i TECHNICAL EFFICIENCY OF SMALLHOLDER IRISH POTATO PRODUCTION IN NYABIHU DISTRICT, RWANDA BY GATEMBEREZI MUZUNGU PAUL A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN AGRICULTURAL& APPLIED ECONOMICS DEPARTMENT OF AGRICULTURAL ECONOMICS FACULTY OF AGRICULTURE COLLEGE OF AGRICULTURE AND VETERINARY SCIENCES UNIVERSITY OF NAIROBI, KENYA JULY, 2011
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i
TECHNICAL EFFICIENCY OF SMALLHOLDER IRISH POTATO
PRODUCTION IN NYABIHU DISTRICT, RWANDA
BY
GATEMBEREZI MUZUNGU PAUL
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN
AGRICULTURAL& APPLIED ECONOMICS
DEPARTMENT OF AGRICULTURAL ECONOMICS
FACULTY OF AGRICULTURE
COLLEGE OF AGRICULTURE AND VETERINARY SCIENCES
UNIVERSITY OF NAIROBI, KENYA
JULY, 2011
i
DECLARATION
I declare that this is my original work and has not been submitted in any to any other
size, experience in farming, access to credit, extension services, and do not influence
technical efficiency of potato production in Nyabihu District.
6
1.6 Justification of the study
A study on Technical Efficiency of Irish potato is justified by a lot of importance given to
agriculture in the Rwanda economic plan known as Vision 2020. It is also justified by
identified great role that agriculture is expected to play to meet Millennium Development
Goals one (MDG1) target.
Its plays an important role both as food and cash crop in the country in general and
Nyabihu District in particular. With 57,000 ha under cultivation, the potato sector is a large
and dynamic segment of agriculture. Hundreds of thousands of Rwandan farmers across the
country are engaged in commercial and subsistence cultivation. Irish potato is a major crop
widely grown in the Rwanda Northern and Western provinces where population depends
on the farming activities for their livelihood.
Annual national production level stands at 1,073,000 tones while and annual
consumption is a very high 125 kg per person, making potato the country's second most
important source of calorie intake after cassava. It is characterized by a high demand for
domestic markets; especially in urban areas.
Moreover, the technical efficiency study will play a significant role in providing useful
information regarding economic inefficiencies in production and helps to identify those
factors, which are associated with inefficiencies that may exist. Therefore, it is expected
from this study to generate adequate understanding of the issues that might lead towards
taking appropriate actions for improvement of efficiencies and the identification of the
extent of inefficiencies as well as the factors associated with them. Furthermore, the
study also came at a time when the efficiency of smallholder family farms is highly
disputed in Rwanda.
7
CHAPTER TWO
LITERATURE REVIEW
This chapter reviews previous literature based on technical efficiency. It provides a
theoretical background on the concepts of technical efficiency as well as the socio-
economic factors influencing it. The present study will help to fill the gap, where no such
study exists that explores efficiency in Irish potato production in Rwanda.
2.1. The Concept of Efficiency Yotopolous et al (1967) relates the efficiency of the firm to a comparison between
observed and optimal values of its outputs and inputs. If the optimum is defined in terms
of production possibilities, the resulting comparison measures technical efficiency. If the
optimum is defined in terms of behavioral goals of the firm(e.g. profit maximization and
cost minimization),then efficiency is economic and is measured by comparing a firm’s
observed and optimum achievement of goals(e.g. profit, revenue and cost) subject to the
appropriate consideration of technology and prices.
The analysis of efficiency dates back to Knight (1933), Debreu (1951) and Koopmans
(1951). Koopmans (1951) provided a definition of technical efficiency while Debreu
(1951) introduced its first measure of the ‘resource utilization’. Following on Debrew in a
seminal paper Farrell (1957), provided a definition of frontier production functions,
which embodied the idea of maximality.
Farrell (1957) proposed that the economic efficiency of a firm consists of two
components: technical efficiency and allocative efficiency. Technical efficiency refers to
8
the ability of a firm to produce maximal potential outputs from a given amount of input
or to use a minimal amount of inputs in order to produce a given amount of output.
Allocative efficiency represents the ability of a firm to utilize the cost-minimizing input
ratios or revenue-maximizing out-put ratios. A firm is allocatively efficient if it uses the
optimal combination of inputs with respect to their prices. First-order conditions from
revenue maximization can be used to determine optimal output ratios based on output
prices and marginal costs.
Heady(1952) defines the efficiency of resource use as the point at which net returns from
a single technical unit are at a maximum when the marginal cost of the resource is equal
to the marginal value product of the resource. He further states that farmers do not always
extend resource use to this point of efficiency use. This inability to equate marginal cost
of resources, either for technical unit or for the farm as a business includes three
considerations; (i) Lack of knowledge or principles, (ii) lack of knowledge of the relevant
input-output relationships and cost structures, (iii) the uncertainty of future prices and
yields and the existence of severe capital limitations
9
2.2 Approaches to measuring efficiency
The literature on the measurement of efficiency is divided into two major approaches that
use either parametric or non-parametric frontiers. The frontier defines the limit to a range
of possible observed production (cost) levels and identifies the extent to which the firm
lies below (above) the frontier. In the parametric frontier analysis the technology of a
decision making unit is specified by a particular functional form for the cost, profit or
production relationship that links the decision making unit’s output to input factors (Delis
et al., 2008).
The most widely applied technique is the Stochastic Frontier Approach (SFA) originally
proposed by Aigner et al., (1977) and Meeusen et al., (1977) .The model is defined by
iiii uvxfY −+= );(ln β , ………………, i = 1,2,…N……………………………(2.1).
The random error, Vi, accounts for measurement error and other factors, such as the
weather, strikes luck, etc, and µi is one -sided component representing technical
inefficiency. Under the SFA, the error term is split into two components, allowing for
both random effects and frontier efficiency, where the random effects usually follow a
normal distribution and the inefficiencies a truncated normal distribution. The non
parametric approaches to efficiency measurement include the Data Envelopment
Analysis and the Free Disposal Hull. The Free Disposal Hull was developed by Deprins
et al., (1984) while the DEA method was first used by Charnes et al., (1978).
10
2.3 Concept of Data Envelopment Analysis (DEA) Data Envelopment Analysis (DEA) is non parametric method of measuring efficiency
that uses mathematical programming approach to frontier estimation rather than
regression. This approach based on the work of Farell (1957) and Fare et al. (1994) has
since been improved upon and extended programming method of DEA, which compares
by Battesse (1992) and Coelli (1995). Charnes et al. (1981) introduced the method of
Data Envelopment Analysis (DEA) to address the problem of efficiency measurement for
Decision Making Units (DMUs) with multiple inputs and multiple outputs in the absence
of market prices.
However, the DEA approach suffers from criticisms that it takes no account of the
possible influence of measurement errors and other noise data that are common in
agriculture, since all observed deviations from estimated frontier are assumed to be the
result of technical inefficiency (Coelli and Battese, 1996). Nevertheless, parametric and
non parametric models differ in two ways. First, the two models differ on assumptions of
the distribution of the error term that represents inefficiency. Second, they differ in the
way the functional form is imposed on the data.
Parametric methods impose functional and distributional forms on the error term whereas
the non-parametric methods do not. An important drawback of the parametric approaches
is that they impose a particular functional form (and hence all its associated behavioral
assumptions), which predetermines the shape of the frontier. If the functional form is
incorrectly specified, the estimated efficiency may be confounded with significant bias.
11
2.4 Technical, Allocative and Economic Efficiency Measurement of economic efficiency requires an understanding of the decision making
behaviour of the producer. A rational producer, producing a single output from a number
of inputs, x = x1……xn, that are purchased at given input prices, w = w1…..wn and
operating on a production frontier will be deemed to be efficient. But if the producer is
using a combination of inputs in such a way that it fails to maximize output or can use
less inputs to attain the same output, then the producer is not economically efficient.
A given combination of input and output is therefore economically efficient if it is both
technically and allocativelly efficient; that is, when the related input ratio is on both the
isoquant and the expansion path (Farrell 1957).
These contentions are best illustrated in the figure 2.1 below. This figure, indicates that
AB is an isoquant, representing technically efficient combinations of inputs, x1 and x2,
used in producing output Q. AB is also known as the ‘best practice’1production frontier.
DD' is an iso-cost line, which shows all combinations of inputs x1 and x2 such that input
costs sum to the same total cost of production. However, any firm intending to maximize
profits has to produce at Q', which is a point of tangency and representing the least cost
combination of x1 and x2 in production of Q. At point Q' the producer is economically
efficient.
1 Coelli (1995) indicates that the production function of the fully efficient firm ‘best practice’ is not known in practice, and thus it must be estimated from the sample of the industry concerned.
12
Figure 2.1: Concept of Technical and Allocative Efficiencies
Source: Coelli, et al, 1998
Turning to measurement of technical, allocative and economic efficiency, the same figure
3 is employed. Suppose a farmer is producing its output depicted by isoquant AB with
input combination level of (X1and X2) in figure 2.1. At this point (P) of input
combination the production is not technically efficient because the level of inputs needed
to produce the same quantity is Q on isoquant AB. In other words, the farmer can
produce at any point on AB with fewer inputs (X1 and X2) in this case at Q in an input-
input space. Therefore, the point Q is technical efficient because it lies on the efficient
isoquant .The degree of technical efficiency (TE) of such a farm is measured as TEi
=OQ/OP. The proportional OQ/OP is reduction of all inputs that could theoretically be
achieved without any reduction in output. In figure 2.1, DD' represent input price ratio or
iso-cost line, which gives the minimum expenditure for which a firm intending to
maximize profit should adopt.
x 2/y
R > Q
P
Q'
2
x1/y
B
D
O
A
D’
13
The same farm using (X1 and X2) to produce output P would be allocatively inefficient in
relation to R. Its level of allocative efficiency( AE) is represented by OR/OQ = AEi ,
since the distance RQ represents the reduction in production costs if the farmer using the
combination of input (X1 and X2) was to produce at any point on D D', particularly R
instead of P. The overall (economic) efficiency (EEi) is measured as the product of
OQ/OP and OR/OQ, which is OR/OP. EEi = OQ/OP * OR/OQ = OR/OP. This follows
from interpretation of distance RP as the reduction in costs if a technically and
allocatively inefficient producer at P were to become efficient (both technically and
allocatively) at Q'. These forms reflect alternative behavioral objectives (i.e. profit
maximization or cost minimization) and can account for multiple outputs (Coelli, 1995).
2.5 Technical Efficiency: Empirical Application
This section presents a review of some of the technical efficiency studies .The stochastic
frontier production function (SFP) was independently proposed by Aigner, et al., (1977)
and Meeusen and Van den Broeck (1977). Some of the main researchers who have
utilized the stochastic frontier approach are: Battese and Coelli (1995); Battese (1996);
Abdulai and Huffman (2000); Thiam, et al., (2001); Awudu and Eberlin (2001); Gautam
and Alwang(2003); Khairo and Battese (2005). Many studies have been carried out on
technical efficiency in Africa and beyond. Lingard et al., (1983) applying a two-
component model to panel data estimated a bias free agricultural production function for
the Philippine rice farmers in Luzon District.
14
The study showed that area was dominant in earlier years when the technology was
introduced, while other variables (such as irrigation, fertilizers and chemicals) became
significant overtime, reflecting full adoption of the technology.
Banik (1994) carried out a study on technical efficiency of irrigated farms in a village of
Bangladesh and used a stochastic production frontier. He used a Cobb-Douglas function
and used the Maximum Likelihood estimates (MLE) method to estimate the parameters
of the stochastic frontier Cobb-Douglas production function. The index of the technical
efficiency level for each individual farm was calculated estimating the one side error
component. The results showed that 88 out of 99 farms had a technical efficiency of 71
percent or above. A very interesting finding was that ten out of thirteen most efficient
farms belonged to the category of small farms. The study also revealed that owner-tenant
farms were technically more efficient than owner farms.
Panda (1996) used a frontier production function which he derived from Cobb-Douglas
production function and estimated by Corrected Ordinary Least Square (COLS) method.
Corrected Ordinary Least squares (COLS) models is among the most commonly used
parametric methods such as ,Ordinary least squares (OLS), and Stochastic Frontier
Analysis (SFA). In other words, the SFA models take both inefficiency and random noise
into account. When using COLS it is good practice to perform quantile analysis. Quantile
analysis helps to overcome the possible effect of outliers on the estimated mean allowing
the analyst to detect the presence of performers on specific or extreme quantiles such as
the lower (25%) or the upper (75%) quantiles.
15
From the estimated equation, Timmer’s measure of technical efficiency and Copp’s
measure of allocative efficiency of various resources utilized in sericulture farms were
examined. The study revealed that the economics of sericulture was highly profitable
both in the traditional and non-traditional areas. The study also identified major
constraints in sericulture development as being inadequate trained manpower.
Kakhobwe (2007) carried out an analysis of technical efficiency of mixed intercropping
and relay cropping Agroforestry technologies in Zomba district in Malawi and a
stochastic production model of parametric approach specified by Battese and Coelli
(1995) to evaluate technical efficiency of Mixed and Relay cropping Agroforestry
Technologies and identify factors that determine the technical efficiency of farmers.
The results revealed that larger proportion of the farmers practicing, and relay cropping
Agroforestry technologies and NA produce maize below their frontier levels implying
that farmers did not effectively use their resources in maize production. The study further
revealed that age of household head and land fragmentation were the determinants of
technical efficiency of relay cropping agroforestry technology.
Belbase and Grabowski (1985) used corrected ordinary least squares (COLS) technique
to measure technical efficiency of farmers in Nuwakot District in Nepal.The
appropriately adjusted (removing the outliers) results showed that the Nepalese farmers
were operating close to the technical frontier. The factors contributing positively to
16
technical efficiency were: nutrition levels, family incomes and education. The structure
(farm size) of the farms was taken as given, yet as noted by Mbowa (1996), the variable
bears a significant influence on technical efficiency. Further, the Belbase and
Grabawoskis’ study did not deal with allocative inefficiency.
Ahmed et al., (2004) carried out a study on Cotton Production Constraints in Sudan:
Economic Analysis Approaches”. The main objective of the economic study was to
identify, analyze and evaluate the major constraints of cotton production in the Gezira
Scheme. To analyze technical efficiency the study employed a stochastic frontier model.
Stochastic Production Frontier Analysis results revealed that 48 percent of cotton yield
variability was due to tenant and scheme management specific factors. And that 25
percent of the variability was due to the tenants’ technical inefficiency and 23 percent is
due to the scheme management’s inefficiency.
Yilma (1996) used three different approaches to estimate smallholder efficiency in coffee
and bananas namely, deterministic parametric, stochastic frontier approaches and DEA in
Masaka district, Uganda. The deterministic parametric approach showed differences in
mean scores of efficiencies in coffee and generally food production. The coefficients
estimated under deterministic parametric frontier model showed lower efficiency than the
stochastic frontier model, agreeing with many earlier studies (Kalirajan and Obwona,
1994 and Lingard et al., 1983). Nevertheless, irrespective of the approach used, all
farmers were found not to be producing on the frontier.
17
Mbowa (1996) used DEA to examine resource use farm efficiency on small and large-
scale farms in sugarcane production in Kwazulu-Natal. The study results showed that
small-scale farmers were technically inefficient than large-scale producers and concluded
that the size of farm operation affects level of efficiency attainable.
Abedullah (2006) did a study on “Technical Efficiency and its Determinants in Potato
Production, Evidence from Punjab, Pakistan” using Cobb-Douglas stochastic production
frontier approach. The result showed that potato farmers are 84 percent technical
efficiency implying significant potential in potato production that can be developed.
There was high correlation between irrigation of the potato crop and technical efficiency.
However, it is different in terms of type of dataset used, focus area, some regressions
used as well as geographical location.
Obwona (2006) estimated a translog production function to determine technical
efficiency differentials between small- and medium-scale tobacco farmers in Uganda
using a stochastic frontier approach. The results showed that, credit accessibility
extension services and farm assets contribute positively towards the improvement of
efficiency. One major drawback of this study is the inability of the author to show in
clear terms whether there is any differential in efficiency between the two groups of
farmers. The estimated efficiencies were explained by socioeconomic and demographic
factors. The results showed that, credit accessibility extension services and farm assets
contribute positively towards the improvement of efficiency. One major drawback of this
18
study is the inability of the author to show in clear terms whether there is any differential
in efficiency between the two groups of farmers.
Olorunfemi et al., (2006) Technical efficiency differentials in rice production
technologies in Nigeria. The study examine technical efficiency differentials between
farmers planting traditional Rice and those planting improved verities in Nigeria,
estimated a Cobb–Douglas production function through a method of ordinary least square
(OLS) and discovered that labour and seed inputs were inefficiently utilized. Farm size
(scale of operation) and the level of technology were not taken into consideration.
Elibariki (2005) this study describes the technical efficiency of sugarcane production and
the factors affecting in Tanzania. This efficiency was estimated using the Cobb-Douglas
production frontier assumed to have a truncated normal distribution. The study
determined and compared the level of technical efficiency of out grower and non-out
grower farmers, and examined the relationship between levels of efficiency and various
specific factors. The results of the estimation showed that there were significant positive
relationships between age, education, and experience with technical efficiency.
Nyagaka (2009) carried out a study on Economic Efficiency of Smallholder Irish
Potato Producers in Kenya: A Case of Nyandarua North District using stochastic frontier
function. The Tobit model Tobit model is used to derive efficiency indices as a function
of a vector of socio-economic characteristics and institutional factors.
19
The result show decreasing returns to scale in production, education, access to extension,
access to credit and membership in a farmers association positively and significantly
influence economic efficiency. According to our knowledge there exists very little
literature dealing with technical efficiency in Rwanda. One study found is for Byiringiro
and Reardon (1996) “Investigated the effects of farm size, soil erosion and soil
conservation investments on land and labour productivity and allocative efficiency in
Rwanda”. The authors concluded that there is a strong inverse relationship between farm
size and land productivity. Furthermore, for small farms, there was evidence of
inefficiency in the use of land and labour, the cause being attributed to factor market
access constraints.
20
CHAPTER THREE
METHODOLOGY
This chapter provides information on methods adopted for data analysis in the study as
well as sampling design, sample size determination and data collection procedure.
3.1 Analytical framework
3.1.1 Theoretical framework: Stochastic frontier production
For a long time, econometricians have estimated average production functions. It is only
after the pioneering work of Farrell (1957) that serious considerations have been given to
the possibility of estimating the so-called frontier production functions in an effort to
bridge the gap between theory and empirical work. (Aigner, Lovell and Schmidt, 1977).
According to Farrell, technical efficiency reflect the ability of the firm to maximize
output for a given set of resource inputs while allocative (factor price) efficiency reflects
the ability of the firm to use the inputs in optimal proportions given their respective
prices and the production technology.
Following Farrell’s (1957) work, there has been a proliferation of studies in the field of
measuring efficiencies in all fields. However over the years, Farrell’s methodology had
been applied widely while undergoing many improvements. And of such improvement is
the development of the stochastic frontier model which enables one to measure farm level
technical and economical efficiency using Maximum Likelihood Estimation (MLE) a
Correction of Ordinary Least Square (COLS). A stochastic model originally was
pioneered by Aigner and Chu (1968) who proposed a composed error term. Building on
21
the work of Aigner and Chu (1968) a stochastic frontier model was developed (Aigner, et
al., 1977, Meeusen and van den Broeck, 1977, Battese and Corra, 1977).
Following the specification stochastic production frontier can be written as:
( ) Niexi
ii fY .............,2,1, == εβ (3.1)
Where Yi is the yield of potatoes for the i-th farm, xi is a vector of k inputs (or cost of
inputs), ββββ is a vector of k unknown parameters, εi is an error term. The stochastic
production frontier is also called “composed error” model, because it postulates that the
error term εi is decomposed into two components: a stochastic random error component
(random shocks) and a technical inefficiency component as follows:
uv iii −=ε (3.2)
The model used in this paper is based on the one proposed by Battese and Coelli et al.,
(1995) and Battese et al., (1996) in which the stochastic frontier specification
incorporates models of technical inefficiencies effects and simultaneously estimate all the
parameters involved in the production function. The stochastic production frontier
functional form which specifies the production technique of the farmers is expressed as
follows:
iiii uvxfY −= exp);( β (3.3)
Where iY represents of potato output, which is measured in kilograms, ix represents the
quantity of input used in the production, iv represents random errors assumed to be
independent and identically distributed Ν(0, σν2) and iu represents the technical
inefficiency effects assumed to be non-negative truncated of the half-normal distribution
22
Ν(µ, σu2). The truncated-normal distribution is a generalization of the half-normal
distribution. It is obtained by the truncation at zero of the normal distribution with
mean µ, and variance,σ 2u . If µ is pre-assigned to be zero, then the distribution is half-
normal. Only two types of distributions are considered such as, half –normal and
truncated-normal distributions. These two distributions allow for a wider range of
distributional shapes but this comes at the cost of computational complexity.
This technique was developed by Coelli (1996) and has been used extensively by various
authors in estimating technical efficiency among crop farmers. The two error components
(v and u) are also assumed to be independent of each other. The variance parameters of
the model are parameterized as:
10;2
2222 ≤≤=+= γ
σσγσσσ and
s
uuvs (3.4)
The parameter γ must lie between 0 and 1. The maximum likelihood estimation of
equation (1) provides consistent estimators for theβ , γ, and σ 2s parameter, where,
σ 2s explains the total variation in the dependent variable due to technical inefficiency
(σ 2u ) and random shocks (σ 2
v ) together (Jondrow et al 1982).
The technical efficiency of individual farmers is defined as the ratio of the observed
output to the corresponding frontier output, conditional on the level of input used by the
farmer.
23
Hence the technical efficiency of the farmer is expressed as:
Nyabihu district is bordered by Rubavu district in West, in North, Democratic Republic
of Congo and Musanze district in East Gakenke district and south Ngororero and Rutsiro
districts. It is divided into 12 administrative sectors2 that is Bigogwe, Jenda, Jomba,
Kabatwa, Karago, Kintobo, Mukamira, Muringa, Rambura, Rugera, Rurembo and Shyira
and 73 cells3.
3.5 Testing of Multicollinearity
Multicollinearity refers to the presence of linear relationships or near linear relationship
among explanatory variables in OLS assumption violation of a regression function
(Gujarati, 2005). Multicollinearity can be caused due to wrong model specification and
the use of lagged variables in a regression model. Economic variables tend to move
together hence causing multicollinearity. For example in times of boom production and
wages are high and the reverse is true in times of recession (Gujarat and Sangeetha,
2007).
However, the OLS estimated coefficients are always unbiased. Due to unbias coefficients
may be statistically insignificant thus causing wrong signs and high R2 at different
times. As a result, hypothesis testing becomes weak so that diverse hypotheses about
parameter values can be rejected. Therefore it was important to evaluate the existence of
multicollinearity. Kennedy, (1985) also states that a value of 0.8 or higher in absolute
terms in one of the correlation coefficients indicates a high correlation between the
independent values in which it refers. Based on this criterion, the correlation coefficients
do not exist in relation to multicollinearity. See Appendix 3 2 The sector ( Umurenge) is the next level of administration in the contry 3 The cell (Akagari ) is the smallest politico-administrative unit of the country
35
CHAPTER FOUR
RESULTS AND DISCUSSIONS
This chapter presents the data and discusses findings of the study. It is organized as
follows; section one presents brief description of important household characteristics of
potato production, section two deals with technical efficiency estimates and factors
explaining the observed inefficiency while section contains discussions of results of the
study.
4.1. Householder Characteristics This section discusses the characteristics of the small holder farmers who are involved in
potato production. The specific household characteristics considered here were:
household size, gender, marital status, education and experience in potato production of
the household head.
4.1.1 Household size Family size can explain the level of production through its effects on labor availability
and food consumption. Figure 4.1 below shows that household size among potato farmers
range between 3 to 12 persons with estimated average of 7 persons. This suggests that
they may have a reasonably large family size which may provide more family labor in
production than other households with different size. In other words, majority of the men
in that region are polygamous.
36
Figure 4.1: Family size
Family size in percentage
0
5
10
15
20
3 4 5 6 7 8 9 10 12
Number of persons in the household
Per
cent
age
Family size in percentage
Source: Author’s presentation, 2010
4.1.2 Gender and Maritial status
A total of 123 household heads from the district were retained in the sample (after
excluding 25 outliers). Table 4.1 shows that 73.1 percent were males involved in potato
production while 15.4 percent are females. Bagamba (2007) contends that men are
capable of doing more tedious work which is usually associated with farming than the
females. He also asserts that farms managed by men were expected to attain higher
technical efficiency than those that were managed by women.
37
Table 4.1 Farmer distribution according to gender and marital status
Sex Marital status
Single Married % Divorced Widowed Total
Male 3 90 73.1 1 0 94
Female 3 19 15.4 3 4 29
Total 6 109 88.6 4 4 123
Percentage 4.8 88.6 3.5 3.5 100
Source: Author’s presentation, 2010
The marital status of the households as illustrated in table 4.1 indicates that 88.6 percent
of the respondents were married well as 3.5 percent of farmers were divorced widows
while 4.8 percent were single. Various factors explain this gender difference engaged in
potato production. For example, women in rural areas are mostly involved in domestic
activities such as collecting water and firewood. Therefore, it may not be easy for women
to afford extra time to do field activities.
38
4.1.3 Education Level A positive relationship is expected between education level and management productivity
(DAtchoarena et al., 1983). A farmer’s level of education is expected to influence his
ability to adopt agricultural innovations and make decisions on various aspects of
farming. Education is therefore highly important for sustainable agricultural growth and
development. Figure 4.2 indicates that education level of respondents were low given that
those who attained formal education in primary were 35.7 percent and those of secondary
level were 2.4 percent.
The results also show that 56.9 percent of potato farmers did not complete primary
education. This is a challenge to the extension staff in the area to ensure that farmers are
trained on modern farming practices. Kalirajan Bravo-Ureta and Evenson (1984, 1994)
contend that the impact of education on efficiency is negative. Their argument is that
when a farmer gets access to better education, he or she may get better opportunities
outside the farming sector to pursue other income earning activities. Therefore, this
reduces labor availability for a farm production in the household thereby lowering
efficiency.
39
Figure 4.2: Education level of household head
Education level of household
56%
36%
5%
3%
0%
Never went to school(56.9)
Primary(35.7)
Tertiary(4.8)
Secondary(2.6)
University(0)
Source: Author’s presentation, 2010
4.1.4 Area under Potato Production Land is a limiting factor of production in Rwanda. The area under potato production
ranges from 0.2 ha to 1.5 ha. The average potato farm area in the study area was 0.34
hectares. The largest cultivatable land for potato production is between 1 and 1.5
equivalents to 67.4 percent of the total land well as that land between 0.5-09 hectares is
the smallest and covers 22.7 percent of the cultivatable area .
40
Table 4.2 Potato Farm area category
Farm size Category(ha) Percentage
0.20-0.49 9.7
0.5-0.9 22.7
1- 1.5 67.4
Total 100
Source: Author’s presentation, 2010
4.1.5 Experience of growing potato
The results of this study as presented in table 4.3 shows that 86.2 percent had farming
experience of growing potato of more than 10 years. On the average all farmers had an
experience of 12 years in potato production. Kabede (2001) argues that increasing
farming experience lead to better assessment of importance and complexities of good
farming decision including efficient use of inputs.
41
Table 4.3 Experience of farmer growing potato
Source: Author’s presentation, 2010
4.1.6 Extension services Table 4.4 shows that 63.4 percent of farmers confirmed that extension agents had visited
them in September 2009 and March 2010 thus providing them basic agricultural skills
while 36.5 percent of the farmers were not able to access extension workers. Farmers
provided with basic agricultural skills were taught modern agricultural technology for
input use and disease control.
Experience of farmer growing potato (Year) Percentage
1-4 1.6
5-8 8.1
9-10 4.0
>10 86.2
Total 100
42
Table 4.4 Extension service visit
Extension services visits Percentage
Yes 63.4
No 36.5
Total 100
Source: Author’s presentation, 2010
4.1.7 Accessibility to credit The results of the study in table 4.5 showed that 89% of the respondents did not have
access to any form of credit while only 11.3% of the farmers had access to credit. Access
to credit improves problem of liquidity and enhances use of agricultural inputs in
production. Lack of credit facilities affected inputs acquisition especially among cash
constrained farmers. Farmers’ accessibility to credit through credit cooperatives can
reduce constraints encountered in production hence increasing the efficiency of farmers.
Table 4.5 Distribution of farmer to access to credit
Access to credit of the farmer Percentage
Yes 11.3
No 88.6
Total 100
Source: Author’s presentation, 2010
43
4.2 Estimation of Technical efficiency in potato production In the analyses of technical efficiency and its determinants, it is necessary to test the
presence of inefficiency in the production of the sample households. The test was carried
out by estimating the stochastic frontier production function. Likelihood-ratio test was
used to test null hypothesis of no technical inefficiency. The test statistics were computed
automatically when the frontier model was estimated using STATA.
4.2.1 Output and input variables in potato production The summary of the production function variables is presented in Table 4.6. The result
indicates that, the mean output per farmer in potato production was about 16,155 kg. The
analysis of the inputs revealed that the average farm size under potato production ranged
from 0.48 ha to 8 ha per farmer of minimum and maximum size of hectares of land
respectively. The mean hired labor was 79.72 man-days while family labor was 8.16
man-days. This shows that potato farmers depend heavily on hired labour to do most of
the farming operations. Labour constitutes the most important input into smallholder
agricultural production in Nyabihu.
The average amount of fertilizers used and pesticide applied was 18.16 Kg and 20.46 kg
respectively. The use of pesticides has been observed as a major labor saving device as
the labor requirement for weeding always accounts for a high proportion of the total farm
cost of labor in potato production. The average quantity of seeds for sampled farmers
planted was 3032.94 kg. The quantity and type of seed planted by potato farmers has a
lot of implications for yield realized.
44
Table 4.6 Summary descriptive statistics of output and input variables in Potato production (Kg, ha, man-day) Variable Mean Std Minimum Max Sample size
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ENTERED BY ______________________________________DAY
___________MONTH_________
DURATION OF INTERVIEW (MINUTES) MIN
1A . Number of persons in the household -------------------------------------------------------------------
74
B. CHARACTERISTICS OF HOUSEHOL
2. What are the main problems that you experience in
Potato production?
Name of household B2A.
sex
1. Male
2.Famale
B3A.
Age
(years)
B4A.
Marital Status
Single=1
Married =2
Divorced/separate
d =3
B5A.
Education level
Never went to school=1
Not finished primary
school =2
Finished primary
school = 3
Professional school = 4
Not finished primary
school =5 Finished
secondary school=6
Not Finished
university=7
Finished university=8
B6A.
For how long
have you been
growing
potato
One year =1
Five years =2
Ten Years =3
More ten
years = 4
75
3. C. LAND
C1A . What is the total size of your farm?
acre =1
half of hectare =2
Hectare = 3
More than hectare = 4
Less than acre =5
-------------------------
C2B Is it your own land : Yes =1 No =2 ----------------
C2C If is yes :
Is it inherited=1 --------------
You Purchased it =2
1&2 = 3
C2D Is it your rented =1
-------------
Is it your borrowed =2
1&2= 3
76
4. D LABOUR USE IN POTATO
D 1A . LABOUR USE IN POTATO
Activity D1A. Land preparation
D1B. Planting
D1C. Weeding
D1D. Fertilizer/ manure application
D1E. Harvesting
D1F. Transportation
D1G .others
Did use the Family labour Yes= 1 No =2
How many
hours – days
How many
person-days
The price
person –day
The total
labour cost
5. E. Do you belong to any to the Association, Cooperative of Potato production? Yes= 1 No = 2 E1A Which position do you have in the cooperative? Chairman =1 Secretary =2 Member only =3
---------------
E2B How the cooperative help you in potato production?
6 . F. POTATO PRODUCTION
F1A. What was the yield
(in kilograms) from last 2
season’s crop?
F2B.size of
Farm
F3C. Seed
used
F4D. Production
(Kgs)
F5D.Price( Kg) F6E.
Total
77
7. G INPUT USE IN POTATO
Did you use inputs (fertilizers, manure, pesticides, others) in your potato last seasons?
Yes/No………………
8. H . SEED USE IN POTATO
H1A What variety(ies) of potato did you grow last
season?
H3A.Which seed did you grow last year? ..................
H4A How many kilograms of seed did you plant?
-------------------------Kg
Improved =1
Local =2 -----------------
---
Names of Fertilizer
Code G1A. Quantity? G1B
Measurement
Kilo=1
Litter= 2
G1C. Price kg G1D. Total
a.Fertilizer
NPK=1
DAP=2
Urée= 3
b. Manure
Imborera =1
Amase =2
Amatungo magufi
=3
Ikimoteri =4
c. Pesticide
Dithane =1
Ridomir=2
Thiodan=3
f. other (specify)
78
H2A Where did you get the seed that you planted
from?
Own =1
Neighbor =2 --------------
-----
Government assistance =3
other= 4
H5A. Did you apply fertilizers on the plot of potato last season?
Yes=1 ------------
No=2
H6A If Yes , how many ?...........................................Kgs.
9.I. PRODUCTION OF LIVESTOCK KEPT
Type I1A. Number Kept
I2A. Number of cows milk kept
I3A.How many days did you milk your cow
I4A.How much liters do you get /days Litres/day
I5A. Which animals assist you to increase fertilizers(manure)
I6A. Production (specify product and units) Meat = 1 Eggs =2
I7A.Price of liter of milk Egg =kilo Meat=kilo
I8A.
Total
Cow=1
Goat=2
Sheep=3
Pig=4
rabbit=5
Poultry
= 6
10. J. LABOUR USE IN LIVESTOCK
Type G1A. Activity G2A. Do you use family
labor .
Yes = 1 -------------
No = 2
G6A If , Yes, How much
Person –day
G3A. Do you pay your lobar
monthly? How many hours – days
79
1. Cow Feeding
Milking
Tick control
Deworming
Others (specify)
2. Poultry Feeding
11. K. ACCESS TO CREDIT
K1A Do you access credit to enhance Potato production? (1) Yes…... (2) No……
K2 A If yes, Please fill the table below:
K2B Source of
credit
K2C
Amount
K2D
Repayment
period
K2C.Interest
rate
K2D. Did the credit
assist you to grow the
potato?
K2E. How did you
utilize it?
K2F If no, why not:
The banks and Micro finance institutions are far = 1
What the requirement to access to the credit? =2
The rate of interest rate is high.=3
Other =4
80
12. L. EXTENSION SERVICES
L1A Do you receive extension officer visit you about
potato production last season?
Yes = 1
No= 2 ------------------
K2A If Yes , How many times in a month 1)Once a month ------------------------