# Determinants of Yam Postharvest Management in the Zabzugu District of Northern Ghana

Authors: Isaac Gershon Kodwo Ansah; Bright K. D. Tetteh
Journal: Advances in Agriculture
 (2016)

---

## Abstract

Postharvest loss reduction has received attention in many policy documents across nations to ensure global food security, particularly in developing countries. Many researchers have examined various options for reducing postharvest losses. We contribute our quota to this scientific discourse by using a different approach. We argue that the human element of managing postharvest loss is central and therefore poses the question of what are the characteristics of the farmer who manages postharvest losses better. We examine this question by using a cross section of yam farmers in the Zabzugu district in Northern Ghana and generate a proportional variable called postharvest management, which measures how effective a farmer works to reduce storage losses. We then use a fractional logistic regression model to examine the determinants of postharvest management. A significant result is that subsistence farmers manage postharvest losses better than commercial farmers. Characteristically, the farmer who effectively manages postharvest losses is a young, subsistence farmer, living in or close to a district capital with fewer household members, has attained formal education, and produces more yam. Efforts to reduce postharvest losses require the provision of access roads to remote towns or providing effective storage techniques and training on postharvest management practices.


---

## Body

## 1. Introduction

Recent trends in global food markets indicate that unless serious attention is given to postharvest losses, the possibility of feeding over 9 billion people in 2020 and beyond is shrouded in uncertainty. This attention is even more crucial in developing countries where postharvest losses are high. In the year 2011, [1] estimated that within 20%–40% of fresh foods are lost during and after harvesting of major staple crops. This report comes many years after the World Food Conference convened in Rome in 1974, which drew attention to the concept of reducing postharvest food loss as a strategic and significant means to increase food availability. According to Bourne [2] and Hodges et al. [3], one of the most important pathways to increase food availability is to reduce food loss and waste. Goldsmith et al. [4] further stipulate that preventing food loss and increasing production are the two realistic alternatives by which the world can meet its ever rising food demand, but increased food production actually comes from preventing losses. In the opinion of [5], postharvest losses impose both economic and environmental impacts.In Ghana, root and tuber crops such as yam and cassava are important food security crops. Next to cereals, roots and tubers serve as important staples to a significant portion of the Ghanaian populace. However, one challenge that limits the success of these crops in improving food security is postharvest losses, and genuine concerns have been raised about these issues. A survey on postharvest losses conducted by [2] reproduced a table from FAO on postharvest losses across the major regions of the world and found that postharvest losses in Sub-Saharan Africa are estimated to be 18%. This figure is substantial, considering the fact that, for every 100 tons of root and tuber crops produced, 18 tons is lost through postharvest handling. Ghana’s Ministry of Food and Agriculture (MoFA) in 2008 conducted a baseline survey on harvest and postharvest (HPH) losses among major crops across some regions, and the general finding was that postharvest losses were prohibitively high. Lack of appropriate processing facilities, postharvest storage techniques, and management practices are responsible for the large postharvest losses. Recent news items by prominent personalities in Ghana, for example, the Minister for Environment, Technology and Innovation, Dr. Oteng-Adjei, in 2013 expressed great worry that close to 50% of food produced for consumption fails to reach the intended user due to postharvest losses. He recounted that about $ 340,000.00 is lost annually in postharvest losses due to poor postharvest management. This reechoes the calls by the numerous food security activists who are calling for a concerted effort to reduce postharvest losses.Broadly defined, postharvest loss is a collective term for food losses all the way along the food production chain, from harvest and handling through storage and processing to packing and transportation [3, 6]. The causes of postharvest losses are varied and complex, depending on several factors including the weather and regional and crop differences, and in the developing world the most significant variables include lack of proper storage [7], inadequate transportation infrastructure, and limited or no information on where or how food is lost. In the context of this study, yam postharvest loss is defined as the quantity of yam tubers that are lost from the time of harvest until the produce gets to the final user (which could be the consumer or industrial user). It is important to note that postharvest loss so defined is a function of the postharvest management practices adopted by the individual farmer.In Ghana and also across most of West Africa, yam is basically used as food, but surplus products are processed into starch for industrial and other uses. Apart from domestic use for food and industrial starch production, many yam farmers derive income through exports of the produce. Yam is also an important food security crop in this region. Statistics show that Ghana is currently the world’s largest exporter of yams and exports about 94% of its yam to the international market. These uses make yam an important economic crop for many smallholder farmers in Northern Ghana, since the bulk of domestic and export yam is produced here. Being an annual crop, yam is harvested only at certain months of the year (roughly in August and September). Even though yam production tends to be seasonal, its consumption is relatively constant within the year. Farmers therefore would have to store yam to stabilize supply throughout the year. Yet, after maturity, losses through physiological processes, rots, and pests attack are quite considerable and constitute a major threat to the economic viability of yam production and the welfare of the farmers involved in it [3]. The Alliance for a Green Revolution in Africa (AGRA) [8] conducted a survey to establish the status of postharvest losses in eleven African countries, where they found that Ghana loses about 60% of its yam ware during storage. This is attributed to a number of factors including poor storage facilities, limited processing facilities, and poor postharvest management practices [9]. A study conducted by [10] established that yam farmers across the country are often very vulnerable to high postharvest losses. This is particularly so in the Zabzugu District located in the Northern Region of Ghana, where yam production is the main economic activity. Farmers in the district usually produce large quantities of yam during the main growing seasons. The major challenges faced by the farmers, however, as stipulated in the reports of [11] are high postharvest losses. The national statistics on yam postharvest loss in 2010 were 21.96% of total harvest and 17.08% in 2012 [12], but district estimates reveal that the postharvest losses recorded in the Zabzugu District in the same period far exceeded the national average. Invariably, revenues lost from postharvest losses are often nonignorable and their effects on the welfare of the farmers are matters of societal relevance and national concern. Although some marked increases in yam production have been witnessed in the Northern Region and particularly in the Zabzugu District as a result of adoption of improved varieties and other production techniques, the payoff after harvest is still small due to high postharvest losses. Inappropriate storage facilities, poor harvesting techniques, and management systems are the reasons for these losses. Therefore, improving the postharvest system is expected to stimulate expanded production. But to be able to improve the postharvest system first of all requires knowledge on what factors affect postharvest management practices.The economic forces of demand and supply also are at play in the Ghanaian yam market. In the main production season prices are often low due to excess supply over demand. For this reason, farmers are more inclined to store their produce in anticipation of better and more rewarding prices during the lean seasons. Now after harvesting the problem that farmers usually face is the availability and/or affordability of appropriate postharvest management strategies that could prolong the shelf life of the produce. In particular, the current state of high postharvest losses that yam farmers face can partly be attributed to the inability of farmers to apply proper postharvest management practices after harvesting. Indirectly, the desire to produce beyond subsistence needs is then reduced, and for that matter most farmers are not well integrated into the market system. Those who get integrated may not be able to make good returns on their investment for the production season.Many empirical studies have attempted to investigate the issues of postharvest losses. Usually, researchers have been concerned with quantifying postharvest losses (see, e.g., [13–15]) and others have examined the physiological causes of postharvest losses (e.g., [16–18]). One critical component of these issues that have escaped the attention of researchers is the human element of postharvest management. Very few empirical studies have considered postharvest management by actors in the agricultural value chain (e.g., see [4]) and none for yam in Ghana. Meanwhile, the role that these actors play, more importantly farmers, in managing postharvest losses and how their actions or inactions do contribute to reducing postharvest losses are nontrivial [4] and therefore need not be overlooked. This leaves a research and knowledge gap, which is the subject of this study. The objective of this study therefore is to examine the factors that affect postharvest management of yam farmers. This is addressed by answering the question: “to what extent do farmers practice good postharvest management and what are the characteristics of these farmers?” When the right management practices are used, postharvest losses can be significantly reduced. Babu et al. [6] outline two important ways by which poor postharvest management could influence food security: (1) output reduces through physical losses and (2) income reduces when products are sold immediately after harvest. We therefore postulate that the methods and practices adopted by farmers in managing postharvest losses have implications on the shelf life of the produce and consequently on the payoff realized from the farm enterprise. If farmers are able to manage postharvest losses effectively, the quantity of yams exported to the world market can improve. With improved exports, foreign exchange earnings would also increase and general economic development is affected positively. It is therefore necessary to investigate the role played by farmers in managing postharvest losses and the factors that contribute to effective postharvest management.


## 2. Materials and Methods

### 2.1. Study Area and Data

In this study, we used a cross section of yam farmers from Zabzugu District in the Northern Region of Ghana. With a total land size of 1,332 km2, the district is dominated by farmers who engage in one season farming due to the monomodal rainfall pattern in the region. The district capital (Zabzugu) lies on latitude 9°17′0′′N and longitude 0°22′0′′E and is located in the Guinea Savannah zone characterized by thinly dispersed vegetation with a long dry period of Harmattan winds. The district’s vegetation has economic trees such as shea-nut, dawadawa, teak, and mango. The 2010 Population and Housing Census put the population of the district at 61,927 people, consisting of 30,543 males (49.3%) and 31,384 females (50.7%). The annual average, minimum, and maximum temperatures stand at 27.6°C, 21.6°C, and 33.0°C, respectively. The annual precipitation is recorded to be 1,235 mm of rain.The data was collected from primary sources through field survey, mainly from yam farmers in the study area, and included information on demographic variables, socioeconomic characteristics, postharvest losses in yam, and postharvest management practices. We used a two-stage sampling technique; in the first stage, purposive sampling was used to select six (6) dominant yam production communities from the district. In the second stage, simple random sampling was used to select two hundred and one (201) respondents from the 6 communities for interview. Using standard questionnaires as a survey instrument, personal interviews were used to obtain information on the variables of interest within the month of January 2015.


### 2.2. Data Analysis

We used descriptive analytical techniques to quantify the extent of postharvest losses and methods used to manage postharvest losses. For the inferential analysis, we used a fractional regression model (FRM) due to the nature of the dependent variable.


#### 2.2.1. Fractional Regression Model

To assess factors that influence postharvest management, the FRM was used. This model was used because of the proportional nature of the dependent variable (PMC). The model posits that there is an underlying rational decision process and that the process has a functional form.The main research interest is on the determinants of postharvest management. What are the characteristics of farmers who adopt good postharvest management practices and why would they do so? Ideally, one may not deviate significantly by assuming that farmers aim to maximize profit and therefore model their decisions to adopt effective postharvest management practices under the framework of profit maximization. But profit maximization is a restrictive concept [19] in the context of smallholder farmers. Many of these farmers have multiple objectives other than simply maximizing profit, and these are very critical in adoption decisions [20]. In this sense, it is much economically appropriate to adopt the broader concept of utility maximization.Farmers make daily choices about maximizing their utility or payoff from their enterprises and this depends on the choices they make in carrying out their productive ventures. Consider a farmer who is faced with two choice alternatives: the first choice is that he/she can adopt effective management techniques, and the second choice is that he/she can choose not to adopt the techniques. If farmeri chooses to adopt these, he does so in order to preserve the produce and also reduce environmental pollution through reduced postharvest losses. The set of management practices adopted will determine the quantity and quality of the produce in a future time period and hence the profit to be obtained from the sale of such produce. One important assumption to make our reasoning hold is that farmers who adopt effective management practices will generate better quality and quantity of produce in the future and will earn higher payoffs than those who use less effective ones or those who sell immediately after harvest [21]. Therefore, adoption of better postharvest management practices increases the utility obtained through reduction of storage losses.In this paper we use as a measure of postharvest management ability the ratio of stored products that remain preserved to the total quantity of products stored after harvest. This is called postharvest management coefficient (PMC), derived from the expression(1)
P
M
C
=
Quantity of yam stored after harvest
-
Quantity lost through storage
Quantity stored after harvest
.This coefficient is a proportion with values closed between 0 and 1 (i.e., 0
≤
P
M
C
≤
1). PMC is the main dependent variable in this study, and it measures the extent or degree of effectiveness of a farmer in managing postharvest losses (in other words, storage quality). The larger the coefficient, the greater the farmer’s ability to manage these losses after harvest. A farmer with PMC = 0 means that all the stored tubers of yam went to waste and indicates that the farmer is very poor in managing postharvest losses. PMC = 1 means that farmer was able to maintain the entire stored yam tubers in desirable form both qualitatively and quantitatively, with none wasting away; such a farmer is characterized as being excellent in managing yam postharvest losses.


#### 2.2.2. The Econometric Modeling of Postharvest Management

Our overarching interest is in the determinants of postharvest management (PMC), so we use an econometric model to achieve this objective. We consider PMC to be a function of several variables(
X
), which may include farmer and farm-specific characteristics, location characteristics, and other socioeconomic characteristics. This is expressed mathematically by model (2) as follows:(2)
E
P
M
C
∣
X
=
X
b
+
e
,where PMC is the dependent variable as defined above, X is matrix of independent variables, b is a vector of coefficients to be estimated, and e represents an error term that accounts for unmeasured variables. We use FRM to estimate the b vector of model (2) because the dependent variable is a proportion which is bounded between zero and one. This does not permit us to use the ordinary least squares (OLS) technique to calculate the parameters of the model [22, 23] consistently and unbiasedly. Other researchers have used normal censored regression techniques such as Tobit regression. In the opinion of Maddala [24] and Baum [25] such approaches are also not appropriate because the observed data are not censored, and values outside the unit interval (
0
,
1
) are not possible in the case of proportional data. The FRM accounts for the proportional nature of the dependent variable in cross-sectional settings. Many applications of the model are found in economics and finance literature to address the specification error due to the fractional response, defined on the closed (
0,1
) interval [26]. The FRM, first proposed by [27], is an alternative to the binary logit, OLS, and beta regression [23] models which cannot produce plausible and statistically sound parameters with fractional values including 0 and 1. In the FRM model, a functional form for the dependent variable is chosen such that it imposes constraints on the response variable to ensure that predicted values would always lie within the closed interval (0, 1). This leads to model (3):(3)
E
P
M
C
∣
X
=
g
X
b
.
g is a nonlinear distribution function which transforms to predicted value of the dependent variable to lie between 0 and 1. A distribution suggested by [27, 28] is a Bernoulli distribution and the parameters in model (3) are estimated using quasi-likelihood estimators such as generalized linear models (GLM). In Stata [25] demonstrates the implementation of the FRM using GLM, and we adopted this method for our analysis. The procedure requires specification of both a link function and a distribution function. The parameters in the model are obtained by maximizing the Bernoulli quasi log likelihood function for the FRM that takes the form of (4)
ln
⁡
L
b
=
∑
j
=
1
N
w
j
P
M
C
j
ln
⁡
g
X
j
′
b
+
w
j
1
-
P
M
C
j
ln
⁡
1
-
g
X
j
′
b
,where P
M
C
j is the dependent variable, N denotes sample size (spanning from 1 to 201), X
j are the independent variables for farmer j, and w
j is an optional weight. We assume that the link function g
· follows a logit distribution with the function shown in model (5): (5)
g
X
j
′
b
=
e
X
j
′
b
1
+
e
X
j
′
b
.This leads us to the empirical specification of the FRM as presented in(6)
E
P
M
C
∣
X
=
g
μ
i
=
b
0
+
∑
k
=
1
9
b
k
X
i
,
k
+
e
i
.The variables in the model are defined under Section 2.2.3 and their a priori expectations are given based on economic logic and intuition.


#### 2.2.3. Definition and A Priori Expectation of Independent Variables

Market Participation
(
X
1
). Market participation defines the extent of market integration (commercialization) of the farmer. A farmer is said to be integrated into the market if part or all of the produce is sold or traded for cash income. Purely commercialized farmers sell all their produce harvested while purely subsistence farmers sell none of harvested produce. It is expected that more integrated or commercialized farmers have the greater likelihood of adopting superior postharvest management practices,ceteris paribus. Therefore, a positive effect is expected on the dependent variable. A coefficient of market participation (MPC), defined as the proportion of produce sold out of the total harvest, was generated and is given by the following expression:(7)
M
P
C
=
Quantity of produce sold
Total quantity harvested
. Based on the MPC, farmers were categorized into three as follows:(i)
Farmers with MPC below 0.25 (i.e.,M
P
C
<
0.25) are considered as subsistence farmers. These farmers are more interested in maximizing the subsistence objective rather than profit maximization.
(ii)
Farmers with MPC larger than or equal to 0.25 and less than or equal to 0.5(
0.25
≤
M
P
C
≤
0.5
) are classified as transition farmers. These farmers, though bothering about household food production, are also interested in earning some cash income from the yam enterprise.
(iii)
Farmers with MPC larger than 0.5(
M
P
C
≥
0.5
) are classified as commercial farmers whose primary objective is to generate cash income from the yam enterprise. We expect that commercial farmers are more profit oriented and are therefore more inclined to adopt measures that could help preserve produce for longer periods in anticipation of better prices in the future.Years of Education
(
X
2
). Studies done by Koundouri et al. [29] pointed to the fact that farmers’ human capital plays a significant role in the decision to adopt improved production and postharvest management practices. Education is considered to improve the quality of human capital. Farmers with more years of formal education are expected to manage postharvest challenges better than less educated ones. By logic, getting some formal education should enrich a farmer’s knowledge level and ability to manage losses not only at the production stage but importantly after harvest. The a priori expectation is a positive effect of education on postharvest management.Household Size
(
X
3
). Household size is measured by the number of people who feed from the same pot. All other things being equal, one could argue that larger households have more readily available and cheaper sources of labour to manage postharvest losses effectively compared to smaller sized households. The expectation is that household size will have a positive effect on the dependent variable.Farm Size
(
X
4
). This variable measures the total arable crop land a farmer cultivates. Farmers with larger farms are expected to allocate and commit more acreage to yam production and therefore have more output. The greater the output a farmer produces, the greater the probability of experiencing greater postharvest losses. The sign of the coefficient is therefore expected to be negative.Age of Farmer
(
X
5
). Age (measured in years) is a continuous variable. As the farmer advances in age, his output also improves because of experience he has gathered over the years of working on the field. We know that productivity increases until it gets to a stage in life where it begins to decline (hence the need for retirement). At that stage any additional input will result in limited or no change in output. We expect this to apply in the case of a farmer’s off-farm activities, including postharvest management. At younger ages, farmers have more strength and zeal to undertake more effective management strategies that reduce postharvest losses. As the farmer advances in age, the ability to effectively manage postharvest losses reduces. Therefore we expect a nonline, U-shape relationship between age and postharvest management. But, since experience increases as one advances in age, we expect a positive coefficient.Type of Labour
(
X
6
). In the Ghanaian agricultural system, two types of labour are commonly used for farm operations and postharvest management practices. These are family labour and hired labour. Although the two are not mutually exclusive, we expect that farmers who use more hired labour should be able to deal better with postharvest losses than those who use mainly family labour. Our reasoning is premised on the fact that hired labour is employed with supervision and hence is expected to follow standard measures that minimize losses on the farm. We dichotomize this variable such that farmers who use mainly family labour are coded 0 and farmers using hired labour are coded 1. Based on the foregoing argument, we anticipate a positive relationship of labour type with postharvest management.Yam Storage Duration
(
X
7
). This variable (measured in days) is the maximum length of time yam tubers are stored after harvest. The length of storage should have both direct and indirect influence on postharvest losses [30], hence on management practices. The longer the tubers are stored, the better the management techniques that must be employed to prolong the shelf life of the produce are. The expected sign for the storage duration coefficient is positive.Distance of Community to the District Capital
(
X
8
). This variable is measured in miles and represents the distance from the communities where farmers and their farms are located to the district capital (i.e., Zabzugu). In many developing country settings, a large proportion of the food produced in the countryside is sent to the district capitals and other relatively more urban cities for sale to urban consumers. Jacoby [31], in his study of access to markets and the benefits of rural roads, concluded that providing good and accessible roads to market centres is great incentive for increased production, because farmers tend to benefit from it. In the study area, many of the roads from production centres to the district capital are in poor conditions, and many communities farther away from the district capital have very poor road conditions, making only heavy duty vehicles (e.g., tractors) ply such roads. Communities farthest from the district market are expected to experience high probability of postharvest losses. However, farmers in remote communities may be forced by poor infrastructural conditions to adopt innovative management practices that prolong the shelf life of farm produce.Production Volume
(
X
9
). This variable is a measure of the extent of yam production. It is computed as the total number of tubers harvested from the yam field in the previous season. With large scale production, farmers are expected to make higher investments and generate larger incomes. Such farmers should be able to adopt effective postharvest management techniques. If this holds, then it is expected that the effect of production volume on PMC should be positive. On the other hand, farmers with large scale production may have challenges managing the resulting output after harvest, especially in terms of available space for effective storage and treatment; hence more yam tubers could be lost due to inadequate storage facilities or other management limitations. In this regard, the direction of the production volume coefficient can be positive or negative.


## 2.1. Study Area and Data

In this study, we used a cross section of yam farmers from Zabzugu District in the Northern Region of Ghana. With a total land size of 1,332 km2, the district is dominated by farmers who engage in one season farming due to the monomodal rainfall pattern in the region. The district capital (Zabzugu) lies on latitude 9°17′0′′N and longitude 0°22′0′′E and is located in the Guinea Savannah zone characterized by thinly dispersed vegetation with a long dry period of Harmattan winds. The district’s vegetation has economic trees such as shea-nut, dawadawa, teak, and mango. The 2010 Population and Housing Census put the population of the district at 61,927 people, consisting of 30,543 males (49.3%) and 31,384 females (50.7%). The annual average, minimum, and maximum temperatures stand at 27.6°C, 21.6°C, and 33.0°C, respectively. The annual precipitation is recorded to be 1,235 mm of rain.The data was collected from primary sources through field survey, mainly from yam farmers in the study area, and included information on demographic variables, socioeconomic characteristics, postharvest losses in yam, and postharvest management practices. We used a two-stage sampling technique; in the first stage, purposive sampling was used to select six (6) dominant yam production communities from the district. In the second stage, simple random sampling was used to select two hundred and one (201) respondents from the 6 communities for interview. Using standard questionnaires as a survey instrument, personal interviews were used to obtain information on the variables of interest within the month of January 2015.


## 2.2. Data Analysis

We used descriptive analytical techniques to quantify the extent of postharvest losses and methods used to manage postharvest losses. For the inferential analysis, we used a fractional regression model (FRM) due to the nature of the dependent variable.


### 2.2.1. Fractional Regression Model

To assess factors that influence postharvest management, the FRM was used. This model was used because of the proportional nature of the dependent variable (PMC). The model posits that there is an underlying rational decision process and that the process has a functional form.The main research interest is on the determinants of postharvest management. What are the characteristics of farmers who adopt good postharvest management practices and why would they do so? Ideally, one may not deviate significantly by assuming that farmers aim to maximize profit and therefore model their decisions to adopt effective postharvest management practices under the framework of profit maximization. But profit maximization is a restrictive concept [19] in the context of smallholder farmers. Many of these farmers have multiple objectives other than simply maximizing profit, and these are very critical in adoption decisions [20]. In this sense, it is much economically appropriate to adopt the broader concept of utility maximization.Farmers make daily choices about maximizing their utility or payoff from their enterprises and this depends on the choices they make in carrying out their productive ventures. Consider a farmer who is faced with two choice alternatives: the first choice is that he/she can adopt effective management techniques, and the second choice is that he/she can choose not to adopt the techniques. If farmeri chooses to adopt these, he does so in order to preserve the produce and also reduce environmental pollution through reduced postharvest losses. The set of management practices adopted will determine the quantity and quality of the produce in a future time period and hence the profit to be obtained from the sale of such produce. One important assumption to make our reasoning hold is that farmers who adopt effective management practices will generate better quality and quantity of produce in the future and will earn higher payoffs than those who use less effective ones or those who sell immediately after harvest [21]. Therefore, adoption of better postharvest management practices increases the utility obtained through reduction of storage losses.In this paper we use as a measure of postharvest management ability the ratio of stored products that remain preserved to the total quantity of products stored after harvest. This is called postharvest management coefficient (PMC), derived from the expression(1)
P
M
C
=
Quantity of yam stored after harvest
-
Quantity lost through storage
Quantity stored after harvest
.This coefficient is a proportion with values closed between 0 and 1 (i.e., 0
≤
P
M
C
≤
1). PMC is the main dependent variable in this study, and it measures the extent or degree of effectiveness of a farmer in managing postharvest losses (in other words, storage quality). The larger the coefficient, the greater the farmer’s ability to manage these losses after harvest. A farmer with PMC = 0 means that all the stored tubers of yam went to waste and indicates that the farmer is very poor in managing postharvest losses. PMC = 1 means that farmer was able to maintain the entire stored yam tubers in desirable form both qualitatively and quantitatively, with none wasting away; such a farmer is characterized as being excellent in managing yam postharvest losses.


### 2.2.2. The Econometric Modeling of Postharvest Management

Our overarching interest is in the determinants of postharvest management (PMC), so we use an econometric model to achieve this objective. We consider PMC to be a function of several variables(
X
), which may include farmer and farm-specific characteristics, location characteristics, and other socioeconomic characteristics. This is expressed mathematically by model (2) as follows:(2)
E
P
M
C
∣
X
=
X
b
+
e
,where PMC is the dependent variable as defined above, X is matrix of independent variables, b is a vector of coefficients to be estimated, and e represents an error term that accounts for unmeasured variables. We use FRM to estimate the b vector of model (2) because the dependent variable is a proportion which is bounded between zero and one. This does not permit us to use the ordinary least squares (OLS) technique to calculate the parameters of the model [22, 23] consistently and unbiasedly. Other researchers have used normal censored regression techniques such as Tobit regression. In the opinion of Maddala [24] and Baum [25] such approaches are also not appropriate because the observed data are not censored, and values outside the unit interval (
0
,
1
) are not possible in the case of proportional data. The FRM accounts for the proportional nature of the dependent variable in cross-sectional settings. Many applications of the model are found in economics and finance literature to address the specification error due to the fractional response, defined on the closed (
0,1
) interval [26]. The FRM, first proposed by [27], is an alternative to the binary logit, OLS, and beta regression [23] models which cannot produce plausible and statistically sound parameters with fractional values including 0 and 1. In the FRM model, a functional form for the dependent variable is chosen such that it imposes constraints on the response variable to ensure that predicted values would always lie within the closed interval (0, 1). This leads to model (3):(3)
E
P
M
C
∣
X
=
g
X
b
.
g is a nonlinear distribution function which transforms to predicted value of the dependent variable to lie between 0 and 1. A distribution suggested by [27, 28] is a Bernoulli distribution and the parameters in model (3) are estimated using quasi-likelihood estimators such as generalized linear models (GLM). In Stata [25] demonstrates the implementation of the FRM using GLM, and we adopted this method for our analysis. The procedure requires specification of both a link function and a distribution function. The parameters in the model are obtained by maximizing the Bernoulli quasi log likelihood function for the FRM that takes the form of (4)
ln
⁡
L
b
=
∑
j
=
1
N
w
j
P
M
C
j
ln
⁡
g
X
j
′
b
+
w
j
1
-
P
M
C
j
ln
⁡
1
-
g
X
j
′
b
,where P
M
C
j is the dependent variable, N denotes sample size (spanning from 1 to 201), X
j are the independent variables for farmer j, and w
j is an optional weight. We assume that the link function g
· follows a logit distribution with the function shown in model (5): (5)
g
X
j
′
b
=
e
X
j
′
b
1
+
e
X
j
′
b
.This leads us to the empirical specification of the FRM as presented in(6)
E
P
M
C
∣
X
=
g
μ
i
=
b
0
+
∑
k
=
1
9
b
k
X
i
,
k
+
e
i
.The variables in the model are defined under Section 2.2.3 and their a priori expectations are given based on economic logic and intuition.


### 2.2.3. Definition and A Priori Expectation of Independent Variables

Market Participation
(
X
1
). Market participation defines the extent of market integration (commercialization) of the farmer. A farmer is said to be integrated into the market if part or all of the produce is sold or traded for cash income. Purely commercialized farmers sell all their produce harvested while purely subsistence farmers sell none of harvested produce. It is expected that more integrated or commercialized farmers have the greater likelihood of adopting superior postharvest management practices,ceteris paribus. Therefore, a positive effect is expected on the dependent variable. A coefficient of market participation (MPC), defined as the proportion of produce sold out of the total harvest, was generated and is given by the following expression:(7)
M
P
C
=
Quantity of produce sold
Total quantity harvested
. Based on the MPC, farmers were categorized into three as follows:(i)
Farmers with MPC below 0.25 (i.e.,M
P
C
<
0.25) are considered as subsistence farmers. These farmers are more interested in maximizing the subsistence objective rather than profit maximization.
(ii)
Farmers with MPC larger than or equal to 0.25 and less than or equal to 0.5(
0.25
≤
M
P
C
≤
0.5
) are classified as transition farmers. These farmers, though bothering about household food production, are also interested in earning some cash income from the yam enterprise.
(iii)
Farmers with MPC larger than 0.5(
M
P
C
≥
0.5
) are classified as commercial farmers whose primary objective is to generate cash income from the yam enterprise. We expect that commercial farmers are more profit oriented and are therefore more inclined to adopt measures that could help preserve produce for longer periods in anticipation of better prices in the future.Years of Education
(
X
2
). Studies done by Koundouri et al. [29] pointed to the fact that farmers’ human capital plays a significant role in the decision to adopt improved production and postharvest management practices. Education is considered to improve the quality of human capital. Farmers with more years of formal education are expected to manage postharvest challenges better than less educated ones. By logic, getting some formal education should enrich a farmer’s knowledge level and ability to manage losses not only at the production stage but importantly after harvest. The a priori expectation is a positive effect of education on postharvest management.Household Size
(
X
3
). Household size is measured by the number of people who feed from the same pot. All other things being equal, one could argue that larger households have more readily available and cheaper sources of labour to manage postharvest losses effectively compared to smaller sized households. The expectation is that household size will have a positive effect on the dependent variable.Farm Size
(
X
4
). This variable measures the total arable crop land a farmer cultivates. Farmers with larger farms are expected to allocate and commit more acreage to yam production and therefore have more output. The greater the output a farmer produces, the greater the probability of experiencing greater postharvest losses. The sign of the coefficient is therefore expected to be negative.Age of Farmer
(
X
5
). Age (measured in years) is a continuous variable. As the farmer advances in age, his output also improves because of experience he has gathered over the years of working on the field. We know that productivity increases until it gets to a stage in life where it begins to decline (hence the need for retirement). At that stage any additional input will result in limited or no change in output. We expect this to apply in the case of a farmer’s off-farm activities, including postharvest management. At younger ages, farmers have more strength and zeal to undertake more effective management strategies that reduce postharvest losses. As the farmer advances in age, the ability to effectively manage postharvest losses reduces. Therefore we expect a nonline, U-shape relationship between age and postharvest management. But, since experience increases as one advances in age, we expect a positive coefficient.Type of Labour
(
X
6
). In the Ghanaian agricultural system, two types of labour are commonly used for farm operations and postharvest management practices. These are family labour and hired labour. Although the two are not mutually exclusive, we expect that farmers who use more hired labour should be able to deal better with postharvest losses than those who use mainly family labour. Our reasoning is premised on the fact that hired labour is employed with supervision and hence is expected to follow standard measures that minimize losses on the farm. We dichotomize this variable such that farmers who use mainly family labour are coded 0 and farmers using hired labour are coded 1. Based on the foregoing argument, we anticipate a positive relationship of labour type with postharvest management.Yam Storage Duration
(
X
7
). This variable (measured in days) is the maximum length of time yam tubers are stored after harvest. The length of storage should have both direct and indirect influence on postharvest losses [30], hence on management practices. The longer the tubers are stored, the better the management techniques that must be employed to prolong the shelf life of the produce are. The expected sign for the storage duration coefficient is positive.Distance of Community to the District Capital
(
X
8
). This variable is measured in miles and represents the distance from the communities where farmers and their farms are located to the district capital (i.e., Zabzugu). In many developing country settings, a large proportion of the food produced in the countryside is sent to the district capitals and other relatively more urban cities for sale to urban consumers. Jacoby [31], in his study of access to markets and the benefits of rural roads, concluded that providing good and accessible roads to market centres is great incentive for increased production, because farmers tend to benefit from it. In the study area, many of the roads from production centres to the district capital are in poor conditions, and many communities farther away from the district capital have very poor road conditions, making only heavy duty vehicles (e.g., tractors) ply such roads. Communities farthest from the district market are expected to experience high probability of postharvest losses. However, farmers in remote communities may be forced by poor infrastructural conditions to adopt innovative management practices that prolong the shelf life of farm produce.Production Volume
(
X
9
). This variable is a measure of the extent of yam production. It is computed as the total number of tubers harvested from the yam field in the previous season. With large scale production, farmers are expected to make higher investments and generate larger incomes. Such farmers should be able to adopt effective postharvest management techniques. If this holds, then it is expected that the effect of production volume on PMC should be positive. On the other hand, farmers with large scale production may have challenges managing the resulting output after harvest, especially in terms of available space for effective storage and treatment; hence more yam tubers could be lost due to inadequate storage facilities or other management limitations. In this regard, the direction of the production volume coefficient can be positive or negative.


## 2.2.1. Fractional Regression Model

To assess factors that influence postharvest management, the FRM was used. This model was used because of the proportional nature of the dependent variable (PMC). The model posits that there is an underlying rational decision process and that the process has a functional form.The main research interest is on the determinants of postharvest management. What are the characteristics of farmers who adopt good postharvest management practices and why would they do so? Ideally, one may not deviate significantly by assuming that farmers aim to maximize profit and therefore model their decisions to adopt effective postharvest management practices under the framework of profit maximization. But profit maximization is a restrictive concept [19] in the context of smallholder farmers. Many of these farmers have multiple objectives other than simply maximizing profit, and these are very critical in adoption decisions [20]. In this sense, it is much economically appropriate to adopt the broader concept of utility maximization.Farmers make daily choices about maximizing their utility or payoff from their enterprises and this depends on the choices they make in carrying out their productive ventures. Consider a farmer who is faced with two choice alternatives: the first choice is that he/she can adopt effective management techniques, and the second choice is that he/she can choose not to adopt the techniques. If farmeri chooses to adopt these, he does so in order to preserve the produce and also reduce environmental pollution through reduced postharvest losses. The set of management practices adopted will determine the quantity and quality of the produce in a future time period and hence the profit to be obtained from the sale of such produce. One important assumption to make our reasoning hold is that farmers who adopt effective management practices will generate better quality and quantity of produce in the future and will earn higher payoffs than those who use less effective ones or those who sell immediately after harvest [21]. Therefore, adoption of better postharvest management practices increases the utility obtained through reduction of storage losses.In this paper we use as a measure of postharvest management ability the ratio of stored products that remain preserved to the total quantity of products stored after harvest. This is called postharvest management coefficient (PMC), derived from the expression(1)
P
M
C
=
Quantity of yam stored after harvest
-
Quantity lost through storage
Quantity stored after harvest
.This coefficient is a proportion with values closed between 0 and 1 (i.e., 0
≤
P
M
C
≤
1). PMC is the main dependent variable in this study, and it measures the extent or degree of effectiveness of a farmer in managing postharvest losses (in other words, storage quality). The larger the coefficient, the greater the farmer’s ability to manage these losses after harvest. A farmer with PMC = 0 means that all the stored tubers of yam went to waste and indicates that the farmer is very poor in managing postharvest losses. PMC = 1 means that farmer was able to maintain the entire stored yam tubers in desirable form both qualitatively and quantitatively, with none wasting away; such a farmer is characterized as being excellent in managing yam postharvest losses.


## 2.2.2. The Econometric Modeling of Postharvest Management

Our overarching interest is in the determinants of postharvest management (PMC), so we use an econometric model to achieve this objective. We consider PMC to be a function of several variables(
X
), which may include farmer and farm-specific characteristics, location characteristics, and other socioeconomic characteristics. This is expressed mathematically by model (2) as follows:(2)
E
P
M
C
∣
X
=
X
b
+
e
,where PMC is the dependent variable as defined above, X is matrix of independent variables, b is a vector of coefficients to be estimated, and e represents an error term that accounts for unmeasured variables. We use FRM to estimate the b vector of model (2) because the dependent variable is a proportion which is bounded between zero and one. This does not permit us to use the ordinary least squares (OLS) technique to calculate the parameters of the model [22, 23] consistently and unbiasedly. Other researchers have used normal censored regression techniques such as Tobit regression. In the opinion of Maddala [24] and Baum [25] such approaches are also not appropriate because the observed data are not censored, and values outside the unit interval (
0
,
1
) are not possible in the case of proportional data. The FRM accounts for the proportional nature of the dependent variable in cross-sectional settings. Many applications of the model are found in economics and finance literature to address the specification error due to the fractional response, defined on the closed (
0,1
) interval [26]. The FRM, first proposed by [27], is an alternative to the binary logit, OLS, and beta regression [23] models which cannot produce plausible and statistically sound parameters with fractional values including 0 and 1. In the FRM model, a functional form for the dependent variable is chosen such that it imposes constraints on the response variable to ensure that predicted values would always lie within the closed interval (0, 1). This leads to model (3):(3)
E
P
M
C
∣
X
=
g
X
b
.
g is a nonlinear distribution function which transforms to predicted value of the dependent variable to lie between 0 and 1. A distribution suggested by [27, 28] is a Bernoulli distribution and the parameters in model (3) are estimated using quasi-likelihood estimators such as generalized linear models (GLM). In Stata [25] demonstrates the implementation of the FRM using GLM, and we adopted this method for our analysis. The procedure requires specification of both a link function and a distribution function. The parameters in the model are obtained by maximizing the Bernoulli quasi log likelihood function for the FRM that takes the form of (4)
ln
⁡
L
b
=
∑
j
=
1
N
w
j
P
M
C
j
ln
⁡
g
X
j
′
b
+
w
j
1
-
P
M
C
j
ln
⁡
1
-
g
X
j
′
b
,where P
M
C
j is the dependent variable, N denotes sample size (spanning from 1 to 201), X
j are the independent variables for farmer j, and w
j is an optional weight. We assume that the link function g
· follows a logit distribution with the function shown in model (5): (5)
g
X
j
′
b
=
e
X
j
′
b
1
+
e
X
j
′
b
.This leads us to the empirical specification of the FRM as presented in(6)
E
P
M
C
∣
X
=
g
μ
i
=
b
0
+
∑
k
=
1
9
b
k
X
i
,
k
+
e
i
.The variables in the model are defined under Section 2.2.3 and their a priori expectations are given based on economic logic and intuition.


## 2.2.3. Definition and A Priori Expectation of Independent Variables

Market Participation
(
X
1
). Market participation defines the extent of market integration (commercialization) of the farmer. A farmer is said to be integrated into the market if part or all of the produce is sold or traded for cash income. Purely commercialized farmers sell all their produce harvested while purely subsistence farmers sell none of harvested produce. It is expected that more integrated or commercialized farmers have the greater likelihood of adopting superior postharvest management practices,ceteris paribus. Therefore, a positive effect is expected on the dependent variable. A coefficient of market participation (MPC), defined as the proportion of produce sold out of the total harvest, was generated and is given by the following expression:(7)
M
P
C
=
Quantity of produce sold
Total quantity harvested
. Based on the MPC, farmers were categorized into three as follows:(i)
Farmers with MPC below 0.25 (i.e.,M
P
C
<
0.25) are considered as subsistence farmers. These farmers are more interested in maximizing the subsistence objective rather than profit maximization.
(ii)
Farmers with MPC larger than or equal to 0.25 and less than or equal to 0.5(
0.25
≤
M
P
C
≤
0.5
) are classified as transition farmers. These farmers, though bothering about household food production, are also interested in earning some cash income from the yam enterprise.
(iii)
Farmers with MPC larger than 0.5(
M
P
C
≥
0.5
) are classified as commercial farmers whose primary objective is to generate cash income from the yam enterprise. We expect that commercial farmers are more profit oriented and are therefore more inclined to adopt measures that could help preserve produce for longer periods in anticipation of better prices in the future.Years of Education
(
X
2
). Studies done by Koundouri et al. [29] pointed to the fact that farmers’ human capital plays a significant role in the decision to adopt improved production and postharvest management practices. Education is considered to improve the quality of human capital. Farmers with more years of formal education are expected to manage postharvest challenges better than less educated ones. By logic, getting some formal education should enrich a farmer’s knowledge level and ability to manage losses not only at the production stage but importantly after harvest. The a priori expectation is a positive effect of education on postharvest management.Household Size
(
X
3
). Household size is measured by the number of people who feed from the same pot. All other things being equal, one could argue that larger households have more readily available and cheaper sources of labour to manage postharvest losses effectively compared to smaller sized households. The expectation is that household size will have a positive effect on the dependent variable.Farm Size
(
X
4
). This variable measures the total arable crop land a farmer cultivates. Farmers with larger farms are expected to allocate and commit more acreage to yam production and therefore have more output. The greater the output a farmer produces, the greater the probability of experiencing greater postharvest losses. The sign of the coefficient is therefore expected to be negative.Age of Farmer
(
X
5
). Age (measured in years) is a continuous variable. As the farmer advances in age, his output also improves because of experience he has gathered over the years of working on the field. We know that productivity increases until it gets to a stage in life where it begins to decline (hence the need for retirement). At that stage any additional input will result in limited or no change in output. We expect this to apply in the case of a farmer’s off-farm activities, including postharvest management. At younger ages, farmers have more strength and zeal to undertake more effective management strategies that reduce postharvest losses. As the farmer advances in age, the ability to effectively manage postharvest losses reduces. Therefore we expect a nonline, U-shape relationship between age and postharvest management. But, since experience increases as one advances in age, we expect a positive coefficient.Type of Labour
(
X
6
). In the Ghanaian agricultural system, two types of labour are commonly used for farm operations and postharvest management practices. These are family labour and hired labour. Although the two are not mutually exclusive, we expect that farmers who use more hired labour should be able to deal better with postharvest losses than those who use mainly family labour. Our reasoning is premised on the fact that hired labour is employed with supervision and hence is expected to follow standard measures that minimize losses on the farm. We dichotomize this variable such that farmers who use mainly family labour are coded 0 and farmers using hired labour are coded 1. Based on the foregoing argument, we anticipate a positive relationship of labour type with postharvest management.Yam Storage Duration
(
X
7
). This variable (measured in days) is the maximum length of time yam tubers are stored after harvest. The length of storage should have both direct and indirect influence on postharvest losses [30], hence on management practices. The longer the tubers are stored, the better the management techniques that must be employed to prolong the shelf life of the produce are. The expected sign for the storage duration coefficient is positive.Distance of Community to the District Capital
(
X
8
). This variable is measured in miles and represents the distance from the communities where farmers and their farms are located to the district capital (i.e., Zabzugu). In many developing country settings, a large proportion of the food produced in the countryside is sent to the district capitals and other relatively more urban cities for sale to urban consumers. Jacoby [31], in his study of access to markets and the benefits of rural roads, concluded that providing good and accessible roads to market centres is great incentive for increased production, because farmers tend to benefit from it. In the study area, many of the roads from production centres to the district capital are in poor conditions, and many communities farther away from the district capital have very poor road conditions, making only heavy duty vehicles (e.g., tractors) ply such roads. Communities farthest from the district market are expected to experience high probability of postharvest losses. However, farmers in remote communities may be forced by poor infrastructural conditions to adopt innovative management practices that prolong the shelf life of farm produce.Production Volume
(
X
9
). This variable is a measure of the extent of yam production. It is computed as the total number of tubers harvested from the yam field in the previous season. With large scale production, farmers are expected to make higher investments and generate larger incomes. Such farmers should be able to adopt effective postharvest management techniques. If this holds, then it is expected that the effect of production volume on PMC should be positive. On the other hand, farmers with large scale production may have challenges managing the resulting output after harvest, especially in terms of available space for effective storage and treatment; hence more yam tubers could be lost due to inadequate storage facilities or other management limitations. In this regard, the direction of the production volume coefficient can be positive or negative.


## 3. Empirical Results and Discussion

### 3.1. Distribution of Yam Postharvest Management across the Sampled Communities

Results indicate that postharvest management varies modestly across the sampled communities. In terms of distance, it is observed that ability to manage postharvest losses increases with proximity to the district capital. Farmers in Zabzugu managed postharvest losses better compared to farmers in the remaining communities (see Table1). This is plausible since farmers in or near the district capital may have access to better storage facilities or more current knowledge about methods of managing postharvest losses.Table 1
Distribution of PMC across communities in the study area.
Community
Distance from district capital (miles)
Mean
Standard deviation
Minimum
Maximum
Lagbani
7
0.67
0.05
0
0.94
Sakpeleengbani
5
0.68
0.06
0
1
Kukpaligu
7
0.57
0.05
0
0.96
Sheini
16
0.46
0.06
0
0.89
Tasundo No. 1
4
0.57
0.05
0
0.98
Zabzugu
0
0.69
0.04
0
0.95Farmers in the Sheini community (farthest from district capital) were found to be losing more than half of their total harvest. This is conceivable because the road connecting it to the district capital is in such a deplorable state and vehicles that ply that road are in bad conditions, thereby contributing to more losses.


### 3.2. Distribution of Yam Postharvest Losses in the Study Area

In the study area, postharvest losses in yam production are quite huge. Except for one respondent (0.50 percent) who is said to have recorded no postharvest loss, the remaining 99.5% of farmers recorded losses that ranged from 10 to 2000 yam tubers, depending on the production volume. In Table2, the postharvest losses are grouped into seven categories. The main sources of these postharvest losses are due to attacks by termites and rodents, as well as excessive heat, which start the process of decomposition before the farmer gets ready to sell the tubers. Reducing the effect of these agents requires farmers to adopt specialized skills using the rudimentary traditional storage methods at their disposal. The differences in farmers’ ability to use their skills bring about the differences in postharvest management.Table 2
Distribution of yam postharvest losses for 2013/2014 production season.
Tubers lost
Frequency
Percent
Mean
Minimum
Maximum
0
1
0.50
0
0
0
1–50
29
14.43
41.90
10
50
51–100
53
26.37
91.91
57
100
101–150
20
9.95
147.50
120
150
151–200
36
17.91
197.36
160
200
201–250
8
3.98
241.88
225
250
Above 250
54
26.87
560.39
300
2000
Overall
201
100
235.64
0
2000
⁢Cedi equivalent of mean tubers lost through postharvest handling and storage = GHȻ 631.20From Table2, on average 236 tubers of yam were lost after harvest, representing 7.5% of the average total tubers harvested (approximately 3140). The statistics in Table 2 indicate that close to fifty percent of the farmers lost above one hundred and fifty tubers of yam, and this is quite significant, considering the fact that on average 100 tubers of yam cost above GHȻ 200.00. In cedi equivalence, farmers were on average losing about GHȻ 631.00 due to poor postharvest management. This is reasonable because yam cultivation is the main source of livelihood activity among the farmers. While some depend on the produce for subsistence, others engage in it as a real commercial venture to support their families and livelihoods. The causes of these losses range from ineffective postharvest storage techniques to poor transport systems and infrastructure. Therefore, providing proper postharvest management techniques to farmers is a panacea to improving farmers’ earnings from yam production and other food security objectives.


### 3.3. Summary Statistics of Variables Used in the Fractional Logistic Regression Model

PMC across the communities was on average encouraging with a mean of 0.60 but market participation (MPC) was not. With a mean MPC of 0.44, it means that the average farmer sold less than 50% of total production. This figure is relatively lower than expected. With yam production being the main business of the inhabitants, we expected them to commit greater percentage of their production to sales in the market. Table3 provides a summary of these statistics.Table 3
Descriptive statistics of variables used in the fractional regression model.
Variable
Mean
Minimum
Maximum
PMC (dependent variable)
0.60
0
1
MPC
0.44
0.08
0.94
Education (dummy)
0.35
0
1
Household size (count)
8.03
1
31
Farm size (acres)
7.28
1
32
Age (years)
40.11
18
90
Labour (dummy)
0.68
0
1
Storage duration (days)
115.63
14
210
Distance to district capital (miles)
5.90
0
16
Production level (count of tubers)
3139.20
300
16,000From Table3, the minimum MPC of 0.08 means that no single farmer was a pure subsistent farmer where the main intention of production was to meet household food needs. Even farmers that dedicated larger proportion of their produce to household consumption still sent few excess tubers to market for cash income or as means of reducing losses. Again, no farmer in the district was a pure commercial farmer whose primary intention was to produce entirely for sale, as reflected in the maximum MPC of 0.94. This means that even farmers who sold larger proportion of their farm produce still reserved some for household food needs. Again, the education dummy has a mean of 0.35 and implies that only 35% of the respondents had formal education while the remaining 65% did not. This is an indication of the low level of educated farmers involved in agriculture in general and yam production in particular. Again, the average farmer was approximately 40 years old with a household size of about 8 members and cultivated 7.28 acres of land for yam production. About 68% of the farmers employed hired labour for their productive activities while the remaining 32% used family labour. This is understandable because yam production is a labour-intensive activity, and usually farmers resort to hired labour for the various activities. The farmers who stored their yam did so for an average of 116 days (i.e., approximately 4 months) using their traditional storage methods. If improved storage techniques are made available the storage period could be extended. The average community was approximately 5.9 miles away from the district capital (Zabzugu).


### 3.4. Factors Influencing Postharvest Management of Yam Farmers

We used the GLM approach to estimate the coefficients of the FRM which are reported in Table4. The results indicate a general good fit of the model, with AIC of 1.068339 showing that the model gives a good fit for the data. In the table, the column titled marginal effect measures the extent to which a unit change in the explanatory variables would influence PMC. For instance, the value of  −0.3907671 on MPC means that when the extent of market participation increases by one percentage point, PMC will fall by 0.39%, holding all the other variables constant. The three stars (
∗
∗
∗
) attached to it mean that this measured effect is not by chance but it is real, at the 1 percent level of significance. The corresponding standard error (0.13725) is used to judge the significance of the measured effect.Table 4
Marginal effects from fractional logit regression (dependent variable is PMC).
Variable
Marginal effects
Std. error
MPC (X
1)
−0.3907671∗
∗
∗
0.13725
Education (X
2)
0.0742587∗
0.04427
Household size (X
3)
−0.0090155∗
0.00505
Farm size (X
4)
0.008634
0.0058
Age (X
5)
−0.0031726∗
0.00184
Labour (X
6)
−0.0465028
0.04947
Storage duration (X
7)
0.03432
0.04397
Distance to district capital (X
8)
−0.0079412∗
0.00477
Volume of production (X
9)
0.14666∗
∗
∗
0.04357
∗
∗
∗ and ∗ indicate significance at 1% and 10%, respectively.We included nine (9) main explanatory variables in the model, but only six were found to have influence on PMC. These include market participation (MPC), household size, age, and distance to district capital, which were found to reduce storage losses or increase storage quality. Education and output were also found to increase storage quality or reduce storage losses. Farm size, type of labour used, and storage duration did not have any influence on storage losses.Extent of market participation (MPC) was found to have a sinking influence on postharvest management of yam. Since this index lies between zero and one, the coefficient can be interpreted as elasticity. The result implies that as the extent of market participation increases by 10 percent, the PMC of an individual farmer is reduced by 3.9 percent (see Table4). This result is very important. This however contradicts a priori expectation of a positive relationship but is not unreasonable. A possible explanation may be offered as follows: commercial farmers are more profit oriented while subsistence farmers are more concerned with meeting the food security needs of the household. Therefore, subsistence farmers look for better and innovative methods to preserve harvested produce for longer periods of time. On the other hand, commercially oriented farmers focus on getting more output for sale. The result may be that subsistence farmers are better managers at preventing losses after harvest compared to the commercially oriented farmers. This observation or finding is also very critical for policy makers, especially those concerned with poverty alleviation and rural farm household food security. If subsistence farm households are assisted with better postharvest management methods, it will go a long way to improve their food security statuses.The positive value for education shows that, all other things being equal, farmers with formal education have higher PMC, indicating that educated farmers are able to reduce postharvest losses significantly. This result was expected and confirms the findings made by [32] that knowledge and skills on postharvest management are important at preventing or reducing losses. Generally, it is believed that education increases the intellectual capacity of an individual and therefore enhances the management ability. The result shows a significant difference in postharvest management between formally educated and noneducated farmers. A marginal effect of 0.07 indicates the difference in postharvest management ability between a person with no education and one with formal education. We find that farmers with formal education are about 7% better at reducing postharvest losses. Policy-wise, this finding reinforces the need to establish postharvest learning platforms [33], where improved postharvest management practices are showcased for farmers to learn.Households with higher members tend to experience lower PMC, indicating that they record more storage losses compared to smaller sized households. This finding is counterintuitive, since one would expect that larger households should have adequate labour to manage postharvest losses compared to smaller sized farm households. Based on the results, if an additional member is added to the household, PMC reduces by 0.9%. Age was another determinant with a negative effect on postharvest management. It is established that older farmers were less effective at managing postharvest losses. Considering two farmers with same characteristics but differing in age by a year, the older will be 0.3% less effective at managing storage losses compared to the younger one.Distance to the district capital was also found to negatively affect PMC. Farmers in communities that were far from the district capital were less effective in managing postharvest losses compared to those who were closer to the district capital. The situation could be due to the bad road network that connects these communities to the district capital. If an aggregation centre is provided for farmers and an access provided for them to easily connect with bigger markets without stress, postharvest losses may be significantly reduced and farmers will be motivated to produce more. The volume of production variable was found to have a positive and significant connection with postharvest management. A unit increase in the production volume increases a farmer’s ability to manage postharvest losses by 15%. Thus, farmers with higher production volumes tend to reduce storage losses eventually. For such farmers they need the yam ware for two main purposes, cash and food security. Therefore, they endeavor to take all requisite measures that will ensure that outputs are available for the greater part of the season until it is time for another crop harvest.


## 3.1. Distribution of Yam Postharvest Management across the Sampled Communities

Results indicate that postharvest management varies modestly across the sampled communities. In terms of distance, it is observed that ability to manage postharvest losses increases with proximity to the district capital. Farmers in Zabzugu managed postharvest losses better compared to farmers in the remaining communities (see Table1). This is plausible since farmers in or near the district capital may have access to better storage facilities or more current knowledge about methods of managing postharvest losses.Table 1
Distribution of PMC across communities in the study area.
Community
Distance from district capital (miles)
Mean
Standard deviation
Minimum
Maximum
Lagbani
7
0.67
0.05
0
0.94
Sakpeleengbani
5
0.68
0.06
0
1
Kukpaligu
7
0.57
0.05
0
0.96
Sheini
16
0.46
0.06
0
0.89
Tasundo No. 1
4
0.57
0.05
0
0.98
Zabzugu
0
0.69
0.04
0
0.95Farmers in the Sheini community (farthest from district capital) were found to be losing more than half of their total harvest. This is conceivable because the road connecting it to the district capital is in such a deplorable state and vehicles that ply that road are in bad conditions, thereby contributing to more losses.


## 3.2. Distribution of Yam Postharvest Losses in the Study Area

In the study area, postharvest losses in yam production are quite huge. Except for one respondent (0.50 percent) who is said to have recorded no postharvest loss, the remaining 99.5% of farmers recorded losses that ranged from 10 to 2000 yam tubers, depending on the production volume. In Table2, the postharvest losses are grouped into seven categories. The main sources of these postharvest losses are due to attacks by termites and rodents, as well as excessive heat, which start the process of decomposition before the farmer gets ready to sell the tubers. Reducing the effect of these agents requires farmers to adopt specialized skills using the rudimentary traditional storage methods at their disposal. The differences in farmers’ ability to use their skills bring about the differences in postharvest management.Table 2
Distribution of yam postharvest losses for 2013/2014 production season.
Tubers lost
Frequency
Percent
Mean
Minimum
Maximum
0
1
0.50
0
0
0
1–50
29
14.43
41.90
10
50
51–100
53
26.37
91.91
57
100
101–150
20
9.95
147.50
120
150
151–200
36
17.91
197.36
160
200
201–250
8
3.98
241.88
225
250
Above 250
54
26.87
560.39
300
2000
Overall
201
100
235.64
0
2000
⁢Cedi equivalent of mean tubers lost through postharvest handling and storage = GHȻ 631.20From Table2, on average 236 tubers of yam were lost after harvest, representing 7.5% of the average total tubers harvested (approximately 3140). The statistics in Table 2 indicate that close to fifty percent of the farmers lost above one hundred and fifty tubers of yam, and this is quite significant, considering the fact that on average 100 tubers of yam cost above GHȻ 200.00. In cedi equivalence, farmers were on average losing about GHȻ 631.00 due to poor postharvest management. This is reasonable because yam cultivation is the main source of livelihood activity among the farmers. While some depend on the produce for subsistence, others engage in it as a real commercial venture to support their families and livelihoods. The causes of these losses range from ineffective postharvest storage techniques to poor transport systems and infrastructure. Therefore, providing proper postharvest management techniques to farmers is a panacea to improving farmers’ earnings from yam production and other food security objectives.


## 3.3. Summary Statistics of Variables Used in the Fractional Logistic Regression Model

PMC across the communities was on average encouraging with a mean of 0.60 but market participation (MPC) was not. With a mean MPC of 0.44, it means that the average farmer sold less than 50% of total production. This figure is relatively lower than expected. With yam production being the main business of the inhabitants, we expected them to commit greater percentage of their production to sales in the market. Table3 provides a summary of these statistics.Table 3
Descriptive statistics of variables used in the fractional regression model.
Variable
Mean
Minimum
Maximum
PMC (dependent variable)
0.60
0
1
MPC
0.44
0.08
0.94
Education (dummy)
0.35
0
1
Household size (count)
8.03
1
31
Farm size (acres)
7.28
1
32
Age (years)
40.11
18
90
Labour (dummy)
0.68
0
1
Storage duration (days)
115.63
14
210
Distance to district capital (miles)
5.90
0
16
Production level (count of tubers)
3139.20
300
16,000From Table3, the minimum MPC of 0.08 means that no single farmer was a pure subsistent farmer where the main intention of production was to meet household food needs. Even farmers that dedicated larger proportion of their produce to household consumption still sent few excess tubers to market for cash income or as means of reducing losses. Again, no farmer in the district was a pure commercial farmer whose primary intention was to produce entirely for sale, as reflected in the maximum MPC of 0.94. This means that even farmers who sold larger proportion of their farm produce still reserved some for household food needs. Again, the education dummy has a mean of 0.35 and implies that only 35% of the respondents had formal education while the remaining 65% did not. This is an indication of the low level of educated farmers involved in agriculture in general and yam production in particular. Again, the average farmer was approximately 40 years old with a household size of about 8 members and cultivated 7.28 acres of land for yam production. About 68% of the farmers employed hired labour for their productive activities while the remaining 32% used family labour. This is understandable because yam production is a labour-intensive activity, and usually farmers resort to hired labour for the various activities. The farmers who stored their yam did so for an average of 116 days (i.e., approximately 4 months) using their traditional storage methods. If improved storage techniques are made available the storage period could be extended. The average community was approximately 5.9 miles away from the district capital (Zabzugu).


## 3.4. Factors Influencing Postharvest Management of Yam Farmers

We used the GLM approach to estimate the coefficients of the FRM which are reported in Table4. The results indicate a general good fit of the model, with AIC of 1.068339 showing that the model gives a good fit for the data. In the table, the column titled marginal effect measures the extent to which a unit change in the explanatory variables would influence PMC. For instance, the value of  −0.3907671 on MPC means that when the extent of market participation increases by one percentage point, PMC will fall by 0.39%, holding all the other variables constant. The three stars (
∗
∗
∗
) attached to it mean that this measured effect is not by chance but it is real, at the 1 percent level of significance. The corresponding standard error (0.13725) is used to judge the significance of the measured effect.Table 4
Marginal effects from fractional logit regression (dependent variable is PMC).
Variable
Marginal effects
Std. error
MPC (X
1)
−0.3907671∗
∗
∗
0.13725
Education (X
2)
0.0742587∗
0.04427
Household size (X
3)
−0.0090155∗
0.00505
Farm size (X
4)
0.008634
0.0058
Age (X
5)
−0.0031726∗
0.00184
Labour (X
6)
−0.0465028
0.04947
Storage duration (X
7)
0.03432
0.04397
Distance to district capital (X
8)
−0.0079412∗
0.00477
Volume of production (X
9)
0.14666∗
∗
∗
0.04357
∗
∗
∗ and ∗ indicate significance at 1% and 10%, respectively.We included nine (9) main explanatory variables in the model, but only six were found to have influence on PMC. These include market participation (MPC), household size, age, and distance to district capital, which were found to reduce storage losses or increase storage quality. Education and output were also found to increase storage quality or reduce storage losses. Farm size, type of labour used, and storage duration did not have any influence on storage losses.Extent of market participation (MPC) was found to have a sinking influence on postharvest management of yam. Since this index lies between zero and one, the coefficient can be interpreted as elasticity. The result implies that as the extent of market participation increases by 10 percent, the PMC of an individual farmer is reduced by 3.9 percent (see Table4). This result is very important. This however contradicts a priori expectation of a positive relationship but is not unreasonable. A possible explanation may be offered as follows: commercial farmers are more profit oriented while subsistence farmers are more concerned with meeting the food security needs of the household. Therefore, subsistence farmers look for better and innovative methods to preserve harvested produce for longer periods of time. On the other hand, commercially oriented farmers focus on getting more output for sale. The result may be that subsistence farmers are better managers at preventing losses after harvest compared to the commercially oriented farmers. This observation or finding is also very critical for policy makers, especially those concerned with poverty alleviation and rural farm household food security. If subsistence farm households are assisted with better postharvest management methods, it will go a long way to improve their food security statuses.The positive value for education shows that, all other things being equal, farmers with formal education have higher PMC, indicating that educated farmers are able to reduce postharvest losses significantly. This result was expected and confirms the findings made by [32] that knowledge and skills on postharvest management are important at preventing or reducing losses. Generally, it is believed that education increases the intellectual capacity of an individual and therefore enhances the management ability. The result shows a significant difference in postharvest management between formally educated and noneducated farmers. A marginal effect of 0.07 indicates the difference in postharvest management ability between a person with no education and one with formal education. We find that farmers with formal education are about 7% better at reducing postharvest losses. Policy-wise, this finding reinforces the need to establish postharvest learning platforms [33], where improved postharvest management practices are showcased for farmers to learn.Households with higher members tend to experience lower PMC, indicating that they record more storage losses compared to smaller sized households. This finding is counterintuitive, since one would expect that larger households should have adequate labour to manage postharvest losses compared to smaller sized farm households. Based on the results, if an additional member is added to the household, PMC reduces by 0.9%. Age was another determinant with a negative effect on postharvest management. It is established that older farmers were less effective at managing postharvest losses. Considering two farmers with same characteristics but differing in age by a year, the older will be 0.3% less effective at managing storage losses compared to the younger one.Distance to the district capital was also found to negatively affect PMC. Farmers in communities that were far from the district capital were less effective in managing postharvest losses compared to those who were closer to the district capital. The situation could be due to the bad road network that connects these communities to the district capital. If an aggregation centre is provided for farmers and an access provided for them to easily connect with bigger markets without stress, postharvest losses may be significantly reduced and farmers will be motivated to produce more. The volume of production variable was found to have a positive and significant connection with postharvest management. A unit increase in the production volume increases a farmer’s ability to manage postharvest losses by 15%. Thus, farmers with higher production volumes tend to reduce storage losses eventually. For such farmers they need the yam ware for two main purposes, cash and food security. Therefore, they endeavor to take all requisite measures that will ensure that outputs are available for the greater part of the season until it is time for another crop harvest.


## 4. Conclusion and Policy Implications

Postharvest loss reduction is important for improving national food security. One way to minimize this effect is to manage the losses, and in doing so the human element is a critical factor. We try to measure the extent of this management ability of farmers by deriving a PMC variable and used FRM to measure factors that influence postharvest management of yam farmers in Ghana, using Zabzugu District as case study. We find that postharvest losses in the district are quite significant, though below the national average. Farmers adopt a variety of traditional techniques to manage these losses but the extent of success is as high as 60%, and this is influenced by a number of factors. A farmer who manages postharvest losses effectively is the one with formal education and a subsistence farmer, who lives close to the district capital and has few household members but with higher volume of production. Policy-wise, stakeholders with interest in minimizing postharvest losses must consider providing formal/informal training on postharvest management to farmers so as to improve knowledge level. Any intervention to improve postharvest management of farmers in the district must also consider providing access to remotely sited communities. The elimination of infrastructural bottlenecks has the potential to remove entry barriers and structural impediments to market participation.



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*Source: 9274017-2016-04-05.xml*