7
ABSTRACT
Demand for citrus fruit has increased over the years in Nigeria. However, post-harvest loss of tropical fruits is high in 
developing nations owing to poor handling and storage facilities along the supply chain. The study therefore assessed levels 
and correlates of postharvest losses of fresh oranges along the orange supply chain. Primary data collected from marketers 
and producers of orange were analysed using descriptive statistics and ordered logit. Orange farming was dominated by 
male (68.4%) while orange marketing was dominated by females. Most of the producers (63.16%) and wholesalers (65.38%) 
experienced postharvest loss of 6-10%, while 46.79% of the retailers experience a postharvest loss of less than 5%. Being 
a male farmer that harvested oranges in the afternoon increased the likelihood of postharvest losses among the farmers 
while having a large household size reduced it. However, education, smallholding marketing and use of storage facilities 
reduced the likelihood of postharvest loss among orange marketers. Postharvest training for farmers and marketers on 
fruit harvesting and storage methods will help to minimise postharvest loss from producers to retailers.
Key words: postharvest losses, producers, marketers, ordered probit
Agricultura 19: No 1: 7-15(2022)
https://doi.org/10.18690/agricultura.19.1.7-15.2022
*Correspondence to:
E-mail: jkemmyade@yahoo.co.uk
INTRODUCTION
Malnutrition is a major global public health and 
development concerns. Nigeria has the second highest 
burden of stunted children in the world, with a national 
prevalence rate of 32 percent of children under five and about 
2 million children suffering from severe acute malnutrition 
in the country (UNICEF, 2021). About 5.1 million Nigerians 
were estimated to be in immediate need of food assistance in 
March-May 2020 (SWAC/OECD, 2020). Micronutrients are 
vital components of good nutrition, and their deficiency in the 
human diet is responsible for many health problems (Kuku-
Shittu et al., 2016). Consumption of fruits and vegetables is 
a panacea for malnutrition. Orange fruit (Citrus sinensis) 
is known to be rich in calories and micronutrients such as 
vitamin C, thiamin, foliate, calcium, fibre and potassium 
(Cervoni, 2012). Of all the citrus fruits, orange is the most 
common and the most widely cultivated and consumed in 
Nigeria (Inienger and Udoh, 2020).
Global orange production for 2021/22 increased from 1.4 
million tons in 2020/2021 to 48.8 million in 2021/2022 due to 
favorable weather (USDA, 2022). The production of oranges 
of Africa and Nigeria increased from 2.73 and 1.5 million 
tonnes, respectively in 1971 to 9.76 and 3.98 million tonnes, 
respectively in 2020 growing at average annual rates of 2.76% 
and 2.13%, respectively (Knoema, 2022). However, owing to 
their green color and small sizes, Nigerian oranges do not 
meet export standards (Dijkxhoorn et al., 2021). Although 
there has been an improvement in orange production in 
Nigeria over time, postharvest loss is still a major challenge 
(Adegbija, 2018).
What Factors Explain Postharvest Losses of Orange Fruit
(Citrus sinensis) from Farm to Fork in the Tropics?
Oluwakemi Adeola OBAYELU1*, David Olusegun DAIRO1, Olukemi Olumuyiwa OLOWE2
1Department of Agricultural Economics, Faculty of Agriculture, University of Ibadan, Nigeria
2Department of Economics, McPherson University, Seriki-Sotayo, Nigeria
8
What factors explain postharvest losses of orange fruit (Citrus sinensis) from farm to fork in the tropics?
Orange fruits do change hands several times among the 
actors along the marketing chain. Farmers sell their fruits to 
the consumers through various intermediate marketers who 
keep the entire price share in the market (Arah, 2015). The 
amount of production that finally gets to the consumer is 
more important than the level of production as post-harvest 
losses of tropical fruits account for the reduction of produce 
that eventually gets to the consumer (Ezekiel et al., 2014). 
Postharvest loss, therefore, includes the food losses across the 
food supply chain from harvesting of crop to its consumption 
(Aulakh et al., 2013). These losses occur all along the supply 
chain, beginning from the time of harvest up to packing, 
storage, transportation, retailing and consumption (Saran et 
al., 2012). However, over 50% of the fruits produced in Nigeria 
are lost in transit between farms and major urban markets 
(Olife et al., 2015). Reduction in postharvest loss enhances 
inclusive economic growth, food and nutrition security by 
increasing food availability (AGRA, 2020; Mahmud, 2020). 
Several factors are responsible for postharvest loss of 
orange in most developing countries are mainly due to the 
combination of poor infrastructures and logistics, poor farm 
practices, poor of postharvest technologies and handling 
knowledge, as well as a convoluted and ineffective marketing 
system (Parfitt et al., 2010; Doki et al., 2019). Mechanical 
damage occurs during transportation of fruits over untarred 
roads, which generates high temperatures that accelerate 
enzyme and microbial activities which contribute to the 
deterioration of fruit after harvests (Agona and Muyinza, 
2008; Mashau et al., 2012). All these problems reduce the 
life span, quality and quantity of orange fruits that get to the 
consumer and hence increase postharvest losses of oranges. 
The quality and nutritional value of fresh orange fruits 
are mostly affected by postharvest handling and storage 
condition (Sablani et al., 2006). The perishable nature of the 
citrus fruits implies that are inherently liable to deteriorate 
under different climatic and other circumstances due to their 
high moisture content (Kitinoja and Kader, 2002. Thus an 
efficient marketing strategy needs to be put in place to avoid 
damage to crops after they are harvested (Mbah et al., 2018). 
Several studies have conducted studies on postharvest 
losses of oranges among farmers (Agada and Uga, 2017; 
Attah et al., 2018; Ikwuba et al., 2019) and marketers (Aminu 
et al., 2019; Girei et al., 2020) in Nigeria. Some studies 
also analysed the determinants of postharvest loss among 
farmers and marketers in Nigeria without disaggregating the 
marketers into wholesalers and retailers (Doki et al., 2019) 
or without identifying the determinants of postharvest losses 
(Adekalu et al., 2019). Owing to a paucity of empirical study 
on postharvest loss along orange supply chain in Nigeria, this 
study therefore identified the socio-economic and postharvest 
handling factors influencing postharvest losses along orange 
supply chain in the tropical southwest Nigeria.
MATERIAL AND METHODS
The study was conducted in tropical Oyo state, Nigeria, 
which is one of the major orange fruit producing states in 
Nigeria (Inienger and Udoh, 2020). A cross-sectional type 
of data was collected from three groups of actors along a 
short supply chain (orange farmers, orange wholesalers and 
orange retailers), using a multi-stage sampling procedure. A 
purposive sampling method was used to select four Local 
Governments Areas (Afijo, Ona Ara, Ogooluwa and Egbeda 
local governments) in Oyo state, where orange farming is 
predominant. A total number of 60 farmers were randomly 
selected, proportionate to the orange farming population of 
the area. Similarly, a multi-stage sampling procedure was 
used to select sample from both orange wholesalers and 
retailers for the study. The four major fruit markets (Odo-
Oba, Oje, Elekara and Egbeda) in Oyo state were purposively 
selected for the study. A simple random sampling was used 
to select 10 orange wholesalers from each of these markets, 
making a total of 40 wholesalers. However, 80 orange retailers 
were randomly selected from the markets proportionate to 
the size of the orange retailers in the markets. Information 
obtained include socio-economic characteristics of the 
actors, percentage of postharvest loss, time of harvesting, 
materials for packaging, perceived causes of postharvest loss, 
among others.
Descriptive statistics were used to describe postharvest loss 
of the actors along the orange supply chain, while the ordered 
probit was used to identify determinants of postharvest loss 
at each of the supply nodes (production, wholesale and 
retailing) of the chain. An ordered categorical dependent 
variable (percentage postharvest losses) was estimated. The 
model is useful in determining a combination of the multiple 
factors contributing to the resultant percentage postharvest 
loss categories on a given crop. Unlike the Ordinary Least 
Squares method, the ordered probit model avoids specification 
of lead equation and recognizes unequal differences between 
ordinal categories in the dependent variable (Greene, 2003). 
The postharvest loss per trip was estimated in percentage 
and categorized into three groups namely: low (1 – 5%) = 0; 
moderate (6 – 10%) = 1; high (> 10%) = 2. The respective 
category for quantity lost is unobserved and is denoted by 
the latent variable Yi*. The latent equation that shows how Yi* 
varies with explanatory variables is given as:
Yi* = Xiβ + εi      (1)
where the latent variable Yi* measures postharvest loss by an 
actor i (0= low (1 – 5%); 1= moderate (6 – 10%); 2 = high (> 
10%); Each actor i belongs to one of the three groups; Xi is a 
vector of exogenous variables; β is a conformable parameter 
vector; and the error term(εi) is independent and identically 
distributed as standard normal. The implied probabilities are 
obtained as:
Pr {Yi = 0| Xi} = Φ(µ0 - Xiβ)
Pr {Yi = 1| Xi} = Φ(µ1 - Xiβ) – Φ(µ0 - Xiβ) 
Pr {Yi = 2| Xi} = Φ(µ2 - Xiβ) – Φ(µ1 - Xiβ)   (2)
where µ is the unknown parameter that is estimated jointly 
with β; and Φ(·) is the cumulative distribution function (cdf) 
of the standard normal (Verbeek, 2008). Estimation is based 
upon the maximum likelihood where the above probabilities 
enter the likelihood function. The interpretation of the β 
coefficients is in terms of the underlying latent variable model 
in equation (1). 
9
What factors explain postharvest losses of orange fruit (Citrus sinensis) from farm to fork in the tropics?
A measure of goodness of fit can be obtained by 
calculating:
ρ2 = 1− [lnLb/ ln Lo]     (3)
where ln Lb is the log likelihood at convergence and ln Lo is 
the log likelihood computed at zero. This measure is bounded 
by zero and one. If all model coefficients are zero, then the 
measure is zero. Although ρ2 cannot equal one, a value close 
to one indicates a very good fit. ρ2 increases as the model fit 
improves (Greene, 2003). 
RESULTS 
A larger percentage of the orange farmers/producers were 
male (68.4%), married (89.5%) with mean and modal ages 
of 52.01 years and 41-50 years (38.5%), respectively; modal 
household size of six people (78.9%); and had primary 
education (35.1%) (Table 1). However, a larger part of the 
orange wholesalers (61.5%) and retailers (80.8%) were 
female, married (96.1%, 93.1% respectively), mean ages of 
46.32 and 44.10 years, respectively; modal household size of 
6 people (53.8% and 52.1%, respectively); and had secondary 
education (42.3%, 42.5%, respectively).
Most of the producers and marketers (retailer and 
wholesaler) had a high level of experience (more than 20 years) 
which constituted about 52.6%, 46.6% and 46.2%, respectively, 
with the mean values of marketing and farming experience 
being 20 and 24 years, respectively (Table 2). About a third 
of the producers (75.4%) belonged to a farmers’ association 
while most of the marketers (retailers and wholesalers) 
which constituted 95.9% and 88.5%, respectively, belonged 
to a marketing group. Most of the wholesalers (61.5%) and 
retailers (91.8%) were primarily engaged in trading, while 
73.7% of the producer were primarily engaged in farming. 
A high percentage of the farmers did not have access to 
credit which constituted about 73.7% while 79.5% and 69.2% 
of the retailers and wholesalers, respctively had access to 
credit. About 80.8% and 65.8% of the orange wholesalers 
and retailers got their source of finance from personal equity, 
while most the producer got theirs from cooperative society 
(47.4%).  
A larger percentage of the producers (80.7%) harvested 
orange early in the morning (Table 3). Arrangements for 
the collection are done in advance so that the farmer can 
harvest accordingly. Further, a majority of the farmers 
(93.0%) transported their oranges in in open trucks without 
any protection; while only a few farmers (7.0%) packed 
their oranges in sacks. A larger percentage of producers 
(54.4%), wholesalers (80.8%) and retailers (52.1%) perceived 
perishability of the produce as the main cause of postharvest 
losses, while lack of storage facilities and bad road networks 
were next to it, respectively. The distribution of the post-
harvest losses (in percentage) showed that most of the 
producers (63.16%) and wholesalers (65.38%) experienced a 
post-harvest loss of 6-10%, while 46.79% and 37.18% of the 
retailers experienced postharvest losses of less than 5% and 
6-10%, respectively. 
The ordered probit regression was used to identify the 
determinants of postharvest losses of orange fruits. The log 
likelihood (−32.7880) and chi-square of (34.03) of the ordered 
probit model for the producers were significant (P<0.01) and 
Pseudo-R2 of 0.3417 implying that all the variables jointly 
explained the variations in the level of post-harvest losses 
(Table 4). Five variables significantly explained the level of 
postharvest losses of orange among farmers at different 
levels. Those that significantly influence orange losses among 
farmers were gender, household size, primary occupation, 
time of harvest and packaging.
Data for both the wholesalers and marketers were pooled 
and analysed as marketers because none of the wholesalers 
had less than five percent post-harvest loss and none of the 
retailers had above 11% post-harvest loss. The log likelihood 
(−78.71568) and chi-square (40.52) with a cut1 of −1.6696 
and cut2 of 0.0803 were significant implying that the model 
had a good fitness of the model (Table 5). Postharvest losses 
of orange among the marketers were influenced by quantity 
of orange purchased, education level and storage facilities.
DISCUSSION
Most of the orange producers in the study area were male 
(Table1). This shows that male farmers are more into orange 
production than females in the study area, which buttressed 
the findings of Attah et al. (2018) that orange farming was 
mostly practiced (80.0%) by the men in Benue State, Nigeria. 
However, female actors were prominent in marketing of 
oranges than their male counterparts. This is consistent with 
finding of Aminu et al. (2020) that most orange marketers in 
Lagos metropolitan markets were females. Most of the actors 
in the supply chain were married with fairly large household 
sizes. This confirms the findings of Attah et al. (2018) and 
Aminu et al. (2019) that the majority of orange farmers 
(95.0%) and marketers (81.4%) were married with mean 
household size of 14 and six people, respectively. Married 
and large households are expected to have an advantage 
with regards to labour availability for their production and 
post-harvest handling techniques (Obayelu et al., 2021). 
Most of the producers and marketers were also in their 
economic active age, buttressing the findings of Ikwuba et 
al. (2019) and Aminu et al. (2019) that orange farming and 
marketing activities were mostly practised by the middle-
aged individuals in Nigeria. 
Moreover, the level of education among the producers was 
low with majority of them having primary education, while 
the retailer and wholesaler were moderately educated. This is 
consistent with the finding of Ezekiel et al. (2014) that most 
orange farmers had primary education; and that of Aminu 
et al. (2019) that 38.1% of orange marketers had secondary 
education. This is expected because formal education is not 
requisite for non-skilled activities. Babalola et al. (2010) also 
found that the majority of farmers had low educational status 
and that only literate farmers could understand and use most 
of the available post-harvest technologies. Orange farmers 
and marketers were moderately experienced in their trades 
suggesting better knowledge and fair handling of postharvest 
techniques. This is in line with the findings of Babalola et al. 
(2010) that inexperienced farming community coupled with 
10
low formal education levels might be contributory to high 
postharvest losses. Household membership of association/
group increased access to information important to 
production and marketing decisions. The result is in line with 
Martey et al. (2012) that the majority of the farmers belong to 
a farmer association and have access to market information 
at all levels of supply chain in the study area. Most of the 
farmers diversified their livelihood activity to complement 
their earnings in order to manage their risks (Baiphethi and 
Jacobs, 2009). A larger percentage of the actors had access to 
credit with a larger percentage of producers obtained loans 
from informal sources, which is an indication that the formal 
sources of financing are difficult to access by actors in orange 
supply chain.
Furthermore, harvesting in the afternoon was reported to 
be a major cause of high postharvest losses because of high 
temperatures and evaporation which causes the vegetables to 
shrink, thus affecting the marketing quality. A majority of the 
producers harvested their oranges early in the morning which 
is known to reduce postharvest losses. Kasso and Bekele 
(2018) found that a larger proportion of farmers in Ethiopia 
preferred to harvest their horticultural crops during the 
coolest time of the day like in the early morning to transport 
to the market for sale. Similar results were also reported by 
Genova et al. (2006) and Kereth et al. (2013) that harvesting 
activities should be completed during the coolest time of the 
day, which is usually in the early morning. However, this was 
contrary to the findings of Muhammad et al. (2012) that a 
complete lack of proper postharvest knowledge was evident 
among farmers, as only 10% of the respondents in their study 
were found to harvest at an appropriate time of harvesting i.e. 
morning and evening. 
In addition, a majority of the producers transported their 
oranges in open trucks from the farm to the first point of 
sale to buyers. This is consistent with the findings of Adekalu 
et al. (2019) and Aminu et al. (2019) that oranges are often 
transported on trucks/vans and are cushioned with grasses 
or paddy straw or moss or banana leaves, which easily expose 
the fruits to adverse weather conditions and losses. Ikwuba et 
al. (2019), however, found that a higher proportion of orange 
farmers in Benue State stored their oranges in bags.
Table 1: Distribution of the actors by their demographic 
characteristics
Demographic 
characteristics
Retailers Wholesalers Producers
(N = 73) (N = 26) (N = 57)
Gender
Male 14 (19.2) 10 (38.5) 39 (68.4)
Female 59(80.8) 16 (61.5) 18 (31.6)
Age of respondents
≤ 30 11 (15.1) 3 (11.5) 0 (0.0)
30-40 20 (27.4) 6 (23.1) 8 (14.2)
41-50 17 (23.3) 5 (19.2) 22 (38.5)
>50 25 (34.2) 12 (46.2) 27 (47.3)
Mean 44.10 46.32 52.01
Household size
≤ 5 35 (47.9) 12 (46.2) 11 (19.3)
6-10 38 (52.1) 14 (53.8) 45 (78.9)
>10 0 (0.0) 0 (0.0) 1 (1.8)
Educational status
No formal 
education 0 (0.0) 1 (1.8) 1 (1.8)
Primary 
education 12 (16.4) 7 (26.9) 20 (35.1)
Secondary 31 (42.5) 11 (42.3) 13 (19.3)
Tertiary 30 (41.1) 7 (26.9) 22 (28.1)
Others 0 (0.0) 0 (0.0) 3 (5.3)
Marital status
Single 3 (4.1) 0 (0.0) 2 (3.5)
Married 68 (93.1) 25 (96.1) 51 (89.5)
Separated 2 (2.7) 1 (3.8) 0 (0.0)
Widowed 0 (0.0) 0 (0.0) 4 (7.1)
Numbers in parentheses are percentages.
Economic 
variables
Retailers Wholesalers Producers
(N = 73) (N = 26) (N = 57)
Marketing/Farming experience
≤ 5 3 (4.1) 1 (3.8) 2 (3.5)
6-10 17 (23.3) 2 (7.7) 6 (10.5)
11-15 14 (19.2) 5 (19.2) 12 (21.1)
16-20 5 (6.8) 6 (23.1) 7 (12.3)
>20 34 (46.6) 12 (46.2) 30 (52.6)
Mean Value 20.2 ± 10.49 19.5 ± 6.71 23.9 ± 11.35
Membership of Trade Association
Yes 70 (95.9) 23 (88.5) 43 (75.4)
No 3 (4.1) 3 (11.5) 14 (24.8)
Primary Occupation
Farming 6 (8.2) 10 (38.5) 42 (73.7)
Trading 67 (91.8) 16 (61.5) 15 (26.3)
Access to 
credit
Yes 58 (79.5) 18 (69.2) 15 (26.3)
No 15 (20.5) 8 (30.8) 42 (73.7)
Sources of finance
Personal 
equity 48(65.8) 21(80.8) 25(43.9)
Cooperative 11(15.1) 2(7.7) 27(47.4)
Money 
lender 6(8.2) 3(11.5) 5(8.8)
Friend and 
relatives 4(5.5) 0(0.0) 0(0.0)
Table 2: Distribution of actors by economic characteristics
Numbers in parentheses are percentages.
What factors explain postharvest losses of orange fruit (Citrus sinensis) from farm to fork in the tropics?
11
The use of sacks does not protect fresh fruits from mechanical 
damage as they create high heat owing to physiological 
change by metabolic reaction, which in turn accelerates 
mechanical damage and microbial attack (Kader and Rolle, 
2004; Kereth et al. 2013). However, all the wholesalers and 
retailers transported their oranges in open trucks, which may 
not protect fresh produce from mechanical damage as they 
cause postharvest losses by crushing (Kereth et al., 2013). The 
use of sacs as major packaging materials could be due to their 
accessibility and low cost (Yeshiwas and Tadele, 2021). 
Postharvest losses were high among orange producers and 
wholesalers due to losses on the farm during harvesting and 
losses due to pest and diseases, duration of storage, poor 
transportation system without cold facility, distance of farm 
to the market, the number of days it takes to sell oranges 
and storage facilities available. The lack of good and cheap 
transportation poses a serious threat to orange supply from 
the point of production to the point of sale (Aminu et al., 
2019). In addition, inexistence of cooling chain increased 
postharvest losses in Nigeria as the trucks do not have 
cooling facilities. Thus, unavailability of appropriate storage 
and transport facilities were major factors affecting trade 
efficiency and the quality of the oranges in its supply chain 
(Dijkxhoorn et al., 2021; Musasa et al., 2013). The retailers' 
lower losses can be due to the fact that they purchase only the 
quantity of oranges they can sell within a given period. 
Gender had a negative relationship with the level of 
postharvest losses among orange farmers. This implies that 
the male farmers are more likely to experience postharvest 
loss than the female farmers. This may be attributed to the 
fact that females are more careful in handling of the fruit than 
their male counterparts (Garikai, 2014. Household size of the 
farmers also had a negative relationship with the level of post-
harvest losses among orange farmers. The reason for this may 
be that larger households increase the numbers of labour that 
will assist both in the production and post-harvest activities 
(Adepoju, 2014; Obayelu et al., 2021).
Primary occupation was negatively related to the quantity 
of orange lost by farmers, suggesting that respondents whose 
major occupation was trading tended to have low postharvest 
losses. This might be as a result of acquisition of marketing skills 
and information. The time of picking or harvesting is a very 
important factor in determining postharvest losses. Harvesting 
in the afternoon can be detrimental to fruits and vegetables due 
to high temperatures. It is therefore desirable that the fruits are 
harvested during the cooler parts of the day to reduce the risk 
of heat injury and sunburn (Bekele, 2018). Time of harvest had 
a positive relationship with the level of postharvest loss among 
orange farmers. This implies that farmers that harvested their 
oranges in the afternoon were more likely to have higher 
postharvest loss than farmers that harvested their produce in 
the morning. These results complement the findings by Kereth 
et al. (2013) that harvesting should preferably be done early in 
the morning. 
Packaging materials used in postharvest handling play an 
important role in reducing postharvest loss. Produce should 
be immobilized by proper packaging and stacking during 
transportation to avoid excessive movement or vibration 
(Kitinoja et al., 2013). Packaging of oranges in open trucks 
had a positive relationship with the likelihood of postharvest 
losses. This implies a low likelihood of postharvest losses 
when oranges were packaged in sacks and baskets when 
transporting. This was in line with the findings of Garikai 
(2014) that use of packaging materials reduced post-harvest 
losses in cabbage and tomato. The coefficient of education 
level of the actor was negative implying that marketers with 
higher level of education experienced less postharvest losses 
than those with lesser years of formal education. Marketers 
with high educational level have the ability to understand and 
appreciate postharvest technologies better than those with low 
educational attainment (Mashau et al., 2012; Abera et al., 2020). 
The coefficient of quantity of orange purchased was positively 
related to postharvest losses among orange marketers. This fact 
is buttressed by the negative coefficient of improved storage 
facilities used. This suggests that use of storage facilities, that 
extend the shell-life of the oranges, minimizes storage losses 
among the marketers.  
CONCLUSIONS
The major limitation of this study was the number of 
samples collected, which was due to funding challenges. In 
Postharvesting 
variables
Retailers Wholesalers Producers
(N = 73) (N = 26) (N = 57)
Time of Harvesting
Morning 46 (80.7)
Afternoon 1 (1.8)
Evening 10 (17.5)
Materials for packaging
None 73 (100.0) 26 (100.0) 53 (93.0)
Sacks - - 4 (7.0)
Perceived causes of losses
None 0 (0.0) 1 (3.8) 0 (0.0)
Perishability 38 (52.1) 21 (80.8) 31 (54.4)
Lack of 
adequate 
storage 
facilities
17 (23.3) 3 (11.5) 21 (36.8)
Bad roads 10 (13.7) 1 (3.8) 5 (8.8)
Mechanical 
damage 4 (5.5) 0 (0.0) 0 (0.0)
Lack of 
processing 
plant
4 (5.5) 0 (0.0) 0 (0.0)
Distribution of postharvest losses
<5 % 5 (6.67) 0 (0) 68 (93.15)
6-10 % 36 (63.16) 17 (65.38) 5 (6.85)
11-15% 16 (28.07) 9 (34.62) 0
Table 3: Distribution of actors by postharvest variables
Numbers in parentheses are percentages.
What factors explain postharvest losses of orange fruit (Citrus sinensis) from farm to fork in the tropics?
12
Table 4: Determinants of postharvest losses among producers/farmers
Variables Coefficient Marginal effect Low loss (1-5%)
Marginal effect 
Moderate loss 6-10%
Marginal effect High 
loss 11-15%
Gender -0.9917 (0.584) * 0.023(0.025) 0.276(0.182) -0.298 (0.189)*
Age -0.002 (0 .042)  -0.00004 (0.001) 0.0004 (0.010) -0.0004 (0.011)
Household size -0.241 (0.110)** 0.007 (0.007) 0.057 (0.028)** -0.064 (0.029)**
Educational level -0.016 (0.520) -0.001 (0.015) -0.004 (0.121) 0.004 (0.137)
Farming experience -0.013 (0.044) -0.0004 (0.001) -0.003 (0.010) 0.003 (0.012)
Member of cooperative 0.035 (0.449) -0.001 (0.013) 0.008 (0.108) -0.009 (0.121)
Farm size -0.087 (0.111) 0.003 (0.004) 0.020 (0.026) -0.023 (0.029)
Primary occupation 1.517 (0.573)*** -0.118 (0.101) -0.166 (0.093)* 0.284 (0.085)***
Access to credit 0.464 (0.474) -0.011 (0.015) -0.123 (0.141) 0.134 (0.150)
Time of harvesting 3.313 (0.773)*** -0.649 (0.16)*** 0.255 (0.178) 0.394 (0.083)***
Packaging -2.010 (0.980)** 0.331 (0.308) -0.112 (0.304) -0.219 (0.069)***
Number of observations 57
LR chi2(11) 34.03
Prob>chi2 0.0004
Pseudo R2 0.3417
Log likelihood -32.788009
/cut1 -0.8912(1.3147)
/cut2 2.3027(1.3236)
*, ** and *** represent 10%; 5% and 1% levels of significance, respectively.
Numbers in parentheses are standard errors.
Variables Coefficient Marginal effect Low loss (1-5%)
Marginal effect 
Moderate loss 6-10%
Marginal effect High 
loss 11-15%
Age -0.011 (0.014) 0.004 (0.0052) -0.0022 (0.003) -0.0017 (0.0023)
Quantity Purchased 0.00003 (8.61 e-06  )*** -0.0001 (0.000)*** 6.25e-06 (0.000)*** 4.82e-06 (0.000)***
Educational level -0.718 (0.342)** 0.234 (0.106)** -0.083 (0.039)** -0.151 (0.091)**
Marketing experience 0.027 (0.018) -0.009 (0.007) 0.006 (0.004) 0.004 (0.003)
Household size -0.034 (0.065) 0.013 (0.024) -0.007 (0.014) -0.005 (0.011)
Distance from farm to 
market 0.211 (0.022) -0.008 (0.008) 0.004 (0.005) 0.003 (0.004)
Storage facilities -0.782 (0.283)*** 0.288 (0.106)*** -0.163 (0.076)** -0.1253 (0.050)**
Access to credit 0.485 (0.341) -0.185 (0.132) 0.120 (0.096) 0.066 (0.041)
Duration  of storage -0.210 (0.142) 0.078 (0.052) -0.044 (0.032) -0.034 (0.024)
Number of observations 99
LR chi2(11) 40.52
Prob>chi2 0.000
Pseudo R2 0.205
Log likelihood -78.716
/Cut1 -1.669 (0.819)
/Cut2 0.080 (0.806)
Table 5: Determinants of postharvest loss at Market level
*, ** and *** represent 10%; 5% and 1% levels of significance, respectively.
Numbers in parentheses are standard errors.
What factors explain postharvest losses of orange fruit (Citrus sinensis) from farm to fork in the tropics?
13
addition, owing to a lack of local or nationally representative 
data on magnitude of losses, socio-economic characteristics 
of actors and associated factors explaining postharvest loss 
along the supply chain of orange, the study, therefore, made 
use of a sample of the actors along the value chain in one 
of the major orange producing states of Nigeria. This study 
concluded that the larger the quantity purchased, the less 
the postharvest loss incurred. The study also concluded 
that harvesting of oranges should be completed during the 
coolest time of the day, which is usually in the early morning, 
in order to minimize postharvest loss by the farmers. There 
were neither cooling transportation nor adequate storage 
facilities for oranges from the point of production to the 
point of sale to consumers. Being a male farmer increased 
the likelihood of postharvest loss among the farmers, while 
large farming household size and harvesting of orange in the 
morning would reduce it. However, education, smallholding 
marketing and use of improved storage facilities reduced the 
likelihood of postharvest loss among the marketers. Owing to 
inadequate storage facilities and non-existence of a cold chain 
in the orange supply, marketers who bought large quantity of 
oranges than they could sell within a few days were likely to 
incur more losses than those who bought less. Thus, both the 
public and the private agricultural financial sectors should 
review the existing credit system in the country and introduce 
innovative credit schemes that would promote credit access to 
smallholder actors along the orange supply chain. Moreover, 
solving the problem of postharvest loss along orange supply 
chain in a developing economy requires the implementation 
of a blend of policies that focus on gender equity, as well as 
provision of storage and logistic infrastructure. In addition, 
governmental and non-governmental organisations should 
emphasize on trainings and awareness programmes on 
harvesting and postharvest handling of oranges for all actors 
at each node of orange supply chain.
CONFLICT OF INTERESTS 
The authors declare no conflict of interest for this 
manuscript.
REFERENCES
Abera, G., Ibrahim, A. M. Forsido, S. F., & Kuyu, C. G. 1. 
(2020). Assessment on post-harvest losses of tomato 
(Lycopersicon esculentem Mill.) in selected districts of 
East Shewa zone of Ethiopia using a commodity system 
analysis methodology,” Heliyon, 6, 4, e03749.
Adegbija, D. (2018). 2. Problems and prospect of commercial 
orange farming in Nigeria. https://infoguidenigeria.
com/problems-prospect-commercial-orange-farming-
nigeria/
Adekalu, O. A., Agboola, D. A., Atanda, S. A., & Akande, 3. 
S. (2019). A survey of post-harvest techniques employed 
in handling and storage of sweet oranges (Citrus sinensis 
Osberk) in Lagos, Ogun and Osun States, Nigeria. Asian 
Journal of Agriculture and Food Sciences, 7, 1, 20-30. 
Agada, M. O., & Uga, M. B. (2017). Effects of farmers’ 4. 
attitudes towards post-harvest losses of citrus in Ushongo, 
Benue State, Nigeria. International Journal of Agriculture 
and Environmental Research, 3, 6, 4168-4186. 
Agona, A., & Muyinza, H. (2008). 5. An overview of 
horticulture in Uganda. Postharvest Programme NARO 
Uganda. 
AGRA (2020). 6. Reducing post-harvest loses key to ensuring 
food security. AGRA Knowledge Series. https://agra.org/
wp-content/uploads/2020/09/Reducing-post-harvest-
loses-key-to-ensuring-food-security-Tanzania.pdf 
Aulakh, J., Regmi, A., Fulton, J. R., & Alexander, C. 7. 
(2013). Estimating post-harvest food losses: Developing 
a consistent global estimation framework; Proceedings 
of the Agricultural & Applied Economics Association’s 
2013 AAEA & CAES Joint Annual Meeting; Washington, 
DC, USA. 4–6 August 2013.
Aminu, F. O., Balogun, E. O. S., & Ojo, O. O. (2019). 8. 
Analysis of post-harvest losses in orange in Lagos 
metropolitan markets, Nigeria. Nigerian Journal of 
Horticultural Science, 25, 143-151.
Attah, A. J., Mbah, E. N., & Okeke, M. N. (2018). 9. 
Assessment of factors limiting production of citrus 
among smallholder farmers in Benue State, Nigeria. 
Journal of Agriculture and Crops, 4, 12, 170-175. 
Arah, I. K. (2015). An overview of post-harvest 10. 
challenges facing tomato production in Africa. Paper 
presented at the 37th Annual Conference of the African 
Studies Association of Australasia and the Pacific 
(AFSAAP) held on 25-26 November, 2014 at Dunedin, 
New Zealand. 
Ayandiji, A., Kehinde, A. L., Adeniyi, O. R., & Omotosho, 11. 
O. (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, 3, 77-84.
Babalola, D. A., Makinde, Y. O., Omonona, B. T., & 12. 
Oyekanmi, M. O. (2010). Determinants of post- harvest 
losses in tomato production: A case study of Imeko-Afon 
local government area of Ogun state. Journal of Life and 
Physical Sciences, 3, 2, 14-18.
Baiphethi, M. N., & Jacobs, P. T. (2009). The contribution 13. 
of subsistence farming to food security in South Africa. 
Agrekon, 48, 4, 459-482. 
Bekele, D. (2018). Review on factors affecting postharvest 14. 
quality of fruits. Journal of Plant Science and Research, 
5, 2, 180. https://www.opensciencepublications.com/
fulltextarticles/JPSR-2349-2805-5-180.html
Cervoni, B. (2021). Orange nutrition facts and health 15. 
benefits. https://www.verywellfit.com/oranges-nutriti-
on-facts-calories-and-health-benefits-4119322
Dijkxhoorn, Y., Talabi, J., & Likoko, E. (2021). 16. Scoping 
study on fruits and vegetables: Results from Nigeria. 
Wageningen, Wageningen Economic Research, Report 
2021-110. 68 pp
Doki, N. O., Eya, C. I., Tuughgba, M. F., Akahi, O. G., & 17. 
Ameh, A. (2019). Determinants of post-harvest losses of 
orange in selected local government areas of Benue State. 
International Journal of New Economics and Social Scien-
ces, 2, 10, 295-308. DOI 10.5604/01.3001.0013.8106
Ezekiel, M. Z., Mzimbiri, R., & Mtunguja, M. K. (2014). 18. 
What factors explain postharvest losses of orange fruit (Citrus sinensis) from farm to fork in the tropics?
14
Assessment of orange losses and existence of post-harvest 
methods (PHM) along the coast belt of Tanzania. Journal 
of Biology, Agriculture and Healthcare, 4, 3, 14-21.
Garikai, M. (2014). Assessment of vegetable postharvest 19. 
losses among smallholder farmers in Umbumbulu area of 
Kwazulu-Natal Province, South Africa. Masters Disserta-
tion, University of KwaZulu-Natal. https://researchspace.
ukzn.ac.za/bitstream/handle/10413/11918/Garikai_
Maremera_2014.pdf?sequence=1&is Allowed=y.
Greene, W. H. (2003) 20. Econometric Analysis. 5th Ed. New 
Jersey: Prentice Hall. 
Ikwuba, A. A., Wegh, F. S., Agwaza, T. A., & Angera, R. 21. 
I. (2019). A study on sweet orange production and its 
post-harvest losses among farmers in northeastern zone 
of Benue State, Nigeria. Journal of Biological Sciences and 
Bioconservation, 11, 3, 64-105.
Inienger, C. C., & Udoh, E. (2020). An assessment of 22. 
citrus farming in Nigeria. International Journal of Recent 
Research in Social Sciences and Humanities, 7, 1, 10-15.
Kader, A. A., & Rolle, R. S. (2004). 23. In the role of post-
harvest management in assuring the quality and safety of 
horticultural produce. FAO, Rome.
Kasso, M., & Bekele, A. (2018). Post-harvest loss and 24. 
quality deterioration of horticultural crops in Dire 
Dawa Region, Ethiopia. Journal of the Saudi Society of 
Agricultural Sciences, 17, 1, 88-96.
Kereth, G. A., Lyimo, M., Mbwana, H. A., Mongi, R. J., 25. 
& Ruhembe, C. C. (2013). Assessment of post-harvest 
handling practices: knowledge and losses of fruits in 
Bagamoyo district of Tanzania. Food Science and Quality 
Management, 11, 8-15.
Kitinoja, L. (2013). Innovative small-scale postharvest 26. 
technologies for reducing losses in horticultural crops. 
Ethiopian Journal of Applied Science and Technology, 
Special Issue 1, 9- 5
Kitinoja, L., & Kader, A. A. (2002). 27. Small-scale 
postharvest handling practices: A manual for horticultural 
crops (fourth ed.), University of California, Postharvest 
Technology Research and Information Center, Davis.
Knoema (2022). Nigeria - Citrus fruit production 28. 
quantity. https://knoema.com/atlas/Nigeria/topics/
Agriculture/Crops-Production-Quantity-tonnes/
Citrus-fruit-production 
Kuku-Shittu, O., Onabanjo, O., Fadare, O., & Oyeyemi, 29. 
M. (2016). Child malnutrition in Nigeria: Evidence from 
Kwara State. Nigeria Strategy Support Program Working 
Paper 33. International Food Policy Research Institute, 
Abuja, Nigeria
Leghari, H. B. (2001). Post-harvest technology in 30. 
Pakistan, The Dawn, December 24, 2001. http://www.
dawn.com/news/11785/post-harvest-technology-in-
pakistan.
Mahmud, S. (2020). 31. Impact of post-harvest loss 
(PHL) on food security: Perspectives from Bangladesh. 
https://postharvestinstitute.illinois.edu/wp-content/
uploads/2020/02/2-Sarwar-Mahmud.pdf
Martey, E., Al-Hassan, R. M., & Kuwornu, J. K. M. 32. 
(2012). Commercialization of smallholder agriculture 
in Ghana: A Tobit regression analysis. African Journal of 
Agricultural Research, 7, 14, 2131-2141.
Musasa, S. T., Mvumi, B. M., Manditsera, F. A., 33. 
Chinhanga, J., Musiyandaka, S., & Chigwedere, C. 
(2013). Postharvest orange losses and small-scale 
farmers’ perceptions on the loss causes in the fruit value 
chain: a case study of Rusitu Valley, Zimbabwe. Food 
Science and Quality Management, 18, 1-8.
Mashau, M. E., Moyane, J. N., & Jideani, I. A. (2012). 34. 
Assessment of postharvest losses of fruits at Tshakhuma 
fruit market in Limpopo Province, South Africa. African 
Journal of Agricultural Research, 7, 4145-4150. 
Mbah, E. N., Okeke, M. N., & Attah, A. J. (2018). Strategies 35. 
for enhancing production of citrus among small-scale 
farmers in Benue State, Nigeria. International Journal of 
Research Studies in Agricultural Sciences, 4, 4, 25-28.
Muhammad, R. H., Hionu, G. C., & Olayemi, F. F. 36. 
(2012). Assessment of the post-harvest knowledge of 
fruits and vegetable farmers in Garun Mallam L.G.A of 
Kano, Nigeria”, International Journal of Development and 
Sustainability, 1, 2, 510-515.
Obayelu, O. A., Adegboyega, O. M., Sowunmi, F. A., & 37. 
Idiaye, C. O (2021). Factors explaining postharvest loss 
of hot pepper under tropical conditions. International 
Journal of Vegetable Science, 27,6, 526-535. https://doi.or
g./10.1080/19315260.2021.1879342
Olife, I. C., Ibeagha, O. A., & Onwualu, A. P. (2015). 38. 
Citrus fruits value chain development in Nigeria.  Journal 
of Biology, Agriculture and Healthcare, 5, 4, 36-47.
Parfitt, J., Barthel, M., & Macnaughton, S. (2010). Review 39. 
of food waste within food supply chains: Quantification 
and potential for change to 2050. Philosophical 
Transactions of the Royal Society B: Biological Sciences, 
365, 1554, 3065-3081.  
Sablani S. S., Opara, L. U., & Al-Balushi, K. (2006). 40. 
Influence of bruising and storage temperature on Vitamin 
C content of Tomato. Journal of Food, Agriculture, and 
Environment, 4, 54-56.
Saran, S., Roy, S. K., & Kitinoja, L. (2012). Identification 41. 
of appropriate postharvest technologies for improving 
market access and incomes for small horticultural 
farmers in Sub-Saharan Africa and South Asia. Part 2: 
Field trial results and identification of research needs for 
selected crops. Proc. XXVIIIth IHC – IS on Postharvest 
Technology in the Global Market. Eds.: M.I. Cantwell 
and D.P.F. Almeida. Acta Hort., 934, 41-52. 
SWAC/OECD (2020). Food and nutrition crisis 2020: 42. 
Analyses & responses. Maps & Facts, No. 3, November 
2020
UNICEF (2020). Nutrition. https://www.unicef.org/43. 
nigeria/nutrition
USDA (2022). Citrus: World markets and trade. United 44. 
States Department of Agriculture Foreign Agricultural 
Service. https://apps.fas.usda.gov/psdonline/circulars/
citrus.pdf
Yeshiwas, Y., & Tadele, E. (2021). An investigation into 45. 
major causes for postharvest losses of horticultural crops 
and their handling practice in Debre Markos, North-
Western Ethiopia. Advances in Agriculture, 1985303. 
https://doi.org/10.1155/2021/1985303.
What factors explain postharvest losses of orange fruit (Citrus sinensis) from farm to fork in the tropics?
15
What factors explain postharvest losses of orange fruit (Citrus sinensis) from farm to fork in the tropics?
Dejavniki, ki pojasnjujejo izgube pridelka pomaranč (Citrus 
sinensis) po obiranju v tropskih območjih v verigi od proizvajalca do 
potrošnika 
IZVLEČEK
Povpraševanje po citrusih se je v Nigeriji v zadnjih letih povečevalo, vendar pa je za države v razvoju značilna precejšnja izguba 
tropskega sadja po obiranju zaradi slabih pogojev pri obiranju in skladiščenju sadja tekom dobavne verige. Cilj raziskave je bil 
oceniti stopnje in korelacije izgub svežih pomaranč po obiranju v celotni dobavni verigi. Podatki, zbrani pri proizvajalcih in 
prodajalcih pomaranč, so bili analizirani z uporabo deskriptivne statistike in ordinalne regresijske analize. Rezultati so pokazali, 
da so bili pridelovalci pomaranč v glavnem moški (68,4 %), pri trženju/prodaji pomaranč pa so prevladovale ženske. Pri večini 
proizvajalcev (63,2 %) in veletrgovcev (65,4 %) so izgube pridelka pomaranč po obiranju znašale 6-10 %, pri 46,8 % trgovcev 
na drobno pa pod 5 %. Pri proizvajalcih je bila verjetnost izgub pomaranč po obiranju večja, če so pomaranče obirali moški v 
popoldanskem času, medtem ko je imela velikost gospodinjstva oziroma število družinskih članov obraten učinek (zmanjšane 
izgube). Pri prodajalcih se je izkazalo, da izobraževanje, trženje na malih posestih in uporaba skladišč zmanjšujejo verjetnost 
izgub pridelka pomaranč. Usposabljanje za pridelovalce in prodajalce pomaranč o metodah obiranja in skladiščenja sadja bo v 
prihodnje pripomoglo k zmanjšanju izgub po spravilu v celotni verigi proizvajalcev in trgovcev na drobno.
Ključne besede: izgube po obiranju, proizvajalci, prodajalci, regresijska analiza