Content uploaded by Rahmat Ullah
Author content
All content in this area was uploaded by Rahmat Ullah on Dec 26, 2023
Content may be subject to copyright.
131
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
Research Article
Estimation of Post-Harvest Losses of Grape Fruit: Evidence
from Recent Study in Balochistan
Rahmat Ullah1, Hamdullah2, Rabbia Yousaf2, Sikander Shehzad3, Kaleem Ullah4,
Muhammad Ilyas5, Mansoor Rasheed6, Ghanha Liaquat1
1 Department of Agriculture Extension, Balochistan Agriculture College Quetta, Pakistan.
2 Department of Agricultural & Applied Economics, The University of Agriculture, Peshawar, Pakistan.
3 Department of Computer Science, Balochistan Agriculture College Quetta, Pakistan.
4 Department of Horticulture, Balochistan Agriculture College Quetta, Pakistan.
5 Department of Statistics, Balochistan Agriculture College Qutta, Pakistan.
6 Department of Agricultural & Applied Economics, Balochistan Agriculture College Quetta, Pakistan.
*Correspondence: rahmatullah364@gmail.com
Abstract
The main objective of this study was to estimate post-harvest losses of grapes and
identify their determinants. Cross-sectional data were collected from two main grape-
growing districts (Killa Abdullah and Pishin) of Balochistan province, Pakistan. A
sample of 180 farmers was drawn from selected areas using multistage sampling
techniques and Cochran's proportional allocation techniques. The data were analyzed
using the linear multiple regression analysis technique. The study findings showed that
post-harvest losses in selected areas ranged from 19% to 29%, and the Killa Abdullah
district was found to face relatively more losses. The results of regression analysis
showed that socioeconomic factors such as age, education, and experience had a
significant negative impact on post-harvest losses, implying that increases in these factors
may reduce post-harvest losses. The results further suggest that distance from farm to
market increases losses. The average post-harvest losses in the district of Pishin were 227
kg/Ton or 22.72%. Within the respective district, losses ranged from 19% to 27%. The
minimum losses were suffered by grapes growers in UC Sheikhalzai whereas the
maximum losses occurred in UC Huramzai. In addition, growers who harvest in the
morning, use shears/scissors as picking tools, color as maturity indicator, and
refrigerated trucks as transport have fewer losses than other growers. However, the
losses in the entire surveyed area range from 18% to 28.54%. The least losses suffering
UC among all UCs was Sheikhalzai (18.84%) while the most affected UC was Pir Alizai
(28.54%) based on the data analysis. It was concluded that the demographics of the
operations involved in postharvest management had a large impact on postharvest
damage. Based on the results suggested by the study, growers should harvest grapes in
the morning using modern picking tools.
Keywords: Fruits and vegetables; Grapes losses; Post-Harvest losses; Pakistan.
________________________________________________________________________________________
Introduction
Grapes (Vitis vinifera) come from the family Vitaceae and remain a significant horticultural
crop. Grapes among other deciduous fruits have a historical link with the development
Journal of Agriculture and Veterinary Science
ISSN: 2959-1198 (Print), 2959-1201 (Online)
Article History
Received: June 12, 2023
Accepted: July 18, 2023
Published: August 23, 2023
Copyright: © 2023 by the authors.
Licensee Roots Press, Islamabad
Pakistan.
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/).
132
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
of humankind and are most widely grown across the world. It is said, grapes are the only
specie of the Vitaceae family which has emerged about 65 million years ago (De Saporta,
1879). In earlier times, wine was considered a major product of grapes (McGovern, 2015).
European states such as Italy, Spain and France, Middle-East North America, and East
Asia are the major producing hubs of grapes. Grapes have tremendous nutritional value
as it contains Vitamins, minerals sugar, and several other valuable ingredients (Kumar et
al., 2017; Ammar et al., 2004).
Literature unveils that grape cultivars existing currently are numbered in thousands
while the global market is subjugated by a few of them. Conferring Food and Agriculture
organization, 76,720 KM2 area is dedicated to grapes and the production remains at 91.50
Million Tonnes. Area wise Spain has allocated the largest area to grapes 0.93 million
hectares, followed by France (0.75 million hectares), China (0.74 million hectares), Italy
(0.69 million hectares) turkey (0.4 million hectares). However, in the production, China is
the top producer with (14.3 million Tonnes) followed by Italy (7.9 million Tonnes), Unites
States (6.2 million Tonnes) Spain (5.8 million Tonnes), and France (5.4 million Tonnes).
Similarly, yield wise china remains first with (32.65 Tonnes/hectare), followed by Egypt
(22.65 Tonnes/ hectare) Saudi Arabia (21.87 Tonnes/hectare) India (21.72 Tonnes/hectare).
Considering the facts Pakistan's position in global grapes production is devastating for
instance in production Pakistan ranks 43rd and yield-wise at 80th position (FAO, 2020).
Pakistan’s economy is reliant on the agriculture sector to a very large extent. This sector
contributed 19.2% to the Gross Domestic Product (GDP) of the country. This sector has
engrossed about 38.5% of the total labor force and about 70% of the country's population
is directly or indirectly associated with it Government of Pakistan (GOP, 2021). Over the
last decade, the agriculture sector experienced several challenges such as climate change,
pest attacks, water crisis, and many others. Due to these issues, the performance of this
sector is declining year to year. However, during the year 2020-21 agriculture sector
observed growth of 2.77% greater than the last year which was 2.67% Government of
Pakistan (GOP, 2020, 2021). The substantial growth of 4.65% was witnessed by the crop
sector last year. After food crops, fruit crops also play a vital role in the enhancement of
this sector. Pakistan allocated 0.746 million hectares to 30 different fruits which produced
6.96 million Tonnes. The major fruits are Mango, Guava, Melons, Apple, Dates, Banana,
Apricot, Peach, and Grapes. All provinces have a noteworthy share in fruit production
however Balochistan particularly contributes the major share in apple, grape, dates,
apricot, and melons. This sector retains the potential to produce surplus rather than
fulfilling the country's domestic demand. This sector is responsible for the provision of
raw materials to the industrial sector. Its advancement has a gigantic impact on
Pakistan’s social and economic development and also poverty alleviation and uplifting
living standard of the farming community Government of Pakistan (GOP, 2021).
Post-harvest losses are the top-ranking issue in the production of fruits. The hike in
population is concerned the food security and thus its issues remain of great interest to
researchers (El-Ramady et al., 2015; Kader, 1992) reported that 5 to 25% of fruits and
vegetables leaving the farm gate are being spoiled and not consumable. Post-harvest
losses are relatively high than vegetables and other crops. It is not the only issue of
133
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
developing states but also the developed states. Recent studies revealed that 30%-40% of
post-harvest losses are experienced in developing nations (Dhatt and Mahajan, 2007).
Even some underdeveloped nations lose more than this percentage of their fruits.
Maturity is the most significant factor to determine the post-harvest life and final quality
such as the color, texture, size, and nutritive value of fruits and vegetables (Kader, 1992).
The literature highlighted various factors that are responsible for losses in fruits.
Inappropriate handling and transportation involved in destroying the fruits. Respiration
of ethylene production, compositional variations, transpiration, physiological
breakdown, physical damage pathological breakdown is some biological factors that are
responsible for destroying the fruit quality. Some climatic factors like temperature,
precipitation, relative humidity, and ethylene also decline the quality of fruits (Negi and
Anand, 2015).
Balochistan the largest province of Pakistan is legitimately called the fruit basket of
Pakistan. It spreads over vast areas and has significant economic importance in terms of
mines, minerals, agriculture, and other natural resources. Broadly speaking, the province
produces more than 20 different fruits and vegetables and other important crops still
there is lack a of self-sufficiency in food crops. The majority of the population is
concerned with agriculture as farming remains a major source of earning for most of the
rural communities of Balochistan. In grapes production interestingly, it produces 98% of
total grapes in the country and has allocated about 99% of the total area (GOP, 2021). In
2021, Balochistan produced 81268 Tonnes of grapes over an area of 15574 hectares. Five
different cultivars; Sunderkhani, Kashmishi, Haita, and Shekhali, are grown mostly
whereas Sahibi and Red globe are also found on a partial level (Aujla et al., 2011). The
Major grapes producing districts are Pishin, Quetta, Killa Abdullah, and Mustang (GoB,
2020). Grapes in Balochistan remain of vital importance. Different cultivars are being
utilized for various purposes for instance Sunderkhani and Kashmishi are mostly
consumed as table grapes whereas Haita is mainly processed into raisins. Farmers' up to
their knowledge and shelf life of grapes decide whether to sell their produce as table
grapes or add value to the process of raisins (Khair and Sattar Shah, 2005).
The production of grapes faces different constraints in Balochistan such as lack of credit
facilities, insufficient supply of inputs, chain of middlemen, substandard packaging,
worst marketing infrastructure, improper post-harvest handling, etc. (Balaji and
Arshinder, 2016). Mainly the post-harvest losses in Balochistan occurred due to improper
post-harvest management. A considerable quantity of fresh fruits is lost at various stages
of marketing due to the non-availability of suitable post-harvest technologies and
infrastructure (Bishnoi et al., 2018; Shah et al., 2002). On the other hand, market
intermediaries exploit producer and consumer both by charging a fixed high margin on
their investment. Moreover, the low returns and huge monetary loss increase the
transportation cost and marketing costs. Ultimately the growers remain poor
economically. Decreasing the post-harvest losses could increase returns to producers and
can reduce the cost of production and distribution (Subrahmanyam, 1986).
Grapes production is directly associated with the primary income of many farming
families. This study not only concentrated on socioeconomic factors i.e. Age, education,
134
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
experience, credit, facilities, etc. but also investigated post-harvest handling factors to
reach a meaningful conclusion in southern districts of Balochistan i.e. Pishin and Killa
Abdullah of Balochistan. This research work attempted to point out the pivotal
socioeconomic and operational determinants of post-harvest losses in grapes production.
The findings of this study are anticipated to facilitate farmers in mitigating the grapes'
post-harvest losses and increasing their returns. Moreover, this research work has put
forward recommendations to policymakers that might assist them in formulating the
policies regarding grapes.
Methodology
Study area description and data collection
The study was carried out in the southern districts of Balochistan namely, Pishin and
Killa Abdullah. The primary source of income for the majority of the population in both
districts is agriculture, with only a few involved in the business sector. Pishin district
stands out as a major contributor to fruit production in Balochistan, particularly known
for cultivating apples, grapes, palms, peaches, and cherries. Apples, in particular, hold a
significant position in the market, commanding higher prices. In the previous year, the
combined output of both districts exceeded 50 thousand tonnes of grapes, representing
more than half of the country's total grape production. Grapes rank as the second most
cultivated fruit, with prevalent varieties in the study areas such as Kashmishi,
Sunderkhani, and Haita. Sunderkhani variety is grown the most because of its
profitability followed by Kashmishi. The Haita variety is grown mainly processed to
make raisins Government of Pakistan (GOP, 2021).
Figure 1. Map of district Pishin and Killa Abdullah.
Sampling technique and sample size
The multistage sampling technique was applied to select a sample of growers in the
135
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
study area in the light of pertinent literature (Hamdullah et al., 2021). In the first stage,
District Pishin and Killa Abdullah were chosen purposively on basis of grapes
production. In the 2nd stage, 4 major producing Tehsils were selected from two selected
districts. In the third stage, the 2 union councils were chosen from each selected tehsil. In
the final stage, farmers were randomly selected in the selected Union council. A random
sample of 180 progressive farmers was drawn keeping in view the limited available
resources, time, and financial constraints. The sample size within the Union council was
drawn by applying the proportional allocation sampling technique (Cochran, 1977).
In the above equation;
n; The total size in all the UCs
N= denotes the total population of growers
Ni; denotes the entire population of grapes growers in the ith UC
ni; is the sample drawn from the ith UC
i; (1,2,3…..8) = UC.
Table 1. Sample size and sampling technique.
District
Tehsil
Union councils
Total growers
Sampled growers
Killa Abdullah
Gulistan
Adbul Rehamzai
88
16
Abdullah khan
128
23
Killa Abdullah
Gulistan
156
28
Pir Alizai
80
15
Pishin
Pishin
Shiekhalzi
102
19
Malikyar
120
22
Huramzai
Manzari
148
27
Hurramzai
162
30
Total
983
180
Source; Author’s survey estimates, 2021.
Data acquisition and data sources
Mainly primary cross-sectional data was utilized for this study. Secondary resources
such as; Government reports and trusted websites were also utilized. In order to collect
data about grapes and post-harvest losses of grapes, a well-designed, interesting, and
smooth interview schedule was advanced to collect data from the growers. The questions
were arranged in such a way that farmers can easily understand and answer accurately.
The survey was carried out in districts Pishin and Killa Abdullah from November-
December, 2021. The respondents were encouraged and assured that the information
gathered will be used for research purposes only. That's why they responded willingly
and provide accurate data which will lead to significant results.
The research aimed to evaluate the quantitative evidence of losses. In the previous
literature, different authors have utilized different economic models to evaluate the
socio-economic factors influencing post-harvest losses. They have made estimations on
various levels. However, in our case, we have focused on the growers who were well
136
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
informed about the magnitude of losses from farm to end consumer. They have revealed
the actual magnitude and thus the analyses were conducted on the aggregate level. Based
on the practical experiences the model was designed by (Maloba et al., 2017; Addo et al.,
2013; Mebratie et al., 2015). Thus, in this research work, most of the significant
determinants were taken into consideration as suggested by the pertinent literature. The
study employed a linear multiple regression model to analyze the data following the
footprint of renowned researchers (Di Bari et al., 2004; Divya et al., 2014; Kulwijila, 2021).
In this study, the model is estimated at the farm producer level to drive reliable results.
The research aimed to evaluate the quantitative evidence of losses. In the previous
literature, different authors have utilized different economic models to evaluate the
socio-economic factors influencing post-harvest losses. They have made estimations on
various levels. However, in our case, we have focused on the growers who were well
informed about the magnitude of losses from farm to end consumer. They have revealed
the actual magnitude and thus the analyses were conducted on the aggregate level. Based
on the practical experiences the model was designed by (Maloba et al., 2017; Addo et al.,
2013; Mebratie et al., 2015). Thus, in this research work, most of the significant
determinants were taken into consideration as suggested by the pertinent literature. The
study employed a linear multiple regression model to analyze the data following the
footprint of renowned researchers (Di Bari et al., 2004; Divya et al., 2014; Kulwijila, 2021).
In this study, the model is estimated at the farm producer level to drive reliable results.
The general form of the model is given below:
Where
is the post-harvest losses of ith farmer
is the independent variable i.e. determinants of post-harvest losses
is the intercept of the model
to n are the estimated parameters
is the random error term
Following the literature, the Empirical model for post-harvest losses of grapes and the
factors responsible for these losses is given below. The data were analyzed using STATA
12.
Where;
Post-harvest losses dependent variable of ith grapes grower (kg/ ton)
Intercept of the model
Age of the grower
Education of the grower
Experiences of the grower
Area under grapes
Distance from market to farm
137
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
Dummy for harvesting time morning OR evening (Morning=1 and evening=0)
Harvesting tool cutter OR saw (cutter=1 saw=0)
Means of or reefer truck OR transportation truck (reefer truck=1 simple truck=0)
Maturity index color OR size (Color=1, size=0)
Estimated parameters
Random error term
Post estimation diagnostics
Histogram, VIF variance inflation factor, and Bruesch pagan test were conducted to
check the model for the issue of normality, multicollinearity, and heteroscedasticity.
Results and Discussion
Post-harvest losses of grapes in the selected districts and union councils
According to results presented in the table 2 compares the mean post-harvest losses and
mean percentage of post-harvest losses between the two districts and among the union
councils in the respective district. The average post-harvest losses in the district of Pishin
were 227 kg/Ton or 22.72%. Within the respective district, losses ranged from 19% to
27%. The minimum losses were suffered by grapes growers in UC Sheikhalzai whereas
the maximum losses occurred in UC Huramzai. Likewise, the average post-harvest losses
in District Killa Abdullah were 243 kg/Ton or 24.32%. The variation in the magnitude of
losses ranged from 20% to nearly 29% among the selected UCs within the district. Our
estimations are almost the same as that of (Aujla et al., 2011) who reported that post-
harvest losses in the same region are (16% to 23%). However, the losses in the entire
surveyed area range from 18% to 28.54%. The least losses suffering UC among all UCs
was Sheikhalzai (18.84%) while the most affected UC was Pir Alizai (28.54%) based on
the data analysis. The average Post-harvest losses in the entire study area were 235
kg/ton or 23.52%. Post-harvest losses in Killa Abdullah were more dominant in
comparison to district Pishin. It is evident from the district profiles of the district that
Pishin is comparatively more developed than Kill Abdullah based on demographic
aspects. The literacy rate in Pishin is higher than in Killa Abdullah. In this way growers
in Pishin are more educated than farmers in Killa Abdullah. Therefore, we can say that
the latest or innovative techniques are more likely to be adopted by growers in Pishin
than in Killa Abdullah. Indeed, socioeconomic factors can influence post-harvest losses
thus the pertinent literature reveals that losses are more common in developing nations
than in developed nations Post-harvest losses in fruits and vegetables are reported to
range from 20% to 40% (Barry et al., 2008; Kumrul et al., 2010; Atanda et al., 2011; Ngowi
and Selejio, 2019). In the same way losses of grapes are said to be ranging from 20% to
53% in developing nations (Rajabi et al., 2015; Kughur et al., 2015).
Model diagnostic tests
A histogram was created to test the symmetrical distribution of the error term. The test
revealed error tern was zero at its mean and symmetrically distributed having constant
variance. This implies that the random error term was normally distributed. Variance
inflation factor (VIF) test for multicollinearity was held whose mean value was less than
two which confirmed that there was no multicollinearity problem existed in the data set.
138
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
For heteroscedasticity, the Bruesch-pagan/cook Weisberg test was performed which
resulted in the calculated chi-square magnitude being 0.72 with P-value insignificant.
Thus, it disclosed that the data set was free of heteroscedasticity.
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of Post-harvest losses Kg/Ton
Chi2 (1)= 0.72
Prob > chi2 = 0.3961
Table 2. Post-harvest losses of grapes in the selected districts and union councils.
Districts
Union Council
Obs
Mean Losses
%Age Losses
Pishin
Sheikhalzai
19
188.42
18.84%
Malikyar
22
206.82
20.68%
Manzari
27
241.48
24.15%
Huramzai
30
272.00
27.20%
Average Pishin
98
227.18
22.72%
Killa Abdullah
Abdul Rehmanzai
16
209.38
20.94%
Abdullah Khan
23
216.96
21.70%
Gulistan
29
261.03
26.10%
Pir Alizai
13
285.38
28.54%
Average Killa Abdullah
81
243.19
24.32%
Total
179
235.18
23.52%
Author’s estimates; survey results, 2021.
Table 3 represents the descriptive statistics of the regress and regressors employed in the
model. Total observations were 180 however one of them was dropped based on
inaccurate and incomplete data. The mean post-harvest losses were recorded at 237 kg/
ton with a standard deviation of 35. The mean age of the respondents was 37 years
having a standard deviation of 9. In the same way, the average education and experience
were 8 and 17 years, and the standard deviation was 5 and 9 years respectively. The area
under the grapes orchard ranged from 1 to 10 hectares with an average of 4 hectares and
a standard deviation of 2. The distance from farm to market varied to a large extent. The
reason behind this variation was that there were mostly 4 market destinations for grapes
grower i.e. Hazarganji market Quetta, Dera Ismail Khan Market Khyber Pakhtunkhwa,
Karachi fruit market, and Islamabad fruit market. the closest market was Hazarganji
Quetta while the far-off was Islamabad market Therefore, farm-to-market distance ranges
from 70 to 895 km, and the mean distance remained 464 Km. Four dummy variables were
utilized in the model.
• D1 Grapes harvesting time 0 for evening and 1 for morning time.
• D2 Grapes ripening, or maturity index was determined on two features i.e. size
and color. Those who preferred size were coded with 0 and for color 1.
• D3 Grapes were plucked from the belly using two tools i.e. traditional Saw and
cutter. Those who used Saw were coded with 0 and 1 for the cutter.
139
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
• D4 Grapes after packing were transported by two means i.e. simple trucks and
reefer trucks. For the simple truck code set was 0 and for the reefer truck 1.
In addition to these, some other variables were not taken in the analysis. For instance, in
the entire study area, corrugated cotton was used as a packaging material. Likewise,
Gender, traditional packed house, pre-cooling, storage facilities, credit access facilities,
and time spent on grapes reaching markets were alike. Therefore, it was understood that
no significant effect of these variables could be extricated. Therefore, these factors were
excluded from the data analysis. The data were analyzed using STATA 12.
Table 3. Descriptive statistics of the variable.
Variable
Obs
Mean
Std. Dev.
Min
Max
Units
Post-harvest Losses
179
237.04d
35.48
170
290
Kg/Ton
Age
179
37.15
9.39
25
58
Year
Education
179
8.36
4.51
0
16
Year
Experience
179
17.78
9.14
5
38
Year
Area under Grapes
179
4.04
2.03
1
10
Hectare (Ha)
Distance farm-MKT
179
463.98
336.64
70
895
Kilometer (KM)
D1Harvesting Time
179
0.84
0.36
0
1
Dummy
D2 Maturity Index
179
0.94
0.24
0
1
Dummy
D3 Harvesting Tool
179
0.55
0.50
0
1
Dummy
D4 Means of Trnsptn
179
0.88
0.32
0
1
Dummy
Author’s estimates; survey results, 2021
Estimates of Multiple regression analysis
Table 4 represents the estimation of multiple regression analysis. R-squared magnitude is
93 which interprets the measure of goodness of fit of the model. It implies that 93% of the
variation in the regress is due to regressors utilized in the model. The major socio-
economic factors revealed an inverse but significant influence on post-harvest losses. The
age variable is negative but statically significant at a 1% level of significance. It revealed
that an increase in age by 1 year can reduce post-harvest losses by 0.33 kg ceteris paribus.
Our findings were not in line with Kulwijila (2021) who reported no significant relation.
Though we can say that a person aged more has higher experiences, this could be the
reason. Education is statically significant at a 1 % level of significance. It shows that an
increase of 1 year in education can decline the post-harvest losses by 1.1 kg/ ton. In
general, it can be perceived that education increase may be associated with more suitable
post-harvest management techniques. Our results are similar to that of Umer et al. (2021)
who found a significant effect of education on post-harvest losses. He reported 0.137
reductions in losses with an increase of one year of education. In the same way,
Experience was statistically significant but had negative behavior concerning grapes
post-harvest losses. The magnitude of experience was 0.34 which reveals that if an
individual’s experience is increased by one year the losses of grapes will decrease by 0.34
kg. The findings are alike to that of Kulwijila (2021) who found that increase in
experience is related to a decline in the post-harvest losses of grapes. Likewise,
140
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
comparable results were elucidated by Ahmed et al. (2015) and Mebratie et al. (2015).
They found that Post-harvest losses have significant inverse relation and unveiled that
the higher the experience lower the losses of Kinnow fruit and Banana after harvesting.
Distance has a vigorous relationship with commodities transportation specifically
perishable commodities are more vulnerable to long-distance. Due to their low shelf life,
they suffer damages in shipping. The results of distance from orchard to market had
positive and statistically significant relation. The results revealed that with a distance of 1
km the post-harvest losses increase by 0.05 kg per ton. Kulwijila (2021) reported equally
similar results that distance had a statically significant and positive influence on post-
harvest damages. The study found increase in distance from farm to market increase the
losses. Likewise, Ayandiji et al. (2011) stated the same results that far-flung markets badly
influenced the tomato crop in Nigeria. Murthy et al. (2009) argued that about 50% of post-
harvest losses occur during shipment to distant markets in form of loose and damaged
berries. The author estimated that 7%-11% of losses are caused by shipment to distant
markets. In our case, far-flung market destinations and sub-standard communications
routes badly influence the quality of fruits in the form of mechanical damages and long
shipment time for such perishable commodities. The mean shelf life of fruits ranges from
3 to 4 days so long-distance shipment and inappropriate handling damage fruits up to
20% before reaching the end consumer Dessalegn et al. (2016). Consequent to
transportation losses Subrahmanyam (1986) is of the view that these losses can only be
minimized by increasing the per-unit cost of transportation and marketing.
In general, most of the post-harvest losses occurred during handling the grapes as
compared to socio-economic factors. The results revealed that growers who harvested
their grapes in the evening suffered losses of 22 kg/ ton more than the growers who
carried their harvesting in the morning. Our findings were in line with that of Ahmed et
al. (2015) with slight variation in the magnitude. The author found morning losses 0.28
times fewer losses suffered during evening harvest. It is evident from the literature that
harvesting grapes in the morning are likely to reduce post-harvest losses. Our findings
are also in conformity with Gangwar et al. (2007), who reported morning time for
harvesting is beneficial comparatively. The same recommendations were being made by
Sharma and Singh (2011) that harvesting in the early morning is more suitable to lessen
the harm.
Growers were cognizant of and practiced two measures (i.e. color and size of grapes) to
decide whether grapes are ready to pluck or not. This study found that growers who
considered size as maturity index suffered losses of 25 kg more than the growers who
considered color as maturity index. Pertinent literature also had found color factor as
maturity index is more suitable as significant criteria under which bunch color remains
uniform. The color index is said to be more appropriate in colored varieties (Sharma and
Singh, 2011). The dummy for the harvesting tool was statistically significant but inversely
on the post-harvest losses. The results revealed that growers who used traditional Saw to
detach a bunch from the belly suffered 4.7kg/ton more than those who used modern
cutters or sharp scissors. Similar results were found by Umer et al. (2021) who reported
that sharp tools for picking were effective to reduce losses as compared to traditional
141
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
picking methods. Mencarelli et al. (2005) also emphasized that a skilled picker can also
play a role in the reduction of losses.
Dummy means of transportation were highly significant and had a positive influence on
losses. The magnitude of means of transportation was 9.54 which implies that growers
who transported their grapes through simple trucks suffered losses of 9.54 kg/ton more
than those who used reefer trucks. In general, small and medium farmers cannot afford
or avail of services of reefer vehicles due to lack of credit facilities and others. A large
number of researchers like Umer et al. (2021) have conveyed that means of transportation
such as road infrastructure, vehicles, etc. play a vital role in post-harvest losses.
Additionally, the area under the grapes orchard was statistically insignificant. The reason
could be that more or less cultivated areas might not assist in post-harvest management.
Nonetheless, it would vary from locality to locality where the post-harvest management
innovative techniques are introduced and followed. Moreover, the post-harvest losses
might be more influenced by demographics rather than orchard size. However, our
findings were dissimilar to Umar et al. 2021 who found a positive relationship between
area under cultivation and losses.
Table 4. Estimates of multiple regression analysis.
Regressors
Coefficient
Std. Err.
t
P>|t|
Age
-0.332
0.094
-3.52
0.001
Education
-1.134
0.334
-3.4
0.001
Experience
-0.343
0.105
-3.26
0.001
Area under Grapes
-0.196
0.373
-0.53
0.600
Distance farm-MKT
0.053
0.005
11.64
0.000
D1 Harvesting Time
-22.322
2.393
-9.33
0.000
D2 Maturity Index
-25.446
3.728
-6.83
0.000
D3 Harvesting Tool
-4.739
2.482
-1.91
0.058
D4 Means of Trnsptn
9.542
2.510
3.8
0.000
Constant
278.066
6.935
40.1
0.000
Number of Obs
179
R-squared
0.9306
F(9, 169)
251.7
Adj R-squared
0.9269
Prob > F =
0.000
Root MSE
9.5954
Author’s estimates; survey results, 2021
Literature reported various other factors that are contributing to post-harvest losses. As
mentioned earlier, some of the essential variables were excluded from the analysis. The
reason behind this was that in both districts those practices were common. As packaging
material was corrugated cartons throughout the study area. Only males were practicing
farming due to ritual constraints. Pre-cooling was not familiar across both districts. Even
growers were not aware of the significance of pre-cooling which is a recommended
technique to overcome the losses. Similarly, lack of credit facilities poses challenges to
farmers to procure new types of equipment, rent reefer vehicles for transportation, and
other requisite inputs. Grapes storage was infrequent for growers. In the same way,
142
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
growers were using traditional pack houses where ventilation of air was poor. Likewise,
improper handling, poor transportation, and climatic factors like temperature, rainfall,
and humidity are factors responsible for post-harvest losses in grapes (Negi and Anand,
2015; Bishnoi et al., 2018).
Post-harvest losses remain one of the highly concentrated research subjects in the
agricultural sector. However, techniques for estimation of post-losses show a wide
discrepancy through the different countries, depending on the crops and post-harvest
management techniques. Based on different scenarios researchers have put to use
different techniques for estimations (Ayandiji et al., 2009; Gangwar et al., 2007; Murthy et
al., 2007). There are only a few that attempted to evaluate different determinants of post-
harvest losses, However, this piece of research focused econometric techniques to
calculate the losses after harvest and the factors responsible for these losses.
Conclusion and Recommendations
The study was executed to estimate the post-harvest losses of grapes and also the causal
factors that contribute to losses. Post-Harvest losses in the study area were ranging from
19% to nearly 29%. District Killa Abdullah suffered more losses than district Pishin. The
linear multiple regression analysis disclosed that socioeconomic factors i.e. age,
education, and experience were the principal factors in reducing the post-harvest losses.
As the increase in the variables was found to decline in the losses of grapes. However,
the main operational factors such as harvesting time, maturity index, a picking tool, and
means of transportation were found to have more impact as compared to socioeconomic
determinants. The picking tool cutter was found more beneficial to reduce losses.
Similarly, morning time, and reefer truck for shipment of grapes were key factors that
could reduce post-harvest losses to a large extent. Most of the recommended and
beneficial post-harvest handling practices were not common in both districts. Generally,
both districts fall in the backward and deprived province of a developing nation which is
why innovative and modern techniques are not common. Growers should use cutters as
picking tools rather than saws, which will help reduce the amount of loss. Agricultural
extension and other agencies should train growers who understand post-harvest
management practices. Governments and other financial institutions should arrange
credit facilities for grape growers so that they can procure the necessary types of
equipment required for post-harvest management.
Conflict of Interest
The authors have not declared any conflict of interest.
Authors Contributions
All the authors contributed equally in the manuscript.
References
Addo, A., I. Oduro and L. Effah-Manu. 2013. Effect of dextrinized sweet potatoes on the
physicochemical and sensory quality of infra-red dried mango leather. Journal of
Food Processing and Technology, 4: 1776.
143
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
Ahmed, U. I., L. Ying, K. Mushtaq and M. K. Bashir. 2015. An econometric estimation of
post-harvest losses of kinnow in Pakistan. International Journal of economics,
commerce and management, 3: 373-83.
Ammar, H., S. López, J. S. González and M. J. Ranilla. 2004. Chemical composition and in
vitro digestibility of some Spanish browse plant species. Journal of the Science of
Food and Agriculture, 84: 197-204.
Atanda, S., P. Pessu, S. Agoda, I. Isong, O. Adekalu, M. Echendu and T. Falade. 2011.
Fungi and mycotoxins in stored foods. African Journal of Microbiology Research,
5: 4373-82.
Aujla, K. M., N. A. Shah, M. Ishaq and A. Fraooq. 2011. Post-harvest losses and
marketing of grapes in Pakistan. Sarhad Journal of Agriculture, 27: 485-90.
Ayandiji, A., O. Adeniyi and D. Omidiji. 2011. Determinant post harvest losses among
tomato farmers in Imeko-Afon local government area of Ogun State, Nigeria.
Global Journal of Science Frontier Research, 11: 23-27.
Ayandiji, A., A. Kehinde, O. Adeniyi and O. Omotosho. 2009. Gross margin analysis of
post-harvest losses in Citrus spp in Ife ADP zone of Osun State, Nigeria. Journal
of Agricultural Extension and Rural Development, 1: 77-84.
Balaji, M. and K. Arshinder. 2016. Modeling the causes of food wastage in Indian
perishable food supply chain. Resources, Conservation and Recycling, 114: 153-
67.
Barry, I., H. Jaenicke, G. Pichop and D. Virchow. 2008. Production and marketing of
African indigenous vegetables in the Arumeru district of Tanzania: assessing
postharvest loss and processing potential. International Symposium on
Underutilized Plants for Food Security, Nutrition, Income and Sustainable
Development 806.
Bishnoi, A., H. Chawla and M. Shah. 2018. Influence of coating developed from oligomer
isolated from lac resin on post-harvest quality and shelf life of peaches (Prunus
persica L.). Canadian Journal of Biotechnology, 1: 80-88.
Cochran, W. G. 1977. Sampling Techniques. John Wiley & Sons.
De Saporta, G. 1879. Le monde des plantes avant l'apparition de l'homme. G. Masson.
Dessalegn, Y., H. Assefa, T. Derso and A. Haileslassie. 2016. Assessment of fruit
postharvest handling practices and losses in Bahir Dar, Ethiopia. African Journal
of Agricultural Research, 11: 5209-14.
Dhatt, A. and B. Mahajan. 2007. Harvesting, handling and storage of horticultural crops.
In.
Di Bari, M., M. Chiarlone, D. Matteuzzi, S. Zacchei, C. Pozzi, V. Bellia, F. Tarantini, R.
Pini, G. Masotti and N. Marchionni. 2004. Thoracic kyphosis and ventilatory
dysfunction in unselected older persons: an epidemiological study in Dicomano,
Italy. Journal of the American Geriatrics Society, 52: 909-15.
Divya, A., S. Jayashree and B. Bhogi. 2014. Effect of storage methods on the nutritional
quality of sapota candy. Asian Journal of Dairy and Food Research, 33: 104-08.
El-Ramady, H. R., É. Domokos-Szabolcsy, N. A. Abdalla, H. S. Taha and M. Fári. 2015.
Postharvest management of fruits and vegetables storage. Sustainable
144
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
Agriculture Reviews, 15: 65-152.
FAO. 2020. Food and agriculture organization of the United Nations. Food and
Agriculture Organization. Rome, Italy.
Gangwar, L. S., D. Singh and D. Singh. 2007. Estimation of post-harvest losses in Kinnow
Mandarin in Punjab using a modified formula. Agricultural Economics Research
Review, 20: 315-31.
GoB. 2020. Government of Balochistan. Development Statistics of Balochistan.
Agriculture and Cooperatives Department, pp. 2019-20.
GOP. 2020. Economic survey of Pakistan (2019-20). Economic Advisor’s Wing, Finance
Division. Islamabad, Pakistan.
———. 2021. Pakistan economic survey 2020-21. Govternment of Pakistan. Islamabad,
Pakistan.
Hamdullah, S. Ali, A. Khan, S. A. Shah and S. U. Khan. 2021. Economic appraisal of
transformative climate change on potential variations in wellbeing of wheat
growers across various ecological zones. Environmental Science and Pollution
Research, 28: 10077-91.
Kader, A. A. 1992. Postharvest biology and technology: An overviewUniversity of
California, Division of Agriculture and Natural Resources. Oakland, California,
USA. pp. 296.
Khair, S. M. and S. A. Sattar Shah. 2005. Grapes drying: An indigenous profitable
enterprise in Balochistan. Journal of Applied Sciences, 5: 563-68.
Kughur, P., G. Iornenge and B. Ityonongu. 2015. Effects of postharvest losses on selected
fruits and vegetables among small-scale farmers in Gboko local government area
of Benue State, Nigeria. International Journal of Science and Innovative Research,
19: 201-08.
Kulwijila, M. 2021. Socio-economic determinants of post-harvest losses in the grape value
chain in Dodoma Municipality and Chamwino District, Tanzania. African
Journal of Economic Review, 9: 288-305.
Kumar, A. R., S. Parthiban, A. Subbiah and V. Sangeetha. 2017. Effect of severity of
pruning on yield and quality characters of grapes (Vitis vinifera L.): A review.
International Journal of Current Microbiology, 13: 818-35.
Kumrul, H., L. Bishan and N. Akhter. 2010. Post-harvest loss assessment: A study to
formulate policy for loss reduction of fruits and vegetables and socioeconomic
uplift of the stakeholders. National Food Policy Capacity Strengthening
Programme Final Report: 188.
Maloba, S., J. Ambuko, M. Hutchinson and W. Owino. 2017. Off-season flower induction
in mango fruits using ethephon and potassium nitrate. Journal of Agricultural
Science, 9: 158-67.
McGovern, R. 2015. Management of tomato diseases caused by Fusarium oxysporum. Crop
protection, 73: 78-92.
Mebratie, M. A., J. Haji, K. Woldetsadik, A. Ayalew and E. Ambo. 2015. Determinants of
postharvest banana loss in the marketing chain of central Ethiopia. Food Science
and Quality Management, 37: 52-63.
145
https://doi.org/10.55627/Javs.02.2.0452
J. Agri. Vet. Sci. 02 (2) 2023. 131-145
Mencarelli, F., A. Bellincontro and G. DiRenzo. 2005. GRAPE: Post-harvest Operations.
Murthy, D. S., T. Gajanana, M. Sudha and V. Dakshinamoorthy. 2007. Marketing losses
and their impact on marketing margins: A case study of banana in Karnataka.
Agricultural Economics Researh Review, 20: 47-60.
Murthy, D. S., T. Gajanana, M. Sudha and V. Dakshinamoorthy. 2007. 2009. Marketing
and post-harvest losses in fruits: Its implications on availability and economy.
Indian Journal of Agricultural Economics, 64: 259-75.
Negi, S. and N. Anand. 2015. Supply chain of fruits & vegetables agribusiness in
Uttarakhand (India): Major issues and challenges. Journal of Supply Chain
Management Systems, 4: 43-57.
Ngowi, E. R. and O. Selejio. 2019. Post-harvest loss and adoption of improved post-
harvest storage technologies by smallholder maize farmers in Tanzania. African
Journal of Economic Review, 7: 249-67.
Rajabi, S., F. Lashgarara, M. Omidi and S. J. F. Hosseini. 2015. Quantifying the grapes
losses and waste in various stages of supply chain. Biological Forum.
Shah, N. A., S. Khan, M. A. Kasi and S. Khair. 2002. Post harvest and cold storage losses
in apple of Balochistan. Asian Journal of Plant Sciences, 1: 65-66.
Sharma, G. and S. Singh. 2011. Economic analysis of post-harvest losses in marketing of
vegetables in Uttarakhand. Agricultural Economics Research Review, 24: 309-15.
Subrahmanyam, K. 1986. Post-harvest losses in horticultural crops: An appraisal.
Agricultural situation in india, 41: 339-43.
Umer, M., M. Mubeen, M. Ateeq, M. A. Shad, M. N. Atiq, M. M. Kaleem, S. Iqbal, A. A.
Shaikh, I. Ullah and M. Khan. 2021. Etiology, epidemiology and management of
citrus black rot caused by Alternaria citri: An outlook. Plant Protection, 5: 105-15.
