Edited by
Agricultural and
Food Systems
Sustainability
The Complex Challenge
of Losses and Waste
Alessandro Suardi and Nadia Palmieri
Printed Edition of the Special Issue Published in Sustai nabi lity
www.mdpi.com/journal/sustainability
Agricultural and Food Systems
Sustainability: The Complex
Challenge of Losses and Waste
Agricultural and Food Systems
Sustainability: The Complex
Challenge of Losses and Waste
Editors
Alessandro Suardi
Nadia Palmieri
MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin
Editors
Alessandro Suardi
CREA Research Centre for
Engineering and Agro-Food
Processing
Italy
Nadia Palmieri
CREA Research Centre for
Engineering and Agro-Food
Processing
Italy
Editorial Office
MDPI
St. Alban-Anlage 66
4052 Basel, Switzerland
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Contents
About the Editors .............................................. vii
Alessandro Suardi, Walter Stefanoni, Simone Bergonzoli, Francesco Latterini, Nils Jonsson
and Luigi Pari
Comparison between Two Strategies for the Collection of Wheat Residue after Mechanical
Harvesting: Performance and Cost Analysis
Reprinted from: Sustainability 2020, 12, 4936, doi:10.3390/su12124936 ................ 1
Ikechukwu Kingsley Opara, Olaniyi Amos Fawole and Umezuruike Linus Opara
Postharvest Losses of Pomegranate Fruit at the Packhouse and Implications for Sustainability
Indicators
Reprinted from: Sustainability 2021, 13, 5187, doi:10.3390/su13095187 ................ 19
Anelle Blanckenberg, Umezuruike Linus Opara and Olaniyi Amos Fawole
Postharvest Losses in Quantity and Quality of Table Grape (cv. Crimson Seedless) along the
Supply Chain and Associated Economic, Environmental and Resource Impacts
Reprinted from: Sustainability 2021, 13, 4450, doi:10.3390/su13084450 ................ 39
Walter Stefanoni, Francesco Latterini, Javier Prieto Ruiz, Simone Bergonzoli, Nadia Palmieri
and Luigi Pari
Assessing the Camelina (Camelina sativa (L.) Crantz) Seed Harvesting Using a Combine
Harvester: A Case-Study on the Assessment of Work Performance and Seed Loss
Reprinted from: Sustainability 2021, 13, 195, doi:10.3390/su13010195 ................. 61
Luigi Pari, Vincenzo Alfano, Walter Stefanoni, Francesco Latterini, Federico Liuzzi,
Isabella De Bari, Vito Valerio and Anna Ciancolini
Inulin Content in Chipped and Whole Roots of Cardoon after Six Months Storage under Natural
Conditions
Reprinted from: Sustainability 2021, 13, 3902, doi:10.3390/su13073902 ................ 73
Luigi Pari, Alessandro Suardi, Walter Stefanoni, Francesco Latterini and Nadia Palmieri
Environmental and Economic Assessment of Castor Oil Supply Chain: A Case Study
Reprinted from: Sustainability 2020, 12, 6339, doi:10.3390/su12166339 ................ 85
Luigi Pari, Alessandro Suardi, Walter Stefanoni, Francesco Latterini and Nadia Palmieri
Economic and Environmental Assessment of Two Different Rain Water Harvesting Systems for
Agriculture
Reprinted from: Sustainability 2021, 13, 3871, doi:10.3390/su13073871 ................103
Maria Angela Perito, Emilio Chiodo, Annalisa Serio, Antonello Paparella and Andrea Fantini
Factors Influencing Consumers’ Attitude Towards Biopreservatives
Reprinted from: Sustainability 2020, 12, 38, doi:10.3390/su12240038 .................117
v
About the Editors
Alessandro Suardi
Dr. Alessandro Suardi is a senior researcher and agricultural engineer for forestry and the
environmental management with a PhD in science and technology. A senior researcher at the
Council for Agricultural Research and Economics (CREA) in Italy since 2008, he has worked on
nine national and eight international projects for the development of new prototypes to optimize
agricultural mechanization logistics, improve farm mechanization, harvesting and management,
valorise agricultural residues, and perform environmental sustainability assessments of agricultural
supply chains with the Life Cycle Assessment method (LCA). He is the author of more than 100
national and international scientific publications.
Nadia Palmieri
Dr. Nadia Palmieri is researcher and economist, with both a Ph.D and a national scientific
qualification in agricultural economics. She is researcher at the Council for Agricultural Research
and Economics (CREA) in Italy. Her main areas of research are consumer behaviour, willingness
to consume, willingness to accept, agricultural economics as well as environmental and economic
assessment of agricultural systems. On these subjects, she was the scientifist responsible for the
economic aspect of 8 research projects and has published more than 50 papers.
vii
sustainability
Article
Comparison between Two Strategies for the
Collection of Wheat Residue after Mechanical
Harvesting: Performance and Cost Analysis
Alessandro Suardi
1
, Walter Stefanoni
1,
*, Simone Bergonzoli
2
, Francesco Latterini
1
,
Nils Jonsson
3
and Luigi Pari
1
1
Consiglio per la Ricerca in Agricoltura e l’analisi dell’Economia Agraria (CREA)-Centro di Ricerca
Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare, 16, 00015 Monterotondo (RM), Italy;
alessandro.suardi@crea.gov.it (A.S.); francesco.latterini@crea.gov.it (F.L.); luigi.pari@crea.gov.it (L.P.)
2
Consiglio per la Ricerca in Agricoltura e l’analisi dell’Economia Agraria (CREA)-Centro di Ricerca Ingegneria
e Trasformazioni Agroalimentari, Via Milano, 43, 24047 Treviglio (BG), Italy; simone.bergonzoli@crea.gov.it
3
Research Institutes of Sweden (RISE), Ultunaallén 4, 756 51 Uppsala, Sweden; nils.jonsson@ri.se
* Correspondence: walter.stefanoni@crea.gov.it; Tel.: +39-06-9067-5205
Received: 25 May 2020; Accepted: 12 June 2020; Published: 17 June 2020
Abstract:
The growing population worldwide will create the demand for higher cereal production,
in order to meet the food need of both humans and animals in the future. Consequently, the quantity
of crop by-products produced by cereal cropping will increase accordingly, providing a good
opportunity for fostering the development of the sustainable supply chain of renewable solid fuels
and natural feedstock for animal farming. The conventional machineries used in wheat harvesting
do not guarantee the possibility to collect the chaff as additional residue to the straw. The present
study investigated the possibility to equip a conventional combine with a specific device, already
available on the market, in order to collect the chaff either separately (onto a trailer), or together with
the straw (baled). The total residual biomass increased by 0.84 t
·
ha
−1
and 0.80 t
·
ha
−1
respectively,
without negatively affecting the performance of the combine when the chaff was discharged on the
swath. Farmers can benefit economically from the extra biomass collected, although a proper sizing
of the machine chain is fundamental to avoid by-product losses and lower revenue.
Keywords: biomass; bioenergy; straw; combine harvester; chaff; by-product
1. Introduction
1.1. Framework
The use of non-renewable sources for meeting the fast-growing energy demand worldwide
could trigger negative effects on the environment in terms of pollution. On the other hand, as the
worldwide population is expected to exceed 9 billion people by 2050 (FAO), the production of several
key commodities will also increase accordingly, in order to meet the fast-growing demand for food.
The production of cereals is expected to grow from the annual 2.1 billion tons up to 3 billion tons by
2050 if animal feeding is also included [
1
]. Consequently, the ongoing conflict on land use for food and
non-food crops will be more serious if new strategies are not promptly undertaken. Regarding the
bioenergy production, the European policy is keen to promote the utilization of agroforestry residues
over the plantation of energy crops [
2
], by applying stringent regulations, in order to meet the climate
and energy targets set in the EU 2030 framework [
3
,
4
]. Hence, a possible strategy could be improving
the collection and the utilization of residual biomass that is normally produced in cereal cropping,
but not effectively exploited yet [
5
]. During the harvesting of cereals, for example, in addition to
the collection of grains, a large quantity of residual biomass is usually produced as straw and chaff.
Sustainability 2020 , 12, 4936; doi:10.3390/su12124936 www.mdpi.com/journal/sustainability
1
Sustainability 2020 , 12, 4936
Among them, straw has been exploited for a long time as natural bedding for animals [
6
] and, recently,
as a valid source for energy production or as raw material for industrial processes. In terms of energy,
one hectare of cereal straw is approximately equivalent to 200 L of oil [
7
] if considered as solid biofuel.
However, the biochemical properties of ligno-cellulosic materials, as straw, make it suitable for further
industrial processing. For instance, Fang and Shen [
8
] reported the suitability of straw for paper
and paperboard production, H
ý
sek et al. [
9
] highlighted the possibility to exploit cereal residues for
composite material production, while Swain et al. [
10
] investigated the hydrolyzation of cellulose and
the hemicellulose of straw into fermentable sugars, which are particularly attractive for bioethanol
production industries. Recently, it has been found that winter wheat straw can be returned to soil as
biochar to enhance the yield in corn and peanut cultivation [
11
]. Conceptually, the development of a
comprehensive, efficient and sustainable straw supply chain can bring benefits to many sectors and to
developing countries as well [12].
On the other hand, the chaff, as the finest part of the grain residue, is normally lost on the
ground after mechanical harvesting. In wheat crops, chaff is available at the rate of the 17% of the
grain yield [
13
], and if considering the European annual production of wheat and spelt estimated in,
approximately, 138 Mt [
14
], the whole European biomass supply chain could benefit from the collection
of 23 Mt
·
yr
−1
more of biomass. This would contribute to increase the availability of solid biofuel
for the production of energy, particularly if baled with the straw [
15
]. Chaff palletization is also
possible, but only if provided as loose product [
16
], as well as for the production of second generation
bioethanol [17].
Nevertheless, the collection of chaff has already been investigated in agriculture, as a promising
tool in organic farming of cereal grain for reducing the accumulation of weed seeds in the soil over
time [
18
,
19
]. In Australia, different mechanical devices were invented and tested on field, with the
specific purpose of removing the chaff for decreasing the amount of weed seeds [
20
–
22
]. The chaff
was then arranged in small hips or in narrow strips for being burnt afterwards. The possibility to
collect chaff for purposes different from weed seeds control, has already been investigated under the
economic aspect by Unger and Glasner [
23
], whose study revealed that the exploitation of that kind of
residue is feasible. Although the simultaneous harvesting of wheat grains and chaff has been recently
investigated [19,24–26], the literature still lacks of specific data from in-field experiment.
Actually, mainly due to the lack of knowledge on the specific devices already available on the
market, uncertainty on the harvesting system to adopt and due to the lack of a dedicated supply
chain for an effective exploitation, the chaff still remains an untapped biomass. There is a real need to
evaluate the cost effectiveness and the performance of systems for harvesting chaff in order to foster
the utilization of this biomass, depending on the final use, and to stimulate the development of a
dedicated value chain. According to this, the aim of the study consisted in filling this knowledge gap
and providing a deeper understanding of the possibility to enhance the current wheat harvesting
method, in order to improve the quantity of biomass collected, including the chaff.
1.2. Main Chaff Utilization
The chaff from cereal crops can be handled differently according to its final utilization.
More recently, the chaff is thought as a source of biomass for energy use, but others are known
in literature. For instance, in Australia, harvest weed seed control (HWSC) systems have been
developed and tested for years, providing good results in terms of alternative strategy for weeds
control. Walsh, Newman, and Powles in 2013 [
20
] reviewed the following systems: chaff chart, narrow
windrow burning, bale direct and Harrington seed destructor (HSD). The first two of them accumulate
the chaff, either in heaps, or in a narrow windrow (50–60 cm wide) on the field for direct burning.
Among the other two systems, apart from the HSD that mechanically destroys the seed weeds, the direct
baling strategy provides multiple choices for chaff utilization. In fact, the chaff is baled with the straw as
soon as they exit the cleaning shoe of the combine harvester. Indeed, baling them addresses two main
problems: the removal of weed seeds and the collection of biomass for livestock (both feeding [
6
,
13
]
2
Sustainability 2020 , 12, 4936
and natural bedding). The presence of chaff into straw bales also increases the adsorbent capacity of
natural bedding [
27
]. Even poultry farming can benefit from loose chaff availability on the market.
A direct interview with a local farmer in France highlighted the positive effects, noticed by farmers,
on the welfare of the animals that could scratch around in search for broken kernels and weed seeds,
which, in turn, contributed to overall feeding. The same experience was reported by Italian farmers.
The use of similar cereal residues is reported in literature as a valid source for littering. Anisuzzaman
and Chowdhury reported that rice husk was a good litter material for rearing broilers [
28
] and it also
has a high adsorbent capacity if compared with sawdust [
29
]. Chaff could also be suitable for further
processing, like briquetting, and used for multiple purposes. Akerlof [
30
] reported the possibility of
producing briquettes of soybean chaff for meeting the needs of livestock in providing complete feeding,
whereas spelt chaff has been proven to be a good raw material for the production of briquettes for
non-feeding purpose, who exhibited different mechanical properties according to the temperature of
compression applied [
31
]. Wheat chaff applications are not fully studied in the sense of both feeding
or not-feeding purposes. The unviability of specific mechanical machines able to collect it without
increase in the harvesting costs, has probably limited the research in that direction. For this reason,
this study addresses an important issue for the development of new production chains based on
cereal residues, showing two possible chaff collection logistics, the limits and operating costs of the
technologies used, laying the foundations for the development of possible supply chains that are
currently underdeveloped or, in some cases, non-existent. In the framework of the H2020 AGROinLOG
project [
32
], a specific test in the Halland region (Sweden) was carried out, to provide evidences on the
possibility of improving the conventional supply chains in wheat harvesting, for increasing the overall
residual biomass collectable in the field. Specifically, the aim of the test was to evaluate if it is possible
to accomplish such a task by equipping a conventional harvester combine with a dedicated device
for chaff recovery, already available on the market and manufactured by the Thierart firm (Thierart,
Le Ch
â
telet-sur-Retourne, France) [
33
]. The device permitted one to flow the chaff, either onto a towed
trailer, or on the straw swath produced by the combine harvester. Therefore, both chaff collection
methods were tested: loose in a towed trailer (CoT), or baled together with the straw (CoS). The trailer
was connected to the combine harvester, therefore no tractor was required for towing it. The amount
of biomass potentially collectable as grains, straw and chaff was quantified, as well as the performance
and quality of the work of all machines involved in the two supply chains. The loss of seeds, straw
and chaff were recorded and an evaluation of the harvesting operating costs was carried out.
2. Materials and Methods
2.1. Field Site and Experimental Design
The test was performed at Lilla Bösld (Halland region, Sweden) (56
◦
35
48” N 12
◦
57
33” E) in the
35th week of 2019 (Figure 1). The field, 15 m a.s.l., exhibited a negligible value of slope.
The wheat (Triticum spp.) variety “Julius” was sown in medium clay soil type (24–29% of clay) in
September 2018, with a seeding rate of 220 kg
·
ha
−1
and cultivated in conventional farming. Fertilization
was carried out with 150 kg
·
ha
−1
of PK 11–21 and 500 kg
·
ha
−1
of Nitrogen fertilizer (27% N + 9% SO3)
and 200 kg
·
ha
−1
of calcium nitrate. For the weed control 1 L
·
ha
−1
of MCPA and 15 g
·
ha
−1
of Express 50
(wetting agent 0.1 L·ha
−1
) were used. For the fungus control, 0.5 L·ha
−1
of Ascra Xpro was applied.
Within the field, a homogeneous area of 3 ha was preliminarily selected. The surrounding wheat
was harvested and the whole biomass removed, in order to avoid edge effects and biased measurement.
The selected area was then divided into three blocks, each of them sub-dived in two rectangular shaped
plots measuring approximately 0.5 ha. Thus, three random replications per treatment were obtained,
for a total of six plots. The chaff was collected in two different ways (treatments): either discharged on
the swath (CoS) or collected on a trailer (CoT).
3
Sustainability 2020 , 12, 4936
Figure 1. Map of the experimental field in Halland region of Sweden.
2.2. Pre-Harvest Tests: Theoretical Biomass Assessment
For management reasons, the test was split into two consecutive days: the first day was dedicated
to the pre-harvesting activities and combine harvesting; the following day occurred the baling operation
and post-harvesting activities. Before harvesting, the whole plants of 10 samples areas of 1 m
2
randomly
chosen were hand harvested. Culms and spikes were weighed separately. Successively, all spikes and
a representative sample of culms were put in sealed bags and shipped to the laboratory of Research
Centre for Engineering and Agro-Food Processing (CREA) for further measurements as: theoretical
yield of grain and chaff, dry weight and moisture content.
In the laboratory, by using a stationary thresher (PLOT 2375 Thresher, Cicoria Company,
San Gervasio, Italy), kernels were separated from the rest of the spikes (rachis, lemma, glumes
and palea). The dry weight and moisture content of culms, kernels and chaff was assessed according
to the EN ISO 18134-2:2017 [34] standard.
2.3. Equipment
The contractor provided all the machines required for the test. Settings of the combine harvester,
as well as the baler, were maintained at a constant rate throughout the experiment.
2.3.1. Combine Harvester and Recovery System
A combine harvester New Holland TX68 with a conventional threshing drum, straw walker and
cleaning shoe was used to perform the test. The header was 7.27 m width and it was specifically
designed for cereal harvesting. The machine was driven by a 209 kW diesel engine and the chassis was
comprehensive of a dedicated hitch for trailer towing.
The device for the chaff recovery was installed at the end of the cleaning shoe of the combine
harvester. As shown in Figure 2, the device is made of a tank that receives the chaff from the cleaning
shoe; within it, there is a steal-made screw that delivers the chaff to the two-stage turbine which,
in turn, blows it through the outlet. According to the company Thi
é
rart [
33
], the device requires a
minimum of 45 L
·
min
−1
of hydraulic oil flow rate to work properly and the cutting bar of the combine
harvester should not exceed 5.5 m in width to properly manage the chaff flow.
4
Sustainability 2020 , 12, 4936
Figure 2.
Device developed and patented by Thierart for chaff recovery: (1) two stages turbine;
(2) specific support for the installation; (3) access hatch to the screw for inspection (source:
https://www.menuepaille.fr/materiels/turbine-a-double-etage/).
Here, a PVC pipe is connected, in order to permit the discharge of the chaff, either on the swath
(Figure 3a) or onto the trailer (Figure 3b). The screw and the twin-stage turbine are driven by the
dedicated hydraulic system.
The trailer used was a single-axed wagon, with a pivoted drawbar directly connected to the hitch
of the combine (Figure 3a). The loading capacity of the trailer was 6 m
3
. The upper part of the trailer
was closed with a thick plastic film, in order to prevent accidental loss of chaff due to wind interference.
The combine harvester was also equipped with auxiliary hydraulic connections, for controlling the
movements of the trailer while discharging the chaff.
Figure 3.
(
a
) Loose in a towed trailer (CoT) = chaff recovery system mounted in New Holland TX68,
for the discharging of the product on a towed trailer (Trailer Agrohill Maskin AB, Halmstad, Sweden);
(
b
) CoS = chaff recovery system mounted in New Holland TX68, for the discharging of the product on
top of the swath.
2.3.2. Residual Biomass Harvesting
In treatment CoT, the chaff collected during the harvesting was systematically discharged into
an auxiliary trailer parked outside the field, then weighted at the end of every plot, using a local
scale. In both treatments, the straw were baled using a round baler John Deer 550 towed by a tractor
John Deere 6830. The baler was completely empty at the beginning of each plot. At the end of each
experimental unit, the machine was forced to close the bale, even if undersized. The last bale was
included in the calculation of the residue production per plot, but not in the calculation of the mean
5
Sustainability 2020 , 12, 4936
weight of the bales, in order to avoid biased mean weights. In treatment CoS the straw swaths, that also
included the chaff, were baled, according to the same methodology applied in CoT. In both treatments
the fuel consumption registered by the on-board computer of the tractor was recorded for a fuel
consumption calculation.
2.4. Harvesting and Baling Performance
Every plot guaranteed the formation of four swaths after harvesting, with minimal overlapping
between the passes. In treatment CoT, the combine had to stop at least once, in order to empty the
trailer and complete the harvesting. At the end of the plot, the trailer was emptied again for total chaff
weight. The time required for discharge operations was recorded as accessory time. To measure the
grain yield, the collected grain was discharged on a trailer and weighted for each plot.
The performance of the machines was evaluated throughthe study of the working times, performed
according to the Comit
é
International d’Organisation Scientifique du Travail en Agriculture (CIOSTA)
methodology and the recommendations from the Italian Society of Agricultural Engineering (A.I.I.A.)
3A R1 [
35
]. The evaluation of the field speed allowed the determination of the theoretical field capacity
(TFC, ha
·
h
−1
), the effective field capacity (EFC, ha
·
h
−1
), field efficiency (FE, %) and material capacity
(MC, t
·
h
−1
). Gathered data were used to define the performance of the machines and the operative
costs. Fuel consumption during baling was recorded by using the measuring system of the tractor.
In the following paragraphs, the biomass unit (t) refers to fresh weight.
2.5. Post-Harvesting Test: Biomass Collected, Losses and Bulk Density
After baling, all bales produced within the plots were weighed singularly for total biomass
baled assessment and average fresh weight measurement (here, the last bale was not included in the
calculation). In treatment CoT, the quantity of the chaff collected was determined by weighing the
chaff collected in each plot on an in situ scale.
Losses of biomass were assessed for stubble, straw and chaff. By knowing the cutting height of
the combine header, stubbles were reconstructed in the laboratory by cutting the basal part of culms
previously harvested for pre-harvest analysis. Straw and chaff losses were determined as the difference
between the theoretical biomass available derived from the pre-harvest analysis and the effective
biomass weighted at the end of the test. The moisture content of each biomass fraction was measured
according to the standard methodology described above. The bulk density (kg
·
m
−3
) of the loose
biomass stored in the trailer was assessed by taking 10 randomly selected samples of chaff and was
measured according to ISO 17828:2015 [
36
]. In each plot, all bales were weighed singularly, and three
of them were randomly selected and their sizes measured for volume assessment. Bulk density was
successively calculated by dividing the mass in kilograms by the volume in cubic meters.
2.6. Cost Analysis
In the economic analysis, the following parameters have been taken into account: purchase and
operating costs that were provided by the contractor via a interview, performance of the machines
derived from the field tests as primary data, and standard values reported in CRPA methodology [
35
].
Hourly costs of machines were calculated on the basis of the market value of the agricultural
machinery [
37
,
38
]. The prices of the machines have been discounted to 2019, applying the lending
rate of 3% provided by Banca d’ Italia Institute [
39
]. The parameters used during the cost analysis are
reported in Tables 1 and 2.
6
Sustainability 2020 , 12, 4936
Table 1.
Parameters used for the economic analysis in CoT treatment. Harvesting stage with the
collection of chaff on a towed trailer and straw baling stage.
Unit Harvesting Baling
Machine
Combine harvester Tractor
Power [kW] 208.8 115.6
Operating machine Thierart Trailer Baler
Financial cost
Investment [€] 230,980 10,000 7000 110,127 30,463
Service life [y] 10 10 10 10 8
Service life [h] 3000 3000 3000 14,000 2500
Resale [%] 19 18 18 32 23
Resale [€] 44,139 1768 1238 40,524 6878
Depreciation [€] 186,841 8232 5762 69,603 23,585
Annual usage
[h·y
−1
]
480 480 480 307 307
Interest rate [%] 33333
Workers [n] 1 - - 1 -
Fixed costs
Ownership costs
[€·y
−1
]
18,684.09 823.16 576.21 11,009.49 2948.11
Interests
[€·y
−1
]
4126.79 176.53 123.57 1652.39 560.12
machine shelter
[m
2
]
62.30 0.00 10.20 9.12 6.93
Value of the shelter
[€·m
−2
]
100.00 0.00 100.00 100.00 100.00
Value of the shelter
[€·y
−1
]
124.59 0.00 30.60 27.36 20.79
Insurance (0.25%)
[€·y
−1
]
577.45 0.00 17.50 275.32 76.16
Variable costs
Repair factor [%] 40.00 45.00 80.00 80.00 90.00
Repairs and maintenance
[€·h
−1
]
49.28 2.40 2.99 1.38 10.78
Fuel cost
[€·l
−1
]
0.57 0.57
Fuel consumption
[L·h
−1
]
32.51 11.60
Fuel cost
[€·h
−1
]
18.66 6.66
Lubricant cost
[€·l
−1
]
3.03 3.03
Lubricant consumption
[L·h
−1
]
0.14 0.09
Lubricant consumption
[€·h
−1
]
0.44 0.27
Worker salary
[€·h
−1
]
11.50 11.50
Cost of baling string
[€·h
−1
]
32.32
Table 2.
Parameters used for the economic analysis in baled together with the straw (CoS) treatment.
The chaff collected with the twin-stage turbine is discharged on the swath and baled afterward.
Unit Harvesting Baling
Machine
Combine harvester Tractor
Power [kW] 208.8 115.6
Operating machine Thierart Baler
Financial cost
Investment [€] 230,980 10,000 110,127 30,463
Service life [y] 10 10 10 8
Service life [h] 3000 3000 14,000 2500
Resale [%] 19 18 32 23
Resale [€] 44,139 1768 40,524 6878
Depreciation [€] 186,841 8232 69,603 23,585
Annual usage
[h·y
−1
]
480 480 307 307
Interest rate [%] 3 3 3 3
Workers [n] 1 1
Fixed costs
Ownership costs
[€·y
−1
]
18,684.09 823.16 11,009.49 2948.11
Interests
[€·y
−1
]
4126.79 176.53 1652.39 560.12
Machine shelter
[m
2]
62.30 9.12 9.89
Value of the shelter
[€·m
−2]
100.00 100.00 100.00
Value of the shelter
[€·y
−1
]
124.59 27.36 29.67
Insurance (0.25%)
[€·y
−1
]
577.45 275.32 76.16
7
Sustainability 2020 , 12, 4936
Table 2. Cont.
Unit Harvesting Baling
Variable costs
Ownership costs [%] 40.00 45.00 80.00 90.00
Repairs and maintenance
[€·h
−1
]
49.28 2.40 1.38 10.78
Fuel cost
[€·L
−1
]
0.57 0.57
Fuel consumption
[L·h
−1
]
32.51 10.7
Fuel cost
[€·h
−1
]
18.66 6.14
Lubricant cost
[€·L
−1
]
3.03 3.03
Lubricant consumption
[L·h
−1
]
0.14 0.09
Lubricant consumption
[€·h
−1
]
0.44 0.27
Worker salary
[€·h
−1
]
11.50 11.50
Cost of baling string
[€·h
−1
]
32.32
In the calculation of the operating costs of the two harvesting systems, the time required for
each operation, the quantity of the products obtained and the respective market value (Table 3) were
considered. The economic allocation in each treatment was derived from the ratio between each
product revenue on the total revenues obtained, as shown in the following formula:
Ea
=
Mp × Y
i
3
i
= 1
R
i
(1)
where:
Ea = Economic allocation of each product or co-product (i.e., grain seed, straw, or chaff) per harvesting
phase (combine harvesting or baling)
Mp = Market price of each product or co-product (i.e., grain seed, straw, or chaff)
Y
i
= Yield of each product or co-product (i.e., grain seed, straw, or chaff)
R
i
= Revenue obtained by multiplying Mp × Y
i
Table 3.
Economic allocation used for the cost analysis of straw and chaff harvesting with the Thierart
technology in Sweden, for each harvesting phase, and treatment.
Treatment
Product
Market Price Yield Revenue Economic Allocation
[€·t
−1
][t·ha
−1
][€·ha
−1
] Harvesting [%] Baling [%]
CoT
Grain
198.5
1
9.83 1951.26 89.2 0.0
Straw
50
1
3.88 194 8.9 100.0
Chaff
50
1
0.84 42 1.9 0.0
Total 2187.26 100.0 100.0
CoS
Grain 198.5 9.5 1881.78 88.9 0.0
Straw 50 3.9 194 9.2 83.0
Chaff 0 0.8 40 1.9 17.0
Total 2115.78 100.0 100.0
Note: prices retrieved from Camera di Commercio di Modena (2019) [40].
2.7. Statistical Analysis
The statistical analysis was performed in ordertodiscriminatethedifferences among the treatments.
All data were subjected to the analysis of variance (ANOVA), using the R 3.6.1 to separate statistically
different means (p ≤ 0.05) [41].
8
Sustainability 2020 , 12, 4936
3. Results and Discussions
3.1. Biomass Fractions
The results of pre-harvesting highlighted that the total aboveground biomass was 18.8 t
·
ha
−1
.
Spikes represented the 57% (10.78 t
·
ha
−1
) of the total (47% seeds and 10% chaff, corresponding to
8.94 t
·
ha
−1
and 1.84 [t
·
ha
−1
], respectively) whereas the whole culms accounted for the 43% (8.02 t
·
ha
−1
).
The moisture content was equal to 14.3% (
±
9.1), 9.0% (
±
3.5) and 14.6% (
±
2.7), for straw, chaff and seeds,
respectively. After the harvesting, the different fractions of biomass collected are shown in Figure 4.
Figure 4.
Effective tons of fresh biomass collected in treatment CoT (
left
) and CoS (
right
). CoT permitted
one to collect the chaff separately from the straw, while in CoS, the chaff was baled with straw and
considered as residual.
3.2. Performance of the Combine
The methodologies studied for chaff collection highlighted some differences in the performance of
the machines involved. According to what was anticipated by Glasner et al. (2019) [
42
], the theoretical
field capacity (TFC) of the combine did not variate among the treatments, as its speed was constant
during the cutting and cleaning processes, although a reduction of 10–25% of cleaning was reported in
the study. Despite that, significant differences were found in EFC, FE and MC (Table 4), where the
accessory times, like the time required for unloading the wagon in CoT, were included. In fact,
the wagon could collect only 6 m
3
of loose chaff and, considering the low bulk density of 41.75 kg
·
m
−3
,
the wagon shortly became full of chaff, forcing the combine harvester to stop and exit the field
for unloading the wagon. A similar value of 42.88 kg
·
m
−3
for chaff bulk density was reported
by Bergonzoli et al. [
26
] and slightly higher values of 56 kg
·
m
−3
and 62.08 kg
·
m
−3
were found by
McCartney et al. [13] and by Suardi et al. [24].
Table 4.
Comparison of the performance of the combine harvester among the two treatments. (TFC =
Theoretical Field Capacity, EFC = Effective Field Capacity, FE = Field Efficiency, MC = Material Capacity).
Treatment
TFC EFC FE MC
[ha·h
−1
] [ha·h
−1
] [%] [t
grain
·h
−1
][t
residue
·h
−1
]
CoT 2.17 ± 0.20 1.02 ± 0.18 47 ± 5 9.87 ± 0.82 0.84 ± 0.05
CoS 2.34 ± 0.43 1.71 ± 0.36 73 ± 8 16.22 ± 3.49 -
ANOVA ns * ** *
Note: (ns) not significant; (*) Significant at p < 0.05; (**) Significant at p < 0.01.
9
Sustainability 2020 , 12, 4936
In CoT, the unproductive times of the combine harvester were 233% higher than in CoS where the
chaff was continually blown over the swath. In fact, as depicted in Table 4, the FE and the MC of the
combine harvester were significantly higher when the CoS system was applied. In Suardi et al. [
19
],
TFC andEFC wererespectively3.72ha
·
h
−1
and 2.28ha
·
h
−1
on averageand the combine fuelconsumption
resulted in 11.8 L·h
−1
.
Different methods for loose chaff collection have been reported in the literature. For instance,
Suardietal.[
24
] tested a continuative dischargingof chaff onto a trailer towed by a tractor side by side the
combine; that the system permitted to collect 1.27 t
·
ha
−1
of loose chaff.Differently, Bergonzoli et al. [
26
]
tested a combine harvester equipped with Harcob system, which had an integrated tank of 9 m
3
in
volume for storage of the chaff collected and that the system allowed to collect 0.6 t
·
ha
−1
. Regardless
of the quantity of the chaff collected, neither of them reported negative impacts on the combine
performance: the trailer volume available for chaff storage in Suardi et al. [
24
] was better dimensioned,
while the Harcob system allows the simultaneous discharging of grain and chaff, avoiding extra
unloading time [
26
]. For that reason, the unproductive times needed were much lower. Similar tests
performed by INRA (Institut National de la Recherche Agronomique) in the frame of the project
«Syst
è
mes de Cultures Innovants» and CUMA (Federation Nationale des Cooperatives d’Achat et
d’ Utilisation de Materiel Agricole) in 2011 and 2012 demodays with similar turbine systems, provided
higher results in terms of quantity of chaff collected: respectively, 1.5 t·ha
−1
and 1.15 t·ha
−1
[43,44].
3.3. Performance of the Baler
Regarding the baling stage, the EFC that includes accessories’ times (e.g., turning time and
unloading time) was lower in CoS, since a higher quantity of biomass in the swath was to be processed
(Table 5). That implied more stops for the discharge of the bales and it also forced the tractor to reduce
the speed, in order to avoid overloading of the baler’s chamber. In fact, the amount of biomass that
the baler could process per unit of time was not statistically different. No significant differences were
found regarding TFC. The fuel consumption of the tractor was also recorded and referred to the unit
of biomass baled. On average, 1.27 (
±
0.17) l of diesel fuel was required for each ton of straw baled,
regardless of the presence or the absence of the chaff in the bales.
Table 5.
Comparison of the performance of the baler within the two treatments. MC is calculated
taking into account the overall quantity of residual biomass produced: straw and chaff together
(TFC = Theoretical Field Capacity, EFC = Effective Field Capacity, FE = Field Efficiency, MC = Material
Capacity). No statistical differences were found between treatments.
Treatment
TFC EFC FE MC
[ha·h
−1
] [ha·h
−1
] [%] [t·h
−1
]
†
CoT 2.86 ± 0.43 1.92 ±0.09 68 ± 7 7.47 ± 0.85
CoS 2.21 ± 0.25 1.57 ± 0.15 71 ± 2 7.37 ± 0.38
ANOVA ns * ns ns
Note: (
†
) Material Capacity refers to tons of fresh residues. (ns) Not significant; (*) Significant at p < 0.05.
Similar tests were performed by Suardi et al. in France in 2018 and 2019 [
24
], on baling the straw
with chaff. The authors reported higher values for TFC, EFC and MC, respectively: 5.23 (
±
0.65) ha
·
h
−1
,
3.46 (
±
0.28) ha
·
h
−1
and 20.79 (
±
0.7) t
·
h
−1
in 2018; whereas 4.64 (
±
0.31) ha
·
h
−1
, 3.09 (
±
0.13) ha
·
h
−1
and
20.20 (
±
2.0) t
·
h
−1
in 2019. When chaff was not included in the bales, the performance of the baler was
not statistically different. The fuel consumption ranged between 0.77 (
±
0.15) l
·
t
−1
and 0.94 (
±
0.12) l
·
t
−1
in the case of straw and chaff baling, while it ranged from 1.01 (
±
0.13) l
·
t
−1
and 0.64 (
±
0.23) l
·
t
−1
,
when the chaff was dispersed on the ground. In similar experiment, the TFC and EFC of straw baling
operation resulted on average 3.96 ha·h
−1
and 2.01 ha·h
−1
, with a mean FE of 51 % [19].
10
Sustainability 2020 , 12, 4936
3.4. Losses of Biomass during the Baling Stage
The theoretical availability of straw, in the present study, was estimated in 8.02 t
·
ha
−1
; in line
with other studies such as Suardi et al. [
24
], where the theoretical straw availability was estimated at
7.39 (
±
0.73) t
·
ha
−1
and 8.33 (
±
0.75) t
·
ha
−1
, in 2018 and 2019 tests, respectively. Nevertheless, during the
present study, the amount of residues baled was on average 3.88 t
·
ha
−1
and 4.68 t
·
ha
−1
with CoT and
CoS treatments, respectively (Table 6). Therefore, the remarkable differences in the residue harvesting
performance can be imputed exclusively to the suitability of the machine chosen by the contractor,
to carry on the baling stage. The round baler John Deere mod. 550 used during the test was equipped
with a pick-up 1.41 m wide, whereas the straw swath produced by the combine harvester measured
1.74 m in width, on average. Hence, 0.33 m of straw swath could not be collected by the baler’s pick-up
system in each pass, due to reduced width of the its pickup system (Figure 5). At the end of the baling
phase, a large quantity of product was still not harvested in the field (Figure 3).
Table 6.
Differences in fresh biomass outputs from wheat crop, due to the use of a twin-stage Turbine
for chaff collection.
Treatment
Machine
Yield
Grain Residue Bale Weight Bale Density Chaff Bulk Density
[t·ha
−1
][t·ha
−1
] [kg] [kg·m
−3
] [kg·m
−3
]
CoT
Combine 9.83 ± 1.26 0.84 ± 0.12 - - 41.75 ± 3.30
Baling - 3.88 ± 0.06 184.6 ± 4.41 76.78 ± 1.83 -
CoS
Combine 9.48 ± 0.45 - - - -
Baling - 4.68 ± 0.23 198.4 ± 3.14 82.22 ± 1.33 -
ANOVA ns ns
†
**
Note: (
†
) In treatment CoT the mean residue value takes into account also the chaff, (-) not performed;
(ns) non-significant; (*) Significant at p < 0.05.
Figure 5.
The narrow pick-p of the round baler (
left
) caused high loss of straw (
right
) along the swath
(areas of the swath not reached by the baler’s pickup system are highlighted in red).
The estimated average loss of residueafter baling was 4.75 t
·
ha
−1
(4.68 t
·
ha
−1
for CoS, and 4.72 t
·
ha
−1
for CoT), corresponding to a loss of biomass of 50% on average, without statistical difference between
the two treatments.
11
Sustainability 2020 , 12, 4936
Bergonzoli et al. [
26
] reported a similar value when a combine harvester mounting Harcob system
(developed for Maize cob harvesting) was modified and used for collecting the chaff in wheat crops,
even if the results were ascribed to the cleaning system of the combine harvester.
Such a level of product losses recorded during the tests exceed the sustainable removal rate of
33% proposed by Unger and Glasner (2019) [
23
]. For this reason, it could be considered positive from
the point of view of soil fertility, even if the economic sustainability is closely linked to the amount of
recoverable biomass. Therefore, low collection efficiencies may render the operation of recovering
residual biomass economically unviable.
However, the scenarios herein proposed provided differences in both the quantity and quality of
residuals biomass collectable from wheat cropping, without affecting the grain yield. Such an aspect is
very important; in fact harvesting, along with storage, is the most responsible factor for loss of grains
throughout the wheat supply chain [
45
]. The presence of the chaff included in the bales increased both
weight and density of the bales by 7.45% and 7.09% respectively, in comparison with bales free of
chaff (Table 6). Increases of 18.0% in bale bulk density, due to the inclusion of chaff, was reported by
Suardi et al. [
24
], when a similar turbine technology for chaff recovery was used. On the other hand,
Suardi et al. reported a non-significant increase in the case of chaff admixing performed with a combi
system (manifactured by Rekordverken Sweden AB, Kvänum, Sweden) [19].
The different methods studied, allowed to harvest 4.68 t
·
ha
−1
and 4.72 t
·
ha
−1
of wheat residues
by baling chaff and straw together, or by harvesting chaff in the trailer and straw baling, respectively
(Table 6), with no statistical differences.
3.5. Cost Analysis
In the analysis of the unitary costs, the running cost of each machinery involved in the supply
chain is related to the market price [
€·
t
−1
] of each product and by-product obtained. The performance
of the machines contributed to the final calculation of the costs. For instance, the reduction in EFC,
FE and MC of the combine harvester (Table 4) found that, when the combine towed the wagon (CoT),
it increased the hourly harvesting cost by 3.41%, the cost per hectare by 73.35%, and the cost per ton of
biomass processed by 67.73% (Tables 7 and 8), in comparison with CoS. Here, the combine harvester
did not waste time to continually stop and unload the wagon.
Table 7.
Costs for unit of time, surface and ton of biomass processed in CoS harvesting system,
considering the productivity and the market price of each product.
Unit Grain Straw Chaff
Total Harvesting
Costs
Market price
[€·t
−1
]
198.5 50 50
Yield
[t·ha
−1
]
9.83 3.88 0.84
Harvesting
Cost allocation [%] 89% 9% 2% 100%
Combine harvester + Twin
stage turbine + Wagon
[€·h
−1
]
123.01 12.23 2.65 137.89
[€·ha
−1
]
120.6 11.99 2.6 135.18
[€·t
−1
]
12.27 3.09 3.09 18.45
Baling
Cost allocation [%] 0% 100% 0% 100%
Tractor + Baler
[€·h
−1
]
116.85 116.85
[€·ha
−1
]
60.86 60.86
[€·t
−1
]
15.69 15.69
Total cost of the harvesting system
[€·h
−1
]
123.01 129.08 2.65 254.74
[€·ha
−1
]
120.6 72.85 2.6 196.04
[€·t
−1
]
12.27 18.78 3.09
12
Sustainability 2020 , 12, 4936
Table 8.
Costs for unit of time, surface and ton of biomass processed in CoT harvesting system
considering the productivity and the market price of each product.
Unit Grain Straw Chaff
Total Harvesting
Costs
Market price
[€·t
−1
]
198.5 50 50
Yield
[t·ha
−1
]
9.48 3.88 0.8
Harvesting
Cost allocation [%] 89% 9% 2% 100%
Combine harvester + Twin
stage turbine
[€·h
−1
]
118.59 12.23 2.52 133.34
[€·ha
−1
]
69.35 7.15 1.47 77.98
[€·t
−1
]
7.32 1.84 1.84 11
Baling
Cost allocation [%] 83% 17% 100%
Tractor + Baler
[€·h
−1
]
96.47 19.89 116.36
[€·ha
−1
]
61.45 12.67 74.12
[€·t
−1
]
15.84 15.84 31.67
Total cost of the harvesting system
[€·h
−1
]
118.59 108.7 22.41 249.7
[€·ha
−1
]
69.35 68.6 14.14 152.09
[€·t
−1
]
7.32 17.68 17.68
On the other hand, when the chaff was blown on the swath, the baler had much more biomass
(straw and chaff) to process. In fact, the baler’s EFC (Table 5) dropped by 18.23% and the costs per
hectare and per ton of biomass processed increased by 21.79% and 101.85%, respectively. The hourly
cost for baling did not change (Tables 7 and 8).
The choice to apply CoS over CoT harvesting method affected both the performance and running
cost of the machines. According to Table 9, the harvesting cost per hectare increased by 28.90%
(from 152.03 €·ha
−1
to 196.05 €·ha
−1
), when the chaff was collected as loose material (CoT).
Table 9.
Economic calculation cost, revenue and the net gain obtained from the collection of grains,
straw and chaff when harvested with the two different methods: CoS and CoT.
Treatment
Product
Yield Market Price Revenue Harvesting Costs Net Gain
[t·ha
−1
][€·t
−1
][€·ha
−1
][€·ha
−1
][€·ha
−1
]
CoT
Seed 9.83 198.50 1951.26 120.60 1830.66
Straw 3.88 50.00 194.00 72.85 121.15
Chaff 0.84 50.00 42.00 2.60 39.40
Total 14.55 - 2187.26 196.05 1991.21
CoS
Seed 9.48 198.50 1881.78 69.35 1812.43
Straw 3.88 50.00 194.00 68.60 125.40
Chaff 0.80 50.00 40.00 14.14 25.86
Total 14.16 2115.78 152.09 1963.69
The same results were obtained by Unger and Glasner in 2019, where the separate chaff collection
and supply led to higher costs [
23
]. However, the overall capacity of CoT system permitted one to
collect more biomass per hectare (0.38 t and 0.04 t of grain and chaff respectively), counterbalancing the
higher costs. In fact, if considering just the net gain per hectare, CoS permitted to gain only 27.52
€·
ha
−1
.
Furthermore, in the present study, a market price for chaff of 50
€·
t
−1
was considered.
However, Unger and Glasner [
23
] highlighted that the potential revenue of chaff could vary depending
the final use and market price that can range from 81
€·
t
−1
to 200
€·
t
−1
, making chaff separate collection
economically viable, and giving the farmer, from year to year, different sales opportunities of the
product to more profitable markets.
13
Sustainability 2020 , 12, 4936
4. Conclusions
The cultivation of the cereals is an important source of staple food around the world, and it also
produces a relevant quantity of ligno-cellulosic biomass, that can be further exploited in order to
improve the economic and environmental sustainability of the whole supply chain. In fact, agricultural
residues are gaining more and more interest, due to their considerable availability and their potential
content of energy, or as raw material for industrial processes. Cereal straw and chaff collected either
separately or baled altogether can be a source of food for animals, particularly in case of shortage,
or natural bedding for livestock. In poultry farming, farmers reported positive experiences on the
use of loose chaff for littering, since it provides wellness to animals and a good adsorbent capacity.
However, possible utilization of chaff is to produce bioenergy. Normally, about two tons of chaff
per hectare are available, but still not collected, due to three major problems: unawareness of proper
mechanical devices available on the market for its collection, uncertainty on the harvesting system to
adopt and the development of a specific supply chain for its exploitation. So far, the literature reports
few cases of chaff collection with the specific purpose of weed seeds removal, but it still lacks specific
experiments on these machines intentionally used for biomass collection. Therefore, the present study
aimed to fill that gap and provide deeper understanding in the possibility to enhance the current
wheat harvesting method, in order to improve the quantity of biomass collected by including the chaff.
This research analyzed the technical and economic feasibility of two different logistic methods for chaff
collection: chaff collected as loose product onto a towed trailer (CoT) and baled altogether with the
straw (CoS).
Our results suggest that upgrading a conventional combine harvester with a twin-stage turbine
for chaff collection increases the total biomass collected by 0.84 t
·
ha
−1
without affecting the grain
yield. Furthermore, the separation of chaff from the straw is performed simultaneously to the cleaning
process of the grain and no additional passes of the machine on the field are needed, and further soil
compaction is prevented.
Even if our results reveal that the collection of the loose chaff into a towed wagon is more costly
than including it into the bales, the market price of the pure chaff should be higher, to offset the extra
costs required by the contractor for the collection and handling. Furthermore, it should be noted
that the trailer system could be used also for other crop by-products; for instance, collecting finely
chopped roughage after a forage harvester, without the strong modification of the combine, reducing
the unitary cost of the investment and increasing the quantity of biomass potentially collectable. In fact,
the unproductive time in CoT was 233% higher than in CoS with an increase of 43.94
€·
ha
−1
for the
harvesting cost. In addition to the higher costs, loss of revenue may take place in case of inappropriate
choice of the machine for accomplishing a specific task. Particularly, the round baler chosen by the
contractor could not collect all the straw windrowed by the combine harvester. Although the subject
is still under discussion, some authors consider that a residue extraction of no more than 33% is
sustainable for the soil fertility. On the other hand, however, an amount of uncollected residue, such as
that found during the study (50% of harvesting losses), could negatively affect the economic feasibility
of the residue collection phase, questioning the investment in specific equipment. In fact, according to
6results from CoT treatment, where the chaff was not included in the straw, only 3.88 t
·
ha
−1
out of
8.02 t
·
ha
−1
of straw available on the field were baled. Considering the straw market price of 50
€·
t
−1
,
this can be translated into a loss of income of more than 200 €·ha
−1
.
Future studies should be focused on the assessment of the sustainability of the chaff collection,
in terms of the effect to the soil fertility, carbon dioxide emissions and soil compaction.
Author Contributions:
Conceptualization and methodology, A.S.; Investigation and data curation A.S., W.S., F.L.;
writing—original draft preparation W.S., S.B.; writing—review and editing, A.S., L.P. and N.J.; supervision, L.P.
and A.S.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by European Union’s Horizon 2020 AGROinLOG project grant number 727961
(http://agroinlog-h2020.eu/en/home/).
14
Sustainability 2020 , 12, 4936
Acknowledgments:
Authors thank the contractor Leif Jönsson (Tjärby Gästgivaregård Laholm, Sweden) and
his team for their valid support and assistance provided during the activities, as well as Sandu Lazar for their
valuable contribution in the field activities. Moreover, the authors thank Consuelo Attolico for looking after the
relationship with the French company ETS Thierart
®
.
Conflicts of Interest: The authors declare no conflict of interest.
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©
2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
17
sustainability
Article
Postharvest Losses of Pomegranate Fruit at the Packhouse and
Implications for Sustainability Indicators
Ikechukwu Kingsley Opara
1
, Olaniyi Amos Fawole
1,2
and Umezuruike Linus Opara
1,
*
Citation: Opara, I.K.; Fawole, O.A.;
Opara, U.L. Postharvest Losses of
Pomegranate Fruit at the Packhouse
and Implications for Sustainability
Indicators. Sustainability 2021, 13,
5187. https://doi.org/
10.3390/su13095187
Academic Editors: Alessandro Suardi
and Nadia Palmieri
Received: 13 February 2021
Accepted: 26 April 2021
Published: 6 May 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1
SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of
AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; ikekings101@gmail.com (I.K.O.);
olaniyif@uj.ac.za (O.A.F.)
2
Postharvest Research Laboratory, Department of Botany and Plant Biotechnology,
University of Johannesburg, Johannesburg 2006, South Africa
* Correspondence: opara@sun.ac.za; Tel.: +27-21-808-4064
Abstract:
Pomegranate fruit, like other types of fresh horticultural produce, are susceptible to high
incidence preharvest and postharvest losses and waste. Several studies have been done to improve
the production and handling of pomegranate fruit to meet market standards, but little has been
done in loss quantification, especially in the early stage of the value chain such as the packhouse.
Therefore, the aim of this study was to quantify the magnitude of pomegranate fruit losses at the
packhouse, identify the causes, and estimate the impacts of losses. The study was conducted on a
case study packhouse in the Western Cape Province of South Africa from February to March 2020.
The direct measurement method, which involved physical identification of the causes of loss on
individual fruit, was used for data collection. Loss quantification involved the calculation of lost fruit
proportional to the amount put in the packhouse processing line. The results showed that losses
ranged between 6.74% to 7.69%, which translated to an average of 328.79 tonnes of pomegranate
fruit removed during packhouse operation per production season at the investigated packhouse.
This magnitude of lost fruit was equivalent to over ZAR 29.5 million (USD 1,754,984) in revenue, in
addition to the opportunity costs of resources used to produce lost fruit.
Keywords:
pomegranate; losses; nutrition; environmental; resources; packhouse; postharvest; im-
pacts
1. Introduction
Pomegranate (Punica granatum L.) is an ancient fruit believed to be first cultivated
around 3000 and 4000 BC, and was mentioned both in the Bible and the Quran [
1
]. Its origin
is traced to the Middle East, in present-day Iran, and it adapts to a variety of soil conditions
in the Mediterranean, subtropical, and tropical climates [
2
,
3
]. Currently, it is grown in
many countries for fresh consumption and industrial uses [
4
,
5
]. As a result, more than
500 cultivars
are grown globally, with some cultivars named differently in different parts
of the world [
2
,
4
–
6
]. The awareness of its numerous uses and benefits has made it popular
among other fruit [
5
,
6
]. Pomegranate can be eaten as fresh produce or juiced and stored in
the appropriate temperature and relative humidity. It is sweet, sour, or acidic depending
on the cultivar and rich in vitamins, minerals, and other organic compounds [
4
,
5
]. The
consumption of pomegranate has been linked with a great health outcome in different
studies [
3
,
7
–
10
]. The phenolic compounds present in pomegranate have been found to be
great anti-inflammatory, anti-oxidative, and anti-carcinogenic chemical compounds, which
helps to reduce tumour growth and chronic inflammation [
11
]. The hypoglycaemic activ-
ity of pomegranate juice has been found to prevent diabetes mellitus [
12
]. Pomegranate
fruit consumption has been reported to reduce cardiovascular diseases [
13
]. Chemical
compounds in pomegranate fruit are also used in the treatment of diseases such as ulcers,
Sustainability 2021, 13, 5187. https://doi.org/10.3390/su13095187 https://www.mdpi.com/journal/sustainability
19
Sustainability 2021, 13, 5187
acidosis, haemorrhage, aphthae, diarrhoea, dysentery, respiratory pathologies, and micro-
bial infections [
3
,
9
]. The manufacturing industries use pomegranate aril and peel as a raw
material in the production of jams, ink, dye, and oil [3].
Trends in Production and Trade of Pomegranate Fruit in South Africa and Globally
There has been a rapid increase in the production of pomegranate globally, but the
trade has grown more locally in the major producing countries [
14
,
15
]. Countries such
as India, China, Iran, and Turkey are the leading producers, while India and Iran are the
highest exporters of the fruit [
2
]. However, because the fruit are often grown and picked
from small farms in different locations in the major producing countries, there are no artic-
ulated data available about global production area [
1
,
2
,
15
]. However, global production
was estimated to have increased from about 3 million tonnes in 2014 to 3.8 million tonnes
in 2017 [1].
The production of pomegranate fruit in different parts of the world is primarily di-
vided into two (Northern Hemisphere and Southern Hemisphere) due to different seasons
of production in the regions [
15
,
16
]. The demand for the fruit, especially in the Northern
Hemisphere, is derived in nature in the sense that it is mainly driven by industrial usage
since there is no close substitute for the antioxidants found in pomegranate [
15
]. The
supply is stratified according to the variation in the production seasons, which allows the
Southern Hemisphere to fill the niche market gap in the Northern Hemisphere. The North-
ern Hemisphere, however, accounts for above 90% of the total production, hence, it has a
higher share in the global trade [
15
]. According to Kahramanoglu [
1
], global pomegranate
production area is increasing, but some of the producing countries are facing quality issues,
which leads to considerable postharvest losses and waste. Europe is the biggest market for
pomegranate followed by Asia and the Middle East, as almost all the producing countries
share the European markets [
1
]. Peru and Chile are the biggest exporters of pomegranate
fruit from the Southern Hemisphere with 74% and 14% respectively, while South Africa and
Argentina have a combined 12% contribution to export from the region [
17
]. Iran, China,
India, Turkey, Spain, and Israel are the highest producers in the Northern Hemisphere, but
most of the production in this region is consumed locally [1,14].
The pomegranate fruit is one of the deciduous fruits grown in South Africa, occupying
about 1024 hectares of land in 2019 from 771 hectares in 2011 [
16
]. It is mostly grown
in the Western Cape, which accounts for about 81% of total production [
16
]. In South
Africa, the majority of the production is exported, which earns export revenue for the
country and income for the farmers and value chain actors. In 2019, about 76% of the
total production was exported [
16
], and in 2018, the local market generated about ZAR
67,000 per tonne [
17
]. Production and export have grown from about 837,250 cartons
(
3.8 kg
equivalent) in 2014 to 1,676,160 cartons (3.8 kg equivalent) in 2019, and are projected
to increase to 2,055,271 cartons (3.8 kg equivalent) by 2024 [
16
]. Between 2014 and 2019,
about 7,557,906 cartons (3.8 kg equivalent) were exported [
16
]. The major market for
South African pomegranate is Europe. About 61% of the total export is in the European
markets and 22% in the Middle East, with Asian and African countries importing a small
amount [
17
]. The main competition for export market share comes from the Southern
Hemisphere countries, whose pomegranate fruit is ready in the market almost in the same
period as that of South Africa.
While the pomegranate industry is growing rapidly in South Africa and globally, fruit
are susceptible to losses and waste (wastage) which reduce profitability due to a wide range
of preharvest and postharvest factors, including pest and disease attack [
18
,
19
], bruise
damage [
20
], moisture loss [
21
,
22
], and mechanical damage [
23
]. Industry estimates in
South Africa also suggest that the incidence sunburn (a preharvest skin defect) alone can
be high, causing grower losses that may exceed 30% of harvested fruit [
24
]. Despite the
identified causes of wastage of the fruit in South Africa, there is a lack of quantitative and
science-based data on the magnitude of losses to guide the implementation of loss reduction
strategies. During typical packhouse operation, fruit are cleaned, sorted, graded, labelled,
20
Sustainability 2021, 13, 5187
and packed, and those that do not meet quality specifications due to the preharvest and
postharvest factors outlined are considered as loss and thus discarded or sold at a nominal
price for juice or animal feed. As the last point of fruit handling and quality control prior
to storage, marketing, and distribution, the packhouse is a critical step in assessing the
magnitude and causes of fruit postharvest loss which are critical pieces of information for
informing loss reduction strategies. Discussions on the magnitude of postharvest losses
and the causes are often based on estimates, without site-level measurements which are
known to be difficult and costly. However, researchers globally agree on the need for
more studies to directly quantify the amount of postharvest food losses and to identify the
site-specific contributing factors along the value chain [
25
–
28
]. Therefore, the objective of
the current research was to assess the magnitude of pomegranate fruit postharvest losses at
the packhouse level based on a case study in South Africa, identify the causes, and estimate
the socio-economic and environmental impacts of the losses.
2. Materials and Methods
2.1. Study Settings
The study was conducted from February to March 2020 in the Western Cape Province
(Latitude 33.2278
◦
S, Longitude 21.8569
◦
E) of South Africa. The province was chosen
because over 80% of total production of pomegranate fruit in the country is done in the
region [
16
] and the case study packhouse is arguably the biggest packhouse in the area.
The study was conducted on the three most commercially grown pomegranate cultivars in
the country, namely ‘Herskawitz’, ‘Acco’, and ‘Wonderful’. ‘Herskawitz’ has a sour taste
with hard seeds, ‘Acco’ has a sweet taste with softer seeds, while ‘Wonderful’ has a vinous
taste with soft seeds [
29
]. The study was carried out by assessing the physical quality
of fruit sorted as ‘waste’ from the packhouse production line. The assessment started at
about
9:00 a.m.
and ended by 3:00 p.m. daily. ‘Herskawitz’, which is the early cultivar, was
assessed by mid-February while ‘Acco’ was assessed by early March. ‘Wonderful’ was
assessed by mid and late March. The handling and packaging practices at the packhouse
were observed. The unit of measurement of lost fruit was the bin [length (1270 mm)
×
width (1070 mm)
×
height (720 mm)]. A total of 251 bins, containing 1300–1500 fruit
each which were processed by the packhouse during the study period, were used to
assess the magnitude of fruit losses (‘Herskawitz’ 84, ‘Acco’ 89, and ‘Wonderful’ 78) by
putting fruit through the packhouse line for sorting and grading. The bins handled by the
packhouse during the study period were all examined in order to provide a sufficient and
representative dataset based on commercial practice. The magnitude of loss was estimated
for each pomegranate cultivar based on the number of waste bins containing lost/rejected
fruit. Fruit sorted into waste bins were sampled into ventilated cartons (length (35 mm)
×
height (25 mm)
×
width (22 mm)) and later examined individually to determine the causes
of loss. A total of 18 bins containing lost (discarded) fruit, six bins for each cultivar, were
used to determine the causes of fruit loss by examining each fruit individually. Given that
the same person carried out all the individual fruit assessment to reduce human error, this
was the maximum number of bins and fruit that could possibly be examined during the
research period. Loss calculations included the number of bins of discarded fruit for defect
reasons proportional to the number of bins put in the processing line. The assessment was
made based on the external quality of fruit. Quantification was done by collecting sample
fruit to identify reasons for loss (defects) and how they contribute to total fruit loss.
2.2. Method of Data Collection
The research method for this study was the sampling method, which has been identi-
fied as a practical method for conducting a study where there is a large variable of data
to consider and also in conditions where data collection is constrained [
30
]. Because the
assessment of this present study was carried out simultaneously during full packhouse
commercial operations, which constrained space and time for data collection, it was neces-
sary to use the sampling method. Researchers have used sampling methods to conduct
21
Sustainability 2021, 13, 5187
postharvest studies [
31
–
33
]. This present study involved the physical identification of the
causes of fruit loss by examining individual fruit sorted into the waste bin at the packhouse.
Qualitative data were also collected by physical observation during packhouse operation
and interaction with the packhouse workers.
The economic impact of fruit losses was estimated using the supermarket retail price
(ZAR 89.99/kg) in Stellenbosch, Western Cape, South Africa during the period of study. The
environmental impacts were estimated using the values from previous studies reported in
literature. The energy used for storage and processing activities and greenhouse gas (GHG)
emission associated with fruit production were estimated using 6.1 MJ/kg and 0.48 CO
2
eq/kg, respectively [
34
]. The values were estimated for apples, which is a deciduous fruit
like the pomegranate and which have similar packhouse processes. The water footprint
was estimated with 910 m
3
ton
−1
[
35
]. The nutritional impacts were calculated using values
from [
36
] and [
37
]. Furthermore, cropland use was estimated by the size of the farm and
the average yield produced.
2.3. Data Collection
The data collection protocol was consistent with the direct measurement method of the
Food Loss and Waste Protocol (FLWP) [
38
]. For fruit loss data collection, a total of
251 bins
(containing 1300–1500 fruit each) were put through the packhouse line and the number
of waste bins (fruit loss) produced for each cultivar were recorded. Altogether, a total
of over 351,400 individual fruit were assessed which comprised of 89, 84, and
78 bins
of
‘Acco’, ‘Herskawitz’, and ‘Wonderful’ pomegranate, respectively. To determine the causes
for the loss, fruit in 18 waste bins (6 per cultivar) were further examined. For each bin, a
sample of 30 fruit was randomly selected each from the bottom, middle, and top and placed
into ventilated cartons. Each fruit was visually assessed based on physical appearance
(presence of rot, Alternaria disease, crack, injury, sunburn, blemish, insect damage), sorted,
counted, and recorded according to each type of defect. In total, 1630 fruit (540 per cultivar)
were examined to determine the quality defects causing fruit loss.
Data collection for each cultivar was done in three days, and six bins (n = 6) were
assessed per cultivar (‘Acco’, ‘Hershkawitz’, and ‘Wonderful’). The waste bins were labelled
and two bins were assessed per day. It is important, however, to mention that pomegranate
fruit losses at the packhouse level are not necessarily cultivar dependent, rather they
originate from direct (primary sources) and indirect (secondary sources) [
39
]. Nonetheless,
it was important to categorise fruit defects by cultivar for ease of data collection and
comparison with historical packhouse data.
2.4. Historical Packhouse Data
Historical data on pomegranate postharvest fruit losses collected by quality control
staff at the case study packhouse for the two years where data were available (2016 and 2019)
were obtained as secondary data. These data are presented and discussed in comparison
with the results obtained in the present study.
2.5. Statistical Analysis
Microsoft Excel 2013 (Microsoft Corporation) was used to collate the data collected. In
order to find the trend of variation between cultivars and fruit defects and to consider their
correlation, data were investigated according to principal component analysis (PCA) using
XLSTAT software Version 2012.4.01 (Addinsoft, Paris). The mean value
±
standard error of
fruit defects was also presented and where there was a statistical significance difference
(p < 0.05), analysis of variance (ANOVA) was performed using Statistica Version 13.5.0 to
evaluate the differences between cultivars and fruit defects. Significant differences between
means were separated using Duncan’s multiple range test.
22
Sustainability 2021, 13, 5187
3. Results
3.1. Magnitude of Fruit Losses and Waste
The magnitude of pomegranate fruit losses at the packhouse was measured by the
proportion of bins of discarded fruit to the number of bins initially put in the fruit processing
line. Loss quantification involved a total of 251 bins of fruit put into the processing line
from the three cultivars studied, of which 18 bins were discarded for failing to meet the
minimum market required standard. The total lost fruit among the three cultivars ranged
from 6.74 to 7.69% (Table 1). ‘Acco’ produced the least lost fruit as 89 fruit bins put in the
processing line produced 6 bins of discarded fruit, while 84 fruit bins of ‘Hershkawitz’
produced 6 bins of discarded fruit. Lastly, ‘Wonderful’ produced the highest amount of
lost fruit as 78 bins of fruit put in the processing line produced 6 bins of discarded fruit.
Table 1.
Amount and percentages of each pomegranate cultivar fruit lost (discarded) based on the
amount of fruit put through the packhouse line.
Pomegranate
Cultivar
Fruit Put through the
Processing Line (Bins)
Discarded Fruit
(Bins)
Loss (%)
‘Acco’ 89.00 6.00 6.74
‘Hershkawitz’ 84.00 6.00 7.14
‘Wonderful’ 78.00 6.00 7.69
Total 251.00 18.00 21.5
Mean 83.60 6.00 7.16
Estimates of pomegranate fruit losses at the packhouse level in South Africa have been
reported in recent years by the Pomegranate Association of South Africa (POMASA). In
2017, POMASA reported 11% loss in ‘Wonderful’, 13% loss in ‘Hershkawitz’, and 11% loss
in ‘Acco’ [
40
]. In 2018, a 7% loss of ‘Wonderful’ was reported, an 8% loss in ‘Hershkawitz’,
and a 9% loss in ‘Acco’ [
18
]. Additionally, in 2019, 9% of ‘Wonderful’ was reported as a loss,
25% loss in ‘Hershkawitz’, and 13% loss in ‘Acco’ [
16
]. Pomegranate fruit loss estimation
at the packhouse is measured throughout the production season with fruit from multiple
farmers with different preharvest and postharvest practices, which could affect the quality
of fruit delivered to the packhouse and, hence, the amount of loss recorded. These factors
account for the higher incidence of postharvest losses at the packhouse based on reported
historical industry-wide data compared with the site-specific results obtained in the current
study through a case study.
Bond [
41
] reported a 20% loss in carrots at the packinghouse level in Norway. The
estimation was done using secondary data from experts in the carrot industry and sur-
veys with semi-structured interviews with managers of packhouses. The study revealed
that mechanical damage (harvesting technique at the farm) is a major source of loss at
the packhouse since the superficial injuries during harvest open wounds for decay and
disease infestation. A postharvest loss assessment of avocado, banana, guava, mango,
papaya, and tomato was carried out among fruit growers and traders in north-western
Ethiopia by Bantayehu et al. [
42
]. The results show that 18–28% of losses occurred during
harvesting, storage, and transportation, while 18–25% of losses were reported at trans-
portation and marketing levels. The major causes of loss are superficial injury, bruising,
sunburn, handling technique, and physiological disorders, which are similar to the causes
of pomegranate fruit loss in this present study. Semi-structured questionnaires and inter-
views were used for data collection in the study. Furthermore, a study in Nepal reported
35% loss in carrots [
43
]. Farmgate loss was estimated at 10%, 2% at a collection point, 5% at
the wholesale market, and 18% at the retail level, and crack and splits were identified as the
major cause of carrot loss [
43
]. Irrespective of the magnitude of loss reported in the stud-
ies, losses due to environmental stress and mechanical damage have remained dominant
among the causes of fruit loss, which are similar to the results of this present study.
23
Sustainability 2021, 13, 5187
3.2. Causes of Packhouse Pomegranate Fruit Losses
The causes of packhouse pomegranate fruit losses were assessed based on the quality
issues of why fruit were removed from the packhouse processing line as waste. These
quality issues have contributory factors, and some are direct (primary source) while some
are indirect (secondary source) [
39
]. The main indirect (secondary) cause of packhouse
pomegranate fruit loss is the high market standard. South Africa exports about 76% of
the total pomegranate production [
16
] and 61% of the total export goes to the European
markets [
17
]. The trend of pomegranate marketing in Europe shows that South Africa
faces strong competition with other countries of the Southern Hemisphere for the market
share [
1
,
16
]. This competition is believed to have raised the market standard, which means
that only premium quality fruit are processed for export at the packhouse. The implication
of this is that pomegranate fruit are sorted again at the packhouse to ensure that only the
best quality fruit are packed for sale. The ‘good fruit’ that are deemed not to meet the
premium quality required in the export market are sold locally. The effect of this is that
more fruit are lost or sold at a cheap price for juicing and other purposes. Additionally,
handling at the packhouse is another source of loss categorised as a direct (primary) source
of loss. Losses due to handling manifested mainly as fruit bruises and superficial injuries.
However, the two major reasons for physical loss as identified in this study were sunburn
and injury. Other reasons are Alternaria, bruises, cracks, being oversized, insect damage,
rot, decay, blemishes, and malformation.
3.2.1. Environmental Stress (Sunburn, Cracks, and Splits)
Sunburn
In the three cultivars assessed, sunburn was recorded as the highest cause of loss.
Losses due to sunburn at the packhouse originated from the farm where pomegranate
fruit were exposed to direct sunlight, which causes discolouration of the rind of the
affected fruit, hence downgrading the fruit quality [
44
]. This shows the effect of high
temperature on the quality of pomegranate fruit. After sorting for premium quality fruit at
the packhouse, sunburn accounted for 28.70% and 29.8% of the discarded fruit in ‘Acco’ and
‘Hershkawitz’, respectively (Table 2). The highest fruit loss incidence was in ‘Wonderful’,
where it contributed to 34.81% of losses. Sunburn showed a positive relationship with
oversized fruit in the correlation analysis result (Table 3). This relationship is the only
positive relationship result in the analysis, which indicates that more oversized fruit with
sunburn were deemed fit for export at the farm level but could not meet the minimum
market standard according to the evaluation of the packhouse. The market standard in
Europe and the Middle East does not allow fruit with noticeable sunburn, which means
that such fruit are sold at a low price locally, mainly for juicing.
Table 2. Percentage fruit loss of three pomegranate cultivars due to different defects at packhouse.
Cultivar
Fruit Defect ‘Acco’ (Loss %) ‘Hershkawitz’ (Loss %) ‘Wonderful’ (Loss %)
Alternaria 4.30 3.10 2.96
Bruise 13.33 12.80 10.94
Injury 23.33 23.70 19.07
Sunburn 28.70 29.80 34.81
Crack 18.70 18.34 17.96
Insect damage 3.90 2.20 2.77
Crown rot 2.22 2.96 1.67
Decay 2.22 1.90 2.22
Blemish 3.30 3.30 3.70
Misshapen 0.00 1.90 1.66
Oversized 0.00 0.00 2.24
24
Sustainability 2021, 13, 5187
Table 3.
Pearson correlation coefficient matrix between defects on three pomegranate cultivars (‘Acco’, ‘Hershkawitz’, and
‘Wonderful’).
Defects Alternaria Oversized Bruise Injury Sunburn Crack
Insect
Damage
Crown
Rot
Decay Blemish Misshapen
Alternaria 1
Oversized
−0.267 1
Bruise
−0.248 −0.179 1
Injury 0.157
−0.356 −0.170 1
Sunburn
−0.020 0.376 −0.179 −0.267 1
Crack
−0.246 −0.112 −0.230 −0.184 −0.402 1
Insect
damage
0.116
−0.133 −0.157 −0.219 −0.208 0.157 1
Crown rot 0.067
−0.218 −0.093 0.042 −0.181 −0.063 0.003 1
Decay
−0.088 0.108 −0.209 0.041 −0.208 −0.076 0.055 −0.011 1
Blemish
−0.362 0.131 −0.100 −0.112 −0.094 0.096 −0.124 −0.160 −0.008 1
Misshapen
−0.088 0.201 −0.007 −0.327 −0.007 0.126 −0.257 −0.130 0.002 0.011 1
Values in bold are significant at p < 0.05.
Temperatures exceeding 35
◦
C and low relative humidity at the farm level contribute
to a higher incidence of sunburn [
45
] and because ‘Wonderful’ pomegranate produces
bigger fruit with a larger surface area and is a late cultivar in South Africa, this results
in fruit hanging on the tree much longer before harvest. With most of the fruit exposed
to direct sunlight outside the tree canopy, the incidence of sunburn is exacerbated. This
combination of factors makes ‘Wonderful’ pomegranate fruit more susceptible to sunburn
than ‘Acco’ and ‘Hershkawitz’.
Cracks and Splits
The results show that the amount of fruit affected by cracks and splits in the three
cultivars studied are similar as they ranked third in the causes of loss in the cultivars.
The highest incidence was in ‘Acco’, where they accounted for 18.70% of losses (
Table 2
).
For ‘Hershkawitz’, cracks and splits contributed to 18.34%, while in ‘Wonderful’, they
accounted for 17.96% of losses. Cracks and splits had a negative relationship with sunburn
according to the correlation analysis result (Table 3). This shows the impact of fruit sorting
at the farm level; otherwise, it is reasonable to believe that higher sunburn would result
in more cracks and splits due to the hardening of fruit rinds due to direct sunlight, which
aids cracking when the moisture content fluctuates. Like sunburn, pomegranate cracks
and splits as observed at the packhouse mostly originated from the farm and were a result
of environmental stress, specifically soil moisture imbalances [
46
,
47
] as pomegranate fruit
are highly sensitive to variation in the soil moisture content [
48
]. Therefore, fruit with
cracks and splits at the packhouse are due to either oversight by the farm fruit sorters or
the assumption that the fruit could meet the minimum market standard.
Cracks and splits create an open wound that enhances moisture loss and disease
infestation, which lowers the quality of the affected fruit [
49
]. Fruit discarded from the
packhouse due to cracks and splits were sold locally for industrial use.
3.2.2. Mechanical and Physical Damage (Superficial Injuries, Bruise Damage,
and Blemishes)
Superficial Injuries
Superficial injuries were the second highest cause of pomegranate fruit loss at the
packhouse after sunburn. Injuries constituted 23.33% of the total loss in ‘Acco’ (Table 2). For
‘Hershkawitz’, injury contributed 23.70% of the loss, which is the highest incidence of injury
recorded among the three studied cultivars. ‘Wonderful’ recorded the least amount of injury
with 19.07% of losses in the cultivar. Superficial injuries showed a negative relationship
with oversized fruit in the correlation matrix (Table 3). This indicates that a higher incidence
of injury was due to handling and not fruit sizes. Some of the superficial injuries observed
were cases of opening fruit with a suspicion of internal disease by packhouse fruit sorters
with false results. Furthermore, losses due to injuries originating from preharvest and
handling technique at the farm level were observed. Injuries in this category were deemed
25
Sustainability 2021, 13, 5187
insignificant at the farm level, but the affected fruit failed to meet market standards by the
packhouse. Pomegranate fruit were only stored for a few days (when necessary) at the
packhouse before they were processed; therefore, chilling injuries were not observed.
Bruise Damage
The results show that bruise damage is the fourth cause of loss in the three pomegranate
cultivars assessed. ‘Acco’ recorded the highest incidence of bruise damage, which ac-
counted for 13.33% of losses in the cultivar (Table 2). Bruise damage contributed to 12.80%
of the losses recorded for ‘Hershkawitz’ and 10.94% of the losses in ‘Wonderful’. Bruise
damage showed no significant relationship with any other defect in the correlation analysis
result (Table 3), which suggests that bruise damage at the packhouse is solely a function of
mechanical damage during transportation and handling at the packhouse.
Like an injury, a bruise is caused by mechanical damage as a result of impact during
harvesting, transportation, and handling [
50
]. Most of the bruises observed were believed
to occur during transportation to the packhouse and packhouse handling. Many farm roads
are rough, thereby causing vibration and compression of the fruit during transportation,
which results in bruising damage [
50
,
51
]. Moreover, vibration and impact occur during
fruit unloading at the packhouse and conveyance to the processing line. These assumptions
were made because the affected areas of the fruit were already brownish in colour and soft,
illustrating that the bruising was not an immediate occurrence. However, there were cases
where the affected fruit were discarded during packaging with no visible discolouration of
the rind but with softness in the affected parts. Bruised fruit do not meet either the export
or the local market standards, and therefore, are sold at a low price for industrial use.
Blemish
Blemish is one of the least frequent causes of loss in the three pomegranate cultivars
studied. For ‘Acco’, it ranked seventh out of eight in the causes of loss and accounted
for 3.30% of losses. It ranked fifth in ‘Hershkawitz’ and contributed to 3.30% of fruit
loss. The highest occurrence of blemish was recorded in ‘Wonderful’ with 3.70% of losses.
The presence of fruit with blemish at the packhouse is usually the result of oversight
from the on-farm fruit sorters as they are unlikely to be caused by packhouse handling
operations. Blemish is mostly a result of mechanical damage during and after pruning
before pomegranate fruit are picked. Again, sharp tree branches scratch fruit when thrown
against them by the wind, leaving blemish marks on the affected fruit. Blemish is a
strong factor in determining pomegranate fruit quality both for export and local market
because external attractiveness of pomegranate fruit depends strongly on a blemish-free
appearance [52].
3.2.3. Biological Damage (Insect Damage)
Insect Damage
The results show that insect damage contribution to pomegranate fruit losses at the
packhouse was low. The highest incidence of insect damage was in ‘Acco’, where it ranked
sixth in the causes of loss and accounted for 3.90% of losses (Table 2). The lowest incidence
was in ‘Hershkawitz’ with 2.20% of losses and ranked eighth in the causes of loss. For
‘Wonderful’, it accounted for 2.77% of losses. Insect damage had no significant relationship
with other defects assessed in the correlation analysis (Table 3). This indicates that insect
damage in this present study occurred independently of other defects and that it was not
as a result of packhouse operation. It could also mean that a significant amount of fruit
damaged by insects were discarded at the farm level.
Insect damages downgrade the quality of pomegranate fruit since a small portion of
the fruit is consumed, which results in a partial loss of the affected fruit and in making
them not meet market standard. The affected fruit were discarded from the processing line
and sold at a low price since part of the fruit could still be used for other purposes such as
the manufacturing of dye and animal feed.
26
Sustainability 2021, 13, 5187
3.2.4. Microbial and Pathological Spoilage (Decay and Rots, Alternaria, Crown Rot)
Decay and Rots
Decay and rots are one of the least frequent causes of pomegranate fruit loss among
the three cultivars assessed at the packhouse. For ‘Acco’, it accounted for 2.22% of losses
(Table 2). They contributed to 1.90% of losses in ‘Hershkawitz’ and 2.22% in ‘Wonderful’.
Decay and rot had no significant relationship with other defects in the correlation analysis
(Table 3). This indicates that decay at the packhouse, in this present study, was not a result
of packhouse operation (handling). Therefore, the decayed fruit were because of a sorting
oversight at the farm level. Decay and rot are a result of microbial pathogens that break
down the rind of the affected fruit, which results in partial or total decay and rot [
20
].
Decayed fruit do not meet market standard and are often buried or composted.
Alternaria
Alternaria disease varied among the three studied cultivars at the packhouse. However,
its contribution to total fruit loss was low. The highest incidence of Alternaria was in ‘Acco’,
where it contributed to 4.30% of losses (Table 2). For ‘Hershkawitz’, Alternaria accounted
for 3.10% of loss and ranked sixth in the causes of loss in the cultivar, and contributed
2.96% of loss in ‘Wonderful’. Alternaria disease occurs at the farm and fruit discarded at the
packhouse due to the disease were due to a sorting oversight at the farm because often, it
is difficult to determine infected fruit physically.
Alternaria is a pomegranate fruit disease caused by the Alternaria alternata pathogen.
The disease causes fruit to decay partially or totally from the inside. In contrast, the rind of
the affected fruit appears healthy [
19
]. The affected fruit are light in weight, which makes
them float during chlorine baths at the packhouse processing line. Alternaria-affected
pomegranate fruit are intensely reddish in colour compared to an Alternaria-free fruit.
These fruits are often buried or composted.
Crown Rot
Crown rot accounted for a low amount of pomegranate fruit loss at the packhouse. It
contributed to 2.22% of losses in ‘Acco’ (Table 2). The highest occurrence of crown rot was
in ‘Hershkawitz’, where it accounted for 2.96% of loss and ranked seventh in the causes of
loss for the cultivar. For ‘Wonderful’, it ranked tenth in the causes of loss and accounted
for 1.67% of losses. Crown rot showed no significant relationship with other defects in
the correlation analysis (Table 3), which suggests that it occurred for reasons outside of
the packhouse. Like Alternaria, crown rot is a farm disease and did not originate at the
packhouse, rather, it was found due to a sorting oversight at the farm.
Crown rot is caused by Coniella granati, a fungi pathogen [
19
], which mostly affects
pomegranate fruit on the farm. The rind of the affected fruit shows the presence of pycnidia
with rotten crown [
19
]. Fruit affected by crown rot were discarded for not meeting the
market standard, and as such, were sold at a cheap price for industrial products such as
ink and dye.
3.2.5. Irregular Fruit Size and Shape (Oversized and Misshapen)
Oversized
Oversized fruit were only observed among the ‘Wonderful’ cultivar and in a very
small quantity. Therefore, oversized fruit contributed little to overall pomegranate fruit
loss in the cultivar. Oversized fruit accounted for 2.24% of loss (Table 2). The oversized
fruit were not able to fit comfortably into the 3.8 kg equivalent carton used for pomegranate
fruit packaging, and therefore, were sorted to be sold and used for other purposes such as
juicing.
Misshapen
Pomegranate fruit discarded for being misshapen were very few and contributed
least to the causes of loss. Such fruit were found only in ‘Hershkawitz’ and ‘Wonderful’.
27
Sustainability 2021, 13, 5187
For ‘Hershkawitz’, it contributed to 1.90% of loss, and in ‘Wonderful’, 1.66% (Table 2).
The misshapen fruit were good fruit with irregular shapes, hence, they did not appear
appealing for the shelves but could be used for producing juice, jam, and dye.
3.3. Comparative Analysis of Pomegranate Fruit Based on Defects
Fruit were discarded from the processing line for not meeting market standard due to
bruising and injury (during handling), and other defects such as sunburn and microbial
and pathological diseases that originated from the farm. Although packhouse defects
are not considered cultivar-dependent, this study evaluated the relationship between
pomegranate fruit defects and the cultivars using principal component analysis (PCA).
The result was observed in biplot axes, which shows a relationship by the clustering of
active variables (defects), in the red colour, around active observations (cultivars), in the
blue colour (Figure 1). The result revealed that oversized and misshapen fruit were most
common amongst the ‘Wonderful’, as evidenced by their clustering around ‘Wonderful’.
At the same time, insect damage and Alternaria were predominant in ‘Acco’. Decay and
crown rot were primarily associated with ‘Herskawitz’. Bruise and injury, which are
mainly due to fruit handling, were observed to affect the three cultivars relatively equally.
Environmental stress factors (sunburn and cracks) were also found to affect the three
cultivars in a similar proportion. A dendrogram cluster analysis was done to evaluate
whether different packhouse management practices would be advisable for the handling
of each cultivar (Figure 2). The result suggests that implementing different packhouse
management practices is not necessary for each cultivar as the three cultivars clustered
around each other in cluster 2 and 3, which supports the fact that packhouse fruit loss is
not cultivar dependent, rather due to postharvest handling practices and preharvest factors
which caused some of the defects ab initio. Cluster 1 consists only of ‘Wonderful’, and this
could be attributed to misshapen and oversized fruit, which were majorly associated with
the cultivar.
ȱ
Alternaria
oversize
bruise
Injury
Sunburn
Crack
Insectdamage
Crownrot
decay
Blemish
Mishapen
Acco
acco
Acco
Acco
Acco
Acco
Acco
acco
acco
acco
Acco
Acco
Acco
acco
acco
Acco
Acco
acco
hersk
Herskawitz
Herskawitz
hersk
hersk
hersk
Herskawitz
Hersawitzk
Herskawitz
Herskawitz
Herskawitz
hersk
Herskawitz
Hersawitzk
hersk
Herskawitz
hersk
hersk
wond
wond
Wonderful
Wonderful
Wonderful
Wonderful
Wonderful
Wonderful
Wonderful
Wonderful
Wonderful
Wonderful
Wonderful
wond
Wonderful
Wonderful
wond
Wonderful
Ͳ4
Ͳ3
Ͳ2
Ͳ1
0
1
2
3
4
5
Ͳ4 Ͳ3 Ͳ2 Ͳ1012345
Biplot
Activevariables Activeobservations
Figure 1. Observation chart showing fruit defects according to cultivars.
28
Sustainability 2021, 13, 5187
ȱ
wond
Wond
wond
wond
acco
wond
wond
wond
acco
wond
hersk
hersk
wond
acco
acco
acco
hersk
wond
acco
hersk
hersk
acco
acco
acco
hersk
acco
hersk
hersk
hersk
acco
hersk
hersk
wond
wond
wond
hersk
hersk
wond
hersk
hersk
wond
wond
acco
acco
acco
hersk
acco
hersk
wond
acco
hersk
acco
acco
wond
0
10
20
30
40
50
60
70
Dissimilarity
Dendrogram
Figure 2.
Dendrogram of cluster analysis of three pomegranate cultivars studied based on defects.
Key: wond = ‘Wonderful’, acco = ‘Acco’, hersk = ‘Hershkawitz’.
The analysis of variance (ANOVA) was performed to evaluate differences in how
the defects affect cultivars, as presented in Table 4. The effects of defects on ‘Acco’ and
‘Hershkawitz’ were similar but different in ‘Wonderful’ except for sunburn and superficial
injury. The defects originated from sources such as environmental stress, mechanical
and physical damage, biological damage, microbial and pathological spoilage, and lastly,
irregular fruit size and shape (Table 4). The results show that environmental stress was
the major cause of pomegranate fruit losses at the packhouse. However, it is important
to note that the environmental factors originated from the farms and the affected fruit
were discarded at the packhouse as they did not meet the required market standard.
Environmental stress accounted for the highest incidence of loss, with 49.44% of the total
losses. Mechanical and physical damage also caused significant loss of fruit, accounting for
37.84% of total fruit losses. The biological damage factor was only insect damage, which
contributed 2.96% of losses while irregular fruit size and shape contributed least to losses
with 1.92% and were mostly in ‘Wonderful’. Lastly, microbial and pathological spoilage
accounted for 7.84% of total losses.
29
Sustainability 2021, 13, 5187
Table 4. Comparison between cultivars and fruit defects contributing to postharvest loss in the case study packhouse.
Cultivar
Defects Acco (Mean)
Acco
(Total)
Hershkawitz
(Mean)
Hershkawitz
(Total)
Wonderful
(Mean)
Wonderful
(Total)
Total
Loss
(%)
Biological
Insect damage
(mean)
3.50
± 0.72
e,
*21
2.00
± 0.68
e,f
12
2.50
± 0.43
d
15
Total 21 12 15 48 2.96
Irregular fruit
size and shape
Misshapen
0.00
± 0.00
f
0
1.67
± 0.42
e,f
10
1.50
± 0.43
d
9
Oversized
0.00
± 0.00
f
0
0.00
± 0.00
f
0
2.00
± 0.63
d
12
Total 0 10 21 31 1.92
Mechanical
damage
Bruise damage
12.00
± 1.61
d
72
11.50
± 0.43
d
69 9.83 ± 1.08
c
59
Superficial
injuries
21.00
± 0.73
b
126
21.33
± 0.80
b
128
17.17
± 0.54
b
103
Blemish 3.00
± 0.52
e
18 3.00 ± 0.37
e
18
3.33
± 0.42
d
20
Total 216 215 182 613
37.84
Environmental
stress
Sunburn 25.83
± 0.87
a
155 26.83 ± 1.47
a
161 31.33 ± 0.61
a
188
Cracks and splits 16.83
± 1.08
c
101 16.50 ± 1.28
c
99
16.17
± 1.14
b
97
Total 256 260 285 801
49.44
Microbial and
pathological
Alternaria 3.83
± 0.83
e
23 2.83 ± 0.70
e
17
2.67
± 0.33
d
16
Crown rot
2.00
± 0.26
e,f
12 2.67 ± 0.61
e
16
1.50
± 0.50
d
9
Decay and rots
2.00
± 0.37
e,f
12
1.67
± 0.21
e,f
10
2.00
± 0.63
d
12
Total 47 43 37 127 7.84
* Mean values in the same row followed by different letters (a–f) indicate significant differences (p < 0.05).
4. Discussion
4.1. Historical Packhouse Data on Pomegranate Fruit Losses at Case Study Packhouse in
Wellington, Western Cape, South Africa
Historical fruit loss data for 2016 and 2019 at the case study packhouse were analysed
in comparison with the results of this present study and presented in Figure 3. The result
suggests that marketing standard is a major source of fruit loss at the packhouse. This
means that some fruit deemed suitable for marketing (export and local) at the farm level
do not meet the packhouse marketing standard as a result of defects originating from the
farm. This is evident in the contribution of sunburn and cracks to fruit losses as compared
to bruise and injury, which are believed to be because of transportation and handling at the
packhouse level. Furthermore, blemish, which also originates from the farm, was found
to account for a significant amount of fruit loss at the packhouse according to both the
packhouse historical data and the result obtained from the present study.
30
Sustainability 2021, 13, 5187
ȱ
0
5
10
15
20
25
30
35
40
Loss(%)
Cultivarsandyears
Alternaria Bruise Superficialinjuries Sunburn
Crack Insectdamage Crownrot Decay
Blemishes Misshapen Oversized Undersized
Figure 3.
Comparison of historical packhouse pomegranate fruit defect data (2016 and 2019) and the
present study.
4.2. Economic, Environmental, and Resource Impacts
The impacts of pomegranate fruit loss estimated in this study are based on the mag-
nitude of incidence of pomegranate fruit loss at the case study packhouse in Wellington,
Western Cape Province and retail price in South Africa. This is to reveal the potential
production inputs and resources that are wasted in producing the pomegranate fruit that
are lost. For example, the energy used for the production of wasted food could be used for
another productive purpose such as cold storage to preserve food. Typically, the amount
of packhouse fruit loss at the national and global level might be different depending on
a range of factors including production practices, postharvest handling, and the market
standard at the importing markets. The estimations are particularly important to raise
awareness on the importance of reducing fruit losses at the packhouse level given several
sustainability challenges that the world is facing, which require prudent use of resources
today to create a future with sufficient material and natural resources [53].
The retail price of pomegranate fruit at the supermarket means that ZAR 88.99 (USD
5.26) is lost per 1 kg of lost pomegranate fruit in South Africa. Based on the annual average
loss of 7.16% at the case study packhouse (Table 5), which translates to 328.79 tonnes, the
monetary loss of the total annual production was estimated at ZAR 29.5 million (USD
1,754,984). During the production of pomegranate fruit, greenhouse gases(GHGs) are
emitted into the atmosphere. Based on the findings of this study, the pomegranate losses
at the packhouse level were estimated to emit about 157,819 CO
2
eq. To sink this amount
of CO
2
eq would require planting about 4 million trees at 0.039 metric ton CO
2
per tree
planted [
54
]. Furthermore, an estimated 2,005,619 MJ of energy and 299,198.9 m
3
of water
were wasted in production. This amount of wasted water could meet the daily water
requirement of up to 109,896 persons in a year at 0.05 m
3
utilised per person per day [
55
].
Again, the production of the lost fruit could take up to 8.54 ha of land, that could have
otherwise been used to provide public utilities such as a shopping complex.
31
Sustainability 2021, 13, 5187
Table 5.
Summary of the magnitude of pomegranate fruit losses and impacts at the packhouse, South Africa, and global
levels.
Factors Case Study Packhouse South Africa Global
Production volume (tonnes) * 4592.00 32,572.11
3139
× 10
3
Average loss (%) 7.16 7.16 7.16
Retail price (ZAR/kg)
a
89.99 89.99 89.99
Estimated physical and economic losses
Physical loss (tonnes) 328.79 2,332.16 224,792.50
Monetary loss (ZAR)
29
× 10
6
209 × 10
6
20,229 × 10
6
Environmental impacts
Estimated GHG emission (CO
2
eq)
b
157 × 10
3
1 × 10
6
107 × 10
6
Estimated energy used (MJ)
c
2 × 10
6
14 × 10
6
1371 × 10
6
Resource impact
Water footprint (m
3
)
d
299 × 10
3
2122 × 10
3
204,561 × 10
3
Equivalent land used to produce lost fruit (ha) 8.54 60.58 5838.77
* Production statistics is estimated from Sonlia packhouse [
56
].
a
Supermarket retail price in Stellenbosch, Western Cape, South Africa.
b,c
Impacts per unit fruit produced estimated from [34].
d
Impact per unit fruit produced estimated from [35].
The economic and environmental impacts of pomegranate fruit losses at packhouse
were also estimated at the national (South Africa) level. Losses at the national level were
estimated at 2332.16 tonnes (Table 5), which translates to an estimated ZAR 209.87 million
(USD 12.64 million) annual revenue loss. Losses at the national level were found to emit
about 1.11 million CO
2
eq. To sink this amount of CO
2
eq would require planting at
least
28 million
trees at 0.039 metric ton CO
2
per tree planted [
54
]. Furthermore, about
14.22 million
MJ of energy and 2.12 million m
3
of water were wasted to grow the lost fruit.
The wasted water could meet the daily water requirement of about 116,289 people for a
year at 0.05 m
3
consumed per person per day [
55
]. Lastly, the land used to produce the lost
fruit was estimated at 60.58 ha of land.
Furthermore, the economic and environmental impacts of pomegranate fruit losses
were estimated at the global level using the incidence of losses and retail price in South
Africa. This assumes a 7.16% loss of total fruit conveyed to the packhouse for processing
globally, which was estimated at 224,792 tonnes (Table 5) and a retail price of ZAR 88.99/kg
(USD 5.26/kg). The revenue loss due to the lost fruit was estimated at ZAR 20.22 billion
(USD 1.2 billion). Based on the estimation, about 107.90 million CO
2
eq were emitted
annually due to losses of pomegranate fruit. To sink this amount of CO
2
eq would require
planting at least 2.7 billion trees at 0.039 metric ton CO
2
per tree planted [
54
]. Additionally,
about 1.37 billion MJ of energy and 204.56 million m
3
of freshwater were wasted. The
wasted water could meet the daily water requirement of about 11.2 million people for a
year at 0.05 m
3
utilised per person per day [
55
]. Lastly, about 5838.77 ha of land was used
to produce the lost fruit. Postharvest losses of pomegranate fruit mean a significant loss of
revenue and resources that could have otherwise been put to beneficial use.
4.3. Nutritional Impacts
The loss of pomegranate fruit contributes to food and nutritional insecurity in South
Africa due to a huge loss of essential nutrients in the lost pomegranate fruit. Some of
the nutrients lost due to postharvest losses at the case study packhouse in Wellington,
Western Cape Province of South Africa during the 2020 season are presented in Table 6.
The nutritional impacts of fruit and vegetable cannot be over-emphasised, especially given
the effect of the COVID-19 pandemic on the livelihood of individuals and their ability to
afford healthy and nutritious food. Based on the annual loss of pomegranate fruit during
operations at the case study packhouse, the lost content of sodium, fibre, carbohydrate,
iron, and ascorbic acid in fruit were estimated to meet the daily recommended nutrition
intake of 1, 7, 25, 5, and 66 people, respectively.
32
Sustainability 2021, 13, 5187
Table 6.
Selected nutritional impacts of pomegranate fruit losses at the case study packhouse, Wellington, in the Western
Cape Province of South Africa.
Case Study Packhouse National (South Africa) Global
Nutrition
factor
Amount lost
(mg100
−1
g) *
Nutritional loss (per
capita/day) **
Amount lost
(mg100
−1
g) *
Nutritional loss (per
capita/day) **
Amount lost
(mg100
−1
g) *
Nutritional loss (per
capita/day) **
Fibre
164.39
##
7.00
1166.08
##
47.00
112
× 10
3##
4496.00
Carbohydrate
3255.02
##
25.00
23,088.38
##
178.00
222
× 10
4##
17,119.00
Protein
460.30
##
10.00
3265.02
##
71.00
314
× 10
3##
6842.00
Iron 98.64 5.00 699.65 39.00
67
× 10
3
3747.00
Ascorbic acid 4931.85 66.00 34,982.40 466.00
337
× 10
4
44,959.00
Calcium 9863.70 10.00 69,964.80 70.00
674
× 10
4
6744.00
Magnesium 3945.48 13.00 27,985.92 90.00
269
× 10
4
8702.00
Sodium 1315.16 1.00 9328.64 5.00
899
× 10
3
450.00
Potassium 56,223.09 12.00 398,799.40 85.00
384
× 10
5
8179.00
* Amount lost is based on [32]. ** Nutritional loss is based on [31].
##
Amount lost is estimated in g100
−1
g.
The nutritional impacts of pomegranate fruit losses were also estimated at the national
(South Africa) level using the incidence of losses at the case study packhouse, in the Western
Cape Province of South Africa (Table 6). Based on the annual losses of pomegranate fruit
at the packhouse level, the estimate at the national level suggests that the lost content
of sodium, fibre, calcium, magnesium, and ascorbic acid in fruit could meet the daily
recommended intake of 5, 47, 70, 90, and 466 people, respectively.
The estimation of postharvest nutritional losses of pomegranate fruit at the global
level showed a huge loss of essential nutrients that could benefit people in a period where
micro and macronutrient deficiency affects not less than a third of the world population
and negatively impacts the quality of life [
57
]. Based on the annual incidence of losses in
South Africa, the selected nutrient loss globally due to pomegranate losses at the packhouse
was estimated (Table 6). The lost content of sodium, fibre, protein, potassium and ascorbic
acid in fruit could meet the daily recommended nutrition intake of 450, 4496, 6842, 8179 and
44,959 people respectively. The findings revealed that postharvest losses of pomegranate
fruit at the packhouse level also contribute to global food and nutrition insecurity.
4.4. Possible Solutions to Overcome and Limit Fruit Loss at the Packhouse
This study identified quality issues that lead to the downgrading of a significant
proportion of the fruit processed at the packhouse. The quality issues are categorised into
indirect (secondary) and direct (primary) causes of loss [
39
]. Indirect sources of loss are
mainly due to high market quality standard at the importing markets. At the case study
packhouse, pomegranate fruit that did not meet market quality standard were majorly
due to the loss of aesthetic and physical appeal because of defects and damages leading
to downgrading. Fruit losses due to high market quality standards could be classified
as unavoidable loss [
58
]. This is because market quality standards are determined by
market specifications on produce quality attributes and economic factors that are beyond
the control of the packhouse operation. Under this situation, the application of the best
available postharvest technologies at the packhouse cannot prevent such losses due to
products that fall short of market standards.
Fruit losses due to direct (primary) causes at the case study packhouse include losses
due to postharvest handling of fruit during transportation from storage to the processing
line, sorting and grading; these are manifested as superficial injuries, cuts, and bruises [
50
].
Since poor postharvest handling practices are a major cause of fruit loss, possible solutions
to reduce loss must be practical and technologically driven [
59
,
60
]. Fruit quality improve-
ment can be achieved by local investment in technological innovations through research
to improve knowledge in life cycle assessment, processing, and handling of pomegranate
fruit at the packhouse [
59
,
61
,
62
]. The conventional manual sorting technique used at
the packhouse is subjective and often leads to damaging wholesome fruit because fruit
sorters most times are unable to make distinction between internally damaged fruit and
a good fruit. Hence, possible technological improvements in the packhouse line such as
non-destructive sorting techniques using remote sensing along the processing line would
33
Sustainability 2021, 13, 5187
limit basic sorting errors leading to cutting fruit open to ascertain the presence of internal
diseases. In addition to technological innovation, the reduction of fruit loss will require
the continuous training of packhouse staff on fruit handling, especially the fruit sorters
and forklift drivers. This is important because reckless transportation from temporal cold
storage to the packhouse processing line causes bruises, which lead to fruit loss. This could
be limited by educating forklift drivers about the impact of vibration and compression on
pomegranate fruit.
5. Conclusions
This study found that pomegranate fruit loss at the case study packhouse in Welling-
ton, Western Cape Province of South Africa ranged between 6.74 to 7.69%. This translates
to 328.79 tonnes of pomegranate fruit removed from the packhouse processing line per
production season. This amount of fruit is removed from the value chain for not meeting
the minimum market standard and are sold at a low price for juicing and as raw material for
dye and ink production. The major direct cause of pomegranate fruit loss at the packhouse,
as identified in this study is handling (bruise and injuries). Environmental stress (sunburn
and cracks) and microbial and pathological diseases were also contributors to loss. It is
interesting to note that the result of the causes of loss in this study is similar to the historical
packhouse report as analysed.
The result of the magnitude of losses shows that the incidence of loss was lowest in
‘Acco’ with 6.74% of losses. The amount of loss in ‘Herskawitz’ and ‘Wonderful’ were
similar with 7.14% and 7.69%, respectively. Market standard (especially the export market)
is greatly influential on the amount of losses recorded at the packhouse. This is because
most of the produce are exported to Europe and the Middle East, where only premium
quality fruit are accepted. This means that fruit deemed marketable at the farm level may
be discarded at the packhouse resulting in loss.
Packhouse fruit losses have a huge economic, environmental, resource, and nutritional
impacts as exemplified in this study. The economic impact reflects the loss of revenue
by farmers and other actors along the value chain. Environmental and resource impacts
are evident in the unsustainable use of resources to produce lost and wasted fruit, and
the nutritional impact results in food insecurity due to the wasted nutrients that would
have otherwise benefitted people. Considering the various impacts of postharvest losses
at the packhouse level, postharvest losses and waste reduction is a sustainable means
of ensuring food and nutritional security. Furthermore, reducing postharvest losses and
waste would help mitigate the effects of global warming and increase revenue for the food
value chain actors.
Author Contributions:
Conceptualisation, U.L.O.; methodology, I.K.O.; software, O.A.F.; validation,
U.L.O. and O.A.F.; formal analysis, O.A.F.; investigation, I.K.O.; resources, U.L.O.; data curation,
I.K.O.; writing—original draft preparation, I.K.O.; writing—review and editing, O.A.F.; visualisation,
I.K.O.; supervision, U.L.O., O.A.F.; project administration, U.L.O.; funding acquisition, U.L.O. All
authors have read and agreed to the published version of the manuscript.
Funding:
This work is based on the research supported by the National Research Foundation of
South Africa (Grant Numbers: 64813). The opinions, findings, and conclusions or recommendations
expressed are those of the author(s) alone, and the NRF accepts no liability whatsoever in this regard.
The APC was funded by the NRF (Grant number 64813).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data are not publicly available to protect the privacy of the case
study packhouse.
Conflicts of Interest: The authors declare no conflict of interest.
34
Sustainability 2021, 13, 5187
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sustainability
Article
Postharvest Losses in Quantity and Quality of Table Grape
(cv. Crimson Seedless) along the Supply Chain and Associated
Economic, Environmental and Resource Impacts
Anelle Blanckenberg
1,2
, Umezuruike Linus Opara
2,
* and Olaniyi Amos Fawole
2,3,
*
Citation: Blanckenberg, A.; Opara,
U.L.; Fawole, O.A. Postharvest Losses
in Quantity and Quality of Table
Grape (cv. Crimson Seedless) along
the Supply Chain and Associated
Economic, Environmental and
Resource Impacts. Sustainability 2021,
13, 4450. https://doi.org/10.3390/
su13084450
Academic Editors: Alessandro Suardi
and Nadia Palmieri
Received: 8 March 2021
Accepted: 7 April 2021
Published: 16 April 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1
Department of Horticultural Sciences, Faculty of AgriSciences, Stellenbosch University,
Stellenbosch 7600, South Africa; 12938033@sun.ac.za
2
Africa Institute of Postharvest Technology, South African Research Chair in Postharvest Technology,
Department of Horticultural Sciences, Faculty of AgriSciences, Stellenbosch University,
Stellenbosch 7600, South Africa
3
Postharvest Research Laboratory, Department of Botany and Plant Biotechnology,
University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa
* Correspondence: opara@sun.ac.za (U.L.O.); olaniyi@sun.ac.za (O.A.F.)
Abstract:
High incidence of postharvest losses is a major challenge to global food security. Addressing
postharvest losses is a better strategy to increase business efficiency and improve food security rather
than simply investing more resources to increase production. Global estimates show that fruit and
vegetables are the highest contributors to postharvest losses and food waste, with 45% of production
lost. This represents 38% of total global food losses and waste. However, the lack of primary data on
postharvest losses at critical steps in the fruit value chain and the unknown economic, environmental
and resource impacts of these losses makes it difficult to formulate mitigation strategies. This paper
quantifies postharvest losses and quality attributes of ‘Crimson Seedless’ table grapes at farm and
simulated retail levels. Table grapes were sampled from four farms in the Western Cape Province of
South Africa, the largest deciduous fruit production and export region in Southern Africa. Mean on-
farm losses immediately after harvest was 13.9% in 2017 and 5.97% in 2018, ranging from 5.51%
to 23.3% for individual farms. The main reason for on-farm losses was mechanical damage (7.1%).
After 14 days in cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), mean grape losses were 3.05% in
2017 and 2.41% in 2018, which increased to 7.41% in 2017 and 2.99% in 2018, after 28 days. After
10 days of further storage under simulated market conditions (5.4
±
0.6
◦
C, 83.7
±
2.9% RH), fruit
losses were 3.65% during retail marketing and 4.36% during export. Storing grapes under ambient
conditions (25.1
±
1.3
◦
C and 46.6
±
6.0% RH) resulted in a higher incidence of losses, increasing
from 7.03 to 9.59 and 14.29% after 3, 7 and 10 days, respectively. The socioeconomic impacts of
these postharvest losses amounted to financial losses of over ZAR 279 million (USD 17 million
according to the conversion rate of 20 October 2020) annually, and this was associated with the loss
of 177.43 million MJ of fossil energy, 4.8 million m
3
of fresh water and contributed to the emission of
approximately 52,263 tons of CO
2
equivalent.
Keywords:
postharvest losses; food waste; physicochemical properties; table grape; shelf-life; decay;
stem browning; SO
2
damage; socioeconomic impacts
1. Introduction
Interest in the mitigation of postharvest losses is heightened due to global concern
about food insecurity. Preserving the food supply after production has since the earliest
times been a problem for humankind [
1
]. However, a dominant challenge of the 21st
century is how to feed the growing world population sustainably, predicted to reach
9.1 billion by 2050, affordably while using the natural resources required equitably and
sustainably [2,3].
Sustainability 2021, 13, 4450. https://doi.org/10.3390/su13084450 https://www.mdpi.com/journal/sustainability
39
Sustainability 2021, 13, 4450
The UN Food and Agriculture Organization (FAO) estimated that feeding the growing
world population by 2050 would require 70% increase in food production [
4
]. However,
the FAO also reports that approximately one-third of the edible portions of the food
produced globally is lost or wasted along the supply chain, from farm to plate [
5
], with a
total of 38% of the volume consisting of fruits and vegetables [
6
]. Lost and wasted food
consumes a quarter of all the water used by agriculture annually, requires farmland area the
size of China, and generates an estimated 8% of global greenhouse gas emissions. In effect,
if lost and wasted food were a country, it would be the third-largest greenhouse gas emitter
on the planet after China and the United States [7–9].
While 95% of all agricultural research investment focuses on increasing crop produc-
tion strategies, only 5% focuses on postharvest issues [
10
]. Reducing postharvest loss and
waste is more cost effective and less time consuming than production strategies. Therefore,
the improvement of postharvest chains must receive as much attention as production
practices. Furthermore, limiting the loss of fresh fruit will reduce the use of land, chemicals,
energy, and other inputs needed to produce horticultural crops, thereby conserving natural
resources and protecting the environment [11,12].
A major obstacle in achieving postharvest loss mitigation is a lack of clear knowledge
of the real magnitudes of losses, making it impossible to measure progress against any loss
reduction targets [13].
Apart from worrying anecdotal information, there is little scientific data available
about food losses in South Africa. A study [
14
] provided a preliminary estimate of the
magnitude of food loss and waste generation in South Africa of around 9.04 million tons
per year. However, this study was based on available data as reported by FAO and further
assumptions as no primary data was collected. While estimates do indicate the size of the
problem, they do not provide accurate and reliable data for specific supply chains.
The lack of primary data on postharvest losses at critical steps in the fruit value chain
and the unknown economic, environmental and resource impacts makes it challenging to
formulate mitigation strategies. There is a lack of standard methods to measure postharvest
losses of food crops, including fruit and vegetable crops. Researchers have developed many
different methods during the past few decades, focusing on different aspects of the value
chain and varying types of food losses [
15
]. The lack of accurate and reliable postharvest
loss data may result in inaccurate assumptions on food wastage [16].
In South Africa, fruit and vegetables account for 47% of food wastage [
14
]. In the first
South African study generating primary data on postharvest losses of vegetables at the
retail level, the authors [
12
] found vegetable losses for carrot, tomato and cabbages to be on
average 17.93%, 15.33% and 21.21%, respectively. There are currently no primary research
data on the magnitude of losses in the fresh fruit value chain.
Fruit is a major contributor to the agricultural industry, considering foreign exchange
earnings and employment creation.
Table grapes account for 32% of the total area planted to deciduous fruits in South
Africa [
17
], with ‘Crimson Seedless’ cultivar accounting for 20% of all vines [
18
]. ‘Crimson
Seedless’ is a mid to late season red seedless grape cultivar with firm berries characterized
by its crisp texture and intensely sweet flavor. The main quality problems during posthar-
vest handling are moisture loss leading to loss of mass, fungal infection and shriveled
rachis, which then become brittle and break easily, and the browning of the stems, which
reduces the visual appeal and price of the product [19–21].
Although table grapes are nonclimacteric fruit with a relatively low rate of physio-
logical activity [
22
], they are subject to severe postharvest losses during storage and long
distance transport [
23
]. Rapid moisture loss, which results in rachis (cluster stem) drying
and browning [
24
,
25
], mass loss [
26
], berry shatter, wilting and shriveling of berries [
27
]
are some of the main quality problems experienced during postharvest handling causing
quantitative and quality losses. It has been suggested that inappropriate handling processes
are the main reason for weakening the natural defenses of grapes and making fresh grapes
more susceptible to decay and subsequent deterioration [28].
40
Sustainability 2021, 13, 4450
As the gross value of production of table grapes has increased significantly from R2 bil-
lion in 2006/07 to R7.1 billion in 2015/16, an increase of 248% [
17
], reduction of postharvest
losses by even a few percentage points will not only reduce the cost of production, trade
and distribution but will also have significant financial implications for all involved in the
supply chain by lowering the price for the consumer and increasing the farmers’ income.
Therefore, this research aimed to fill a gap in the knowledge by generating primary data
on postharvest losses at critical steps in the supply chain for table grape ‘Crimson Seedless’
to inform future action to reduce and better manage the food waste problem.
2. Materials and Methods
2.1. Harvesting Techniques and Berry Preparation
Data collection protocols were similar to those used by [
29
]. Grapes were harvested
from four farms during the last week of February and the first week of March in 2017,
and during the first three weeks of February in 2018. Both times were during the commercial
harvest. In 2017 a total sample size of 1200 bunches (300 bunches per farm) were collected,
while 1600 bunches (400 bunches per farm) were collected in 2018. The grapes were
collected from four farms that each have their own packhouse on site, near De Doorns,
Robertson and Piketberg in the Western Cape Region of South Africa. Bunches were
weighed in the packhouse as they came in from the vineyard, each bunch was then tagged
with a unique label identifying the farm of origin (V; K; R or D), the supply chain scenario
the bunch was destined for (A; B; or C in 2017 and A; B; C or D in 2018) and the bunch
number (1–100) e.g., VA29 (Table 1).
Table 1. Description of the supply chain scenarios studied.
Supply Chain Scenario Description Environmental Condition
A
Table grapes were harvested and stored
under ambient conditions, typical in areas
that lack cold storage facilities
Measurements were taken at harvest and
after 3, 7 and 10 days
Under ambient conditions for 10 days:
25.1
± 1.3
◦
C
46.6
± 6.0% RH
B
Handling of table grapes for domestic
supply chain
Measurements were taken at harvest, after
14 days in cold storage, after 10 days at retail
conditions and then after 3, 7 and 10 days at
ambient conditions
Cold store for 2 weeks:
−
0.3
◦
C
±
0.7
◦
C and
81.3% ± 4.1% RH
Retail store for 10 days: 5.4
◦
C ± 0.6
◦
C and
83.7%
± 2.9% RH
Consumer/home (ambient) store:
25.1
± 1.3
◦
C and 46.6 ± 6.0%RH
C
Shipping to export markets
Measurements were taken at harvest, after
28 days in cold storage, after a further
10 days at retail conditions and then at 3,
7 and 10 days at ambient conditions
Cold storage for 4 weeks at
−0.3 ± 0.7
◦
C,
81.3
± 4.1% RH
Retail store for 10 days: 5.4
◦
C ± 0.6
◦
C and
83.7%
± 2.9% RH
Consumer/home (ambient) ‘shelf’ store:
25.1
± 1.3
◦
C and 46.6 ± 6.0%RH
D
Reefer container containing export fruit are
left open on arrival for 2 days before fruit is
unloaded. ‘Abusive’ treatment of fruit within
the export chain
Measurements were taken at harvest, after
28 days in cold storage then after 2 days
exposure to ambient conditions, after a
further 10 days at retail conditions and then
at 3, 7 and 10 days at ambient conditions
Cold store for 2 weeks:
−
0.3
◦
C
±
0.7
◦
C and
81.3% ± 4.1% RH;
Ambient storage for 2 days: 25.1
± 1.3
◦
C,
46.6
± 6.0% RH;
Retail store display for 10 days:
5.4
◦
C ± 0.6
◦
C and 83.7% ± 2.9% RH;
Consumer/home (ambient) ‘shelf’ store:
25.1
± 1.3
◦
C and 46.6 ± 6.0% RH
The grape bunches were then trimmed by expert packers according to commercial
practice and packed into standard 9-kg cartons (internal dimensions of the 9-kg boxes were
58 cm long
×
34 cm wide
×
13.2 cm high) with a riffled sheet at the bottom, a plastic liner
41
Sustainability 2021, 13, 4450
as well as an SO
2
cover pad on top with a slow release of sodium metabisulfite (Na
2
S
2
O
5
)
(Uvasys
®
, Cape Town, South Africa). Sodium metabisulfite generates sulphurous anhy-
dride gas (S0
2
) when in contact with humidity, inhibiting the development and growth of
fungi in table grapes during refrigerated packaging and transport.
2.2. Supply Chains Simulated
In 2017, 18 cartons per farm were collected and divided equally into three simulated
supply chain scenarios. In 2018, 24 cartons per farm were collected and divided into four
simulated supply chain scenarios. From each farm and for both years, 100 bunches were
used for each supply chain scenario simulated, i.e., 400 bunches per scenario. Four supply
chain scenarios were studied (Table 1), representing the range of postharvest handling
practices that occur in local and export marketing of table grapes in the South African fresh
fruit industry. According to export grape producers (pers. communication Amelia Vorster,
Technical Advisor (Quality)—Karsten Western Cape), scenario D is a common occurrence
and leads to tension between role-players as to whether the fruit was mishandled before
the report was written and who is responsible for the losses if it is higher than expected.
2.3. Fruit Loss Evaluation and Quality Measurements
2.3.1. Postharvest Losses
The base measurement for losses at harvest occurred in the packhouse on each farm
after the bunches were trimmed for packaging and the resulting berries sorted into cate-
gories based on the reason for being cut from the bunch, (1) berry too green in color, (2)
mechanical damage or (3) decayed. It was quantified as the weight of the berries removed
as a percentage of the original weight of the bunches before trimming. At each evaluation
date thereafter, physical losses were quantified as the decrease in bunch weight and the
amount lost due to decay or SO
2
damage (a high concentration of SO
2
can damage table
grapes by causing bleaching, cracking or causing early browning of the rachis) expressed
as a percentage of the initial berry numbers per bunch.
2.3.2. Quality Attributes
The following attributes were measured at each evaluation time:
1. Weight loss
Expressed as a percentage of the initial bunch weight.
30 bunches × 4 farms = 120
bunches per supply chain scenario.
2. Stem browning
Rated on a 5-point scale, with 1 being fresh/green and 5 being dry/brown [
15
,
16
].
In total, 30 bunches × 4 farms = 120 bunches per supply chain scenario.
3. Total soluble solids (TSS) concentration
Fruit juice was extracted using a manual juice extractor (TMS
®
hand press commercial
pro manual juice squeezer). TSS of juice was measured with a digital refractometer (Atago,
Tokyo, Japan). A total of 12 bunches per supply chain scenario were used.
4. Titratable acidity (TA)
TA of juice was determined by titration to pH 8.2 using a Metrohm 862 compact
titrosampler (Herisau, Switzerland). A total of 12 bunches per supply chain scenario
were used.
5. Peel color
Color was assessed using a colorimeter (Minolta CR-400, Minolta Corp, Osaka, Japan)
and expressed as CIE L*, a*, b* coordinate where L* defines lightness, a* denotes the
red/green value and b* the yellow/blue value [
30
]. A total of, 120 berries per supply chain
scenario were evaluated for peel color.
6. Firmness
Berry firmness (N) was measured by compression (TA.XT.plus, Stable Micro Systems
Ltd., Surrey, UK) [
31
]. In total, 120 berries per supply chain scenario were evaluated
for firmness.
42
Sustainability 2021, 13, 4450
2.4. Environmental and Economic Impacts of Postharvest Losses
Total greenhouse gas emissions were calculated using values provided by [
32
]. That
study examined the annual cycle for grape production, beginning with establishment costs,
raw material extraction for production of inputs used on the vineyard and included the
factors of fertilizer, tillage, irrigation, pest management, electricity and fuel consumption,
ending at delivery of grapes. For every ton of grapes produced, stored and transported
to the retail market, approximately 0.91 ton of CO
2eq
is emitted into the atmosphere.
The energy cost for producing and marketing the lost produce was obtained using a
reference value of 6529 MJ/ton provided by [
33
], and the water footprint was determined
by multiplying the quantity of lost produce with the reference water footprint value of
210.35 m
3
/ton provided by [
34
]. The value of table grapes lost was calculated using
values provided by [
17
], R13134/ton for locally sold produce and R21002/ton for exported
produce.
2.5. Statistical Analysis
Data on farm losses at harvest were subjected to a one-way analysis of variance
(ANOVA) and the physicochemical analysis data for firmness, total soluble solids (TSS),
titratable acidity (TA), peel color, weight loss, decay, SO
2
damage, and stem browning
were subjected to mixed model analysis of variance (ANOVA) using Statistica version 13.2
(TIBCO Software Inc., Palo Alto, CA, USA) with ‘farm’ and ‘time’ as fixed effect and cartons
as a random effect.
3. Results
3.1. Physical Losses at Farm Level
In 2017 the measured losses at harvest for individual farms were 7.5%, 9.7%, 15.7%,
and 23.3% for V, K, R, or D, respectively, while in 2018 the same farms lost 6.17%, 6.39%,
5.51%, and 5.85%. The average loss in 2017 was 13.9% and 5.97% in 2018. The main reasons
for the losses in 2017 were mechanical damage (7.1%), poor berry color (5%), and decay
(1.8%). In 2018 the reasons remained the same, although the amounts lost differed with
mechanical damage (3.09%), poor berry color (1.77%), and decay (1.11%).
3.2. Physical Losses along the Simulated Supply Chain
3.2.1. Weight Loss, Decay and SO
2
Damage
Supply chain scenario A (handling and marketing fruit under ambient conditions)
There was no statistically significant weight loss (p = 0.28) after harvest in 2017
(
Table 2
); however, there was a decrease in weight of 2.34% after 3 days, 4.47% after 7 days
and 7.6% after 10 days, while in 2018 a decrease of 1.55% was noted after 3 days, 1.83% after
7 days and 4.43% after 10 days. (p = 0.19). While not statistically significant, this decrease
in weight is important in terms of losses as it could affect the profit margin as fruit are sold
by weight. The incidence of decay increased significantly (p < 0.01) over time from 1% after
3 days to 3.3% after 7 days and 7.6% after 10 days in 2017 and from 0.85% after 3 days to
1.67% after 7 days and 2.67% 10 days after harvest in 2018 (p < 0.01).
43
Sustainability 2021, 13, 4450
Table 2.
Physical losses of ‘Crimson Seedless’ table grapes measured as weight loss (%) and de-
cayed and SO
2
damaged berries (%) after 3, 7 and 10 days at ambient conditions (25.1
±
1.3
◦
C
and
46.6 ± 6.0%RH
). Mean values with different letter(s) in the same column indicate statistically
significant differences (p < 0.05).
Season 2017 2018
Time
Weight
Loss (%)
Decay (%) SO
2
(%)
Weight
Loss (%)
Decay (%) SO
2
(%)
Harvest - 0 0 - 0 0
3 days 2.34
a
1.05
b
1.85
b
1.55
a
0.85
b
0.37
b
7 days 4.47
a
3.34
b
2.31
a
1.83
a
1.67
b
0.92
a
10 days 7.63
a
7.60
a
2.57
a
4.43
a
2.67
a
1.19
a
p-value 0.28 <0.01 <0.01 0.19 <0.01 <0.01
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
The incidence of SO
2
berry damage increased significantly over time from 1.85% after
3 days to 2.31% after 7 days and 2.57% after 10 days in 2017 and from 0.37% after 3 days to
0.92% after 7 days and 1.19% after 10 days in 2018 (p < 0.01).
Supply chain scenario B (to local retail markets)
There was no statistically significant weight loss (Table 3) in both seasons (2017;
p = 0.91
, 2018; p = 0.99). However, the weight decreased by 1.41% after 14 days in cold
storage, 1.87% after 10 days at retail conditions, and then by 2.53%, 3.78 and 5.36% after 3,
7 and 10 days under ambient conditions, respectively, in 2017. While in 2018, the decrease
in weight was 1.85% after 14 days in cold storage, 2.57% after 10 days at retail conditions,
and then 4.03%, 4.40 and 6.76% after 3, 7 and 10 days under ambient conditions. The inci-
dence of berry decay increased significantly (p < 0.01) over time, although it remained at
zero for the 14 days duration in cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH) in 2017 with
a small incidence of 0.4% in 2018. After 10 days at local retail conditions (5.4
±
0.6
◦
C,
83.7 ± 2.9% RH
), there was a decay incidence of 2.1% in 2017 and 1.2% in 2018. After
3 days under ambient conditions (25.1
±
1.3
◦
C and 46.6
±
6.0%RH), the incidence of
decay increased significantly (p < 0.01) to 2.5% in 2017 and 2.2% in 2018. After 7 days,
this increased to 5.5% in 2017 and 3.3% in 2018, and after 10 days to 8.6% and 4.7% in
2017 and 2018, respectively.
Table 3.
Physical losses of ‘Crimson Seedless’ table grapes measured as weight loss (%) and decayed
and SO
2
damaged berries (%) after 14 days cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), after
another 10 days at retail conditions (5.4
±
0.6
◦
C, 83.7
±
2.9% RH) and then 3, 7 and 10 days at
ambient conditions (25.1
±
1.3
◦
C and 46.6
±
6.0%RH). Mean values with different letter(s) in the
same column indicate statistically significant differences (p < 0.05).
Season 2017 2018
Time
Weight
Loss (%)
Decay (%) SO
2
(%)
Weight
Loss (%)
Decay (%) SO
2
(%)
Harvest - 0 0 - 0 0
14 days (
−0.5
◦
C) 1.41
a
0
1.0
b
1.85
a
0.4
e
0.8
b
10 days (5
◦
C) 1.87
a
2.1
c
1.8
ab
2.57
a
1.2
d
1.7
ab
3 days 2.53
a
2.5
c
2.1
a
4.03
a
2.2
c
2.1
a
7 days 3.78
a
5.5
b
2.2
a
4.40
a
3.3
b
2.1
a
10 days 5.36
a
8.6
a
2.4
a
6.76
a
4.7
a
2.1
a
p-value 0.91 <0.01 0.02 0.99 <0.01 <0.01
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
The incidence of SO
2
berry damage in this study was 1% after 14 days in cold storage
in 2017 (p = 0.02) and 0.8% in 2018 (p < 0.01). SO
2
damage increased significantly after
44
Sustainability 2021, 13, 4450
10 days at local retail conditions from 1.8% in 2017 and 1.7% in 2018 to 2.1% after 3 days at
ambient conditions in both years, after which there was no further significant change.
Supply chain scenario C (to international retail markets)
In 2017, weight decreased by 4.82% after 28 days in cold storage, 5.50% after 10 days
at retail conditions and 6.61%, 7.90% and 10.22% after 3, 7 and 10 days under ambient
conditions, respectively. In 2018, percentage decreases in weight were 1.89% after 28 days
in cold storage, 2.45% after 10 days at retail conditions and 2.64%, 3.95% and 5.18% after 3,
7 and 10 days under ambient conditions, respectively. (Table 4). Percentage decay increased
significantly over time (p < 0.01). After 28 days in cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1%
RH), there was a 2.14% incidence of decay in 2017 and 0.94% in 2018. After 10 days at retail
display conditions (5.4
±
0.6
◦
C, 83.7
±
2.9% RH) this increased to 3.2% in 2017 and 2.6% in
2018. After being moved to ambient temperature and humidity conditions (25.1
±
1.3
◦
C
and 46.6
±
6.0%RH) for 3 days, decay increased to 4.44% in 2017 and 3.16% in 2018. After
7 days, decay increased to 6.53% in 2017 and 4.95% in 2018, and after 10 days, it reached
9.92% in 2017 and 8.30% in 2018. SO
2
damage remained low. After 28 days in cold storage
plus 10 days at retail conditions, less than 0.5% damage was visible. In 2017 it increased
significantly (p < 0.01) after removal from cold storage to 1.39% after 3 days at ambient
conditions, 1.68% after 7 days and 1.85% after 10 days, but remained below 2% overall.
In 2018, however, it did not increase significantly (p = 0.26) over time and remained below
1% overall.
Table 4.
Physical losses of ‘Crimson Seedless’ table grapes measured as weight loss (%), decayed
and SO
2
damaged berries (%) after 28 days cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), after
another 10 days at retail conditions (5.4
±
0.6
◦
C, 83.7
±
2.9% RH) and then for 3, 7 and 10 days at
ambient conditions (25.1
±
1.3
◦
C and 46.6
±
6.0%RH). Mean values with different letter(s) in the
same column indicate statistically significant differences (p < 0.05).
Season 2017 2018
Time
Weight
Loss (%)
Decay (%) SO
2
(%)
Weight
Loss (%)
Decay (%) SO
2
(%)
Harvest - 0 0 - 0 0
28 days
(
−0.5
◦
C)
4.82
a
2.14
d
0.47
c
1.89
0.94
d
0.25
a
10 days
(5
◦
C)
5.50
a
3.20
cd
0.94
c
2.45 2.60
c
0.62
a
3 days 6.61
a
4.44
c
1.39
b
2.64 3.16
c
0.62
a
7 days 7.90
a
6.53
b
1.68
ab
3.95
4.95
b
0.62
a
10 days 10.22
a
9.92
a
1.85
a
5.18 8.30
a
0.62
a
p-value 0.85 <0.01 <0.01 0.84 <0.01 0.26
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
Supply chain scenario D (simulated ‘abusive’ treatment of fruit within the export chain)
There was no statistically significant difference in bunch weight over time (p = 0.19),
although a 1.35% decrease in weight is noted after 28 days in cold storage, 2.17% after
2 days at ‘abusive’ ambient conditions, 3.04% after 10 days at retail conditions and 3.96%,
4.74% and 5.70% after 3, 7 and 10 days under ambient conditions, respectively (Table 5).
Percentage decay increased significantly over time (p < 0.01). After 28 days in cold storage,
there was a 0.84% incidence of decay, increasing to 1.34% after breaking the cold chain
with 2 days at ‘abusive’ ambient conditions, 10 days at retail conditions increased this to
2.35%, and decay kept increasing significantly at ambient conditions to 3.24% after 3 days,
4.46% after 7 days and 6.7% after 10 days. SO
2
damage remained low, with only 0.88%
visible after 28 days in cold storage and did not increase significantly (p = 0.99) over time,
remaining around 1%.
45
Sustainability 2021, 13, 4450
Table 5.
Physical losses of ‘Crimson Seedless’ table grapes measured as weight loss (%), decayed and
SO
2
damaged berries (%) after 28 days cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), 2 days ‘abusive’
temperature and humidity (25.1
±
1.3
◦
C and 46.6
±
6.0%RH), another 10 days at retail conditions
(5.4
±
0.6
◦
C, 83.7
±
2.9% RH) and then for 3, 7 and 10 days at ambient conditions (25.1
±
1.3
◦
C
and 46.6
±
6.0%RH). Mean values with different letter(s) in the same column indicate statistically
significant differences (p < 0.05).
Season 2018
Time Weight Loss (%) Decay (%) SO
2
(%)
Harvest - 0 0
28 days (
−0.5
◦
C) 1.31
a
0.84
e
0.88
a
2 days (ambient) 2.17
a
1.33
e
0.98
a
10 days (5
◦
C) 3.04
a
2.35
d
1.06
a
3 days 3.96
a
3.24
c
1.09
a
7 days 4.74
a
4.46
b
1.10
a
10 days 5.70
a
6.70
a
1.21
a
p-value 0.19 <0.01 0.99
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
3.2.2. Total Amount of Physical Losses
When the amount of weight loss, decay and SO
2
damage are combined, the total
amount of losses along the different supply chain scenarios are as follows (Figures 1 and 2):
Figure 1.
Total physical losses of grape ‘Crimson Seedless’ at harvest 2017 along different supply chain (SC) scenarios
where SC-A represents marketing at ambient conditions, SC-B represents the supply chain to the local retail market, SC-C
represents the international supply chain, and SC-D represents the international supply chain including 2 days ‘abusive’
ambient temperature and humidity.
46
Sustainability 2021, 13, 4450
Figure 2.
Total physical losses of grape ‘Crimson Seedless’ at harvest 2018 along different supply chain (SC) scenarios
where SC-A represents marketing at ambient conditions, SC-B represents the supply chain to the local retail market, SC-C
represents the international supply chain, and SC-D represents the international supply chain including 2 days ‘abusive’
ambient temperature and humidity.
Supply chain scenario A (marketing at ambient conditions)
In 2017, 13.9% was lost at harvest, followed by 5.25% after 3 days, 7.83% after 7 days,
and 13.47% after 10 days. The total losses were 27.37%. In 2018 only 5.97% were lost at
harvest, 2.77% after 3 days, 2.87% after 7 days and 6.51% after 10 days. The total losses
were 12.48%, less than half that of the previous season.
Supply chain scenario B (to local retail markets)
After 14 days in cold storage in 2017, 2.41% were lost, 4.36% after 10 days at retail
condition followed by an additional 5.27% after 3 days at ambient conditions (shelf-life),
8.98% after 7 days, and 12.64% after 10 days. When this is added to the initial 13.9% lost
at harvest, the total for this supply chain simulation is 26.54%. In 2018, 3.05% were lost
after 14 days in cold storage, 3.65% after 10 days under retail conditions, 5.80% after 3 days
under ambient conditions and remained thus after 7 days while increasing again to 10.22%
after 10 days. The total loss along this simulated supply chain in 2018 was 16.19%.
Supply chain scenario C (to international markets)
In 2017, 7.41% were lost after 28 days in cold storage, another 4.36% after 10 days
under retail conditions and a further 7.03% after 3 days under ambient conditions, 9.59%
after 7 days and 14.29% after 10 days. A total of 28.19% was lost in this export supply
chain simulation in 2017. In 2018, 2.99% were lost after 28 days in cold storage, 3.79% after
10 days under retail conditions, 3.97% after 3 days under ambient conditions, 6.92% after
7 days, and 10.22% after 10 days. A total of 16.99% were lost in 2018.
Supply chain scenario D (simulated ‘abusive’ storage conditions of fruit within the
international supply chain)
This simulation was done during the 2018 season only. It was found that 3.03% was
lost after 28 days in cold storage, 3.19% after 2 days of ‘abusive’ ambient condition, 4.27%
after 10 days at retail conditions. After 3 days under ambient condition losses increased to
5%, 6.66% after 7 days, and 8.91% after 10 days.
47
Sustainability 2021, 13, 4450
3.3. Quality Losses along the Supply Chain
Supply chain scenario A (marketing at ambient conditions)
In the 2017 season, the berry color became lighter (L) over time, (Table 6) although
the change was just not statistically significant (p = 0.06), during the 2018 season
(Table 7
),
however, the change in lightness (L) of berry color became statistically significant (
p < 0.01
).
The measurements for a* denoting the red/green values (p = 0.45) and b* indicating the
yellow/blue values (p = 0.98) in 2017 and a* (p = 0.21) and b* (p = 0.75) in 2018, did not
change significantly. There was no significant difference in firmness in 2017 or 2018
(
p = 0.79
), although the values decreased over time. The TSS (p = 0.85) and TA (p = 0.50)
values did not change significantly in 2017 or in 2018, TSS (p = 0.75) and TA (p = 0.73).
For both seasons, the stem color changed from fresh and green to mostly dry and brown
within 7 days after harvest (p < 0.01).
Table 6.
Supply chain scenario A (2017): changes in quality attributes of color (L, a* and b*),
firmness (N), total soluble solids (TSS) (Brix
◦
), titratable acidity (TA) (%), and stem browning index
for ‘Crimson Seedless’ table grapes at harvest and after 3, 7 and 10 days at ambient conditions
(
25.1 ± 1.3
◦
C
and 46.6
±
6.0%RH). Mean values with different letter(s) in the same column indicate
statistically significant differences (p < 0.05).
Year 2017
Time L a* b*
Firmness
(N)
TSS
(Brix
◦
)
TA
(%)
Stem Browning
Index
Harvest 29.55
a
5.85
a
6.77
a
98.22
a
18.72
a
0.89
a
1
d
3 days 30.81
a
4.98
a
6.56
a
97.80
a
18.16
a
0.75
a
2.4
c
7 days 31.07
a
5.08
a
6.59
a
96.87
a
18.34
a
0.73
a
4.4
b
10 days 30.91
a
4.88
a
6.75
a
95.47
a
19.15
a
0.80
a
5.0
a
p-value 0.06 0.45 0.98 0.79 0.85 0.50 <0.01
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
Table 7.
Supply chain scenario A (2018): changes in quality attributes of color (L, a* and b*), firmness
(N), TSS (Brix
◦
), TA (%), and stem browning index for ‘Crimson Seedless’ table grapes at harvest
and after 3, 7 and 10 days at ambient conditions (25.1
±
1.3
◦
C and 46.6
±
6.0%RH). Mean values
with different letter(s) in the same column indicate statistically significant differences (p < 0.05).
Year 2018
Time L a* b*
Firmness
(N)
TSS
(Brix
◦
)
TA
(%)
Stem Browning
Index
Harvest 27.46
c
7.91
a
6.95
a
98.15
a
20.88
a
0.86
a
1
c
3 days
27.64
bc
6.80
a
6.74
a
97.73
a
16.77
a
0.76
a
2.5
b
7 days
27.96
b
6.84
a
6.83
a
96.96
a
18.60
a
0.79
a
4.6
a
10 days 28.94
a
7.85
a
7.00
a
95.55
a
19.15
a
0.85
a
4.8
a
p-value 0.06 0.45 0.98 0.79 0.85 0.50 <0.01
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
Supply chain scenario B (to local retail markets)
In 2017, there were no significant changes in any color attributes for lightness (L)
denoting black/white values (p = 0.79), a* denoting the red/green values (p = 0.49) or b*
indicating the yellow/blue values (p = 0.25) (Table 8). In 2018, no significant differences
were measured for a* (p = 0.31) and b* (p = 0.19). However, lightness (L) values increased
(p < 0.01), although this only became significant after 10 days at ambient conditions as
there was no significant difference between baseline measurements, 14 days in cold storage,
10 days at retail conditions, or even after a week at ambient temperature and humidity
(Table 9). Berry firmness (p = 0.21) in 2017 and (p = 0.49) in 2018, showed no statistically
48
Sustainability 2021, 13, 4450
significant changes. While TA (p = 0.27) and TSS (p = 0.73) showed no significant changes
in 2017, in 2018 the values did indicate an increase for both TA (p < 0.01) and TSS
(p < 0.01
)
over the storage period. For both seasons, bunch stems and rachi remained fresh and
green during the 14 days in cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), but changed
significantly (p < 0.01) during 10 days at retail display conditions (5.4
±
0.6
◦
C, 83.7
±
2.9%
RH), and after 7 days storage under ambient conditions (25.1
±
1.3
◦
C and 46.6
±
6.0%
RH), the stems were mostly dry and brown.
Table 8.
Supply chain scenario B (2017): changes in quality attributes of color (L, a* and b*), firmness
(N), TSS (Brix
◦
), TA (%), and stem browning index for ‘Crimson Seedless’ table grapes at harvest
after 14 days cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), after another 10 days at retail conditions
(5.4
±
0.6
◦
C, 83.7
±
2.9% RH) and then for 3, 7 and 10 days at ambient conditions (25.1
±
1.3
◦
C
and 46.6
±
6.0%RH). Mean values with different letter(s) in the same column indicate statistically
significant differences (p < 0.05).
2017
Time L a* b*
Firmness
(N)
TSS
(Brix
◦
)
TA
(%)
Stem Browning
Index
Harvest 33.65
a
7.76
a
7.28
a
98.23
a
17.34
a
0.99
a
1
d
14 days
(
−
0.5
◦
C)
30.78
a
6.60
a
7.08
a
100.14
a
19.39
a
0.91
a
1.4
d
10 days
(5
◦
C)
29.78
a
7.64
a
7.74
a
106.59
a
18.97
a
0.75
a
2.4
c
3 days 30.61
a
6.11
a
7.41
a
92.02
a
19.63
a
0.77
a
3.5
b
7 days 30.52
a
6.70
a
8.44
a
87.59
a
19.52
a
0.70
a
4.7
a
10 days 31.38
a
6.86
a
9.20
a
88.88
a
17.10
a
0.68
a
4.9
a
p-value 0.79 0.49 0.24 0.21 0.73 0.27 <0.01
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
Table 9.
Supply chain scenario B (2018): changes in quality attributes of color (L, a* and b*), firmness
(N), TSS (Brix
◦
), TA (%), and stem browning index for ‘Crimson Seedless’ table grapes at harvest
after 14 days cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), after another 10 days at retail conditions
(5.4
±
0.6
◦
C, 83.7
±
2.9% RH) and then for 3, 7 and 10 days at ambient conditions (25.1
±
1.3
◦
C
and 46.6
±
6.0%RH). Mean values with different letter(s) in the same column indicate statistically
significant differences (p < 0.05).
2018
Time L a* b*
Firmness
(N)
TSS
(Brix
◦
)
TA
(%)
Stem Browning
Index
Harvest
27.46
b
7.91
a
2.23
a
111.06
a
17.84
b
0.72
c
1
d
14 days
(
−
0.5
◦
C)
27.88
b
8.22
a
2.87
a
114.59
a
17.99
b
0.85
b
1.3
d
10 days
(5
◦
C)
27.32
b
7.32
a
4.05
a
121.69
a
19.14
a
0.82
b
2.7
c
3 days
27.84
b
7.05
a
2.19
a
115.23
a
18.95
a
0.78
c
3.7
b
7 days
27.56
b
6.56
a
1.94
a
119.03
a
19.13
a
0.89
a
4.6
a
10 days 30.30
a
6.01
a
3.45
a
120.27
a
18.95
a
0.85
b
4.8
a
p-value <0.01 0.31 0.19 0.49 <0.01 <0.01 <0.01
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
Supply chain scenario C (to international retail markets)
No significant difference in any color attributes (Table 10) was observed in 2017, L
(p = 0.12)
,a*(p = 0.15) and b* (p = 0.72). In 2018, however, the attribute for lightness (L)
changed significantly (p < 0.01), with berries becoming a bit lighter after removal from
49
Sustainability 2021, 13, 4450
cold storage but darkening again after 7 days under ambient conditions (Table 11), while
a* (
p = 0.26
) and b* (p = 0.22) remained the same. The average berry firmness remained
unchanged in 2017 (p = 0.90) and in 2018 (p = 0.11). No changes were observed in TSS
in 2017 (p = 0.67) or in 2018 (p = 0.30). There is a trend, however, indicating that TA
may decrease somewhat over time, but with a p-value of 0.06, it was just not statistically
significant in 2017, while it was significant in 2018 (p < 0.01). Stem color exhibited the
same pattern for both years, remaining mostly fresh and green during the 28 days cold
storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH) only changing significantly (p < 0.01) during the
10 days at retail display conditions (5.4
±
0.6
◦
C, 83.7
±
2.9% RH) to mostly green with
some smaller stems that have turned brown. After 3 days at ambient temperature and
humidity (
25.1 ± 1.3
◦
C
and 46.6
±
6.0%RH), most of the smaller stems (rachi) are brown,
but the main stem is still green, after 7 days, however, most stems are dry and brown.
Table 10.
Supply chain scenario C (2017): changes in quality attributes of color (L, a* and b*), firmness
(N), TSS (Brix
◦
), TA (%), and stem browning index for ‘Crimson Seedless’ table grapes at harvest
after 28 days cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), after another 10 days at retail conditions
(5.4
±
0.6
◦
C, 83.7
±
2.9% RH) and then for 3, 7 and 10 days at ambient conditions (25.1
±
1.3
◦
C
and 46.6
±
6.0%RH). Mean values with different letter(s) in the same column indicate statistically
significant differences (p < 0.05).
2017
Time L* a* b*
Firmness
(N)
TSS
(Brix
◦
)
TA
(%)
Stem Browning
Index
Harvest 30.04
a
10.37
a
9.36
a
98.69
a
17.80
a
1.13
a
1
e
28 days
(
−
0.5
◦
C)
29.94
a
8.80
a
9.16
a
94.41
a
18.56
a
0.74
a
1.14
e
10 days
(5
◦
C)
28.61
a
7.89
a
9.54
a
98.70
a
19.15
a
0.64
a
1.97
d
3 days 31.41
a
6.81
a
8.72
a
97.00
a
19.28
a
0.73
a
3.08
c
7 days 31.78
a
6.52
a
8.10
a
99.70
a
19.17
a
0.67
a
4.53
b
10 days 31.69
a
5.76
a
8.10
a
97.20
a
18.57
a
0.67
a
5.00
a
p-value 0.12 0.15 0.72 0.90 0.67 0.06 <0.01
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
Table 11.
Supply chain scenario C (2018): changes in quality attributes of color (L, a* and b*), firmness
(N), TSS (Brix
◦
), TA (%), and stem browning index for ‘Crimson Seedless’ table grapes at harvest
after 28 days cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), after another 10 days at retail conditions
(5.4
±
0.6
◦
C, 83.7
±
2.9% RH) and then for 3, 7 and 10 days at ambient conditions (25.1
±
1.3
◦
C
and 46.6
±
6.0%RH). Mean values with different letter(s) in the same column indicate statistically
significant differences (p < 0.05).
2018
Time L* a* b*
Firmness
(N)
TSS
(Brix
◦
)
TA
(%)
Stem Browning
Index
Harvest
27.45
b
7.91
a
2.23
a
106.60
a
17.80
a
0.72
b
1
e
28 days
(
−
0.5
◦
C)
27.75
b
7.06
a
1.76
a
106.02
a
18.69
a
0.77
a
1.97
e
10 days
(5
◦
C)
28.09
ab
7.20
a
2.47
a
104.34
a
18.58
a
0.77
a
2.67
d
3 days 28.42
a
6.96
a
2.36
a
110.07
a
18.77
a
0.72
b
3.61
c
7 days 27.30
c
6.60
a
2.10
a
110.21
a
18.55
a
0.69
c
4.45
b
10 days
27.79
b
7.23
a
2.41
a
112.65
a
18.53
a
0.73
b
4.78
a
p-value <0.01 0.26 0.22 0.11 0.30
p < 0.01
<0.01
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
50
Sustainability 2021, 13, 4450
Supply chain scenario D (simulated ‘abusive’ treatment of fruit within the export chain)
Significant changes (p < 0.01) in color attribute (L) for lightness were observed,
(
Table 12
), with berries becoming lighter with increased temperature and lower humidity
and darker when returned to lower temperatures and increased humidity. No significant
difference in color attributes a* (p = 0.86) and b* (p = 0.21) were observed. The average
berry firmness remained unchanged (p = 0.23). There were significant changes observed
in TSS (p < 0.01) with values increasing and TA values (p < 0.01) that decreased during
storage. Stem color changed significantly over time (p < 0.01). While stems remained fresh
and green during the 28 days in cold storage, the 2 days at ambient conditions, to simulate
the abusive treatment, affected the stems to such an extent that by the time they reached
retail conditions, most of the stems were already brown.
Table 12.
Supply chain scenario D (2018): changes in quality attributes of color (L, a* and b*),
firmness (N), TSS (Brix
◦
), TA (%), and stem browning index for ‘Crimson Seedless’ table grapes at
harvest after 28 days cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), after 2 days ‘abusive’ temper-
ature and humidity (25.1
±
1.3
◦
C and 46.6
±
6.0%RH), after another 10 days at retail conditions
(
5.4 ± 0.6
◦
C
,
83.7 ± 2.9% RH
) and then for 3, 7 and 10 days at ambient conditions (25.1
±
1.3
◦
C
and
46.6 ± 6.0%RH
). Mean values with different letter(s) in the same column indicate statistically
significant differences (p < 0.05).
2018
Time L* a* b*
Firmness
(N)
TSS
(Brix
◦
)
TA
(%)
Stem Browning
Index
Harvest
27.46
b
7.91
a
9.36
a
116.60
a
17.80
d
0.79
a
1
e
28 days
(
−0.5
◦
C)
27.49
b
7.89
a
9.16
a
118.54
a
18.54
c
0.76
b
1.89
e
2 days
(ambient)
28.21
a
7.65
a
9.23
a
115.72
a
17.75
d
0.75
b
3.25
d
10 days (5
◦
C)
27.71
b
7.88
a
9.54
a
123.01
a
19.55
ab
0.72
c
4.01
c
3 days
27.60
b
8.02
a
8.72
a
121.96
a
17.27
d
0.72
c
4.62
b
7 days 28.23
a
7.69
a
8.34
a
116.28
a
19.11
b
0.75
b
4.94
a
10 days
27.82
b
7.83
a
8.12
a
123.24
a
19.74
a
0.76
b
4.94
a
p-value <0.01 0.86 0.21 0.23 <0.01 <0.01 <0.01
Note: Mean values within the same column with different letters are significantly (p < 0.05) different by Duncan’s
Multiple Range test (DMRT).
3.4. Socioeconomic Impacts of Postharvest Losses
Based on the percentage losses along the simulated supply chains, estimates were
made to determine the volume of table grapes that could be lost at the national level
(
Table 13
). In 2017, South Africa produced approximately 325,061 tons, of which 20,046 tons
were sold locally, and 305,015 tons were exported [
17
]. The ranges provided in the following
data are estimates made from the lowest losses which were recorded in the 2018 season to
the highest losses that were recorded in 2017. It thus provides a range of losses that could
occur in any given season.
Supply chain scenario A (marketing at ambient temperatures and relative humidity)
Losses translated were between 555 and 1052 tons after 3 days. This equates to a
financial loss of R7.3 million–R13.8 million, 3,623,595–6,868,508 MJ of energy, 116,744–
221,288 m
3
water used in production and 505–957 tons CO
2
eq emissions. After 7 days,
the losses increase to 575–1904 tons, R7.5 million–R25 million, 375,417–12,431,216 MJ,
120,951–400,466 m
3
and 523–1733 tons CO
2
eq. After 10 days, 1305–2068 tons were lost,
R17.1 million–R27.1 million, 8,520,345–13,461,972 MJ of energy, 274,507–435,003 m
3
and
1188–1882 tonnes CO
2
eq.
51
Sustainability 2021, 13, 4450
Table 13.
Impact of postharvest losses in terms of magnitude, monetary value, energy used, water footprint, and greenhouse
gas emissions in the production and distribution of table grapes along different supply chains.
Year
Supply Chain
Scenario
Storage Condition
Estimated Physical and
Economic Losses
* Estimated Environmental and Resource Impacts
Time Temp (
◦
C) and Humidity (%)
Physical
(ton)
Value (ZAR) Energy (MJ)
Water
Footprint (m
3
)
Emissions
CO
2
eq (ton)
2017 A 3 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH 1052
a
13,816,968
a
6,868,508
a
221,288
a
957
a
2017 A 7 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
1904
b
25,007,316
b
12,431,216
b
400,466
b
1733
b
2017 A 10 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
2068
b
27,161,112
b
13,461,972
b
435,003
b
1882
b
2017 B 14 days −0.3 ± 0.7
◦
C; 81.3 ± 4.1%RH 493
a
6,475,062
a
3,218,797
a
103,703
a
449
a
2017 B 10 days 5.4 ± 0.6
◦
C; 83.7 ± 2.9%RH
874
ab
11,479,116
ab
5,706,346
ab
183,846
ab
795
ab
2017 B 3 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
1431
bc
18,794,754
bc
9,342,999
bc
301,011
bc
1302
bc
2017 B 7 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
2074
cd
27,239,916
cd
13,541,146
cd
436,266
cd
1887
cd
2017 B 10 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
2534
d
33,281,556
d
16,544,486
d
533,027
d
2306
d
2017 C 28 days −0.3 ± 0.7
◦
C; 81.3 ± 4.1%RH 13,299
a
279,305,598
a
86,829,171
a
2,797,445
a
12,102
a
2017 C 10 days 5.4 ± 0.6
◦
C; 83.7 ± 2.9%RH
21,443
b
450,345,886
b
140,001,347
b
4,510,535
b
19,513
b
2017 C 3 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
22,602
b
474,687,204
b
147,568,458
b
4,754,331
b
20,568
b
2017 C 7 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
29,251
b
614,329,502
b
190,979,779
b
6,152,948
b
26,618
b
2017 C 10 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH 43,587
c
915,414,174
c
284,579,523
c
9,168,525
c
39,664
c
2018 A 3 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH 555
a
7,289,370
a
3,623,595
a
116,744
a
505,05
a
2018 A 7 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH 575
a
7,552,050
a
3,754,175
a
120,951
a
523,25
a
2018 A 10 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
1305
b
17,139,870
b
8,520,345
b
274,507
b
118,755
b
2018 B 14 days −0.3 ± 0.7
◦
C; 81.3 ± 4.1%RH 611
a
8,024,874
a
3,989,219
a
128,524
a
556
a
2018 B 10 days 5.4 ± 0.6
◦
C; 83.7 ± 2.9%RH
728
ab
9,561,552
ab
4,753,112
ab
153,135
ab
663
ab
2018 B 3 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
1163
bc
15,274,842
bc
7,593,227
bc
244,637
bc
1058
bc
2018 B 7 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
1303
cd
17,113,602
cd
85,07,287
cd
274,086
cd
1186
cd
2018 B 10 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
1856
d
24,376,704
d
12,117,824
d
390,410
d
1689
d
2018 C 28 days −0.3 ± 0.7
◦
C; 81.3 ± 4.1%RH 29,861
a
627,140,722
a
194,962,469
a
6,281,261
a
27,174
a
2018 C 10 days 5.4 ± 0.6
◦
C; 83.7 ± 2.9%RH
11,560
b
242,783,120
b
75,475,240
b
2,431,646
b
10,520
b
2018 C 3 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
12,109
b
254,313,218
b
79,059,661
b
2,547,128
b
11,019
b
2018 C 7 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
21,107
ab
443,289,214
ab
137,807,603
ab
4,439,857
ab
19,207
ab
2018 C 10 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH 31,173
a
654,695,346
a
203,528,517
a
6,557,241
a
2,836,743
a
2018 D 28 days −0.3 ± 0.7
◦
C; 81.3 ± 4.1%RH 9242
a
194,100,484
a
60,341,018
a
1,944,055
a
8410
a
2018 D 2 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH 9730
a
204,349,460
a
63,527,170
a
2,046,706
a
8854
a
2018 D 10 days 5.4 ± 0.6
◦
C; 83.7 ± 2.9%RH
13,024
ab
273,530,048
ab
85,033,696
ab
2,739,598
ab
11,852
ab
2018 D 3 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
15,251
bc
320,301,502
bc
99,573,779
bc
3,208,048
bc
13,878
bc
2018 D 7 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH 20,314
c
426,634,628
c
132,630,106
c
4,273,050
c
18,486
c
2018 D 10 days 25.1 ± 1.3
◦
C; 46.6 ± 6.0%RH
27,177
d
570,771,354
d
177,438,633
d
5,716,682
d
24,731
d
Note: a,b,c Values in a column without a common superscript are significantly different (p < 0.05). * Estimated values obtained using the
volume of table grapes sold locally, 20,046 t and exported, 305,015 t [
17
]. This section may be divided by subheadings. It should provide a
concise and precise description of the experimental results, their interpretation as well as the experimental conclusions that can be drawn.
Supply chain scenario B (to local retail markets)
After 14 days in cold storage, the losses were between 493 and 611 tons with a financial
loss of R6.5 million–R8 million, lost energy of 3,218,797–3,989,219 MJ, a water footprint
of 103,703–128,524 m
3
, and 449–556 tons CO
2
eq. After 10 days at retail conditions, losses
were 728–874 tons, R9.6 million–R11.5 million, 4,753,112–5,706,346 MJ, 153,135–183,846 m
3
water lost, and 663–795 tons CO
2
eq.
Once moved to ambient conditions, the losses after 3 days were 1163–1431 tons,
R15.2 million–R18.8 million, 7,593,227–9,342,999 MJ, 244,637–301,011 m
3
, and 1058–1302 tons
CO
2
eq. After 7 days, 1303–2074 tons, R17.1 million–R27.2 million, 8,507,287–13,541,146 MJ,
274,086–436,266 m
3
, and 1186–1887 tons CO2eq. After 10 days, 1856–2534 tons, R24.3 million–
R33.3 million, 12,117,824–16,544,486 MJ, 390,410–533,027 m
3
, and 1689–2306 tons CO
2
eq.
52
Sustainability 2021, 13, 4450
Supply chain scenario C (to export retail markets)
After 28 days in cold storage, the losses were between 13,299 and 29,861 tons with
a financial loss of R279.3 million–R627.1 million, 86,829,171–194,962,469 MJ of energy,
2,797,445–6,281,261 m
3
of water, and 12,102–27,174 tons CO
2
eq. After 10 days at retail condi-
tions, 11,560–21,443 tons, R242.8 million–R450.3 million, 75,475,240–140,001,347 MJ, 2,431,646–
4,510,535 m
3
, and 10,520–19,513 tons CO
2
eq. After 3 days at ambient conditions, losses
were 12,109–22,602 tons, R254.3 million–R474.7 million, 79,059,661–147,568,458 MJ, 2,547,128–
4,754,331 m
3
and 11,019–20,568 tons CO
2
eq. After 7 days: 21,107–29,251 tons, R443.3 million–
R614.3 million, 137,807,603–190,979,779 MJ, 4,439,857–6,152,948 m
3
, and 19,207–26,618 tons
CO
2
eq. After 10 days at ambient, the losses were 31,173–43,587 tons, R654.7 million–R915.4 mil-
lion, 203,528,517–284,579,523 MJ, 6,557,241–9,168,525 m
3
, and 28,367–39,664 tons CO
2
eq.
Supply chain scenario D (simulated ‘abusive’ treatment of fruit within the export chain)
After 28 days in cold storage, the losses were 9242 tons with a financial value of
R194.1 million, 60,341,018 MJ of energy, 1,944,055 m
3
water, and 8410 tons CO
2
eq. Af-
ter 2 days at ‘abusive’ ambient temperature and humidity before entering retail con-
ditions, the losses were 9730 tons, R204.3 million, 63,527,170 MJ, 2,046,706 m
3
water,
and 8854 tons CO
2
eq. After 10 days at retail conditions, losses were 13,024 tons, R273.5 mil-
lion, 85,033,696 MJ, 2,739,598 m
3
, and 11,852 tons CO
2
eq. At 3 days of ambient conditions,
losses were 15,251 tons, R320.3 million, 99,573,779 MJ, 3,208,048 m
3
, and 13,878 tons
CO
2
eq. After 7 days ambient: 20,314 tons, R426.6 million, 132,630,106 MJ, 4,273,050 m
3
and 18,486 tons CO
2
eq. After 10 days at ambient conditions, the losses were 27,177 tons,
R570.8 million, 177,438,633 MJ, 5,716,682 m
3
, and 24,731 tons CO
2
eq.
4. Discussion
4.1. Physical Losses at Farm Level
The losses measured on farm level in 2017 were higher than the findings of [
35
],
who concluded in an economic analysis of the South African table grape supply chain that
approximately 9.5% was lost between farm and intake. However, the data used in that
study was based on perception data gathered from different role-players in the table grape
industry, the authors own elaborations and from [18] as no primary data was collected.
The authors of [
36
] reported similar losses at agricultural production level of 15%,
where table grape losses were quantified along the supply chain in Iran. The materials and
methods of that study are unclear, however, as it divides the supply chain into production,
postharvest, processing, distribution, and consumption stages without clearly describing
the various stages. In the present study, the losses at farm level include what it seems they
refer to as production, postharvest and processing into one. If that is the case, the losses
experienced in Iran were much higher than our findings and amounted to 46% of total
production. In [
36
], the authors used data from government and private sources with
estimates and interviews, and no primary data was collected in that study.
In the present study, the average loss measured on farm level in 2018 was 5.97%.
This is less than reported by [
35
,
36
] but very similar to the findings of [
37
] that reported
losses of table grapes in Pakistan of 4.8% at farm level and [
38
] where losses of table grapes
at farm level in India were reported as 3.4% for grapes prepared for the domestic market
while grapes prepared for the export market sustained losses of 7.82% as the requirements
for export produce are stricter.
The losses measured in 2017 are similar to that of [
38
] for export grapes (Thomson
Seedless), but the reasons for the losses differ, water berries (6.72%), harvest injury (0.57%),
mummies (0.02%), and immature (0.26%). The authors of [
39
] report losses of table grape
(Nana Purple) on farm level as 8–10% due to insufficient coloring, while in [
36
,
37
] the
authors do not specify the reasons for losses.
The large differences between farms in 2017, were unexpected and although it was
initially thought that it could have been due to different climatic conditions or soil types
it appears to have been related to different vineyard management practices as there was
no statistical difference between losses on the farms in the 2018 season and the average
53
Sustainability 2021, 13, 4450
losses were less than half recorded in 2017. The reasons for this could be two-fold. Firstly,
by having been made aware of how high the losses were during the previous season,
the farm managers took steps to reduce this during the 2018 season, workers were trained
to be more careful when handling the crates after harvest. Secondly, the farm that sustained
the highest losses (23.3%) in 2017 harvested later than was optimal and therefore the
bunches stayed on the vines too long. The 2018 harvest occurred two weeks earlier than in
2017, and the grapes were in better condition leading to fewer losses on farm level.
4.2. Physical Losses along the Simulated Supply Chain
Supply chain scenario A (handling and marketing fruit under ambient conditions)
These results support the research of [
40
] that reported the mass of grapes cv. ‘Crimson
Seedless’ always decreased with time at all combinations of storage temperature and
RH. Grapes stored at higher temperature lost weight faster than those maintained at
lower temperatures. The increased vapor pressure deficit and respiration rates of the
stems of grapes stored at higher temperatures accelerated transpiration rates of fruit.
In [
19
], the authors further found a linear profile for the mass decreasing in grapes of
cv. ‘Thompson’ and ‘Superior’. Similar findings were also reported by [
39
], noting a 5%
weight loss in grapes transported in trucks in temperatures of 35–40
◦
C before reaching the
wholesale market.
The findings on the incidence of decay were similar to the findings of [
20
], who found
a severe incidence of decay (2–5 infected berries per carton) of ‘Regal Seedless’ table
grapes after 7 days shelf life at 24.33
±
0.04
◦
C in similar packaging to that used in this
study. The authors of [
41
] reported much higher levels of 40.5% after 7 days at 15
◦
C
for cv ‘Thompson Seedless’. Visible decay in a carton could make the carton hard to sell,
even though less than 10% of the berries are affected, as from the consumer’s perspective,
appearance is the first factor that influences purchase decision, followed by perceived
value for money and fruit eating quality [
42
]. This is similar to the findings of [
20
] who
found severe incidence of decay (2–5 infected berries per carton) of ‘Regal Seedless’ table
grapes after 7 days shelf life at 24.33
±
0.04
◦
C in similar packaging to that used in this
study. The authors of [
41
] reported much higher levels of 40.5% after 7 days at 15
◦
C
for cv ‘Thompson Seedless’. Visible decay in a carton could make the carton hard to sell,
even though less than 10% of the berries are affected, as from the consumer’s perspective,
appearance is the first factor that influences purchase decision, followed by perceived value
for money and fruit eating quality [42].
Supply chain scenario B (to local retail markets)
After 10 days at retail conditions, the reported decay was less than half the amount
of 4.56% reported by [
38
]. Similar to [
43
] reporting 1% SO
2
damage for cv ‘Red Globe’
after 15 days in cold storage, the incidence of SO
2
berry damage in this study was 1% after
14 days in cold storage. The authors of [
20
,
44
] reported that the combination of free water
(100% RH), as occurs with the formation of condensation when cartons are removed from
cooler conditions to ambient conditions, combined with SO
2
in nonperforated liners may
result in the formation of acidic conditions that may increase SO
2
injury, this seems to be the
case in this study also. It is suggested that after a few days at ambient conditions, the free
water evaporates, and the damage stops. Based on the investigated seasons, the decreases
of 5.4% and 6.8% in weight noted for the two seasons were more than double the amount
of 2% weight loss after 14 days of cold storage for cv. ‘Thompson Seedless’ reported by [
45
],
while findings on decay during cold storage were similar to the 0% decay reported.
Supply chain scenario C (to export retail markets)
The measured weight loss was similar to the 5% after 10 days and 10% after 14 days at
room temperature (25
◦
C) and 45–70% relative humidity reported by [46] for cv ‘Victoria’.
After 28 days in cold storage (
−
0.3
±
0.7
◦
C, 81.3
±
4.1% RH), there was a 2.14% incidence
of decay in 2017 and 0.94% in 2018. These results were similar the mean of 1.28% decay
reported by [
47
] for cultivars ‘Red Globe’, ‘Sunred Seedless’ and ‘Thompson Seedless’
under similar conditions for the same time period. After 10 days at retail display conditions
54
Sustainability 2021, 13, 4450
(5.4
±
0.6
◦
C, 83.7
±
2.9% RH) this increased to 3.2% in 2017 and 2.6% in 2018. These results
were slightly higher than the 2.5% reported by [
35
] for the perceived losses of table grapes
at retail level and correspond to the lowest end of the 3–7% range of loss reported for fresh
fruit under retail conditions in the UK and Spain by [
48
]. Both [
35
,
48
], however, used data
collected through interviews with managers in food manufacturing and questionnaires
completed by other role players in the table grape supply chain while no primary sampling
data was collected. Three days after being moved to ambient temperature and humidity
conditions (25.1
±
1.3
◦
C and 46.6
±
6.0%RH), decay increased to 4.44% in 2017 and 3.16%
in 2018. The authors of [
49
] reported decay of more than a hundred berries per kg after
4 weeks at 0
◦
C and 3 days shelf-life at 20
◦
C for cv. ‘Thompson Seedless’. Taking the
average weight of a ‘Thompson Seedless’ berry as 5g reported [
50
] the data translates to
500g infected berries per kg or 50%. That is much higher than the decay rate measured in
this study. The results for SO
2
damage seem similar to the rating of 4 (11–20 berries per
replicate consisting of 10 bunches) with SO
2
damage after 65 days at 0
◦
C and 3 days at
20
◦
C reported by [
51
] although it is not easy to compare as that study used a rating of
1–5 for describing SO
2
damage and not % and it is unknown exactly how many berries
were in a replicate of 10 bunches.
Supply chain scenario D (simulated ‘abusive’ treatment of fruit within the export chain)
The 5.7% decrease in weight noted is half the weight loss of around 12% reported
by [
52
] for ‘Crimson Seedless’ table grapes under similar conditions and for the same
time period.
Decay and S0
2
damage are disorders that can be caused or aggravated by wet berries
in combination with elevated temperature [
53
]. Results indicate that the decay in this
trial was lower than that recorded for supply chain scenario C, which was the same in all
regards except for the 2 days under ambient conditions. This could be due to condensation
evaporating, leaving less free moisture that would exacerbate decay.
Total Amount of Physical Losses
Supply chain scenario A (marketing at ambient conditions)
For both years, the losses were considerably less than the 53% reported by [
36
] for table
grape losses along the supply chain under ambient conditions in Iran. No sampling data
was recorded in that study. However, the data used for their calculations were collected
through government and private data sources with horticulture expert estimates, grape
grower interviews, agriculture cooperation interviews, and market consultations.
Supply chain scenario B (to local retail markets)
When these losses are taken only from harvest to retail level, it translates to 18.26%
loss in 2017 and 9.62% in 2018. The result for 2018 is similar to [
38
], who reported losses of
7.96% from the field to retailer in India.
Supply chain scenario C (to export retail markets)
The 2018 data was similar to [
38
] reporting export supply chain losses for table grapes
in India, of 19.95%, as well as the approximate figure of 15.5% reported by [
35
] for the
South African table grape supply chain, although no sampling data was collected for those
studies. The 2017 data of this study was significantly higher and showed how variable the
yearly losses could be.
Supply chain scenario D (simulated ‘abusive’ treatment of fruit within the export chain)
In terms of quantity of losses, therefore, the 2 days at ambient conditions in the middle
of the cold chain did not cause a significant difference. It did, however, create a difference
in quality, as will be illustrated in the next section.
4.3. Quality Losses along the Supply Chain
Supply chain scenario A (marketing at ambient conditions)
Results for firmness over time supports the findings of [
23
] reporting no difference in
firmness for cv. ‘Mystery’ after 6 days under ambient conditions (22–28
◦
C) but contrasts
with the findings of [
54
] for cv. ‘Muscat Hamburg’ reporting a significant decrease of
55
Sustainability 2021, 13, 4450
firmness over time under ambient conditions. Similar findings of unchanging TSS and TA
values were reported for both cv. Mü¸sküle and cv. Red Globe by [
27
] during the first week
of that research project. The results on change in stem color support the report by [
19
].
The author reported major rachis browning during marketing at ambient temperatures
and relative humidity.
Supply chain scenario B (to local retail markets)
The increase in lightness measured in 2018 differ to the findings of [
45
] that found the
L values for the black table grape cv. ‘Alphonse Lavallée’ decrease during cold storage,
indicating that the grapes became darker over time. Berry firmness results that showed
no significant difference over time is similar to findings of [
55
] reporting no significant
difference in firmness for cv. ‘Italia’ after cold storage and 7 days shelf life. The increase
in TA measured during the 2018 season is similar [
27
] also describing such a significant
increase in TA after 2 and 3 weeks in storage for cv. Mü¸sküle and cv. Red Globe. The au-
thors of [
23
,
56
] describe increases in TSS of grapes during cold storage due to water loss.
In contrast to the 2018 findings, yet similar to the 2017 findings of this study, TSS levels
could remain stable under different storage conditions and for different cultivars. In [
24
]
the authors reported that TSS levels in cv. Red Globe, after up to 12 weeks in storage under
different controlled atmosphere (CA) conditions at 0
◦
C, was almost equal to that measured
at harvest.
Supply chain scenario C (to export retail markets)
No significant difference in any of the color attributes were found in 2017, similar to
findings of [
57
] for cv. ‘BRS Isis’ after 50 days in cold storage plus 5 days under ambient
conditions. In 2018, however, results indicated that berry color became lighter, which
differs from [
28
] reporting that L decreased with storage time for cv. ‘Thompson Seedless’,
while [
55
] reported no significant changes in color for cv. ‘Italia’ after 50 days of cold
storage and 7 days under ambient conditions.
No change in firmness was observed. Similarly, the authors of [
55
] reported no
significant difference in firmness for cv. Italia after up to 50 days in cold storage and 7 days
shelf life. No changes were observed in TSS, similar to findings of [
43
] for cv. ‘Red Globe’.
Results for changes in TA are similar to findings reported by [
28
] that berry TA underwent
a progressive decrease during storage for cv. ‘Thompson Seedless’. Stem color changes
support the findings of [
21
,
53
] also noted for cv. ‘Thompson Seedless’ that the average
stem condition deteriorated more during the shelf life period than cold storage.
Supply chain scenario D (simulated ‘abusive’ treatment of fruit within the export chain).
Unchanged berry firmness was also reported by [
23
] for cv. ‘Mystery’ and [
43
] for
cv. ‘Red Globe’. [
19
], however, reported a decrease in firmness during shelf life trials for
the white grape cultivars ‘Superior’ and ‘Thompson’ held in high (>95%) and low (70%)
relative humidity at 20 or 10
◦
C for up to 11 d. Several researchers similarly reported
general increases in TSS of grapes during cold storage, including [
23
,
56
]. This is attributed
to the gluconeogenesis pathway or water loss. The authors of [
23
] also reported similar
findings on stem browning for cv. ‘Mystery’ where cooling delays of 48 h resulted in a
rachis browning score of 5, indicating very severe damage.
4.4. Socioeconomic Impacts of Postharvest Losses
The socioeconomic impacts of these postharvest losses indicate a financial loss of
between R279 million and over R600 million annually for the table grape export indus-
try. The authors of [
35
] estimated losses of approximately 9.5% between production and
intake stages for the South African table grape industry, translating into a financial loss
of R270.5 million with an additional 2.2% or R93.2 million between intake and export
and 3.8% or R400,000 between the importer to retail depot. It is unclear how 3.8% equals
R400,000 when it was also stated that 2.2% equals R93,200,000, the accuracy of the estimates
are therefore uncertain. The values given for losses between production and intake does,
however, approximate the values this current study recorded for the 2018 season, while the
losses during the 2017 season were more than double that amount.
56
Sustainability 2021, 13, 4450
Additionally, as much as 177.43 million MJ of fossil energy and 4.8 million m
3
of
freshwater resources were lost. At the Eskom tariff rate of R0.90 per kWh, the lost energy is
worth R44.36 million [
58
]. The fresh water lost could sustain at least 263,013 individuals
daily for a whole year at daily minimum usage rate of 0.05 m
3
per day. Losses also
contribute to unwanted emission of approximately 52,263 tons of CO
2
eq, contributing to
environmental degradation from greenhouse gases.
5. Conclusions
The highest loss in the supply chain was measured at the farm level. It is therefore
important to include this stage when studies are conducted on the quantification of posthar-
vest losses. As the main reason for losses at this stage was mechanical damage due to
the rough handling of bunches and crates causing berries to drop off the bunches as well
as the crushing of berries due to loading too many bunches in crates, these losses could
be improved by making workers more aware of the necessity to handle crates with care.
The harvest timing is also essential, as delayed harvesting reduces shelf life and results in
an increased postharvest loss.
The main quality problem, among all supply chain scenarios, was rachis and stem
browning at temperatures higher than
−
0.5
◦
C. This caused berries to drop faster and
bunches to look less fresh, as well as causing bunches to weigh less when sold. While
500 g or 1 kg punnets are routinely kept at around 5
◦
C at the retail level, during peak
season 4.5–10 kg cartons are often stacked on the floor under ambient conditions. Therefore,
the table grapes would have a maximum shelf-life of 7 days before the stems have browned,
and too many berries per bunch are decayed to sell. It would be advisable to keep cartons
at
−
5
◦
C and high RH and only place bunches in punnets in 5
◦
C display fridges as the
stock sells.
The increase in weight loss and especially stem browning recorded in Scenario D
(‘abusive’ treatment of fruit within the export chain), compared to Scenario C (shipping to
export markets) indicated the importance of eliminating the delay between reefer delivery
and quality checking as a break in the cold chain of 2 days has a significant impact on the
quality of the bunches and therefore also the price it can be sold at.
This study was conducted on farms with good infrastructure, cultivation practices
and cooling facilities, where nonetheless, farm-level losses of up to 23% were recorded.
It is significant that during preseason interviews with farm management, the highest
estimate of losses was 13%, most of them lower. As ‘Crimson Seedless’ is a high value
crop, even relatively small improvements in future could have a large financial impact for
producer-exporters.
In the changing local agricultural environment of many more upcoming farmers
entering the industry, this situation deserves much more attention than what was the case
so far. On a global level, as we are approaching population levels of around nine billion
people, the choice is obvious: not only must we produce more food, but we should also
waste much less.
Author Contributions:
Conceptualization, U.L.O.; Formal analysis, A.B.; Funding acquisition,
U.L.O.; Investigation, A.B.; Methodology, A.B., U.L.O., O.A.F.; Supervision, O.A.F. and U.L.O.;
Validation, O.A.F. and U.L.O.; Visualization, A.B.; Writing—original draft, A.B.; Writing—review
and editing, A.B., U.L.O., O.A.F. All authors have read and agreed to the published version of
the manuscript.
Funding:
This work is based on the research supported wholly/in part by the National Research
Foundation of South Africa (Grant Numbers: 64813).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
57
Sustainability 2021, 13, 4450
Data Availability Statement:
Design of the study; in the collection, analyses, or interpretation of
data; in the writing of the manuscript, or in the decision to publish the results. The opinions, findings
and conclusions or recommendations expressed are those of the author(s) alone, and the NRF accepts
no liability whatsoever in this regard.
Acknowledgments: The authors offer their heartfelt thanks to the Karsten Group.
Conflicts of Interest: The authors declare no conflict of interest.
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60
sustainability
Article
Assessing the Camelina (Camelina sativa (L.) Crantz) Seed
Harvesting Using a Combine Harvester: A Case-Study on the
Assessment of Work Performance and Seed Loss
Walter Stefanoni
1
, Francesco Latterini
1
, Javier Prieto Ruiz
2
, Simone Bergonzoli
3,
*, Nadia Palmieri
1
and Luigi Pari
1
Citation: Stefanoni, W.; Latterini, F.;
Ruiz, J.P.; Bergonzoli, S.; Palmieri, N.;
Pari, L. Assessing the Camelina
(Camelina sativa (L.) Crantz) Seed
Harvesting Using a Combine
Harvester: A Case-Study on the
Assessment of Work Performance and
Seed Loss. Sustainability 2021, 13, 195.
https://doi.org/10.3390/su13010195
Received: 30 October 2020
Accepted: 23 December 2020
Published: 28 December 2020
Publisher’s Note: MDPI stays neu-
tral with regard to jurisdictional claims
in published maps and institutional
affiliations.
Copyright: © 2020 by the authors. Li-
censee MDPI, Basel, Switzerland. This
article is an open access article distributed
under the terms and conditions of the
Creative Commons Attribution(CC BY)
license (https://creativecommons.org/
licenses/by/4.0/).
1
Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA),
Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari,
Via della Pascolare 16, 00015 Monterotondo, Italy; walter.stefanoni@crea.gov.it (W.S.);
francesco.latterini@crea.gov.it (F.L.); nadia.palmieri@crea.gov.it (N.P.); luigi.pari@crea.gov.it (L.P.)
2
Camelina Company Espana, Camino de la Carrera, 11, Fuente el Saz de Jarama, 28140 Madrid, Spain;
jprieto@camelinacompany.es
3
Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA),
Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Milano 43, 24047 Treviglio, Italy
* Correspondence: simone.bergonzoli@crea.gov.it
Abstract:
The growing demand in food and non-food industries for camelina oil is driving the
interest of farmers and contractors in investing in such feedstock. Nonetheless, the cost, performance
and critical aspects related to the harvesting stage are still not properly investigated. In the present
study, an ad-hoc test was performed in Spain in order to fulfill this gap. The results support the
hypothesis to harvest camelina seeds with the same combine harvester used for cereal harvesting
without further investment. Theoretical field capacity (TFC), effective field capacity (EFC), material
capacity (MC), and field efficiency (FE) were 4.34 ha h
−1
, 4.22 ha h
−1
, 4.66 Mg h
−1
FM, and 97.24%,
respectively. The harvesting cost was estimated in 48.51
€
ha
−1
. Approximately, the seed loss of
0.057
±
0.028 Mg ha
−1
FM was due to the impact of the combine harvester header and dehiscence of
pods, whilst 0.036
±
0.006 Mg ha
−1
FM of seeds were lost due to inefficiency of the threshing system
of the combine harvester. Adjustment of the working speed of the combine and the rotation speed of
the reel may help to reduce such loss.
Keywords:
work productivity; harvesting costs; harvesting efficiency; wheat header; seed loss;
header impact
1. Introduction
The European Union is currently fostering the replacement of fossil-based products
with bio-based surrogates [
1
,
2
]. Oil crops play a key role concerning this issue, thanks to
their suitability to synthesize molecular structures which could be used to displace substan-
tial amount of petroleum oil derived compounds [
3
,
4
]. Worldwide production of vegetable
oil is given for 75% by few crops, such as soybean, oil palm, cottonseed, rapeseed and
sunflower; while the remaining 25% is obtained from other minor oilseeds [
1
]. On the other
hand, some of these minor oilseeds show particular features, which make them particularly
suitable in the concept of bio-economy. In particular, camelina (Camelina sativa (L.) Crantz)
belonging to Brassicaceae family [
5
] and originating from South-East Europe and South-West
Asia [
6
], is a very promising oil crop for multiple reasons [
7
]. Camelina oil can indeed
be used as edible oil rich in omega-3 fatty acids [
8
], and its oil and meal are also suitable
sources of protein for both fish and ruminant diets [
9
–
12
]. Camelina oil has also multi-
ple industrial applications, such as biodiesel and jet-fuel production, even if with some
drawbacks related to cetane number, iodine value, oxidation stability and linolenic acid
methyl ester content [
13
,
14
]. Furthermore, camelina oil can be used in the production of
Sustainability 2021, 13, 195. https://doi.org/10.3390/su13010195 https://www.mdpi.com/journal/sustainability
61
Sustainability 2021, 13, 195
plasticizers, lubricants, polyols, resins, composites, coatings, elastomers, and adhesives [
15
].
Other interesting features of camelina are related to its cultivation. This species is indeed
resistant to both drought and frost stress [
16
]. It has low nutritional requirements [
17
–
19
],
with subsequent positive effects on the environment highlighted by life cycle assessment
(LCA) studies [
20
,
21
], and can be grown on poor soils, also in a Mediterranean context [
22
],
even if both seed yield and oil yield show substantial variability, i.e., 1.0–3.0 Mg ha
−1
and
30–49% w/w respectively [
23
]. Finally, camelina, considering the presence of both winter
and spring cultivar and the relatively brief life cycle, is suitable for double cropping with
small grain cereals, soybean, and sunflower [24–29].
On the other hand, one of the main issues in camelina cultivation is the high costs of
the supply chain [
30
]. Indeed, the higher percentage of costs for biodiesel production are
related to the feedstock [
31
] and optimizing harvesting operation can lead to a substantial
decrease of such costs [32].
According to this, costs of harvesting and logistic have to be evaluated, in order
to make camelina cultivation fully sustainable and give support in the decision-making
process to farmers and other stakeholders. Currently, mechanical harvesting of camelina is
mainly carried out by using a combine harvester equipped with wheat header [
33
], only
few experiences on cutting and swathing are reported [
34
]. However, seed loss can be very
high, as a consequence of the tiny dimension of the seeds which are very small and light in
weight [
35
,
36
] moreover, presence of weeds can further increase seed loss amount. Indeed,
the entrance within the combine harvester of the green material of weeds, which generally
shows higher moisture content than camelina, can reduce the efficiency of the threshing
and cleaning system of the combine harvester, leading to higher seed loss [
37
]. Considering
this, appropriate setting of the combine harvester and adjustment of working speed are
fundamental to reduce seed loss [
38
]. However, combine harvester settings are not the only
important aspects to take into account. In fact, camelina suffers seed loss for shattering
as the ripeness is completed. Pods can easily open as consequence of external mechanical
input, as the cutting bar of the combine harvester can provide. Hence, it is also important
to finely regulate the rotary speed of the reel as well as the working speed of the machine
in order to reduce such a phenomenon as much as possible. Some authors also suggest to
consider the swathing method for harvesting in case of uneven ripeness [38].
Notwithstanding the centrality of this topic in the optic of a sustainable cultivation
of camelina, few studies have focused on the evaluation of work performance, harvesting
costs, and seeds loss.
The only comprehensive study reported in literature is Stefanoni et al. (2020), who
reported a work productivity of 3.17 ha h
−1
, with harvesting costs of 65.97
€
ha
−1
and seed
loss of 7.82% w/w for a John Deere combine harvester (John Deere, Moline, IL, USA) [
39
].
In a previous work related to harvesting loss evaluation using a plot combine Sintim et al.
(2016) found seed loss of 11.60% w/w [
40
], while Stolarski et al. (2019) reported harvesting
cost per surface unit of 46.70
€
ha
−1
with a New Holland (New Holland, PA, USA) combine
harvester [41].
Considering what is written above, there is still a need to investigate such a topic with
specific field tests, in order to fill the knowledge gap that still exists. The aim of the present
work is properly to provide the literature with significant information for both farmers and
contractors; about work performance, costs and seed loss when collecting camelina seeds
by combine harvester.
2. Materials and Methods
2.1. Experimental Field
Harvesting test was performed in the town of Astudillo, Palencia (Castilla y Leon,
Spain) during the 27th week of 2020 (Figure 1). The experimental field (WGS84-UTM30T
coordinates 390,896 E; 4,661,826 N) was flat and it measured 24.00 ha in surface and
893 m a.s.l in altitude.
62
Sustainability 2021, 13, 195
Figure 1. Experimental field location.
Camelina cultivation in the experimental field was carried out in conventional farming
regime. Cultivar Alba (commercial variety provided by Camelina Company España) was
sown in the first half of December 2019 with a seeding rate of 8 kg ha
−1
. The previous crop
was Barley. Fertilization was provided two times, with a rate of 250 kg ha
−1
of NPK 8-15-15
in winter using a trailed fertilizer spreader and 250 kg ha
−1
of liquid Nitrogen fertilizer
(32%) in April by means of a mounted liquid fertilizer spreader. Chemical control of weeds
was carried out before the nitrosulphate ammonium distribution by using a graminicide
(Pilot, Quizalofop-p-ethyl 10%) to control the narrow-leaf weeds.
2.2. Pre-Harvest Test
Prior to the harvesting operation, 10 squared sample plots of 1 m
2
each were ran-
domly established in order to assess the amount of the whole epigeous biomass (straw,
siliques, and seeds). Camelina plants were cut at ground level with a shear, counted and
then measured in both weight and height. Siliques and seeds were pulled and weighed
separately. Consequently, siliques, seeds and a sample of straw from each plot were closed
in sealed bags and transferred to the laboratory of Research Centre for Engineering and
Agro-Food Processing (CREA-IT, Monterotondo, Rome, Italy) in order to perform further
analysis. In particular, potential seed yield (PSY), dry weight (DW), 1000 seed-weight,
bulk density and moisture content were evaluated. Dry weight and moisture content were
estimated according to EN ISO 18134-2:2017 standard [
42
]. Seeds bulk density (kg m
−3
)
was calculated according to ISO 17828:201 [43] in 15 randomly selected samples.
2.3. Combine Harvester Model and Setting
The harvesting machinery was provided by the contractor. In particular the operation
was carried out with a Claas Lexion 570 (Westfalia, Harsewinkel, Germany) combine
harvester equipped with a conventional cleaning shoe and a 6.6 m wide cereal header.
The machine had 273 kW diesel engine and the applied setting was as follow: rotor speed
800 rpm, cleaning fan speed 700 rpm, opening of the upper sieve 5/22 mm while lower
sieve was closed. The combine harvester was moreover equipped with a straw chopper
system, to thresh the straw and spread it on the ground.
63
Sustainability 2021, 13, 195
2.4. Work Productivity
Harvesting productivity was tested in 6 sample plots randomly established in the
study area. The area of each plot ranged between 420 to 950 m
2
, and the evaluation of
the working times was performed according to the methodology developed by Reith et al.
(2017) [
44
]. The investigated parameters were: working speed (km h
−1
), Theoretical Field
Capacity (TFC, ha h
−1
, calculated knowing the working speed and the width of the header),
Effective Field Capacity (EFC, ha h
−1
, calculated taking into account accessory times) and
Material Capacity (MC, Mg h
−1
, calculated knowing the EFC and the effective seed yield).
The percentage ratio between EFC and TFC is named field efficiency (FE, %).
After harvesting operation, the collected material was unloaded onto a trailer and
transported to the farm scale in order to be weighted.
2.5. Cost Analysis
Purchase and operating costs of the machinery were obtained interviewing the contrac-
tor, whilst the work productivity of the combine harvester was derived from the results of
field tests and standard values for calculation were obtained from CRPA (Research Centre
on Animal productions) methodology [45] as reported in Suardi et al. (2020) [46–48].
Hourly costs of harvesting machinery were calculated taking into account the market
value of the combine harvester. The price of the combine harvester was discounted to 2019,
using the lending rate of 3% provided by Banca d’Italia [
49
]. The parameter used for cost
analysis are given in Table 1.
Table 1. Applied parameters for cost analysis.
Parameter Measure Unit Value
Machine Power kW 240
Financial costs
Investment € 362,615
Service life year 10
Service life H 3000
Resale % 19.00
Resale € 68,896.85
Depreciation € 293,718.15
Annual usage
h year
−1
312
Interest rate % 3
Fixed costs
Ownership costs
€ year
−1
29,371.82
Interests
€ year
−1
6472.67775
Machine shelter
m
2
35.64
Value of the shelter
€ m
−2
100
Value of the shelter
€ year
−1
71.28
Insurance
€ year
−1
906.5375
Variable costs
Repair factor % 40
Repairs and
maintenance
€ h
−1
50.28
Fuel cost
€ l
−1
0.57
Fuel consumption
lh
−1
42.50
Fuel cost
€ h
−1
24.23
Lubricant cost
€ l
−1
3.03
Lubricant
consumption
lh
−1
0.38
Lubricant cost
€ h
−1
1.14
Worker salary
€ h
−1
11.5
2.6. Seed Loss Evaluation
Camelina seed loss was evaluated by counting the number of the seeds lying on the
ground after the passage of the combine harvester. Specifically, two different areas behind
the machine were selected as shown in Figure 2a: (A) in correspondence of the swath; (B)
beside the swath but within the maximum cutting bar width. Ten squared sampling plots
64
Sustainability 2021, 13, 195
10 cm
×
10 cm (Figure 2b) were randomly selected within each region. Thus, in A, the
seed loss was due to natural shattering (SS), impact of the header (ISL) and inefficiency
of the cleaning shoe (CLS). On the other hand, in B, the seed loss was due to SS and ISL.
Consequently, CLS was calculated as difference between the total seeds found in A and B
regions. Since the loss due to CLS was concentrated in 1.6 m (the width of the swath), the
difference in seed number between A and B was divided by 4.125 (the ratio between the
cutting bar width and the swath width). By knowing the 1000-seed weight, the amount of
seed loss was calculated in weight and referred to hectares.
Figure 2.
On the left (
a
), identification of the areas A and B behind the combine harvester. On the
right (b), example of a sample plot to detect seeds on the ground.
Furthermore, the effective seed loss (ESL) was also estimated by calculating the
difference between the potential seed yield (PSY), measured in the pre-harvesting plot, and
effective seed yield (ESY), measured by the farm scale after weighing the trailer.
The difference of the two methodologies was used to estimate the SS.
2.7. Statistical Analysis
The analysis of variance (ANOVA) was performed using the R 3.6.1 software to
separate statistically different means among the groups (p ≤ 0.05) [50].
3. Results
3.1. Pre-Harvest Test
Results of the pre-harvest tests are shown in Table 2. Before harvesting, 424 plants per m
2
were standing on the field and the mean plant height was 60 cm. Straw, siliques and seed
moisture were 44.40%, 9.91%, and 6.45% respectively.
As reported in Figure 3, the largest aboveground portion was straw (69.62% w/w),
then siliques and seeds (14.44% w/w and 15.94% w/w respectively). The harvest index (HI)
was 0.223 and the potential seed yield was 1.17 Mg ha
−1
FM.
Figure 3. Percentage of straw, siliques and seeds of the aboveground biomass.
65
Sustainability 2021, 13, 195
Table 2.
Results of pre-harvest test reporting the mean quantity of available aboveground biomass,
moisture content, and allocation among siliques, seeds and stalks. Weigh and bulk density of seeds is
also reported.
Parameter Measure Unit Average St.Dev.
Harvested surface ha 24 -
Number of plants
Nm
−2
424 176
Plant height cm 60 8
Straw weight
Mg ha
−1
FM
5.10 1.15
Straw moisture content % 44.40 6.21
Siliques weight
Mg ha
−1
FM
1.06 0.25
Siliques moisture content % 9.91 0.49
Potential seed yield (PSY)
Mg ha
−1
FM
1.17 0.18
Seed moisture content % 6.45 0.40
1000-seed weight g 1.04 0.07
Seed bulk density
kg m
−3
687.82 13.60
3.2. Work Productivity and Costs
Working performance of the combine harvester is reported in Table 3. The work-
ing speed was estimated being 6.57 km h
−1
, while TFC and EFC were 4.34 ha h
−1
and
4.22 ha h
−1
, respectively. Considering the effective seed yield (1.10 Mg ha
−1
FM), the MC
and FE resulted in 4.66 Mg h
−1
FM and 97.24%, respectively.
Table 3.
Evaluation of the work performance of the combine harvester: theoretical and effective field
capacity, field efficiency and material capacity.
Parameter Measure Unit Average St.Dev.
Working speed
km h
−1
6.57 1.00
Theoretical Field
Capacity (TFC)
ha h
−1
4.34 0.66
Effective Field
Capacity (EFC)
ha h
−1
4.22 0.63
Field Efficiency (FE) % 97.24 0.41
Material capacity
(MC)
Mg h
−1
4.66 0.69
The analysis of working performance allowed to estimate the harvesting costs which
were: 205.17 € h
−1
, 48.51 € ha
−1
and 43.92 € Mg
−1
FM.
3.3. Seed Loss Evaluation
The seed loss calculated for each source of is reported in Table 4. TSL was 0.093
±
0.033 Mg ha
−1
, or 7.95
±
0.28% of PSY. The majority of seed loss (4.87
±
2.35% w/w) is linked to
the impact of the header and the natural shattering. The latter further estimated in 1.97% w/w of
the PSY as difference between TSL and ESL. On the other hand, CLS accounted for 3.08
±
0.54%
w/w of the TSL. The effective seed loss measured as the mere difference between the PSY and
ESY, was 0.07 Mg ha
−1
.
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Sustainability 2021, 13, 195
Table 4.
Seed loss assessment according to the two methodologies. Common letters within columns
denote the absence of significant difference (p < 0.05).
Parameter
Average
Mg ha
−1
FM %
Area A (CSL) 0.036 ± 0.006 b 3.08 ± 0.54
Area B (SS+ISL) 0.057
± 0.028 a 4.87 ± 2.35
Total seed loss (TSL) 0.093
± 0.033 7.95 ± 0.28
Potential Seed Yield (PSY) 1.17 ± 0.18
Effective Seed Yield (ESY) 1.10 *
Effective seed loss (ESL) 0.07 * 5.98 *
Note: (*) this value was not replicated since all grains were collected within one trailer and weighted
only once at the end of the harvesting.
4. Discussions
4.1. Aboveground Biomass Yield
The potential seed yield assessed in the pre-harvest test of 1.17
±
0.18 Mg ha
−1
FM
(seed moisture content of 6.45
±
0.40%) is in line with the findings reported in Mauri et al.
(2019) and Stefanoni et al. (2020) for similar experiments conducted in Spain [
39
,
51
]as
well as in USA as reported by Schillinger et al. 2019 [
52
]. Higher values of seed yield
are reported by Royo-Esnal et al. (2018) in Eastern Spain with seed yield ranging from
0.92 to 2.31 Mg ha
−1
FM after a comparison of different sowing rates: 8 kg ha
−1
and
11 kg ha
−1
[
27
]. However, the authors did not find a significant effect of the sowing rate
upon potential seed yield, nor with the weed coverage. Similarly, Zanetti et al. (2020)
reported a negligible effect of the plant density on seed yield, whilst later sowing could
improve oil content [
22
]. In the present study, instead, fertilizer and chemicals were used
to both providing nutrients and controlling the weeds. In similar studies, where camelina
was grown under conventional farming, the potential seed yield doubled in comparison
with not fertilized fields (namely, 0.93 Mg ha
−1
FM and 1.81 Mg ha
−1
FM) [
53
]. Comparing
with other herbaceous oilseeds, for instance, camelina performs slightly lower than castor
(Ricinus communis L. up to 4.4 Mg ha
−1
), canola (Brassica napus L. 2.19 Mg ha
−1
FM),
sunflower (Heliantus annuus L. 1.97 Mg ha
−1
FM) [
33
], although it is suitable for cropping
in marginal land [
54
]. After harvesting, seeds usually face some storing which could be
also long in time before being processed. This condition can lead to low quality product, or
even loss of the entire product if moisture is too high. In the present study, seed moisture
was found as low as 6.45
±
0.4% which is far below the threshold of 8% as reported by [
55
].
1000 seed-weight was also recorded and it averaged to 1.04
±
0.07 g in fresh weight which is
consistent with the value found by other authors [
39
]. If compared with other Brassicaceae
family and seed weight, it is rather low. In fact, Kuai et al. (2015) reported 3.3 and 3.5 g in
rapeseed (Brassica napus L.) [
56
], Zhu et al. (2016) reported values ranging from 6.0 to 9.5 g
per thousand seeds in crambe (Crambe abyssinica)[57].
Despite the seeds that find application on both food and non-food sectors, straw and
siliques from camelina (5.10
±
1.15 Mg ha
−1
FM and 1.06
±
0.25 Mg ha
−1
FM respectively)
can also be attractive for energy industry. In fact, they both are valid feedstocks for
bioenergy production via pyrolysis due to the low nitrogen content (0.4–0.5%) and the low
char production (approximately 25.5%) [
58
]. However, the chemical-physical properties
of camelina residual biomass can vary according to the growth conditions. For instance,
camelina grown in the Central Italy exhibits high cellulose and hemicellulose content
in comparison with camelina grown in the Northern Italy while the ash content is not
affected by such factor [
59
]. This implies that different scenarios are opened for the
exploitation of camelina residual biomass in a sustainable green chemistry approach.
Moreover, the development of a proper value chain of the residual biomass may contribute
to the reduction of greenhouse gas emissions that occur during the degradation of the
organic matter in the soil as reported in other oil crops [60].
67
Sustainability 2021, 13, 195
4.2. Work Productivity and Costs
In the present study, a conventional combine harvester equipped with a cereal header
was used. The literature still lacks the knowledge on such kind of strategy for harvesting
camelina seeds, therefore a comparison is possible relying on the findings reported in
Stefanoni et al. (2020) [
39
]. Here, the working speed of the machine was 30.1% higher.
This caused the increase of TFC, EFC, FE, and MC by 29.29%, 33.12%, 3.54%, and 54.82%
respectively. Interestingly, the cutting bars of the combine harvesters measured 6.6 and
6.7 m wide. Such a negligible deviation leads to the conclusion that the difference in the
performance are exclusively related to the different working speeds.
Interestingly, comparing the performance of the combine harvester found in the
present study with those reported in similar studies but conducted on wheat grains har-
vesting, here again the findings are higher. Normally, TFC ranges from 2.61 ha h
−1
to
3.72 ha h
−1
, while the EFC is 1.92–2.28 ha h
−1
and the FE is as high as 83% [
46
–
48
].
However, it is important to underline that such high working performance is related to
the dimensions and to the particular shape of the experimental field, which allowed to
minimize the turnings, thus decreasing the accessory time and increasing the EFC and FE.
The harvesting cost was assessed in 48.51
€
ha
−1
and 43.92
€
Mg
−1
FM which are con-
sistent with the cost shown by Stolarski et al. (2019) [
41
], but much lower than that calcu-
lated in a similar harvesting trial performed in Spain on camelina crop (65.97
€
ha
−1
and
69.42
€
Mg
−1
FM) [
39
]. Other trials performed on wheat and corn grain harvesting with com-
bine harvester showed harvesting costs being 77.98 and 129.51 € ha
−1
, respectively [46,61].
4.3. Seed Loss Evaluation
The evaluation of the seeds loss during harvesting stage is an important parameter
to take into account since it contributes to reduce the revenue of farmers and contractors
therefore, the loss of seeds should be as low as possible. Generally, the amount of seed lost
is calculated as the difference between the potential seed yield (1.17
±
0.18 Mg ha
−1
FM)
and the effective seed yield (1.10 Mg ha
−1
FM) which, in this specific trial, was 0.07 Mg ha
−1
FM (5.98% w/w). Higher values were found by Stefanoni et al. (2020) and Sintim et al.
(2016) which found 7.82 and 11.70% w/w, respectively [
39
,
40
]. In other herbaceous oil
crops, seed loss ranges from 1% as in sunflower [
62
,
63
] or in canola [
64
,
65
], and 3% as in
safflower [
33
], or even higher as in castor bean harvesting [
33
]. However, such information
only provides evidence regarding the total amount of seed loss, but it fails in pointing out
what is responsible for that loss. A combine harvester is a complicated machine which
can generate different sources of loss particularly if seeds are small and light in weight
(only 1.04
±
0.07 g FM per 1000 seeds). The main sources of loss are the impact of the
header and the inefficiency of the cleaning shoe. If some actions have to be taken against
the seed loss in camelina harvesting, their respective contribution to the TSL must be
investigated. According to Table 4, the inefficiency of the cleaning shoe of the combine
harvester (CSL) triggered the loss of 0.036
±
0.006 Mg ha
−1
FM (3.08
±
0.54% w/w)of
the seeds, while 0.057
±
0.028 Mg ha
−1
FM (4.87
±
2.35% w/w) of seeds were lost due
SS and ISL. Interestingly, TSL and ESL differed for 0.023 Mg ha
−1
FM (1.97% w/w of the
PSY) which can be partially explained as the loss due to SS (natural pod shattering) which
occurs spontaneously in camelina as it ripens. In fact, late harvesting can lead to a loss
of seeds due to SS as high as 25% w/w in some cultivars [
66
]. Moreover, pod shattering
can be triggered by a minimum external input in completely ripened pods. Therefore, the
mechanical disturbance provided by the combine harvester can contribute significantly
to increase such phenomenon, particularly as working and rotation speed of the reel (the
latter value was not measured in the present study).
5. Conclusions
Camelina is gathering more and more attention throughout the Europe since its multi-
purpose oil as well as the aboveground suitability for bioenergy purposes. However, the
related value chain is still not well developed, partially because the crucial phase of the
68
Sustainability 2021, 13, 195
harvest has not been comprehensively investigated so far. Our findings support the hy-
pothesis that a combine harvester equipped with wheat header is suitable for camelina seed
harvesting, which is particular convenient for farmers and contractor who use camelina as
rotation crop in winter cereals since the same machine is valid for both crops. Furthermore,
the cost and the performance are similar. Little concern may arise regarding the seed loss
which are mainly linked to impact of the header of the combine harvester, and the inability
of the cleaning shoe to efficiently discriminate the seeds from the other portions of the
biomass. This latter problem can be partially addressed by simply reducing the speed of
the machine. Instead, natural pod shattering contributes marginally to the loss of seeds.
Author Contributions:
Conceptualization, W.S., F.L., S.B., and L.P.; methodology, W.S., F.L., S.B., and
J.P.R.; data curation, W.S., F.L., S.B., N.P., and J.P.R.; writing—original draft preparation, W.S., F.L.,
S.B., and N.P.; writing—review and editing, W.S., F.L., S.B., L.P., and J.P.R.; supervision, L.P.; funding
acquisition, L.P. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by PRIMA foundation, project 4CE-MED, grant Number 1911
and by Horizon 2020 project Panacea, grant Number 773501.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to restrictions e.g., privacy.
Acknowledgments:
The authors wish to thank Camelina Company (Camino de la Carrera, 11-11,
28140 Fuente el Saz, Madrid, Spain) for the organization of the tests and the support during the trials
and Sandu Lazar for the help in performing the field and laboratory tests.
Conflicts of Interest: The authors declare no conflict of interest.
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71
sustainability
Article
Inulin Content in Chipped and Whole Roots of Cardoon after
Six Months Storage under Natural Conditions
Luigi Pari
1
, Vincenzo Alfano
1,
*, Walter Stefanoni
1
, Francesco Latterini
1
, Federico Liuzzi
2
, Isabella De Bari
2
,
Vito Valerio
2
and Anna Ciancolini
3
Citation: Pari, L.; Alfano, V.;
Stefanoni, W.; Latterini, F.; Liuzzi, F.;
De Bari, I.; Valerio, V.; Ciancolini, A.
Inulin Content in Chipped and Whole
Roots of Cardoon after Six Months
Storage under Natural Conditions.
Sustainability 2021, 13, 3902. https://
doi.org/10.3390/su13073902
Academic Editor:
Anastasios Michailidis
Received: 12 March 2021
Accepted: 29 March 2021
Published: 1 April 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1
Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA)—Centro di Ricerca
Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare, 16, 00015 Monterotondo, Italy;
luigi.pari@crea.gov.it (L.P.); walter.stefanoni@crea.gov.it (W.S.); francesco.latterini@crea.gov.it (F.L.)
2
ENEA Centro Ricerche Trisaia, 75026 Rotondella, Italy; federico.liuzzi@enea.it (F.L.);
isabella.debari@enea.it (I.D.B.); vito.valerio@enea.it (V.V.)
3
NOVAMONT SpA, 28100 Novara, Italy; anna.ciancolini@novamont.com
* Correspondence: vincenzo.alfano@crea.gov.it; Tel.: +39-06-9067-5315
Abstract: Industries currently rely on chicory and Jerusalem artichoke for inulin extraction but also
cardoon is proved to synthetize and store high quantity of inulin in roots as well. Cardoon is a
multipurpose crop, well adapted to marginal lands, whose main residues at the end of cropping cycle
consist of roots. However, cardoon roots are a suitable source of inulin, that is of high interest for new
generation biodegradable bioplastics production. On the other hand, a sustainable supply chain for
inulin production from cardoon roots has not been developed yet. In particular, in the inulin supply
chain the most critical part is storage, which can negatively affect both cost and inulin quantity. In
the present study the effect on inulin content in cardoon roots stored as dried chipped roots (CRt)
and dried whole roots (WRt) was investigated in a 6-month storage trial. Our findings suggest that
chipping before storage did not affect the inulin content during the storage. Furthermore, it reduced
the time needed for drying by 33.3% and increased the bulk density by 154.9% with the consequent
reduction of direct cost for drying, transportation and storage.
Keywords:
Cynara roots; biorefinery; marginal lands; multipurpose crop; fermentable sugars;
agricultural residues exploitation
1. Introduction
Cardoon (Cynara cardunculus L.) is one of the most promising feedstocks for biorefinery
in the Mediterranean areas since it is a multipurpose perennial crop well adapted to drought
environments and low productive marginal lands [
1
–
4
]. The cultivation of this species
mainly focuses on the exploitation of the aerial biomass as seeds, leaves and stalks [
5
–
7
].
The vegetable oil extracted from seeds is rich in monounsaturated fatty acids useful to
produce important intermediates such as azelaic acid or pelargonic acid, that are highly
demanded by synthetic fertilizer industries as well as cosmetic industries worldwide [
8
,
9
].
On the other hand, leaves and stalks represent an important source of lignocellulosic
biomass potentially suitable for the production of intermediate compounds, like bioethanol
and Bio-butanediol, which are widely used for producing bioplastics [10–12].
However, the potential of cardoon as a multipurpose crop has not been fully exploited
yet, in particular regarding the presence in the roots of inulin suitable for nutraceutical,
pharmaceutical and other biorefining applications [13–15].
Inulin is a linear fructan, i.e., a polymer of fructose units linked by
β
(2
→
1) glycosidic
bonds with a variable degree of polymerization (DP), between 3 and 60, and usually a
glucose molecule at the end [
16
]. Inulin can be used in food and pharmaceutical industry
for several purposes, such as prebiotics to stimulate the growth of probiotic gut bacteria,
for nutritional purposes as low caloric soluble dietary fiber and also as a mediate sugar
Sustainability 2021, 13, 3902. https://doi.org/10.3390/su13073902 https://www.mdpi.com/journal/sustainability
73
Sustainability 2021, 13, 3902
and lipid metabolism in diabetic and hypercholesterolemia [
17
]. In medicine, inulin is
also used as a diagnostic agent for the determination of kidney function [
18
]. In the
biorefinery industry, inulin and inulin-rich biomass are gaining interest for the production
of fructose by enzymatic hydrolysis of inulin, as alternative way to the current approaches
based on acid hydrolysis of sucrose [
19
,
20
]. In the biorefinery, moreover, the availability
of fermentable sugars is crucial to produce ethanol, and inulin is a good feedstock for
bioethanol production by fermentation after hydrolysis [21,22].
Inulin is a reserve carbohydrate accumulated mainly in the roots and tubers of many
plants belonging to the Asteraceae family, like Cardoon [16].
Among the Asteraceae family plants, Chicory (Cichorium intybus L.) and Jerusalem
artichoke (Helianthus tuberosus L.) are currently the major industrial sources of inulin [
23
]. In
a comparative study aimed at evaluating different types of inulin, extracted from Cardoon
roots, Jerusalem artichoke tubers and Chicory roots, the inulin amount resulted respectively
in 115, 390 and 550 g kg
−1
of d.m. [24].
In the perspective of new generation biorefineries and the circular bioeconomy concept,
the recovery of inulin from cardoon roots at the end of crop cycle, in addition to various high
added-value raw materials from seeds and stalks, seems to be an interesting opportunity.
However, the full development of an effective value chain for the biochemical industries,
implies well-organized logistics. Storage phase, in particular, has a high impact on the
quality of the raw material and on the overall costs of the value chain [25].
Effects of storage conditions on inulin content have been investigated in different
inulin-containing crops. In the case of the storage of Jerusalem artichoke tubers, inulin
composition remained stable under frozen storage (
−
18
◦
C) during 3 months of study, while
a significant degradation of inulin to sucrose and fructo-oligosaccharides was observed
after 4 weeks when the storage was performed at 4
◦
C[
26
]. In another study, inulin
hydrolase activity in the tubers of Jerusalem artichoke peaked at the 15th day of storage
at ambient conditions: inulin underwent depolymerization causing a decrease in inulin
content and an increase in soluble sugars [
27
]. In a 28 day storage trial a decrease of
70% and 96% was reported for artichoke heads after storage at 4 and 18
◦
C, respectively.
Similarly, in storage of sliced artichoke heads at 4
◦
C, about 60% decrease of inulin content
was observed during the first 11 days of storage [28].
In addition to the temperature, moisture plays a key role to start the enzymatic
hydrolysis of the inulin. For example, it has been observed that the enzymatic hydrolysis
of the inulin during storage of chicory roots depends on the moisture content, while the
generated sugars favored the loss of material as a result of cell respiration and microbial
activity [
29
]. In fact, microorganisms require minimum thresholds of moisture to maintain
and optimize metabolism, that is the breakdown and the consumption of the sugar-based
components of dry matter. Moisture below 10% is generally considered to be low enough
to prevent microbial degradation and allow for safe long-term storage of biomass [
30
]. In
this framework, the thermal drying technologies have gained interest as effective tools
for extending the length of storage as well as reducing the handling cost and ease the
transportation that affect the value chain at the industrial level [31,32].
On the other hand, a suitable storage system for a biorefinery has not only to focus
on the capacity of keeping a high content of a given product, but there is also the need of
finding a suitable solution regarding the economic sustainability, with particular reference
to transport costs which can have a substantial impact on the overall value chain [
33
,
34
].
Under this point of view biomass chipping is an interesting approach. Indeed, the higher
bulk density achieved by the chipped material improves the logistics by reducing the
space needed during transport as well as in the storage area [
35
]. On the other hand,
chipping increases the exposed surface area and reduces the air permeability [
36
] with an
expectable opposite effect on drying efficiency as well as on the maintenance of dry matter
and inulin content.
In order to set up a suitable value chain for inulin production from cardoon roots
biomass, there is the need of investigating a storage system which combines effective long-
74
Sustainability 2021, 13, 3902
term maintenance of inulin and economic sustainability. Considering the current lack of
knowledge on this particular topic, a specific task of the Italian Project Cometa-Autoctone
Mediterranean crops and their valorization with advanced green chemistry technologies
(funded by Ministry of Education, Universities and Research), was addressed to develop
an effective handling and storage strategy for cardoon roots aimed to inulin production. In
this framework the present study aimed to investigate the effect of chipping and drying
approach on inulin content of cardoon roots biomass over 6 months of storage.
2. Materials and Methods
2.1. Location of the Field and Plant Material
Cardoon roots were taken in May 2020 from three year old plants cultivated in Terni
(42.561335 N latitude, 12.62860 E longitude) (Umbria Region, Central Italy) on clay soil.
Cardoon (cultivar Trinaseed) were grown from seeds sowed in November 2017 with
a precision sowing machine at the rate of 3.0 kg/ha of seed and spacing distance of
0.75 × 0.17 m.
At the sowing, the seedbed was prepared between October and November with
ploughing at 20 cm, followed by harrowing at 10–15 cm. During the first year, mineral
fertilization with 46 kg of P2O5 and 64 kg/ha of N was added, and a.i. pendimethalin was
used to control weeds. Starting from the second year, 46 kg/ha of P2O5 and 18 kg/ha of N
were applied during the vegetative growth of the plants in autumn and in the early spring
before the stem elongation phase. The fertilization rates were calculated on the basis of the
soil fertility and crop nutrient uptake. Crop water requirements were satisfied by rain.
Approximately 500 plants were randomly uprooted using an excavator carefully
avoiding damage to both canopy and root systems. The whole plants were put in sealed
bags and carried to the laboratory of The Research Centre for Engineering and Agro-Food
Processing of the Council for agricultural research and economics (CREA) in Monterotondo,
Central Italy (42 10019
N latitude 12 62066
E longitude) for sampling. Firstly, the soil was
removed from roots using a cold-water pressure washer. Afterwards, the plants were left to
dry naturally for a few minutes. Then the roots were mechanically cut off from the plants.
The bulk density of roots was determined using a box with an internal volume of
0.0064 m
3
, the value was reported as kg m
−3
. The box was filled with roots and weighed
with a KERN GmbH dynamometer (CH 50K50 model-range of measurements 50 kg and
sensitivity 50 g). Three samples were taken for mean value. In 30 randomly chosen plants
the fresh weight of canopy and roots were measured using a precision scale (Kern PCB
6000-0). The length of leaves and roots of this plants sample was measured with a ruler.
Roots were further investigated by determining the moisture content according to [37].
2.2. Chipping, Drying and Storage
After cleaning, roots were divided into two groups (treatments) to monitor the inulin
content in dried chipped roots (CRt) and dried whole roots (WRt) of cardoon in 6-months
storage. CRt was obtained by selecting 15 kg of randomly chosen roots that were fresh
chipped using an electric 2.0 kW bio-shredder (Zanon, mod. BIO 3). The particle size
distribution (PSD) of the chipped material produced was analyzed according to [38].
Chips were collected and put into the oven for drying at 60
◦
C until they reached
constant weight. Simultaneously, a further 15 kg of randomly chosen whole roots were
collected and put in the oven for drying. At constant weight, the whole roots were removed
to obtain WRt. The apparent bulk density was measured three times in CRt and WRt,
respectively, according to [39] for mean value estimation.
Storage of CRt and WRt was performed outside the building, under a farm shed.
Specifically, chips from CRt were collected in jute bags while the whole roots from WRt
treatment were piled up as shown in Figure 1 after being labeled and weighed individually.
75
Sustainability 2021, 13, 3902
Figure 1.
Storage under a farm shed of dried chipped roots (CRt) on the left side and dried whole
roots (WRt) on the right side.
Monthly, approximately 200 g of chips from CRt and five roots from WRt were sampled
and sent to ENEA laboratory for inulin determination. WRt roots were bioshredded
before the shipment. A representative subsample from each treatment was kept for dry
matter assessment.
2.3. Weather Data Monitoring
During the entire storage period of 6 months, the main weather-climatic parameters
such as temperature, precipitation and air humidity were recorded with a weather station
“DAVIS VANTAGE PRO 2” (Davis Instruments, 3465 Diablo Avenue, Hayward, CA 94545-
2778, USA) located in the proximity of the storage site and connected to wireless net. Data
are shown in Figure 2.
Figure 2.
Trend of main climatic parameters: temperature (
◦
C), precipitation (mm) and air humidity
(%) recorded during the storage of cardoon roots from May to November 2020.
76
Sustainability 2021, 13, 3902
2.4. Inulin Content Determination
The collected samples from CRt and WRt were grinded to 0.5 mm in a ZM200 Retsch
®
ultracentrifugal mill (Retsch GmbH, Haan, Germany).
After elimination of residual humidity by drying at 50
◦
C for 4 h in a ventilated oven,
the inulin content was determined by using a modified Raccuia method [
19
]. In particular,
the quantitative extraction of the inulin from the roots was performed by suspending
1 g of powdered dry root in 20 mL of deionized water at 100
◦
Cfor1hinaBenchmark
Scientific (South Plainfield, NJ, USA) Multi-Therm Heat-Shake kept under constant stirring
at 500 rpm. Subsequently, the sample was centrifuged at 3500 rpm for 5 min and 4 mL of
0.75 M HCl were added to 2 mL of supernatant. The acid solution containing the inulin was
then hydrolyzed in the heat-shake system for 15 min under the same conditions (100
◦
C,
500 rpm).
After centrifugation at 3500 rpm for 5 min, the supernatant was filtered through
0.45
μ
m PTFE filter (Whatman, USA) and carbohydrates were analyzed by using an HPIC
DX 300 cromatographic system (Dionex, Sunnyvale, CA, USA) equipped with a Nucleogel
®
Ion 300 OA column (Macherey–Nagel, Düren, Germany) and sulphuric acid 10 mN as
eluent. The detector was a Shodex RI101 refractive index (Showa Denko, Japan). All
reagents and standards were purchased from Sigma-Aldrich (St. Louis, MO, USA). The
extraction and hydrolysis processes were conducted in triplicate for each sample.
The inulin content was determined by the following Equation (1):
Inulin %
=
C
f
+ C
g
× 0.9 × 3
C
R
× 100 (1)
where C
f
and C
g
are the concentration in gL
−1
of fructose and glucose, respectively; 0.9 is
the correction factor applied for the oligomer-to-monomer hydration; 3 is the dilution
factor for the HCl hydrolysis; C
R
is the concentration in gL
−1
of the initial suspended roots.
2.5. Statistical Analysis
Statistical analysis was performed to assess significant differences among the mean
values of dry matter and inulin content. Normality and homoscedasticity of the data were
tested with Shapiro test and F test, respectively. T-test was performed to investigate signifi-
cantly different means (p
≤
0.05) among treatments. Statistical analysis was performed by
R 3.6.1 software to separate statistically different means [40].
3. Results and Discussion
3.1. Characterization of Cardoon Roots and Evaluation of Drying Times
The growth analysis of sample plants was performed in order to estimate the available
aboveground and belowground biomass. The average fresh weight of canopy and roots
was, respectively, 0.9 and 0.45 kg per plant (71 and 35 t f.w. ha
−1
). The moisture content of
roots was assessed as 70% w/w of fresh weight. Hence, the expected quantity of dry roots
per hectare can be estimated in 10.6 t, similarly to 9.8 t DM ha
−1
reported by [
13
]. Results
of roots’ characterization are given in Table 1.
Table 1.
Characteristics of roots and canopy of three year old cardoon plants (mean
±
sd) sampled in
May 2020.
Taproot Length Root Fresh Weight Canopy Height Canopy Fresh Weight
cm kg cm kg
35.2 ± 14.9 0.45 ± 0.23 69.4 ± 6.6 0.9 ± 0.6
The bulk density of either fresh chips and dried chips was about 2.5 times higher than
the bulk density of fresh whole roots and dried whole roots, respectively (Table 2).
77
Sustainability 2021, 13, 3902
Table 2. Bulk density of the whole roots and the chipped material, before and after drying
Bulk Density
(kg m
−3
)
Fresh whole roots 164.2 ± 15.9
Dried whole roots 61.79
± 1.11
Fresh chips 418.8
± 50.8
Dried chips 157.2
± 31.6
Therefore, chipping represents an advisable option to reduce the volume needed for
both transportation and storage. Consequently, the cost of the operations can be reduced
as well. Although it was not investigated in the present study, sieving can follow the
chipping phase to help removing unwanted debris from chips, which could be detrimental
for further industrial processes. With this aim the chipping should be carried out with a
forestry chipper able to produce a more homogeneous product.
On the contrary, in our case, as shown by PSD analysis (Figure 3), 90% of the chipped
material was less than 8 mm length, making it not possible to separate by sieving unwanted
debris from chips.
Figure 3. Particle size distribution of the chipped roots.
Moreover, the drying process can also benefit from chipping by reducing time and
energy required [
41
]. According to our results, indeed, chipped roots could reach constant
weight after 48 h, whilst whole roots needed 24 h more to dry completely (Figure 4).
Figure 4.
Reduction of the average weight of whole roots and chipped roots during drying in a
thermo-ventilated oven at 60
◦
C.
78
Sustainability 2021, 13, 3902
3.2. Inulin Content
Inulin content at T0 (beginning of storage, immediately after drying) was 43.5
±
0.65%
and 47.1
±
1.30% w/w in WRt and CRt, respectively (Figure 5). Drying time negatively
affected the inulin content in WRt which resulted in 3.54% w/w lower than CRt. Conceivably,
in WRt treatment, the inner tissues of the roots took longer to dry out than the outmost
tissues of the same root. Hence, the metabolic activities naturally occurring in living cells
stopped later and this partially explains the loss of inulin in WRt.
Figure 5.
Inulin content in dry whole and chipped roots of cardoon at T0 and after 6 months of
storage (T6). Common letters denote the absence of significant difference (p < 0.05).
Regardless of the difference in inulin content found among treatments, the values
herein reported are consistent with the literature although some authors also highlight that
possible changes in inulin content may be experienced according to the harvest season [
14
].
Higher values are usually found in spring, particularly between full blossom and fruit
ripening. For this reason, root sampling was performed in May when the concentration is
supposed to peak.
After 6 months storage (T6), inulin content did not change significantly in comparison
with the initial content measured in the respective treatment, namely: 42.3
±
0.82% w/w
in WRt and 48.3
±
1.12% w/w in CRt. Therefore, the drying process performed before
the storage prevented the degradation of inulin, at least over the following 6 months of
storage. This finding highlights the possibility for industries to exploit the drying process
at the industrial scale to help storing the cardoon roots for longer time since inulin loss is
prevented effectively. Artificial drying is certainly costly in terms of money and energy,
but it is surely less costly than freezing. Additionally, the machinery required for drying is
easier to run and cheaper to buy (e.g., a ventilated oven) with enormous advantage also for
the transportation which does not longer require the ice-chain. Chipping also contributes
to enhance the inulin supply chain as the higher bulk density of chipped material would
require less space for drying, storage and transportation.
3.3. Moisture Content and Dry Matter Content
The moisture content at the first month of storage (T1) in WRt and CRt increased
significantly up to 12.1% and 10.8%, respectively. This was probably due to the reabsorption
of humidity from the external environment. In fact, as shown in Figure 6, the monthly
moisture content measured in WRt and CRt traces the air moisture pattern recorded by
weather station. The moisture increase in roots was more evident in WRt where 16.3% w/w
79
Sustainability 2021, 13, 3902
of moisture was recorded at T6 (i.e., 3.4% higher than CRt). This was probably due to a
greater exposure of the whole roots to air humidity with respect to the chipped material
stored inside the jute bag.
Figure 6.
Moisture content (mean
±
SD) in WRt and CRt during the 6 months storage in comparison
with air moisture recorded by a weather station over the same period.
During the first month of storage, a significant reduction in weight in both treatments
was recorded: 10.9% and 3.5% in WRt and CRt, respectively (Figure 7). Despite this,
during the following months the roots’ dry weight remained constant in both treatments.
However, a significant difference of approximately 6% was recorded between the treatments
throughout the trial. Both results were probably due to the higher water content recorded
in the whole roots—not completely removed after drying (less drying efficiency) or greatly
reabsorbed from the external environment (higher exposure to air humidity)—which
promoted microbial activity.
Figure 7.
Dry matter content in dried whole roots (WRt) and dried chipped (CRt) roots at the first
month of storage (June, T1) and after 6 months of storage (T6). Common letters denote the absence of
significant difference (p < 0.05).
80
Sustainability 2021, 13, 3902
4. Conclusions
In the perspective of new generation biorefineries and the circular bioeconomy frame-
work, the exploitation of cardoon also for inulin production is rather appealing, particularly
if plants have been previously exploited for the production of further high added-value
raw materials like seeds and stalks. Due the limited favorable period for harvesting the
roots when inulin content is maximum, industries need to store enormous quantities of
roots and process them gradually. Hence, storage plays a fundamental role in supply chain.
Our findings suggest that during a 6-month storage inulin loss is negligible if roots are
previously dried. Furthermore, chipping could also be a good practice since it is possible
to reduce the volume required for storage (and also transportation) while it promotes a
quicker drying; thus, less energy is required to dry out the roots.
In conclusion, our results highlight the possibility to chip cardoon roots meant for
inulin extraction to ameliorate the supply chain of such a material. Although drying
remains a costly strategy, chipping would help to reduce such cost by reducing the time
required. However, further studies should provide clues to improve also the harvesting
and cleaning process.
Author Contributions:
Conceptualization, L.P. and V.A.; methodology, V.A., W.S. and F.L. (Francesco
Latterini); validation, A.C.; formal analysis, V.A., W.S., F.L. (Francesco Latterini), F.L. (Federico Liuzzi)
and V.V.; investigation, V.A., W.S., F.L. (Francesco Latterini), F.L. (Federico Liuzzi) and V.V.; resources,
A.C.; writing—original draft preparation, V.A., F.L. (Federico Liuzzi), V.V.; writing—review and
editing, V.A., W.S. and F.L. (Francesco Latterini).; supervision, L.P. and I.D.B.; funding acquisition,
L.P. and I.D.B. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was performed within the Italian Project COMETA-Autoctone Mediterranean
crops and their valorization with advanced green chemistry technologies. The project was funded
by Ministry of Education, Universities and Research in the frame of PON “Ricerca e Innovazione”
2014–2020 and FSC/ARS01_00606 COMETA/CUP. B2G18000180004/Azione II.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement:
Data is not publicly available, though the data may be made available
on request from the corresponding author.
Acknowledgments:
The authors would like to thank Salvatore Antonino Raccuia for providing
suggestions and sharing his knowledge on cardoon roots and storage methods.
Conflicts of Interest: The authors declare no conflict of interest.
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83
sustainability
Article
Environmental and Economic Assessment of Castor
Oil Supply Chain: A Case Study
Luigi Pari, Alessandro Suardi *, Walter Stefanoni, Francesco Latterini and Nadia Palmieri
Consiglio per la Ricerca in Agricoltura e l’analisi dell’Economia Agraria (CREA)—Centro di Ricerca Ingegneria e
Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy;
luigi.pari@crea.gov.it (L.P.); walter.stefanoni@crea.gov.it (W.S.); francesco.latterini@crea.gov.it (F.L.);
nadia.palmieri@crea.gov.it (N.P.)
* Correspondence: alessandro.suardi@crea.gov.it; Tel.: +39-06-9067-5248
Received: 30 June 2020; Accepted: 5 August 2020; Published: 6 August 2020
Abstract:
Among the species currently cultivated for industrial vegetable oil production, castor could
be a good candidate for future investments due to the good resistance to pests, tolerance to drought,
and suitability for marginal lands cultivation. In addition, the production of castor oil from Ricinus
generates a large quantity of press cake, husks, and crop residues that, in a framework of bioeconomy,
could be used as by-products for different purposes. Using a case study approach, the work presents
results of the environmental impact assessment and economic feasibility of the production of castor
oil from two different castor hybrids comparing four by-products management scenarios and two
harvesting systems (manual vs. mechanical). Castor hybrid C-856 harvested manually and that
involved only the soil incorporation of press cake obtained by the oil extraction resulted as the most
sustainable. The hybrid C-1030 resulted as more profitable than C-856 when harvested with the
combine harvester. The ratio between gross margin and GWP emissions was applied to calculate
the economic performance (gross margin) per unit of environmental burden. Findings showed that
Sc1B scenario in case of C-856 cultivar hybrid had a better ratio between economic performance and
greenhouse gas (GHG) emitted into the atmosphere (€3.75 per kg CO
2
eq).
Keywords:
bioeconomy; life cycle assessment; life cycle costing; Ricinus communis, L.; castor oil;
harvesting; by-product; residue management
1. Introduction
The world’s population is estimated to exceed 9 billion people by 2050 according to FAO (2009).
Thus, increasing in food and energy demand worldwide cannot be avoided: projections show that the
overall food production is expected to rise by 70% [
1
] while the global demand for energy will increase
by more than a quarter to 2040 [
2
]. Bioenergy production primarily aims at the greenhouse gas (GHG)
reduction and achieving such a goal may lead to indirect land use change. Competition for land use
among food and non-food crops is a serious issue that European Commission has been addressing for
decades, and more stringent policy measures regarding sustainable production of food and energy are
on the Agenda. On 1 January 2021 the proposed new directive RED II will enter into force, setting the
new thresholds for minimum renewable energy share [
3
]. Investments on biofuel production from
non-food feedstock are largely promoted by UE. New policy measures aim to achieve a 27% renewable
energy share consumed by the electricity, heating and cooling, and transportation sectors by 2030 [
3
].
The adoption of energy crops could generate benefits from the reduction of fossil energy dependence,
improvement of rural economies, and the achievement of environmental goals [
4
]. Biodiesel production
from vegetable oils is feasible and widely accepted as an alternative strategy to meet these goals: It has
similar properties to oil-derived diesel and, furthermore, it produces lower sulfur emission. Among the
species currently cultivated for industrial vegetable oil production, castor could be a good candidate
Sustainability 2020 , 12, 6339; doi:10.3390/su12166339 www.mdpi.com/journal/sustainability
85
Sustainability 2020 , 12, 6339
for future investments due to the good resistance to pests, tolerance to drought, and suitability for
marginal lands cultivation [
5
]. According to FAO, in 2017, almost 1.8 million tons of castor seed had
been produced worldwide, and Europe is the main user [
6
]. Furthermore, according to industry
executives, the worldwide castor oil market is growing: The global castor oil market was $1180 million
in 2018 and is expected to reach $1470 million by the end of 2025, growing at a compound annual
growth rate (CAGR) of 2.8 percent between 2019 and 2025, according to international reports [
7
].
The price of castor oil in the beginning of 2019 in the international market reached 1600 dollars per ton
compared with 1300 dollars per ton of 2018 [8].
In addition, the productionof castor oil from Ricinusgeneratesa large quantityofpress cakes, husks,
and crop residues [
9
] that, in a framework of bioeconomy, could be used as by-products for different
purposes. In this framework, the European Project MAGIC (Marginal lands for Growing Industrial
Crops—Grant Agreement number: 727698-MAGIC-H2020-RUR-2016-2017/H2020-RUR-2016-2) aims
towards the development of resource-efficient and economically profitable industrial crops to be
grown on marginal land. Among industrial crops considered in the Project, there is Ricinus communis,
L. (castor) that is cultivated for its seed oil, which is employed extensively in medicine, pharmaceuticals,
and biorefineries[
10
]. Castor is a vigorous fast-growingherbaceous plant native to tropical Africa [
11
,
12
]
which is tolerant to salinity and drought stresses, with additional benefits of providing a multi-purpose
oilseed production [
13
]. In the world, the most productive country is India (more than 80% of
the worldwide production) along with Mozambique, China, Brazil, Myanmar, Ethiopia, Paraguay,
and Vietnam. These are all developing countries that benefit from low labor costs, and the economic
impact of the harvesting phase is thus sustainable. The lack of possibility to harvest the seeds
mechanically is dictated by a high amount of aboveground biomass produced by wild cultivars that
cannot be processed by common combine harvesters. In fact, clogging may occur in the case of a high
quantity of aerial biomass production, and high seed losses. In order to solve this problem, breeders
around the world are struggling to produce hybrids of castor exhibiting high productivity but shorter
in height and with homogeneous ripening of the capsules.
To our knowledge, limited studies have been dedicated to the environmental and economic
sustainability of castor [
14
,
15
]. Some studies are focused on the sustainability assessment of the residue
biomass utilization [
16
] or biodiesel production [
17
] without investigating in detail the impact of the
various castor agricultural stages and different residue management. In particular, a comparison the of
the environmental sustainability between different castor hybrids, harvesting methods, and by-products
management have not been presented in the literature. Using a case study approach [
18
,
19
], the
work aimed to present results concerning the estimation of the environmental impacts caused by two
different castor hybrids harvested both manually and mechanically (manual vs. mechanical harvestings).
Both hybrids had similar seed yields, even though hybrid C-856 is shorter than hybrid C-1030. The latter
reported a higher amount of epigeal biomass. Various scenarios of on-farm by-products managements
were analyzed. Starting by the same approach, the study carried out an economic assessment to identify
the most advantageous scenario for each castor variety and residue management.
2. Materials and Methods
2.1. Study Sites
The study area is located in Geaca Municipality, Cluj District (Romania). Cluj District lies in the
northwestern half of the country, between parallels 47
◦
28’ in North and 46
◦
24’ in South, meridians
23
◦
39’ in west and 24
◦
13’ in east, respectively. It is located in the contact zone of three representative
natural units: Apuseni Mountains, Some
s
,
Plateau, and Transylvanian Plain. Cluj District is the 12th
largest in the country and accounts for almost 3% of Romania’s area. It is bordered to the northeast by
Maramure
s
,
and Bistri
t
,
a-N
ă
s
ă
ud counties, to the east by Mure
s
,
District, to the south by Alba District,
and to the west by Bihor and Sălaj counties.
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Sustainability 2020 , 12, 6339
The trials were carried out on the 8th and 9th of October 2019 in two different experimental fields
where castor was harvested (Figure 1).
Figure 1. Study area and experimental field location (Geaca Municipality, Cluj District, Romania).
Main features of the experimental fields are given in Table 1. Data were taken both from GIS
analysis and from field relieves with clinometer.
As highlighted in Table 1, all fields have southern exposition and the prevalent altitude of 313 m
a.s.l. was recorded in Field 2 while the maximum slope was recorded in Field 1. However, both fields
can be considered flat terrains. The surface of the field 2 was 0.27 ha higher than Field 1. A view of
experimental fields positioning on Sentinel-2 image dated 3 September 2019 is given in Figure 2.
Figure 2.
Experimental fields positioning. Base map Google Satellite Images dated 3 September 2019.
87
Sustainability 2020 , 12, 6339
Table 1. Main features of the experimental fields for castor harvesting.
Experimental
Field
Prevalent
Slope
[%]
Minimum
Slope
[%]
Maximum
Slope
[%]
Prevalent
Exposition
Prevalent
Altitude
[m a.s.l.]
Surface
[ha]
1 8.5 7.9 8.7 South 294 0.25
2 5.7 4.1 7.2 South 313 0.47
2.2. Crop Characteristics and Management of the by-Products
The main data of two dwarf hybrids of Ricinus communis (C-856 and C-1030) collected during the
trials are reported in Table 2. Plants were cultivated in Romania, and seeds were provided to local
farmer (Ecoricinus—National association of Ricinus growers) by the Israelian company KAIIMA.
Table 2.
Primary data: Two castor hybrids. Pre-harvest data collection: Height of the plants, aerial
biomass produced, and Harvest Index.
Hybrid
Cultivar
Height of Plants
[cm]
Husks
[Mg ha
−1
]
Seed
[Mg ha
−1
]
Straw Fresh Weight
[Mg ha
−1
]
Harvest Index
[%]
d.w. ssf.w. f.w. d.w.
C-856 74.4 c 1.40 2.80 a 4.13 b 0.87 b 52.5 a
C-1030 112.8 a 1.60 2.90 a 8.35 a 1.61 a 43.4 b
Note: Common letters within columns denote the absence of significant difference (p < 0.05).
The dwarf hybrids tested were two of the various chosen by the association Ecoricinus to evaluate
their behavior and productivity in Romania. Although hybrid C-856 has already been analyzed in
productive and morphological terms by Alexopoulou et al. [
6
] in Greece and Italy, hybrid C-1030 has
never been described in the literature and in the present study, it has been analyzed only to assess its
productivity and the amount of epigeal biomass available for the LCA study.
Despite the significant difference found in height, straw production, and the harvest index (HI)
between the two hybrids, in both cases, the aboveground biomass produced was lower than the
quantity produced by wild varieties commonly cultivated in Romania (data not shown). Therefore,
more suitable for mechanical harvesting. The farmer reported that fertilization and plowing took place
in 2018 between week 47 and 48, while harrowing and sowing occurred in week 23 and 24 2019 at the
depth of 8–10 cm with the sowing density of 3.6 seeds m
−2
. Mature cow manure was applied at the
quantity of 6 Mg ha
−1
and no irrigation was provided. No chemicals were used for both weed control
and desiccation of leaves.
On the basis of farmer’s survey, two scenarios for each castor variety with a different mix of
by-products management were considered (Table 3).
Table 3. Scenarios analyzed in the study for each castor variety (Sc1 = scenario 1; Sc2 = scenario 2).
Harvesting Systems Manual Harvesting Mechanical Harvesting
Scenarios Sc1A Sc1B Sc2A Sc2B
Products and
co-products
Straw Soil incorporation Sale Soil incorporation Sale
Husk Sale Sale Soil incorporation Soil incorporation
Press cake Soil incorporation Soil incorporation Soil incorporation Soil incorporation
Castor oil Sale Sale Sale Sale
In the case of manual harvesting (Sc1A and Sc1B), the castor fruit is harvested as whole, and the
separation of the spiny capsules from the seeds takes place on the farm. According to Parascanu (2017),
castor husk might be deemed as the best candidate for the combustion process due to its high heat
release [
20
]. Therefore, in the Sc1A and Sc1B scenarios, the sale of spiny capsules has been assumed at a
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Sustainability 2020 , 12, 6339
market price of crushed olive stones due to similar lower heating value (LHV) that results in 16.48 [
20
]
and16.50 MJ/kg [21], respectively.
In the scenarios Sc2A and Sc2B involving castor mechanical harvesting, castor husk has always
been considered incorporated into the soil because it was discharged on the ground by the combine
harvester as residue and not collected. Castor straw is a residue with an LHV of 17.68 MJ/kg and an ash
content of 1.70 wt% [
20
]. For this reason, both manual and mechanical harvesting scenarios have been
considered, both sold as solid biofuel (Sc1B and Sc2B) and incorporated into the soil (Sc1A and Sc2A).
According to the farmer, press cake that resulted during the oil extraction phase is used as fertilizer
and for this reason was always considered incorporated into the soil. Castor oil is the main product in
the supply chain, and it has always been considered as sold.
2.3. Data Sampling and Measurements
Pre-harvest tests were conducted directly in the field. Four plots of 1.5m x 2m each were randomly
selected within the two experimental fields in order to measure the growth of the plants and estimate
the aboveground biomass produced. In each plot, plants were counted and cut at the collect level,
then brought outside the field for height measurements as well as straw and capsules fresh weight
determination. The height of the plant was taken by measuring the distance between the collect and
the tip of the longest raceme. Samples of straw and the total capsules collected in each plot were
put in sealed bags and brought to the laboratory for dry weight determination. In the laboratory,
capsules were separated manually from the seeds. Thus, seeds were weighed for seed yield estimation.
Simultaneously, samples of straw and empty capsules (husks) were dried at constant temperature of
105
◦
C in a ventilated oven until constant weight was reached (EN ISO 18134-2:2015). Then, the dry
matter and humidity content were calculated. All data were subjected to the analysis of variance
(ANOVA) to separate statistically different means (P < 0.05).
2.4. Life Cycle Assessment of Castor Oil Supply Chain
An environmental impact analysis of castor oil production was carried out using the life cycle
assessment methodology (LCA) according to UNI EN ISO 14040: 2006 [
22
] and UNI EN ISO 14044:
2006 [
23
], by means an attributional approach [
24
–
27
], including the following statements: (a) Goal
definition and scoping: Defining the goals of the study, the functional units, the boundaries of the
system, and the required data; (b) life cycle inventory: data collection; (c) life cycle impact assessment:
Estimation of the potential environmental impacts; (d) life cycle interpretation and improvement:
Final step where the risks are evaluated and checked to draw conclusions.
2.4.1. Goal Definition and Scoping
The considered system is defined by all the agricultural processes that occurred during the Ricinus
communis growing phase and subsequent oil extraction phase carried out at farm level.
The boundary of the system (Figure 3) is given by the life cycle stages of castor to be included in
the LCA. Cultivation phases and extraction phase of oil were studied from cradle-to-farm gate.
The functional unit represents the reference unit used to quantify all inputs and outputs from the
boundaries of the system. It is defined as 1 Mg of castor oil produced by the farm.
Firstly, the environmental impact of each single hybrid cultivar was separately analyzed for each
scenario; then, each scenario was assessed to identify the best hybrid cultivar.
Allocation describes how environmental impacts are shared between the main product and
co-products along the supply chain [
28
]. Castor oil is the main product, while crop residues (castor
husks and straw) and press cakes are considered co-products [
9
]. In an LCA study, the co-product
handling is a crucial issue because it could impact on the final results [
29
]. Agricultural products are
particularly sensitive to allocation methods because of the different share that their co-products can
have. In our case, an economic allocation method that takes into account market prices and mass of
product and by-products per each scenario was used [
30
] (Table 4). Castor market prices are not easy
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to be find, especially for castor co-products that do not have a market. For this reason, the selling
price of castor seed was considered to be 600 euros per Mg, while the price of castor oil for cosmetic
purposes was 30 euros per liter according to informal local market. As described above, in the absence
of a market, husk and straw prices for energy purposes have been assimilated to solid biomass with
similar characteristics (olive stones and wheat straw) used for energy purposes and with known market
prices. In fact, following information from the informal local market as happen in other studies [
31
],
the price of husks for energy purposes was 180 euros per Mg, while the price of straw was considered
55 euros per Mg. The prices used are those indicated by the Ricinum producers National Association of
Romania (Ecoricinus Productie Comert Srl, Cluj-Napoca, 10, Fanatelor st. jud, Cluj, Romania). Even if
the press cake corresponds to an important amount of biomass, due to its returns to the soil as fertilizer
internally at the farm, according to the economic allocation type used and due to an absence of a
market and a market price, the impact generated by the press cake was assumed to be very low (0.07%)
with a minimum price of 0.1 € per Mg of by-product.
Figure 3. System boundary.
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Table 4. Economic allocation factors for each castor variety.
Phases Product and by-Products Cultivar Hybrids
C-856 C-1030
Agricultural phases
Husks with seed 97.50% 95.84%
Straw 2.50% 4.16%
Total 100.00% 100.00%
Dehulling
Castor seed 86.96% 85.80%
Husks 13.04% 14.20%
Total 100.00% 100.00%
Oil extraction phase Castor oil 99.93% 99.93%
Press cake 0.07% 0.07%
Total 100.00% 100.00%
Source: CREA elaboration.
2.4.2. Life Cycle Inventory Analysis
Data resulting from a survey carried out by field technicians were utilized for the life cycle
inventory analysis. The Simapro code database 8.0.2 (Pr
è
Consultants, Amersfoort, The Netherlands)
was used for data not identifiable by survey.
The primary data were relative to the technical characteristics of the tractors and agricultural
equipment utilized and diesel consumption (Table 5). Regarding the hypothesis of mechanical
harvesting, all data for the costs, performance, and specifications of a conventional combine harvester
were derived from personal communication and literature. Moreover, the primary data were relative
to different castor varieties.
Table 5. Technical characteristics of the machineries, diesel consumption, and agricultural phases.
Agricultural Operation
Manual
Fert.
Plough. Harr. Sow.
Manual
Hoeing
Manual
Harv.*
Mech
Harv.**
Dehull.
Oil
Extrac.
Machinery
Machinery
power (kW)
- 78 78 78 - - 146 7.7 3
Machinery weight (kg) - 3750 3750 3750 - - 10700 250 1900
Fuel consumption
(l ha
−1
)
- 45 15 5 - - 25 5.1 -
Lubrificant consumption
(l ha
−1
)
0.10 0.07 0.03 - - 0.05 0.18 -
Lifetime
(h ha
−1
)
- 12,000 12,000 12,000 - - 3000 2000 -
Instrument used (type) Shovel
Moldboard
plow
Rolmako
Row
planter
- - ---
Instrument power (kW) 66 63 44 - - - - -
Weight instruments (kg) 1.5 795 2860 830 - - - - -
Lifetime
(h)
4000 2000 2000 1500 - - - - -
Product utilized (type) Manure - - Seeds - - - - -
Quantity
(kg ha
−1
)
6000 - - 15 - - - - -
Source: CREA elaboration on survey data. *Scenario 1. ** Scenario 2.
The secondary data referred to the emission generated by the machines during different agricultural
phases and from fertilizers.
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Emissions in air, soil, and underground water (leaching) due to manure storage, as well as
by-products and manure incorporation into the soil per each scenario, were calculated using the model
proposed by [32] and values of the references reported in Table 6.
Table 6.
Secondary data: Source of the emissions considered in the study for storage and soil
incorporation of the manure, and by-products (press cake, straw, and husk according to the
scenario—Table 2).
Emissions Source
Manure storage emissions
Emissions of methane (CH
4
) and nitrous oxide (N
2
O)
[33–35]
Ammonia (NH
3
) emissions due to manure storage [35,36]
Nitrogen oxides (NOx) emissions [37], using the factor by [34]
Emissions related to soil incorporation different combinations of by-products
CO
2
emissions [6,38]
N
2
O emissions [32]
Emissions due to soil incorporation of manure
N
2
O, NH
3
, NOx and nitrate leaching [32]
Emission factor of Potassium, Copper and Zinc [36]
The exhaust gases emissions from agricultural tractors and combine harvester were calculated
using the standard emission factors for diesel engines reported by Directive 2004/26/EC for carbon-
nitrogen oxides (g NOx ha
−1
), hydrocarbons (g HC ha
−1
), monoxide (g CO ha
−1
), and particulate
matter (g PM ha
−1
), according to the method reported by [
39
]. The amount of released carbon dioxide
(kg CO
2
ha
−1
) was calculated by multiplying the fuel consumption (kg ha
−1
) by an air emission factor
of 2.6 (kg CO
2
emitted per kg of diesel fuel consumed), according to [40,41].
2.4.3. Land Use Change (LUC)
The direct and indirect land use change (LUC) associated with crop production can produce
changes in the carbon from soil and vegetation [
42
]. Castor oil can be in the form of herbaceous or
arborescent plant, annual or perennial, depending on the climatic conditions of the region. In the
present study, castor oil is cultivated as annual oil crop in cropland that had not undergone any land-use
conversion for a period of more than 20 years [
34
]. Following the indications of the Intergovernmental
Panel on Climate [
34
] there is no net accumulation of biomass carbon stocks for annual crops. On the
other hand, emission from soil carbon mineralization per each scenario has been taken into consideration
because there are changes in the management activities on croplands, and in particular, in the amount
of biomass that has been considered incorporated into the soil according to the different scenario
considered (Table 2). Even if the soil’s organic carbon was considered in the steady state, and the farm
analyzed employed crop rotations, different crop residue management considered in the study and
the amount of GHG emitted during the different scenario were calculated according to the following
formula:
GHG
res
=
3
i=1
Res
i
× C
res
i
× C
min
i
× aw
CO
2
(1)
where
GHGres = Greenhouse gases emissions from soil incorporation of residue “i” per scenario (
Mg CO
2
ha
−1
)
Res
i
= Amount of residue “i” incorporated into the soil (Mg ha
−1
)
Cres
i
= Organic carbon content in the residues “i” (%) [6]
Cmin
i
= Organic carsbon in the residues “i” mineralized in soil (%) [38]
awCO
2
= atomic weight of carbon dioxide equal to 44/12
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2.4.4. Life Cycle Impact Assessment
The environmental impact of 1 Mg of castor oil was based on GHG emissions. The carbon
footprint was defined as the sum of all GHGs emitted within the system boundary and expressed in
CO
2
equivalent applying the IPCC 2007 method (100-year life span).
A parallel economic assessment is integrated with LCA also using a life cycle perspective that
covers all activities in the supply chain up to the farm gate. The economic sustainability is critical
because when it comes to assessing the different products and by-products management, the attention
of farmers does not fall solely on environmental impacts, but also (and mainly) on economic aspects.
For this reason, an economic assessment was carried out.
2.5. Economic Assessment
The study followed the steps in LCA identified in the relevant international standard [
22
,
23
] with
the corresponding steps in life cycle costing (LCC) introduced in parallel. Life cycle costing (LCC)
is a methodology that. aimed to assess the costs across the entire life cycle of a product, process,
or service [
43
] concentrating on the economic cost at each stage [
44
]. A conventional cradle-to-gate
LCC was applied here and includes the assessment of all costs associated with the life cycle of the
castor-oil cultivation specific to each scenario. In particular, the LCC assessment is focused on internal
costs (value of goods and services consumed, including raw materials, services, other operating
expenses, and labor costs). It is important to underline that the contractors provide all phases of
the preparation of the field up to sowing (bottom fertilization, ploughing, harrowing, and sowing).
Everything afterwards (weed control and harvesting) is performed by the owners of the field for Sc1A
and Sc1B. In Sc2A and Sc2B, all agricultural phases are in subcontractor account. Later, to evaluate
the gross margin of farm, the revenues for each product (multiplying between prices and quantity of
products) are calculated. Gross margin refers to the difference between revenue from crop sales and
the variable costs related to the agricultural activities [
44
] and it is a profitability indicator of a farm.
All data (Table 7) come from the budget (year 2018) of the farm studied.
Table 7. Economic data expressed in €/ha per year.
Cultivar Hybrids
Costs (€/Year) C-856 C-1030
Manual fertilization 200.00 200.00
Ploughing 120.00 120.00
Harrowing 60.00 60.00
Sowing 60.00 60.00
Manual hoeing 375.00 375.00
Manual harvesting 625.00 625.00
Mechanical harvesting 180.00 180.00
Dehulling 150.00 150.00
Oil extraction 390.00 390.00
Revenues (€/year)
Straw for sales 49.5 88.00
Husks for sales 255.00 288.00
Castor oil for sales 26,206.32 27,142.26
Source: CREA elaboration.
3. Results and Discussions
According to the literature, castor yield can change appreciably with genotype [
45
]. Arnaud
(1990) observed a seed yield from 2000 to 2620 kg ha
−1
in France [
46
], while Anastasi (2015) reported
a yield between 1790 to 4750 kg ha
−1
in Italy [
47
]. In the present research, the genotypes of castor
grown showed similar productions of 2800 and 2900 kg ha
−1
for C-856 and C-1030, respectively.
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However, the C-1030 hybrid, which is higher than C-856 and has a significantly higher HI (Table 2),
produced 85% more straw than C-856, with the same inputs used.
Alexopoulou et al. [
6
], from the comparison of various castor hybrids planted in Greece and Italy,
found an average amount of stems and leaves of 1.08 Mg
dm
ha
−1
, and the hybrid C-856, that resulted as
133 cm tall (79% taller than in our study) in Greece (Aliartos area, Greece in 2014), allowed for obtaining
1.13 Mg
dm
ha
−1
of stems and leaves against 0.87 Mg ha
−1
obtained in the present study. In the same
study, the C-856 hybrid produced a straw quantity of 0.585 Mg ha
−1
, much more similar to that obtained
in this study in 2012 in Greece (Aliartos area, Greece in 2014) [
6
]. In general, Alexopoulou et al. [
6
]
highlighted that C-856 resulted as the best-performing hybrid in Italy while in Greece, its yields were
quite low, probably related to the high percentage of immature racemes (60%) at harvest. This suggests
the influence of the climate and crop management on the phenotype expression of this hybrid. To the
best of our knowledge, there is no information in the literature about the C-1030 hybrid.
The type of harvesting represents a critical phase that can also have a significant influence on the
amount of product that can be collected per unit area. Mechanized harvesting allows for collecting
about 3 t/h of castor oil seeds (considering a harvesting rate between 0.75 and 1.5 hectares per hour)
ready to be pressed. On the other hand, according to farmers, manual harvesting shows extremely low
losses <5%. On the contrary, castor mechanized harvesting needs to be improved due to the major
losses, which can be up to 50% as evidenced by [
48
]. So far, only one machine manufacturer has started
the first harvesting tests using a specific castor header, which would be able to reduce losses to 5% [
48
],
and Zhao et al. [
49
] reported the possibility to harvest the capsules using a vibrating system instead of
a cutting bar [
49
]. In the present study, losses were not considered given the uncertainty of the data to
be scientifically verified in specific tests.
3.1. LCA
The impact analysis allowed for identifying the processes that had higher impacts on
the environment.
What emerged from the analysis was that fertilization was the agricultural phase with the most
impact. This result is common to various studies [
50
–
56
]. In the present study, for all cultivar hybrids
and scenarios, the environmental impacts of fertilization phase were due to emissions of methane (CH
4
),
dinitrogen monoxide (N
2
O), and carbon dioxide (CO
2
) from manure management and its incorporation
into the soil. In fact, fertilization emitted 74 to 89% of the GHG of the castor oil production. The LCA
study of biodiesel production from rapeseed published by Malça et al. [
57
] reported that the cultivation
stage impacted 66 to 79% and fertilization was the main cause of GHG emissions [
57
]. According
to our results, the higher GHG emissions were mainly due to the characteristics, and the direct and
indirect emissions were generated by manure itself. It should be highlighted that, as suggested by
Aguilera et al. [
58
], organic fertilizers applied at similar N rates to synthetic fertilizers generally make
smaller contributions to the leached NO
3
−
pool, and can mitigate N
2
O emissions [
58
]. The different
by-product management also influenced the indirect emissions of GHG due to their degradations
during soil incorporation.
In the case of castor oil produced by both C-856 and C-1030 cultivar hybrids, as expected,
the manual harvesting resulted as more sustainable (Sc1A and Sc1B), and Sc1B scenario was always
the least impactful, followed by scenarios 1A and 2B (Figures 4 and 5).
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Figure 4.
Carbon footprint of 1 Mg of castor oil hybrid C-856 for each scenario ((Sc1 = scenario 1;
Sc2 = scenario 2).
Figure 5.
Carbon footprint of 1 Mg of castor oil hybrid C-1030 for each scenario (Sc1 = scenario 1;
Sc2 = scenario 2).
Moreover, among cultivar hybrids and all scenarios, Sc2A_C-1030 is more impactful than the
other treatments analyzed, while the Sc1B_C-856 is less burdensome than others. These results were
due to both different combinations of on-farm by-products (castor press cake incorporation into the
soil in case of 1B_C-856, and castor press cake, straw and husks incorporation into the soil in case of
2A_C-1030) and yields (2.8 Mg per ha in case of C-856 vs. 2.9 Mg per ha in case of C-1030). In general,
the incorporation of by-products in the soil at farm level has resulted in higher GHG emissions than
their sale. For this reason, the highest impact observed in the mechanized harvesting treatments (Sc2A
and Sc2B) is largely due to the non-collection of husks that are left in the field by the combine and then
buried (unlike manual harvesting where husks are separated from the seeds on the farm and then sold
as solid fuel). Obviously, the study focused on the impacts related to the production of castor oil on the
farm, not considering the whole process downstream of the supply chain and the related impacts that
could completely reverse the results obtained.
The life cycle of scenario 1B, in which manual harvesting was assumed (less burdensome in the
case of hybrid C-856, slightly less productive, and with less press cake), and with the incorporation of
the pressed cake alone and the sale of the other by-products, resulted in the emission of 8.14 Mg CO
2
eq
per Mg of castor oil (8.14 kg CO
2
eq per kg of castor oil extracted). On the other hand, the life cycle of
scenario 2A_C-1030, in which mechanized harvesting with combine harvesters and the incorporation
of straw, husks, and press cakes was assumed, resulted in the emission of 18.9 Mg CO
2
eq per Mg of
castor oil produced (18.9 kg CO
2
eq per kg of castor oil extracted).
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Although, in Sc1A and Sc1B scenarios, there is the de-hulling phase that there is not in Sc2A
and Sc2B, this has a very small impact always <8% (on average 0.698 Mg CO
2
eq Mg
−1
of castor oil
produced) of the total CO
2
emissions. The oil extraction impacted less than 5% of the total CO
2
emitted
(on average 0.412 Mg CO
2
eq Mg
−1
of castor oil). Sanz Requena (2010) reported that for each ton of
crude sunflower, rapeseed, and soybean oil extracted, an average of 2.2 Mg of CO
2
was emitted, but it
should be highlighted that after the mechanical extraction, a treatment with a solvent (hexane) was
included [54].
Spinelli (2012) reported a total emission of 13.7 Mg CO
2
eq Mg
−1
of sunflower oil produced [
59
].
However, according to the study and the allocation used, the emissions became 4.52 Mg CO
2
eq Mg
−1
of sunflower oil and 9.18 Mg CO
2
eq Mg
−1
of sunflower cake produced. The lack of allocation of a
higher share of emissions from the press cake makes castor oil production inevitably more impactful
in GHG emitted than other vegetable oils, although the variety C-856 with manual harvesting have
relatively low and promising overall emissions.
3.2. Economic Assessment
The economic gross margin is related mainly to the yield level (product and by-products) and to
the cost of inputs for each scenario and cultivar hybrid. As far as the yield is concerned, the values
for each farm and crop have been previously discussed and the data are reported in Table 8, showing
higher yields per ha for C-1030 than for C-856 crops. For these reasons, the C-1030 cultivar shows
lower total costs per Mg cultivated than C-856 ones (Table 8). Moreover, for both cultivar hybrids,
the total costs of manual harvesting scenario are higher than mechanical harvesting scenario ones.
This finding was due to labor costs in harvesting phase. In fact, in case of the manual harvesting
scenario, five workers are required to harvest castor seed, contributing 32% to the total costs; while in
case of mechanical scenario one worker (with machinery) is required contributing just 13% to the total
costs. The impact that manual harvesting has on costs can be equated to that reported by Silalertruksa
(2012) in Thailand, where manual harvesting accounts for 22% of total costs in the palm oil sector [
60
].
Table 8.
Economic gross margin for each scenario expressedin
€
/FU(
1 Mg of castor oil
)—(
Sc1 = scenario 1;
Sc2 = scenario 2).
Cultivar Hybrid: C-856 Cultivar Hybrid: C-1030
Scenarios Scenarios
Manual Mechanical Manual Mechanical
Sc1A Sc1B Sc2A Sc2B Sc1A Sc1B Sc2A Sc2B
Costs (€/Mg) *
Manual fertilization 72.42 71.42 71.42 71.42 68.96 68.96 68.96 68.96
Ploughing 42.85 42.85 42.85 42.85 41.38 41.38 41.38 41.38
Harrowing 21.43 21.43 21.43 21.43 20.69 20.69 20.69 20.69
Sowing 21.43 21.43 21.43 21.43 20.69 20.69 20.69 20.69
Manual hoeing 133.93 133.93 133.93 133.93 129.31 129.31 129.31 129.31
Harvesting 223.21 223,21 64.28 64.28 215.52 215.52 62.07 62.07
Dehulling 53.57 53.57 - - 51.72 51.72 - -
Oil extraction 139.28 139.28 139.28 139.28 134.48 134.48 134.48 134.48
Total Costs (€/Mg) 708.12 708.12 494.62 494.62 682.75 682.75 477.58 477.58
Revenues (€/Mg)
Straw for sales - 4,58 - 4,58 - 7.61 - 7.61
Husks for sales 76.24 76.24 - - 81.57 81.57 - -
Castor oil for sales 31,166 31,166 31,166 31,166 31,166 31,166 31,166 31,166
Total Revenues (€/Mg) 31,242 31,246 31,166 31,170 31,247 31,255 31,166 31,173
Gross Margin (€/Mg) 30,533 30,537 30,671 30,675 30,564 30,572 30,688 30,695
Source: CREA elaboration on budget data (year 2018). * For each agricultural phase are included the internal
costs (i.e., value of goods and services consumed, including raw materials, services, other operating expenses and
labor costs).
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Table 9 shows that the 2B_C-1030 scenario had higher gross margin than other scenarios; while the
1A_C-856 scenario had the lowest gross margin.
Table 9.
Gross margin and carbon footprint for each scenario, expressed in
€
/FU (1 Mg of castor
oil)—(Sc1 = scenario 1; Sc2 = scenario 2).
Cultivar Hybrid: C-856 Cultivar Hybrid: C-1030
Scenarios Scenarios
Unit Manual Mechanical Manual Mechanical
Sc1A Sc1B Sc2A Sc2B Sc1A Sc1B Sc2A Sc2B
Gross Margin (€/FU)
30,533 30,537 30,671 30,675 30,564 30,572 30,688 30,695
GWP (kg CO
2
eq/FU) 9070 8140
18,100 15,800 14,600 11,600 18,900 16,300
Gross Margin/GWP ratio (%) 3.37 3.75 1.69 1.94 2.09 2.63 1.62 1.88
Source: CREA elaboration on both budget data (year 2018) and environmental findings.
In addition, the ratio between gross margin and GWP emissions was applied to calculate the
economic performance (gross margin) per unit of environmental burden (Table 9). The ratio is based on
data from both environmental and economic accounting systems. The higher the ratio value, the higher
the economic performance per unit of GWP emitted.
Findings showed that scenario 1B in the case of C-856 cultivar hybrid had a better ratio between
economic performance and GHG emitted into the atmosphere (
€
3.75 per kg CO
2
eq); while the
2A_C-1030 scenario showed the worst ratio between economic and environmental performances
(
€
1.62 per kg CO
2
-eq) confirming the environmental results. These results were due to different
combinations of on-farm by-products (see Table 3), different revenues (see Table 8), and yields
(see Table 2).
4. Conclusions
There has been a critical increment in interest for sustainable and biodegradable items so as to
diminish reliance on petrochemicals. This is one of the essential elements which is driving the growth
of the worldwide castor oil market. The research focused on the evaluation of the environmental and
economic sustainability of two different castor hybrids (C-856 and C-1030) comparing manual and
mechanical harvesting methods, and by-product management.
Comparing all the proposed scenarios, the cultivation of the manually harvested castor hybrid
C-856 and the by-product management that involved only the soil incorporation of press cake obtained
by the oil extraction resulted as the most sustainable. On the other hand, the mechanized harvesting of
hybrid C-1030, which involved the incorporation of all the by-products of the cultivation of castor
and production of castor oil (husk, straw, and press cake) showed the highest CO
2
emissions per Mg
of castor oil (+132%). It is therefore clear how, with the same inputs used, the castor-oil cultivation
method affects the management of by-products and how, while residues are a source of organic matter
for the soil, they cause greenhouse gas emissions during the degradation process in the soil.
From an economic point of view, a difference in Gross Margin (
€
/Mg) between the hybrids used
was only evident when comparing the scenarios in which mechanized harvesting was used, i.e.,
C-856_Sc2A vs. C-1030_Sc2A and C-856_Sc2B vs. C-1030_Sc2B, resulting in an increase in Gross
Margin of 6 and 7%, respectively, using the hybrid C-1030. The two hybrids when harvested manually
did not show appreciable increases in Gross Margin (0.1%). In general, the scenario that produced
most Gross Margin was the C-1030_Sc2B where mechanized harvesting of the plants, the incorporation
of husk and press cake, and the sale of castor oil and straw were carried out.
In the end, to determine the most economically and environmentally convenient scenario, the ratio
between gross margin and GWP emissions was applied to calculate the economic performance (gross
margin) per unit of environmental burden. Findings showed that scenario Sc1B in the case of C-856
cultivar hybrid had a better ratio between economic performance and GHG emitted into the atmosphere
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(
€
3.75 per kg CO
2
eq); while the Sc2A_C-1030 scenario showed the worst ratio between economic and
environmental performances (€1.62 per kg CO
2
eq) confirming the environmental results.
Although Sc1B represents a good economic–environmental compromise, including manual
harvesting, it clashes both with the need to innovate the castor production chain, and with the costs
and availability of labor that may vary over time, affecting the sustainability of the chain, costs,
and market prices.
Furthermore, an important aspect that was not considered in the study is the loss of product during
harvesting. This is particularly relevant in the case of very high losses that are reflected in the impacts
per unit of product. With the implementation of well-functioning mechanized castor harvesting
systems, the resulting seed losses will also necessarily have to be considered in future studies.
Moreover, it is important to highlight that the study did not consider the whole process downstream
of the castor oil extraction and the related impacts that could completely reverse the results obtained,
which should be investigated in future researches.
Ultimately, the lack of official economic data on the market prices of products and by-products,
and the difficulty of finding the costs resulting from the various cultivation practices, within the castor
production chain, as old as it is, currently undergoing improvement and remodernization, represents a
limit to obtaining exhaustive answers on its economic sustainability. For this reason, this research does
not have the presumption to provide a definitive answer to the questions related to the environmental
and economic sustainability of the castor-oil production chain, which will need further study and
analysis as the production methods are refined.
Author Contributions:
Conceptualization and methodology, A.S., N.P., L.P.; investigation and data curation
N.P., A.S., W.S., F.L.; writing—original draft preparation N.P., W.S., F.L., A.S.; writing—review and editing, A.S.;
supervision, L.P. and A.S.; funding acquisition, L.P. All authors have read and agreed to the published version of
the manuscript.
Funding:
This research was funded by European Union’s Horizon 2020 Magic—Marginal lands for Growing
Industrial Crops: Turning a burdeninto an opportunity project grant number 727698 (http://https://magic-h2020.eu).
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the
writing of the manuscript, or in the decision to publish the results.
Acknowledgments:
Authors thank the ricinum producers National Association of Romania (Ecoricinus Productie
Comert Srl, Cluj-Napoca, 10, Fanatelor st. jud, Cluj, Romania) and his team for their valid support and assistance
provided during the activities, the field data and data for the economic analysis, as well as Sandu Lazar for his
valuable contribution in the field activities.
Conflicts of Interest: The authors declare no conflict of interest.
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©
2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
101
sustainability
Article
Economic and Environmental Assessment of Two Different
Rain Water Harvesting Systems for Agriculture
Luigi Pari, Alessandro Suardi, Walter Stefanoni, Francesco Latterini and Nadia Palmieri *
Citation: Pari, L.; Suardi, A.;
Stefanoni, W.; Latterini, F.; Palmieri,
N. Economic and Environmental
Assessment of Two Different Rain
Water Harvesting Systems for
Agriculture. Sustainability 2021, 13,
3871. https://doi.org/10.3390/
su13073871
Academic Editor: Hossein Azadi
Received: 10 March 2021
Accepted: 26 March 2021
Published: 31 March 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Consiglio per la Ricerca in Agricoltura e l’analisi dell’Economia Agraria (CREA)—Centro di Ricerca Ingegneria e
Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy;
luigi.pari@crea.gov.it (L.P.); alessandro.suardi@crea.gov.it (A.S.); walter.stefanoni@crea.gov.it (W.S.);
francesco.latterini@crea.gov.it (F.L.)
* Correspondence: nadia.palmieri@crea.gov.it; Tel.: +39-069-067-5219
Abstract:
Increasing aridity and subsequent water scarcity are currently among the major problems
of agriculture. Rainwater harvesting could represent a way to tackle this issue, and, as a consequence,
scientific research has been more and more focused on such topic. On the other hand, few scientific
studies related to economic and environmental assessment of rainwater harvesting systems in
agriculture are available. The present study carried out an economic and environmental analysis
of two different systems for rainwater harvesting: a typical pond and an innovative flexible water
storage system (FWSS). The environmental and economic performance of the systems was compared
using the Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methodologies, referring to a
functional unit (FU) of 1 m
3
of storable water. The FWSS showed better environmental end economic
performance than the pond system, resulting with both lower environmental impacts (17.04 g per m
3
CO
2
vs 28.2 g per m
3
CO
2
) and lower costs (16.94
€
per m
3
vs 20.41
€
per m
3
). Moreover, the pond
system was more impactful than the FWSS for all the 17 categories investigated. Therefore, the FWSS
can be a suitable solution for water harvesting in agriculture sector, showing interesting features
for farmers.
Keywords:
ecoefficiency; life cycle assessment (LCA); life cycle costing (LCC); run-off; pond; flexible
water storage system
1. Introduction
Water scarcity and water supply are among the major concerns that countries world-
wide have been struggling to address during the last decades. Usually, European countries
are not arid, but some, like Cyprus, Bulgaria, Belgium, Spain, Malta and Italy, are currently
exploiting 20% or more of their long-term water supplies every year. Agriculture is among
the main responsible sector for freshwater consumption accounting for the 24% of the
abstracted water that can go up to 80% in southern regions [
1
]. The need to rely on natural
fresh water basins or on underground water is further fostered by the effect of climate
change on the rainfall pattern in the Mediterranean region, where heavy rainfall events are
occurring more frequently and only in a limited period of the year [
2
]. Farmers struggle
to plan field activities, and plants need to be irrigated artificially more often than before.
They mainly rely on underground water, but the overexploitation of such resource has
detrimental effects on the environment [3]. Public awareness of agriculture impact on the
environment is driving the change from conventional farming to organic farming, the latter
of which seeks to burden the environment with as little water depletion as possible [
4
].
Although organic farming also contributes to GHGs reduction, it is not the resolutive
strategy to cope with this problem. Interest is growing in arid and semi-arid regions of
the planet concerning the possibility of collecting and storing rainwater for urban and
agriculture purposes [5].
Sustainability 2021, 13, 3871. https://doi.org/10.3390/su13073871 https://www.mdpi.com/journal/sustainability
103
Sustainability 2021, 13, 3871
The present water scarcity leads to a new paradigm in water resource manage-
ment, and the application of sustainable water supply solutions is essential [
6
,
7
]. Some
studies [8,9]
have catalyzed interest in alternative approaches to ensure water security by
applying, for example, rainwater harvesting systems.
Rain water harvesting (RWH) is the process of accumulating incident raindrops
on ground surfaces and roofs, with the help of cisterns, tanks, and underground check
dams [
10
]. RWH has been applied for centuries by humankind to meet water supply
needs and nowadays can still represent an important practice to improve the efficiency
of the use of water in the urban future [
5
] by reducing household expenditures on water
consumption [11] and offering many opportunities for agriculture.
In fact, irrigation of rainfed crops through RWH could represent a good option to
increase crop yields due to an improvement in the water productivity [5].
Jiang et al. (2013) highlighted that the rainwater supplementary irrigation could
increase crop yield by more than 30% [
12
]. Furthermore, even if rainfed agriculture in arid
and semi-arid areas represents to up to 90% of the total production of cereal of these regions,
in many countries, productivity remains low due to the sub-optimal rainfall characteristics,
disadvantageous land conditions, and a deficiency in good management of these resources.
On the other hand, increasing productivity of rainfed areas could lead to an increase in
food security, reduction in irrigation frequency, and improvement in livelihoods and rural
conditions. Furthermore, as reported by Ghimire et al. (2017) [
13
], RWH could reduce
“impacts on the environment and human health, stormwater runoff and combined sewer
overflows, and economic viability”. As observed by various authors, RWH represents a
valid system to reduce stormwater runoff, improving water management in an affordable
manner [
14
–
16
]. Surface runoff is a phenomenon that, at the farm level, is triggered by 10
to 25% of rainwater falling in arid and semi-arid areas, and it can have negative effects on
soil erosion and the accumulation of nutrients, chemicals, and sediments into rivers and
streams [
5
]. Storage can be achieved with various types of storage systems that can also
differ remarkably in terms of costs and environmental impacts [5].
Nevertheless, the environmental sustainability of different strategies to store rainwater
has been seldomly investigated at the farm level; particularly, it has not been taken into
account in the decision-making phase which should include this aspect along with the
economical and feasibility aspects. Only few studies dealt with the environmental aspect
of fresh water storage system, and, if so, they mainly focused on drinkable water [
17
,
18
].
Because agriculture is a highly demanding activity in fresh water (more than a simple
beverage), much attention is ought to be paid to such aspect. Life Cycle Analysis (LCA) is
widely recognized as a standardized method [
19
,
20
] that is used to evaluate the potential
environmental impacts of products, processes, or services during the entire life cycle.
Similar to LCA, Life Cycle Costing (LCC) methodology [
21
,
22
] is one of the main tools
used to embed economic factors into the assessment of sustainability.
In general, as noted by several authors, irrigation in agriculture also leads to increased
environmental impacts [
23
–
26
]. Most of the works in the literature focus on the emissions
generated by the irrigation phase, focusing mainly on the amount of water resources used
or on the energy related to the irrigation phase [
23
,
24
], and often without specifically
analyzing the infrastructure (irrigation system) used [25,26].
On the other hand, some studies assessed the sustainability of rain water supply
systems. Yan et al. (2018) [
9
] compared the environmental impacts of decentralized and
centralized potable water supply, and they found a water-saving efficiency laying between
0.6 and 100%, depending on rainfall. Their results suggested that potable water produced
from this decentralized system currently performs poorer than centralized water from
an environmental perspective [
9
]. Other authors [
8
] performed a comparative LCA for
greywater treatment within a circular economy framework and evaluated the environ-
mental impacts of three greywater treatment alternatives (i.e., photocatalysis, photovoltaic
solar-driven photocatalysis, and membrane biological reactor). Their results showed that
104
Sustainability 2021, 13, 3871
photovoltaic photocatalysis driven by solar energy is the most sustainable scenario from
the environmental point of view [8].
In this scenario, the implementation of a circular economy strategy results in a promis-
ing approach [
8
]. However, LCA studies performed relying on experimental data for the
analysis of the environmental impacts of crops irrigated with reclaimed water are still
missing [
27
], although irrigation plays a critical role in boosting crop yield; furthermore,
40% of freshwater global resources are consumed by agricultural production [28].
In particular, to the best of our knowledge, the literature lacks LCA and LCC studies
concerning the impact of RWH systems for agricultural purposes, particularly as tool in
the decision-making process. In this paper, the authors investigated the environmental and
economic impact of a conventional water storage system, as a pond, against an innovative
flexible water storage system (FWSS) that could bring about practical advantages to farmers
because of its flexibility and the easy-to-move feature. A comparison has been performed
via LCA and LCC assessment starting for the hypothesis that 400 m
3
(average commercial
pond’s volume capacity available on the market) of rainwater can be collected and stored
locally for crop irrigation purposes. Furthermore, there are not studies evaluating the
ecoefficiency of different rainwater harvesting systems. For this reason, this study fills a
knowledge gap in the current literature.
2. Materials and Methods
Farms usually rely on underground reservoir or on aqueduct or, sometime, on channels
that naturally occur outside the field during the winter to pump the water needed for
watering plants or cleaning machineries. All those sources are temporary available. Thus,
it is important to catch as much water as possible during the fall-winter season and store it
for the following dry season.
Water storage in ponds is quite common in farms. Thus, the research focused on the
economic and environmental sustainability assessment of two systems for water harvesting
and storage: the pond and the flexible water storage system (FWSS) (Figure 1).
Figure 1.
Schematic view of the pond (
A
) and the FWSS (
B
) meant for agroforestry application. Numbers refer to the main
components of both systems: (1) seasonal water stream, (2) loading system (including electric pump, pipes and connections),
(3) water storage system, (4) electric pump for water delivery, (5) water usage (e.g., irrigation system).
105
Sustainability 2021, 13, 3871
2.1. Pond
Ponds represent a common strategy for water storage because they are relatively
easy to build and low demanding in maintenance although they exhibit short lifespan of
5–10 years that could represent a limit for the system [
29
]. Furthermore, there are other
drawbacks that usually are not taken into account like the permanent disturbance of the
soil, the reduction of the arable surface which reduces the available arable land and other
concerns related to water quality and safety. Moreover, in areas with high evaporation
potential ponds are not very suitable [30] and need to be covered with shade net [29].
Building a pond implies permanent changes in the soil, particularly the shallower
horizons which are more fertile and suitable for cropping. In order to accommodate the
assumed 400 m
3
of water, authors made the hypothesis of removing an equivalent volume
of soil (336.6 m
2
and 1.4 m depth) by using a 90-kW excavator. According to the estimations,
digging requires 20.5 h and 328.1 l of fuel (data not shown). Consequently, a double layer
1350 g m
−2
PVC is applied. A detailed list of the components is shown in Table 1.
Table 1. Summary table of components involved in the loading, storing and water distribution in the pond system.
Item Unit Quantity Unitary Weight (kg) Total Weight (kg)
Polyvinyl
Chloride
Metal
Polyvinyl
Chloride
Metal
Loading system
Electric water pump (n) 1.00 - 7.02 7.02
Electric cable (m) 30.00 0.11 0.07 3.24 2.16
Socket (n) 1.00 0.12 0.06 0.12 0.06
Pipe connection-equal elbow (n) 2.00 0.18 0.35
Pipe connection-adapter (n) 4.00 0.15 0.60
Anti-cross flow (n) 1.00 0.16 0.16
Ball valve (n) 2.00 0.18 0.35
Pipe (m) 330.00 0.31 102.30
Pond
Double PVC layer
(m
2
)
477.56 2.35 1122.27
Fuel (n) 328.1 328.10
Irrigation system
Electric water pump (n) 1.00 7.02 7.02
Electric cable (m) 330.00 0.11 0.07 35.64 23.76
Pipe connection-equal elbow (n) 2.00 0.13 0.26
Pipe connection-adapter (n) 4.00 0.11 0.45
Ball valve (n) 2.00 0.13 0.26
Pipe (n) 400.00 0.23 93.00
Dripper (n) 135.00 0.02 2.70
Total 1689.79 40.01
The Flexible Water Storage System
The flexible water storage system (FWSS) is an alternative solution to ponds. Inter-
estingly, it can be easily folded and moved elsewhere according to the domestic needs
of the farm; the installation does not require a concrete base, just a little slope is desired
to ease the outflow of the water, which is ensured by a secondary electric pump, though.
Contrary to the pond, water is not directly exposed to sunlight; thus microbial activities
are not promoted and higher water quality is expected [31].
The Flexible Water Storage System is made of polyvinyl chloride (PVC) 930 g m
−2
thick and equipped with inlet and outlet pipe connections. FWSSs find many applications
in agricultural sector for storing non-potable water, wastewater or sewage water produced
by livestock. According to Rigamonti et al. (2019) the service life of a rain water harvesting
system based on a polyethylene storage tank is 50 years [32]. Loading is performed by an
electric water pump that pumps the water via a filter from a near seasonal water stream
directly into the FWSS. When the tank reaches its maximum capacity, the blow-off valve
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Sustainability 2021, 13, 3871
opens preventing over-pressure. The water can be stored as long as it is needed without
leak of water or smell. During the dry season the water can be used for irrigation to reduce
the exploitation of the underground water. Components of the FWSS are listed in Table 2.
Table 2. Summary table of components involved in the loading, storing and water distribution in the FWSS.
Item Unit Quantity Unitary Weight (kg) Total Weight (kg)
Polyvinyl
Chloride
Metal
Polyvinyl
Chloride
Metal
Loading system
Electric water pump (n) 1.00 7.02 7.02
Electric cable (m) 30.00 0.11 0.07 3.24 2.16
Socket (n) 1.00 0.12 0.06 0.12 0.06
Pipe connection-equal elbow (n) 2.00 0.18 0.35
Pipe connection-adapter (n) 4.00 0.15 0.60
Anti-cross flow (n) 1.00 0.16 0.16
Ball valve (n) 2.00 0.18 0.35
Pipe (m) 330.00 0.31 102.30
FWSS
Plastic fabric HPVi09
(m
2
)
678.84 0.93 631.32
Pipe connection-reducer (n) 2.00 0.30 0.60
Lid (n) 1.00 0.35 0.35
Blow-off valve (n) 1.00 0.15 0.29 0.15 0.29
Ball valve (n) 4.00 0.80 3.20
Irrigation system
Electric water pump (n) 1.00 7.02 7.02
Electric cable (m) 330.00 0.11 0.07 35.64 23.76
Pipe connection-equal elbow (n) 2.00 0.13 0.26
Pipe connection-adapter (n) 4.00 0.11 0.45
Ball valve (n) 2.00 0.13 0.26
Pipe (n) 400.00 0.23 93.00
Dripper (n) 135.00 0.02 2.70
Total 871.84 43.50
2.2. LCA and LCC Methods
The environmental impact analysis was carried out using the life cycle assessment method-
ology (LCA) according to UNI EN ISO 14040:2006 [
19
] and UNI EN ISO 14044:2006 [
20
],
including the following statements: (a) Goal definition and scoping; (b) life cycle inventory;
(c) life cycle impact assessment; (d) life cycle interpretation and improvement. Moreover,
the study followed the steps in LCA with the corresponding steps in life cycle costing (LCC)
introduced in parallel. Life cycle costing (LCC) is a methodology that aimed to assess the
costs across the entire life cycle of a product [33] focusing on the cost at each stage [34].
2.2.1. Boundary of the System for the Life Cycle Assessment (LCA) and Life Cycle Costing
(LCC) Analysis
The considered system (for LCA and LCC analysis) is defined by all the processes
that occurred during the production and installation phases of two different water tanks
(Figure 1). The functional unit is 1 m
3
of storable water of the studied RWH systems. It
represents the reference unit used to quantify all inputs and outputs from the studied
systems [9].
2.2.2. Life Cycle Inventory Analysis
Primary data of the materials used for the construction of the pond were obtained
through interviews to local enterprises, which main activity consists of ponds’ construction
or, in general, digging jobs. For the FWSS, the primary data was calculated according
to [
35
,
36
]. Secondary data were obtained by Simapro code database 8.0.2 (Prè Consultants,
Amersfoort, The Netherlands) (Tables 3 and 4).
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Table 3. Technical data of the pond.
Data
PVC
(kg)
Iron
(kg)
Diesel
(kg)
Loading system 107.11 9.23
Pond 1122.27 278.89
Irrigation system 132.31 30.77
Source: data collected from either datasheet or direct weighting of spares.
Table 4. Technical data of the FWSS.
Data
PVC
(kg)
Iron
(kg)
Diesel
(kg)
Loading system 107.11 9.23
FWSS 632.42 3.49
Irrigation system 132.31 30.77
Source: collected from either datasheet or direct weighting of spares.
2.2.3. Life Cycle Impact Assessment
Environmental impacts per m
3
of storable water was assessed using both ReCiPe2008 [
37
]
and GWP100 methods. In particular, ReCiPe 2008 method includes categories of envi-
ronmental impact and environmental damage, i.e., at the midpoint and endpoint level.
Initially, the inventory data were associated to the midpoint level using factors of character-
izations. Lately, they have been converted and clustered into the endpoint level considering
three damage categories (i.e., HH, EC, RE damage categories) and by using weighting
factors. It is important to underline that it was applied the method ReCipe Endpoint (H)/
Europe ReCipe H/A by considering weighing factors referred to the mean values of the
hierarchical perspective. Moreover, GHG emission was chosen to link the environmental
issue to the economic aspect of the water harvesting systems in order to determine their
eco-efficiency [
38
]. The carbon footprint was defined as the sum of all GHGs emitted
within the system boundary and expressed in CO
2
equivalent according to IPCC 2007
method (100-year life span). A parallel economic assessment is integrated with LCA using
a life cycle perspective. It is important to underline that the economic sustainability is an
important aspect to consider for farmers.
2.2.4. Economic Assessment
The possibility of conducting a LCA study integrated with Life cycle costing (LCC)
contributes to improve the ecoefficiency of farms [
39
], and thus reducing their impacts on
the environment, while reducing costs [40].
A conventional cradle-to-gate LCC was applied here encompassing the assessment of
all costs associated with the life cycle of both RWH systems studied.
The cost of the items included in the analysis referred to raw materials, services, other
operating expenses, and labor costs. The economic data (Tables 5 and 6) derive from
informal local market as proposed in other studies [41,42].
Table 5. Economic data of the pond.
Data
Total
Costs (Euro)
Loading system 669.47
Pond 6356.93
Irrigation system 1140.37
Total 8166.77
Total costs include raw materials, services, other operating expenses, and labor costs for each step. Source: data
retrieved from informal local market.
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Table 6. Economic data of the FWSS.
Data
Total
Costs (Euro)
Loading system 669.47
FWSS 4968.00
Irrigation system 1140.37
Total 6777.84
Total costs include raw materials, services, other operating expenses, and labor costs for each step. Source: data
retrieved from informal local market.
3. Results and Discussion
A large majority of literature deals with the environmental impact of water manage-
ment in agriculture purely in terms of water and energy used [23–26].
It is clear that irrigation might tip the scale towards a less sustainable scenario as
observed by Stephenson et al. (2010) [
23
]. Some authors have evaluated the emissions
generated by water harvesting systems and the related costs [
32
], even if the majority of
the studies focused on the impact of RWH systems in urban environment [18,43–46].
In fact, rain and storm water harvesting systems are widely used in urban and agri-
cultural areas especially where the weather conditions are unfavorable, with periods of
drought alternating with periods characterized by floods and torrential rains. These aspects
were already highlighted by Ghimire et al. (2017) that among the benefits of RWH indicated
the reduction of stormwater runoff and combined sewers overflows events, as well as the
potential impact reduction on the environment and human health, remarking a lack of
understanding in the magnitude of these positive effects [
13
]. This is especially true in
agriculture where, to the best of our knowledge, few studies analyzed the environmental
and economic impacts of RWH infrastructure.
3.1. Environmental Assessment
The impact analysis allowed to identify the RWH infrastructure and its installation
which has higher environmental impact. It is important to underline that with the weighing
it is possible to assess the importance of each category of impact obtaining aggregate
results as damage categories [
47
]; while the characterization permits to quantify the general
impacts concerning different impacts categories [47].
Figure 2 reports the LCA results of each studied RWH system at endpoint level.
Comparing the outcomes of weighing (Figure 2) and characterization (Figure 3) helps to
identify the environmental performance and impacts of each RWH system. The highest
damage categories were resources and human health, while ecosystem damage was the
lowest in all systems.
The pond causes the highest impact on resources and human health due to used
raw material. Polyvinyl chloride (PVC) production had the highest impact on all damage
categories (especially in the pond system); this finding was due to the characteristics of PVC
production. Additionally, Ghimire et al. (2017) found out that storage tanks represent the
second most important cause of environmental impact (after energy usage) [
13
]. In order
to reduce the impacts, the material used for the construction of a RWH device represents
an important aspect to take in consideration. This is true also for the life span and volume
of the storage tank that may lead to differing impacts [
48
]. In fact, Ghimire at al. (2014)
observed lower impact of polyethylene (PE) when compared with a concrete storage tank,
even if the latter has an expected life span of 70 years (50 years for the PE storage tank) [
48
].
According to Ghimire et al. (2017), the PE storage tank resulted as a good alternative to
the RWH fiberglass storage tank that was less sustainable for the ozone depletion and
freshwater withdrawal impact categories [
13
]. However, to the best of our knowledge, no
other studies reported information about the specific impact generated by PVC used for
RWH construction.
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Sustainability 2021, 13, 3871
Figure 2.
Result of the weighing, comparison of two different RWH systems with functional unit of 1 m
3
of storable water.
The acronyms of the different damage categories are HH (Human Health), EC (Ecosystem), RE (Resources).
10
20
30
40
50
60
70
80
90
100
%
Pond FWSS
Figure 3.
Result of the characterization, comparison of two different runoff water storage systems with functional unit
of
1m
3
of storable water. The values are expressed as percentages in relation to the 100% given to RWH systems with
the biggest impact in each category (i.e., Pond = 100% in all the considered impact categories). The acronyms of the
different impact categories are CCHH (climate change human health), OD (ozone depletion), HT (human toxicity), POF
(photochemical oxidant formation), PMF (particulate matter formation), IR (ionizing radiation), CCE (climate change
ecosystems), TA (terrestrial acidification), FE (freshwater eutrophication), TE (terrestrial ecotoxicity), FRE (freshwater
ecotoxicity), ME (marine ecotoxicity), ALO (agricultural land occupation), ULO (urban land occupation), NLT (natural land
transformation), MD (metal depletion), FD (fossil depletion).
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Sustainability 2021, 13, 3871
The characterization analysis (Figure 3) showed the environmental performance of
each RWH system in relative terms reporting midpoint environmental impacts.
The analysis showed that the pond system is more impactful than the FWSS in all
impact categories. In particular, PVC production was the most impactful phase (11 out of
17 impact categories) regardless of the RWH system; this was due to the characteristics of
the PVC production itself. In particular, for each studied system, the environmental impacts
on CCHH category were due to carbon dioxide emissions coming from PVC production.
This production also impacts the HT category (due to dioxin emission), POF (caused by
nitrogen oxides emissions), PMF (due to sulfur dioxide emissions), CCE (caused by carbon
dioxide emissions) and TA (due to sulfur dioxide emissions) impact categories. Moreover,
the PVC production impacts other categories: FE (caused by phosphorus emissions in
the water) and TE (due to chlorine emissions) impact categories. In addition, the impact
of PVC production on the FRE category was due to nickel emission in the water for the
pond system and copper emission (in the water) for the FWSS. The impacts on the ME
category were due to chlorine emissions coming from PVC production. Finally, the PVC
production also impacted the fossil depletion (FD) category, and this was because of the
energy necessary for the production.
It is interesting to observe that 3 out of 17 impact categories (i.e., OD, IR and NLT
impact categories) are affected only by environmental impact due to the pond system,
while these categories are not involved in the FWSS. In fact, the extraction of oil and the
production of fuel and its combustion during the excavation contributed to OD, IR and
NLT impact categories only for the pond system.
Moreover, it is important to underline that the pond system is more impactful than
the FWSS in the ALO and ULO impact categories due to a different occupation of land
between the two water storage systems. In fact, for the same volume of stored water, unlike
the pond, the FWSS does not permanently occupy arable land and can be installed on the
uncultivated soil of the farm (e.g., over ditches or between rows of permanent crops that do
not require machine passage). This is a major advantage especially in farms where space is
a limiting factor.
Finally, the impacts on the MD category were due to the irony parts of excavator (used
to create the pond), loading and irrigation systems (for both RWH systems).
3.2. The Economic Aspects and Ecoefficiency Analysis
The LCC analysis of the water systems was carried out in similar phases corresponding
to LCA standard. Table 7 shows the total costs referred to the functional unit of 1 m
3
of
storable water.
Table 7. The economic aspect of each system per 1 m
3
of water.
Data
Total
Costs (€ per m
3
)
Pond system 20.41
FWSS 16.94
All data are referred to 1 m
3
. Source: Authors’ elaboration.
Findings showed that the FWSS’s costs are slightly lower than the pond system’s;
in fact, the FWSS shows total costs of 16.94
€
per m
3
of storable water, versus 20.41
€
per m
3
in the pond system. The current literature lacks studies dealing with costs of an
FWSS, though this is quite obvious considering that this system represents an innovation
in the sector, which has not been studied yet. On the other hand, several studies analyzed
economic aspects related to ponds’ construction for rainwater harvesting, with particular
reference to irrigation purposes. A literature review carried out by Lasage et al. (2015)
reported an average cost for water storage ponds, with dimensions ranging from 30
to 300 m
3
, of 17.16
€
per m
3
[
49
]. This value is slightly lower than what was found
in the present study. Surprisingly, this higher cost is not related to the different lining
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Sustainability 2021, 13, 3871
material (cement-wire in the cases studied in the review and plastic film in this paper’s),
considering that cost for lining with cement and wire is generally higher than the cost
for the same operation performed with plastic film. Several further studies were carried
out especially in India. Reported ponds’ construction costs were substantially lower
than what was found in the present study. In details, Deshmukh et al. (2016) reported
1.61
€
per m
3
[
50
], while 3.71
€
per m
3
were found for a pond lined through an HDPE
(high-density polyethylene) geomembrane by Reddy at al., (2020) [
51
]. Finally, Shalander
et al. (2016) reported construction costs for unlined ponds in India ranging from 1.80 to
4.35 € per m
3
[
52
]. Such markedly lower construction costs for ponds are related to both
the absence of lining material (in most cases) and to the lower labor costs compared to the
Italian ones. Interestingly, Shalander et al. (2016) also reported construction costs for a
particular rainwater storage system typical of the Jodhpur region, locally named Tanka [
52
].
This consists of an underground cistern made of concrete. This system is, under some
aspects, comparable to the FWSS, considering that water is not stored in direct contact with
air. Construction costs of a Tanka resulted equal at 21.25
€
per m
3
—therefore higher than
FWSS ones, highlighting how such innovative water storage system is also suitable outside
of Europeon contexts.
An important aspect of the ecoefficiency analysis of two different water storage
systems was the variability of their main components, as was demonstrated by differences
in carbon footprint (as an environmental indicator) and life cycle costs (as an economic
indicator) in relation to RWH systems.
The highest value of the carbon footprint and costs was obtained for the pond sys-
tem, while the FWSS exhibited the lowest value of GHG emission and aggregated costs
(
Figure 4
). These findings showed that the FWSS was the best solution under the economic
and environmental point of view. In fact, the FWSS and the pond system cost 16.94
€
with
an emission of 17.4 gr CO
2
eq per m
3
of storable water and 20.41
€
with an emission of
28.2 gr CO
2
eq per m
3
of storable water, respectively.
Figure 4.
Combined results of the LCC and GWP (LCA analysis) of each RWH system per m
3
of
storable water. Source: Authors’ elaboration.
4. Conclusions
The present work aimed to perform the environmental and economic analysis of two
different RWH systems for irrigation purposes. In particular, LCA and LCC assessments
were carried out to compare, under environmental and economic aspect, the water storage
of a typical pond and an innovative flexible water storage system (FWSS). The current
literature encompasses only few studies assessing the environmental and economic per-
formance of different rainwater harvesting strategies in agriculture. For this reason, the
present work represents a first attempt of such kind of evaluation in the topic—mainly
regarding the evaluation of eco-efficiency—and the first step to fulfill such knowledge gap.
112
Sustainability 2021, 13, 3871
Findings showed that the FWSS performed better in both environmental and economic
aspects, resulting in a suitable and sustainable solution for water harvesting in agriculture.
Evaluating the environmental and economic performance of a given system and
carrying out comparisons among possible alternatives is crucial for a proper decision-
making process. Considering the importance of water scarcity and water harvesting topics
in a circular economy framework, carrying out this kind of scientific analysis could provide
an important contribution to the decision-makers. In particular, our findings are useful
not only for academics but also for farmers and practitioners working in the topic of
water management.
Further studies should focus on a real case study, providing tangible evaluation of the
environmental and economic performance based on existing RWH systems. The sustain-
ability of such system highlighted by our results—along with the valuable features of the
FWSS associated with flexibility and nonpermanent disturbance of the environment—paves
the way for interesting abroad applications, particularly where water scarcity jeopardizes
human health and food security in arid countries of the globe.
Author Contributions:
Conceptualization N.P., A.S., W.S. and F.L.; formal analysis, N.P.; investiga-
tion, N.P., W.S. and F.L.; data curation and methodology N.P.; writing—original draft preparation N.P.,
A.S., W.S. and F.L.; In particular, Introduction paragraph, N.P and A.S.; Materials and Methods para-
graphs N.P. and W.S.; Results and Discussion paragraph N.P., A.S. and F.L; Conclusions paragraph
N.P., A.S., W.S. and F.L.; writing—review and editing N.P., A.S., W.S. and F.L.; supervision, project
administration and funding acquisition, L.P. All authors have read and agreed to the published
version of the manuscript.
Funding:
This research was funded by the AGROENER project (D.D. n. 26329, 1 April 2016) funded
by the Italian Ministry of Agriculture (MiPAAF) and the APC was funded by AGROENER project.
The ideas expressed do not represent those of the Italian Ministry of Agriculture (MiPAAF).
Conflicts of Interest: The authors declare no conflict of interest.
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115
sustainability
Article
Factors Influencing Consumers’ Attitude
Towards Biopreservatives
Maria Angela Perito
1,2,
*, Emilio Chiodo
1
, Annalisa Serio
1
, Antonello Paparella
1
and Andrea Fantini
1
1
Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo,
64100 Teramo, Italy; echiodo@unite.it (E.C.); aserio@unite.it (A.S.); apaparella@unite.it (A.P.);
afantini@unite.it (A.F.)
2
UR ALISS, INRAE, Université Paris-Saclay, 94205 Ivry-sur-Seine, France
* Correspondence: maperito@unite.it
Received: 5 November 2020; Accepted: 8 December 2020; Published: 10 December 2020
Abstract:
Biopreservatives have received considerable attention in recent years as natural alternatives
to synthetic preservatives. This seems to be a response to an increased demand for natural and organic
foods. This study investigates the potential market for products enriched with biopreservatives in
Italy. Data were collected from a sample of Italian consumers (N = 479) using a web-based survey.
The main results indicate that 64% of respondents declared themselves to be willing to consume
biopreservatives only if they replaced synthetic preservatives. Principal component analysis (PCA)
was applied to reduce the number of variables. The factorial scores of the components obtained
from PCA were used for a Cluster Analysis related to consumers’ perceptions about biopreservatives.
Moreover, the survey highlights that the respondents had positive opinions about biopreservatives,
although they showed difficulty in perceiving the exact meaning of the term. The study could provide
useful implications for food manufacturers and facilitate the design of marketing strategies for foods
enriched with biopreservatives.
Keywords: biopreservatives; shelf life; essential oil; organic foods; consumers’ attitude; willingness
to pay
1. Introduction
Delivering food in good conditions from the production site to the consumer often requires the use
of additives. Food additives are defined by Regulation (EC) No 1333/2008 [
1
] as “substances that are
not normally consumed as food itself but are added to food intentionally for a technological purpose”,
to promote food safety and extend product shelf life. In fact, traditionally, the control of food-spoiling
and pathogenic microorganisms is ensured by the use of synthetic substances. Despite the benefits that
some food additives apparently have, consumers feel that additives should be reduced in their foods
and that they are ‘bad’ for their health [
2
]. Arbindra et al. [
3
] and Shim et al. [
4
] pointed out that the
consumers’ perception of food additives is generally negative.
Consumers face many food choices associated with food additives every day. In general,
they prefer food with no additives, but if not available, consumers will choose foods containing
natural additives over synthetic analogues [
5
–
11
]. In particular, natural additives [
12
] have been
gaining interest from producers and consumers. Generally, consumers have a high-risk perception
of industrially produced and processed foods [
13
]. In contrast, the word “natural” on food labels
evokes mainly positive associations [
14
]. According to Lockie et al. [
13
], Rozin [
14
] and Siipi [
15
]
“naturalness” is an attribute that enhances the positive perception of foods, making these products
more desirable to the corresponding non-natural ones. During past decades, the market demand for
natural and organic products has grown in many industrialized countries [
16
–
18
], as well as the request
Sustainability 2020 , 12, 38; doi:10.3390/su12240038 www.mdpi.com/journal/sustainability
117
Sustainability 2020 , 12,38
for ready-to-eat food products and fresh fruits and vegetables [19]. Consumers consider ready-to-eat
products as very perishable and perceive freshness as the most important factor influencing food
choice, both during purchase and consumption [
20
–
22
]. Nevertheless, these products are the most
exposed to contamination by spoiling and even pathogenic microorganisms, thus requiring particular
care to ensure consumer safety and product shelf life [23].
In this general trend, consumer attention is growing towards the properties of medicinal plants
(especially organic ones) and the production of essential oils (EOs) and hydrosols (or hydrolates),
which are both obtained during the distillation of aromatic plants. The use of EOs as natural
preservatives may be useful to improve food safety, mainly because of their antioxidant and
antimicrobial activities [
24
]. EOs are hydrophobic compounds and can be directly applied to food
products by means of surface treatments (application on the surface by dipping, spraying, brushing,
and panning), or included within the matrix (e.g., in minced meat). EOs can be also enclosed in
antimicrobial edible films or coatings, generally made of biomaterials such as proteins or carbohydrates,
with the addition of plasticizers and, eventually, jellying agents [
25
]. The micro- or nano-encapsulation
of EOs and bioactive molecules into edible carriers (e.g., cyclodextrins, proteins, etc.) is another
technique that allows us to increase the stability and bioactivity of the active compounds and to reduce
the impact on the flavour of the food product. On the contrary, hydrosols are hydrophilic, and therefore
they are usually applied to food products by means of washing treatments (e.g., vegetables, fruit,
ready-to-eat salads, etc.) [23].
Nevertheless, in spite of these wide possibilities of application, few of the preservatives containing
EOs for use in food products are currently available on the market, and they are typically used in the
food industry as flavouring agents [
26
]. Biopreservatives can represent a source of natural alternatives
to conventional preservatives to improve food shelf life and safety [27].
The regulations are strengthening to reduce the food-related risk of consumption and to preserve
the health of consumers [
28
], and preservatives are included in the legal category of food additives,
according to Regulation (EC) 1333/2008 [
1
]. However, EOs used as biopreservatives, as long as they
are not listed in the EU Regulation on food additives, are usually included in the ingredient list
as an ingredient: e.g., organic essential oil of oranges. If the EO is manufactured and distributed
according to Regulation (EC) 1334/2008 [
29
] on food flavourings, Regulation (EU) 1169/2011 [
30
] on food
labelling gives us another option for the ingredient list, which is “natural flavourings”. Finally, if the
manufacturer can demonstrate that the EO is added to obtain a certain technological purpose during
processing, leaving residues that do not present any health risk and do not have any technological effect
on the finished product, then it can be considered as a “processing aid” (Regulation (EC) 1333/2008, art.
3), and according to Regulation (EU) 1169/2011, is not reported in the ingredient list.
In any case, among the information contained on the label, the consumers pay particular attention
on the expiry date and they consider it an important quality attribute [
31
,
32
]. Stranieri and Baldi [
33
]
investigated a sample of Italian consumers, and they highlighted how consumers pay attention
to product shelf life, especially for fresh-cut vegetables. As a consequence, considering that these
vegetables are very perishable products, the consumers’ choices in purchase are mostly guided by the
perceived level of freshness [
34
,
35
]. In this respect, EOs can have a significant antimicrobial impact.
Nevertheless, their use in foods is quite limited due to both a high cost and a possible adverse impact
on sensory characteristics and product acceptability [
36
]. Moreover, from a regulatory point of view,
there is not much on the topic of natural preservatives, and in fact the term "biopreservative" is neither
regulated nor used on the label.
Therefore, the main objective of this research was to identify the potential for the development of
the biopreservatives market through the analysis of consumer perception and acceptance of natural food
preservatives as an alternative to synthetic preservatives. In particular, to understand the real consumer
acceptance of biopreservatives, we concentrated our analysis on three fresh products (fresh-cut
vegetables, meats and cheeses). The study also aims to provide information for food manufacturers
regarding the most appropriate marketing levers for the enhancement of biopreservatives.
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Sustainability 2020 , 12,38
2. Materials and Methods
The direct survey of consumers was concerned with the perception of natural preservatives as
an element for both improving the shelf life of food products and replacing synthetic preservatives
with natural preservatives, with the aim of understanding the type of message communicated to
the consumer.
The questionnaire was distributed throughout Italy by ADICONSUM (Consumers and
Environment Defence Association), who administered the questionnaire via email to its members
and through Facebook, collecting 479 complete answers. Participation took place in an absolutely
anonymous form, and the participants were informed in advance that the data collected would have
been treated in an extremely confidential manner and used only for scientific research purposes.
However, respondents were recruited from diverse community centres.
The self-administered questionnaire contained questions with closed-ended response alternatives
on a five-point Likert-type scale. The content validity of the questionnaire was ascertained by a pre-test
to collect elements to assess completeness and clarity of the questionnaire. Specifically, the survey
allowed us to investigate the consumer perception and knowledge about biopreservatives and to assess
the interest in the purchase and the willingness to pay towards food products treated with natural
preservatives, as an alternative to synthetic preservatives.
The questionnaire consists of questions on the knowledge of biopreservatives to investigate
the awareness about the preservatives. Some of these questions contained definitions from the EU
Regulation, which indirectly allowed us to evaluate whether the legislation is understandable for the
consumer or if there is an actual lack of clarity from the regulatory point of view, as highlighted on more
than one occasion in the literature about novel food products or technologies [
37
–
39
]. In particular,
the focus of the investigation was on consumer knowledge of the definition of preservative and the
difference between natural and synthetic preservatives. The perception of information that can be
transmitted by product labelling was analysed considering both the mandatory and hypothetically
voluntary information on the labels [
40
]. The aim of these questions was to analyse the importance to
the consumers of information contained on the label and their perception of different claims [
41
,
42
].
Other questions were related to consumer preferences, their willingness to pay for natural preservatives
(as percentage of synthetic preservatives), and the use on several food products.
To understand the consumers’ acceptance regarding natural preservatives, we also asked questions
about their purchasing habits of food products and the frequency of purchase of organic products to
verify whether or not there was a correlation between the consumption of organic products and interest
in purchasing and willingness to pay for products treated with natural preservatives. According to
Dickson-Spillmann et al. [
43
], consumers consider organic foods to be healthier, uncontaminated,
and purer than conventional foods, and not altered or polluted by synthetic additives or by excessive
human interference. Finally, we also asked about information relating to personal data (gender, age,
profession, level of education, etc.).
A Principal Component Analysis (PCA) was applied (computed using IBM SPSS Statistics-version
20.0.0) on the original data to reduce the initial diversity of a certain number of variables [
44
].
The factorial scores of the components obtained from PCA were used for a Cluster Analysis related to
consumers’ perceptions about biopreservatives.
3. Results and Discussion
A total of 479 respondents completed the questionnaire. In the sample, 64% of respondents
were 25–44 years old and 58.2% were male. The sample contained a high percentage of graduates.
The apparent imbalance of the sample can be considered a strength of the questionnaire, because it
allowed us to investigate the capacity of highly educated people to evaluate natural preservatives.
To evaluate the knowledge of consumers, we asked them to define preservatives and natural
preservatives. In this respect, 94.8% of the sample answered the question “what is a preservative”
correctly, according to the definitions given by Regulation (EC) 1333/2008. As for the term
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“biopreservative”, although 46% of the sample answered correctly, it is true that there is still much
uncertainty about the meaning of this term. Indeed, when consumers were asked the difference between
natural and synthetic preservatives, only 38.8% of the respondents answered correctly. With regards to
the definition of flavourings, 65.8% of the sample answered correctly.
Although most respondents read food labels (87% from often to always), there was no significant
correlation between the frequency of reading labels and the knowledge about biopreservatives.
Moreover, considering different information that consumers can find on the label, the data obtained
demonstrate a greater concern of the respondents for the expiry date and the presence of food additives
(Figure 1).
Figure 1.
The level of attention paid to different information on the label (mean and standard deviation).
(Attributes importance (scale 1 to 5: where 1 = Not important and 5 = Very important)).
Furthermore, the claims “without preservatives”, “without added preservatives” and “organic”,
which are actually used in food labels, have been compared with the claims “natural” or “bio”
preservatives, which are not currently included in the labelling regulation, so that we can consider
these as hypothetical labels. The results showed that the new claims would be less appreciated than
the current ones, with the claim “without preservatives” receiving a significantly higher value than the
claim “with natural preservatives” (p < 0.05), and the claim “with biopreservatives” being assessed as
worse than the others (p < 0.01) (Figure 2).
The results showed that most participants agreed with the statement about the safety of food
biopreservatives. A total of 79% of respondents considered biopreservatives to be less harmful to health
than synthetic preservatives, and 55% of them thought that biopreservatives cause less damage to the
environment than conventional preservatives. Only 22% of respondents think that biopreservatives
improve the flavour of the food compared to the synthetic preservatives. With regards shelf life,
only 17% of respondents considered biopreservatives to be better than the synthetic ones. However,
the respondents appreciated the use of biopreservatives, especially in the preparation of fruit (71%),
fresh-cut vegetables (47%), and meat (42%). They seemed less concerned about processed products.
In fact, only 33.6% of respondents said they would prefer the use of biopreservatives in bakery products,
and only 6% in processed food products. Finally, the majority of the respondents answered that they
were willing to consume biopreservatives only if they replaced the synthetic ones (64.3%). A total of
13.6% thought that they would be willing to consume biopreservatives only if they increased the shelf
life of the food products.
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Sustainability 2020 , 12,38
Figure 2.
The importance of the claims on hypothetical labels for the product choice (mean and standard
deviation). (Attributes importance (scale 1 to 5: where 1 = Not important and 5 = Very important)).
Moreover, we have tried to understand the willingness to pay for food products treated with
biopreservatives as preservative replacers. A total of 67.8% are willing to pay for fresh-cut vegetables
with "only" biopreservatives; of these, 55.7% are willing to spend between 10–20% more and 12.1%
are willing to pay up to 30%. In the case of semi-hard cheeses treated with synthetic preservatives on
the rind, 63% of respondents were willing to pay between 10–20% more for the same product treated
with biopreservatives, and 13% of respondents were willing to pay up to 30%. Conversely, 24% of
respondents were unwilling to pay more for cheese treated with biopreservatives.
To understand the determinants in the preference of a product treated with biopreservatives over
conventional products, we have completed our study with a Principal Component Analysis (PCA).
This method allowed us to reduce the initial diversity of certain number of variables to a smaller
number of Principal Components and to simplify the interpretation of the phenomenon. The PCA can
be used when the sample adequacy of the model, indicated by the KMO index [
45
], is more than 70%;
the total variance explained should be greater than 65–70% of the total variance represented by the
variables used.
The PCA was applied to variables obtained by the questionnaire answers, representing three main
aspects of the phenomenon: sensitivity to label claims (influence of different label claims in the product
choice, level of attention paid to different information present on the label and to the price); consumers’
behaviour and choices; and personal characteristics. The KMO of our data is equal to 0,703 (Table 1)
and the total variance explained reaches a good level (68.35%), with the first eight main components
identified (Table 2).
Table 1. The results of the Principal Component Analysis (PCA)—KMO and Bartlett’s Test.
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.703
Bartlett’s Test of Sphericity
Approx. Chi-Square 2638.368
Df 190
Sig. 0.000
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Sustainability 2020 , 12,38
Table 2.
The determinants of consumers’ preference towards biopreservatives. A rotated component
matrix is shown.
Variables
Components
CP1 CP2 CP3 CP4 CP5 CP6 CP7 CP8
Sensitivity to label claims
Influence of the label: “with
natural preservatives”
0.780 0.235 0.165 −0.103
Influence of the label: “with
biopreservatives”
0.766 0.246 0.234 −0.108
Influence of the label “Organic
product”
0.512 0.640 0.148
Level of attention to Ingredients 0.297 0.739 0.180 −0.142
Level of attention to the Product
origin
0.143 0.570 0.314 0.147 0.207 0.270 0.110
Level of attention to Nutritional
Information
0.415 0.545 0.148 0.127
Level of attention to Organic
certification
0.378 0.300 0.704
Level of attention to Brand 0.118 0.104 0.755
Level of attention to Price 0.140 0.126 0.186 0.722
Consumers behaviour
Frequency of reading label 0.819 0.201 0.116
Frequency of organic food
consumption
0.190 0.803 0.169
Unwillingness to pay more for a
cheese treated with natural
preservatives
0.914
Unwillingness to pay more for a
salad treated with natural
preservatives
0.106 0.913 0.103
Willingness to pay more for food
treated with natural preservatives
0.909 0.116
Willingness to pay more for food
treated with natural preservatives
in substitution of synthetic ones
0.115 0.165 0.789 0.269
Willingness to pay more for food
treated with natural preservatives
if the shelf life of the product
increase
0.842 0.110
Preference for a ready-to-eat fruit
salad treated with
biopreservatives, expiring at 5
days respect the same product
without preservatives expiring at
3 days
0.127 0.225 0.650 0.213
Personal Characteristics
Age 0.154 0.115 0.460 0.197 0.438
Knowledge about
biopreservatives
0.131 0.135 0.679
Gender 0.450 0.122 0.219 0.614
Description of the seven principal main components identified:
CP1
(21.2% of the total variance explained): mainly a female component, susceptible to the
indication on the label of “natural preservatives” and “biopreservatives”, as well as to organic products
and organic certification, where the willingness to pay (WTP) a product treated without synthetic
preservatives is weak.
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Sustainability 2020 , 12,38
CP2
(8.92% of the total variance explained): a youth component that pays attention to the label,
to ingredients, and the origin and nutritional information, but not the brand.
CP3
(7.88% of the total variance explained): older people, with a fair knowledge of the topic,
the frequent consumption of organic products, susceptible to the indication on the label of organic
products and organic certification and who give little importance to the price.
CP4
(7.48% of the total variance explained): a component with no willingness to pay more for
products treated with natural preservatives, no interest in biopreservatives, slightly price sensitive,
not interested in organic production or in the products’ origin.
CP5
(6.96% of the total variance explained): a component very favorable to the purchase of
products treated with biopreservatives, even if they do not replace synthetic preservatives and albeit
with a reduced shelf life.
CP6
(5.47% of the total variance explained): a component with high WTP for products treated
with natural preservatives if the shelf life of the product increases; generally attentive to the price.
CP7
(5.25% of the total variance explained): a male and youthful component, sensitive to the
brand and price and attentive to the origin of the products.
CP8
(5.15% of the total variance explained): a male and elderly component, characterized by a
very limited knowledge about biopreservatives, moderately favorable to the use of biopreservatives
and having little interest in the origin of the food products.
The factorial scores of the previous eight main components obtained from PCA were used for a
Cluster Analysis. A two-step clustering method was applied adopting the squared Euclidean distance
algorithm for case processing.
As the eighth component was not very explanatory, we decided to reduce to seven the principal
components for the analysis. A four-cluster solution showed the most distinctive profile [
46
] and was
thus the solution retained (Figure 3). The four identified clusters possess an acceptable measure of
cohesion and separation, with a silhouette coefficient of 0.43 [
47
]: Cluster 1 (16.7% of the sample);
Cluster 2 (60.3%); Cluster 3 (10.6%); Cluster 4 (12.3%).
Figure 3. The results of the Cluster Analysis with seven principal components.
Their description is given below (Table 3).
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Sustainability 2020 , 12,38
Table 3.
Main characteristics of consumers segments (n = 479): mean scores of variables within the
groups (standard deviation within brackets).
Clusters 1 (Enthusiastic) 2 (Rational) 3 (Disinterested)
4 (Conscious and
Attentive)
Total
Personal
characteristics
Gender (0 = female, 1=
male)
0.45 (0.501) 0.40 (0.491) 0.51 (0.505) 0.37 (0.488) 0.42 (0.494)
Age 38.05 (10.467) 36.75 (11.515) 37.24 (12.239) 35.98 (13.309) 36.923 (11.641)
Knowledge about
biopreservatives *
2.35 (0.873) 2.47 (0.858) 2.39 (0.918) 2.56 (1.038) 2.46 (0.890)
Sensitivity to label
claims
Level of attention to
Nutritional
Information *
3.94 (1.173) 3.72 (1.183) 3.43 (1.118) 3.68 (1.105) 3.72 (1.169)
Level of attention to
Brand *
2.64 (1.183) 2.86 (1.120) 2.55 (1.101) 2.86 (1.025) 2.79 (1.120)
Level of attention to
Organic certification *
3.63 (1.354) 3.23 (1.249) 2.65 (1.230) 2.81 (1.238) 3.18 (1.292)
Level of attention to
Product origin *
4.06 (1.205) 4.00 (1.091) 3.31 (1.516) 3.53 (1.056) 3.88 (1.182)
Level of attention to
Price *
3.81 (1.092) 3.59 (1.080) 3.96 (1.183) 4.03 (0.909) 3.72 (1.084)
Level of attention to
Ingredients *
4.24 (1.046) 4.16 (1.055) 3.86 (1.281) 3.85 (1.096) 4.10 (1.090)
Consumers behavior
Frequency of reading
label *
3.05 (1.146) 2.94 (1.054) 2.75 (1.214) 2.81 (1.025) 2.92 (1.084)
Frequency of organic
food consumption *
1.76 (0.945) 1.72 (0.940) 1.37 (0.958) 1.49 (0.858) 1.66 (0.939)
* Scores on a five-point Likert-type scale (1 = minimum; 5 = maximum).
Enthusiastic (Cluster1—80 cases): a cluster withlessknowledge about the meaningof preservatives
and biopreservatives than other individuals, who frequently purchase organic products and declare an
unconditional interest in the purchase of products treated with biopreservatives. These consumers
are not interested in the shelf life of food products and are willing to pay higher prices than other
respondents. It is a niche market with a high prevalence in families of people with professional work,
e.g., lawyers, engineers, etc., which would deserve the positioning of a differentiated product of high
added value.
Rational (Cluster 2—289 cases): a cluster composed mainly of women (60%), with high educational
levels, knowledge of the properties of preservatives, and a high WTP (both for cheese and fresh-cut
vegetables treated with biopreservatives). In this group, we find consumers of organic products,
attentive to the reading of the label, and in particular to the ingredients and health aspects. The interest
in buying is combined with the search for products in which natural preservatives replace synthetic ones.
Disinterested (Cluster 3—51 cases): a cluster with equal representation of men and women,
more so than the rest of the sample, with lower-than-average levels of education and very limited
knowledge of preservatives. This group expresses a very low WTP, practically zero in the case of cheese
and fresh-cut vegetables, consistent with the indication of high importance attributed to the price.
Conscious and attentive (Cluster 4—59 cases): a cluster composed largely of workers or the
unemployed, attentive to the price and shelf life of the food products purchased. They show good
knowledge about preservatives in food processing, despite a low level of education.
The marketing positioning opportunities of biopreservatives on the consumers basically depends
on the strategic objective that the offer (single or aggregate) has. In this context, if the goal is to
maximize profit or minimize costs (communication and distribution), the most interesting positions,
from the point of view of potential turnover (segment size and WTP), appear to be the clusters 2 and
124
Sustainability 2020 , 12,38
1. Even cluster 4, although small in size, deserves attention, but it would require a marketing-mix
oriented towards products, in particular fruit and vegetables, which are able to combine a good shelf
life with low prices.
In general, our results showed that consumers have different levels of understanding of
biopreservatives in different situations and do not perceive food additives and biopreservatives
in the same way. In fact, as previous literature suggested [
4
,
48
,
49
], consumers hardly recognized food
additive information on product labels, showing limited awareness of food additives. The provision
of accurate information on the presence/absence of food additives is considered to be an important
factor affecting the purchase decision [
50
,
51
]. Consumers tend to amplify the risk when a food item
or a technology is unknown [
52
]. Numerous surveys have shown that consumers express concerns
about their daily diet, and they are worried about being exposed to synthetic preservatives [
43
,
53
].
However, in recent years, consumers have been exploiting new media to become more informed,
and social media has become more and more influential in determining their concerns about food
quality attributes [54].
Another relevant finding of our study is that the less organic food the respondents consumed,
the more they cared for price and the less for the presence of preservatives in convenience foods. Regular
organic food consumers preferred biopreservatives compared to synthetic preservatives. However,
our results pointed out that even when consumers profess a strong support for environmental attributes,
they are still extremely price sensitive. These findings are in line with the literature related to the
consumer’ approach to the decision to purchase sustainable food [18,55,56].
4. Conclusions
We investigated the factors affecting the acceptance for products treated with EOs as natural
preservatives through a direct survey of consumers. Our aim was to analyse consumer knowledge and
perception of the information currently used by companies and to verify acceptance of biopreservatives.
Moreover, consumer acceptance and willingness to pay were analysed with respect to shelf life and
replacement of synthetic preservatives with natural preservatives.
The results of our direct survey highlighted the difficulty in perceiving the exact meaning of the
term "biopreservative", which, however, was generally associated with positive opinions such as the
reduction of damage to health or positive environmental impact. Moreover, the term had a lower
impact on the consumer than the claim “without preservatives”.
Our results suffer from two main limitations. Firstly, the sample analyzed in this study is not
representative of the whole Italian population. However, the relationships between the variables
analyzed and the consumers’ perceptions about biopreservatives remain valid and allow us to obtain
interesting results. Secondly, there is not a clear understanding by consumers of biopreservatives and
our study might suffer from hypothetical bias which could have affected the estimation of consumers’
acceptance [10,41,42].
The findings of this research constitute an opportunity for food companies; suppliers of foods
added with biopreservatives should thus concentrate on organic and high-quality foods with a low
level of processing. The results obtained could also help scientists in addressing the research in this field,
with the aim of meeting the requirements of both consumers and food industries. Moreover, this study
also expresses the need for a consumer campaign and a better education on food biopreservatives.
This study shows how research on consumer preferences and priorities (e.g., naturalness vs.
shelf life; biopreservatives vs. price; etc.) can be of paramount importance to promote the scientific
and technological evolution of food manufacturing toward practical applications of biopreservatives,
which could be fundamental in the future market for their potential to decrease the negative impact of
foods on health and environment.
Author Contributions:
Conceptualization, M.A.P., E.C. and A.F.; Data curation, M.A.P., E.C. and A.F.; Funding
acquisition, A.P.; Investigation, M.A.P.; Methodology, M.A.P. and A.F.; Software, A.F.; Supervision, M.A.P., A.P.;
125
Sustainability 2020 , 12,38
Writing—original draft, M.A.P.; Writing—review & editing, M.A.P., E.C. and A.S. All authors have read and
agreed to the published version of the manuscript.
Funding:
This research was supported by the grant of Regione Abruzzo, for the project “PSR 2017–2013 Misura
1.2.4” cod. CUA 2446850691, title “Sviluppo di sistemi convenzionali e innovazioni per la produzione di composti
bioattivi da materie prime vegetali per l’impiego nel settore alimentare”.
Conflicts of Interest: The authors declare no conflict of interest.
: Data Availability:
The data supporting the findings of this study are available from the corresponding author
(M.A.P.) and the first author (M.A.P.) upon reasonable request.
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