The importance of health as a form of human capital can not be over emphasized.
Good health and productive agriculture are important in the economy of
any nation especially in the fight against poverty. Health enhances work
effectiveness and the productivity of an individual through increase in
physical and mental capacities.
According to Schultz (1999) and Strauss and Thomas (1998), there is a
positive relationship between health and productivity of skilled and unskilled
labour. Good health as related to labour output or better production organization
(since people of good health generally have better intellectual capacities),
can enhance farmer`s/household income and economic growth.
The process of agricultural production and the output it generates can
contribute to both good and poor health among the producers as well as
the entire society. Being an agricultural producer is a determinant of
health relative to income and labour (Corinna and Ruel, 2006). Labour
equally predisposes producers to a range of occupational health hazards
including accidents, strains, diseases and poisoning.
Health affects Agricultural systems by affecting the health of the producers.
Poor health will result in loss of work days or decrease worker capacity,
decrease innovation ability and ability to explore diverse farming practices
and by such makes farmers to capitalize on farm specific knowledge.
Ugwu (2006), Clifford et al. (2006), Donald (2006) and Bradley
(2002) opined that health capital is affected by a number of preventable
diseases: Malaria, musculoskeletal disorders, HIV/AIDS, farminjuries,
yellowfever, typhoidfever, Schistosomiasis, Onchocerciasis, Diarrho real
diseases respiratory diseases and skin disorders, etc. These diseases
according to Ngambeki and Ikpi (1982) makes farmers not to utilize fully
all inputs at their disposal and debilitates farmer`s physical performance
and equally impacts negatively on the farm profit levels.
Amidst the alarming report of effects of diseases on farmers, Nigerian
subsistent farmers spend as much as 13% of total household expenditure
on treatment of malaria alone. This gives enough evidence that the cost
of combating diseases and health problems by the farmers are quite enormous,
considering the frequency and prevalence of diseases among the Nigerian
Recent studies estimated the economic cost (both direct and opportunity
cost) of a farmer becoming sick once to be N29, 225.53 (Ugwu, 2006). Adewale
et al. (1997) valued the opportunity cost of Guinea worm infection
on the farmer at N9, 566 bags of potential cocoa output lost due to ineffective
supervision of farms occasioned by ill health. The farmer loses on the
average 22 working days when incapacitated by one sickness or the other
per time (Ugwu, 2006; Ashagidigbi, 2004).
Developing country`s agriculture is characterized by a widespread productivity
decline (Fulginito and Perrin, 1998). Despite concerted efforts by different
Nigerian government in terms of human and material input into agricultural
system in order to attain self-sufficiency in food production, the rate
of productivity decline has persisted (FAO, 1987; Anonymous, 2006; NPC,
2006; Falusi, 1995).
In addition, Nigeria agriculture is labour intensive oriented (Rahji,
2005), implying that labour is indispensable factor of production in Nigeria`s
agriculture. That not withstanding, recent studies (FAO, 1987) has indicated
a declining agricultural labour as well as a decreasing farm size. Agulanna
(2006), Schultz (1999) and Strauss and Thomas (1998) opined that there
is a correlation between health and productivity of labour or better production
organization. Good health enhances work effectiveness and productivity
of an individual through increases in Physical and mental capacities.
It is therefore extremely difficult to separate agriculture labour supply
from the agricultural producer and health stock. The health status of
the producer of the agricultural producer determines the output of his
labour supply and hence agricultural productivity. The role of health
capital on agricultural productivity manifests in the incalculable opportunity
cost incurred when the farmers is impaired. It is therefore imperative
to reprioritize the relevance and contribution of production input variables
to agricultural productivity by all Stakeholders.
This study focuses on the principal farm operator and his productivity
conditioned on his health. The study seeks to answer the following research
questions: Do health status affect the productivity of the farmer? What
is the share of adverse health to farmers` efficiency? Would an improvement
in the health status of the farmer pay off in terms of higher productivity?
The Stochastic Frontier approach was employed to separate the impact of
adverse health on principal farmer`s productivity as well as measure the
level of technical inefficiency caused by adverse health alongside other
This study is an attempt to assess the impact of adverse health on the
technical efficiency of the principal farm operators, with a view to determine
adequate response, that will address health issues in agriculture.
MATERIALS AND METHODS
The study area Kainji lake Basin North-Central Nigeria has a population
of 102, 370 (NPC, 2006) scattered in 60 communities that are purely agrarians.
A multistage random sampling procedure was adopted to collect the data
with respect to farm specialty of communities. A total of twenty farming
communities were selected for the study, while six respondents were purposively
selected for interview from each of the twenty communities chosen. The
data was collected through well-structured questionnaires in the early
months of year 2006. A total of 120 respondents were surveyed.
The study made use of the Stochastic production function, in particular,
the translog functional form. The choice of this model is because this
model allows for the presence of technical inefficiency while accepting
that random shocks (weather or disease) beyond the control of the farmer
can affect output. The model specifies output (Y) as a function of input
(x) and a disturbance term (ei). That is;
||Output of the ith farm,
||Vector of actual input quantities used by the ith farm,
||Vector of parameter to be estimated,
||Composite error term denoted as Coelli and Batesse (1996).
||Decomposed error term measuring technical efficiency
of the ith farm,
||The inefficiency component of the error term.
The symmetric component (Vi) represent the variation in output
due to factors (weather or disease attack) beyond the farmer`s control.
This symmetric component of the error term is independently and normally
distributed as N (O, δv2). A one sided component (Ui>O)
shows technical inefficiency relative to the Stochastic frontier. Hence,
if Ui = 0, production lies below the frontier and Ui
is assumed to be independently and identically distributed and truncated
at zero with the variance δu2 (N 0, δv2).
The parameter estimators (β) and the variance parameters were obtained
by the maximum likelihood estimation method.
||The variance ratio parameter (Gamma) and by Batesse and Corra (1997),
γ = (o≤γ≤I).
The variance ratio parameter (γ) has two important characteristics:
||When δv2 tends to zero, it is the predominant
error term in Eq. 1 implying that the output of
the sample farmers differs from the maximum output mainly because
of the difference in technical efficiency.
||When δv2 tends to zero, v is the predominant error
term in Eq. 1 and so γ tends to zero, thus
differences between farmers output and the efficient output can be
determine based on the value of γ (Kalirajan, 1981).
The empirical model of the translog Stochastic production frontier function
is specified as follows:
||Value of output of ith farm in kg,
||Land area cultivated measured in hectares,
||Labour used measured in total hours worked in the farm by main farm
||Quantity of fertilizer used in kg,
||Quantity of seed used in kg,
||Quantity of insecticides used in litres,
||As defined above,
||1,2,3 ----------n, farms.
The technical efficiency for individual farm is computed as an index
and the average technical efficiency for the production system determined.
Based on a number of socio-economic factors identified to be influencing
the technical efficiency of the farms, the Coelli and Battese (1996) inefficiency
model was employed to estimate the parameters of the variables. The model
assumes that the inefficiency effect (ui) is independently distributed
with mean Ui and variance δ2.
The model is specified as:
= δ0+ δ1z1+ δ2z2+
||Dummy variable representing the primary occupation of respondents,
||Dummy variable denoting level of education,
||Dummy variable denoting the sex of the respondents,
||Actual age of respondents in years,
||Health status of respondent,
||Farming experience measure in years,
||A random disturbance following half normal distribution.
β, δ, δ2 and γ are unknown parameters
to be estimated. δ2 and γ = coefficients are diagnostic
statistics that indicates the relevance of use of the Stochastic production
frontier function and the correctness of the assumptions of the disturbance
of the error term. The gamma (γ) indicates that the symmetric influence,
the are not explained by the production function are the dominant sources
of random errors. The statistical significance of gamma shows that in
the specified model, there is the presence of a one-sided error component
(vi). This implies that the traditional OLS response function
cannot adequately represent the data and hence the use of stochastic production
frontier function estimated by the maximum likelihood estimation method
is most appropriate. The computer programme frontier version 4.1 (Coelli,
1994) was used to run the maximum likelihood analysis.
RESULTS AND DISCUSSION
The maximum likelihood estimates of parameters which reflect the best
practice farm at the existing level of technology is shown in Table
1. From the table; sigma squared δs2 =
δv2 + δu2 = 0.369,with a t-ratio
of 0.565. This is the ratio of performance of the farm specific efficiency
indices to the total variation in output due to technical inefficiency.
Batesse and Corra (1977) defined gamma (γ) as the total variation
of output from frontier which can be attributed to technical efficiency.
It indicates the estimate of the Stochastic frontier, which show the best
practice performance i.e., efficient use of available technology. It can
be observed that the estimate of g = 0.114. This implies (1-0.114) = 0.886
or 88.6% of the total variance in output of the farmers is due technical
in efficiency. Thus, on the average, the farmer`s are just realizing about
11.4% of their potential output feasible in the prevailing socio-economic
physical and health environment. In other words, the observed differential
output is resulting from farm specific performance and not just statistical
random variability. This therefore, requires attention of policy makers.
||Maximum likelihood estimate and the inefficiency function
|Source: Computer Print Out, 2006; No. of periods = 1;
No. of observation = 120; No. of iterations = 27; Values in the table
has been corrected to three significant figures; ** = Statistically
significant at 5%
The result indicates that the output of the farmers are affected not
only by the traditional input variables: land, labour and capital (fertilizer,
seed and insecticide) but equally by socio-economic factors: age, experience
and health as well as series of dummy variables such as sex, education
and type of primary occupation. The signs of the estimated coefficients
were as expected. Thus, the elasticity`s of land, labour and fertilizer
are positive while seed and insecticides are negative. This implies that
increasing the quantities of any of these inputs will increase output
except for seed and insecticide which were inversely related to output.
Fertilizer has the largest coefficient (elasticity = 0.405) meaning that
fertilizer has the largest impact on the output of the farmers in the
study area. If additional quantity of fertilizer is used on the farm,
output will increase appreciably. Land and labour were not significant
because all the farmers have access to land and land is not a problem
in the study area. All the farmers source family labour, so there is no
significant difference in the amount of labour supplied. Seed and insecticides
were inversely related to output which implies that increasing their quantities
will results in decrease output. This follows theory, that there is a
limit to increasing quantity of variable input relative to fixed inputs
in production, which if not obeyed will at a point cause output to decline.
Suggesting probably an over utilization of resources on fixed factor of
||Estimate of technical efficiency for the principal farm
|Source: Calculation from Computer Print Out, 2006
In the inefficiency model, the negative sign of the parameters indicates
that associated variables have a positive effect and vice versa. All variables
carry the expected signs. Health, education and age have positive coefficients
implying that these variables decrease efficiency of the principal farm
operator while occupation, sex and experience carry negative coefficients
implying positive effects on the efficiency.
The positive sign of age follows a prior expectation, since productivity
decreases with old age. Education has a positive sign and decreases efficiency
because of high level of illiteracy of the principal farm operators in
the study area. The health variable which is measured as average days
lost to incapacitation i.e., periods of sickness when the farmer could
not attend to his farm due to sickness multiplied by frequency of occurrence
of that sickness, thus reflecting adverse health. Health has a positive
coefficient in the model and is statistically significant at 5%. This
follows a prior expectation that adverse health impacts negatively on
the productivity of farmers. The coefficient of health is large (0.31)
i.e., 31% implying that one percent improvement on the health condition
of the farmer will lead to 31% increase in the efficiency of the farmer.
Of all the variables in inefficiency model, health has the largest impact
on the efficiency of farmers. The individual farmer`s technical efficiency
obtained from the estimated stochastic frontier is presented in the frequency
distribution (Table 2). The predicted technical efficiency
differs substantially among the farmers as it ranges from 0.28-0.99 with
a mean technical efficiency of 0.85. This implies that there is a potential
of about 15% to improve the output of the farmers.
Out of the entire variables specified in the inefficiency model, health
has the largest coefficient and is statistically significant at 5%. This
implies that the greater part of the inefficiency of the farmer is as
a result of adverse health. Put another way, it means that improvement
of the health condition of the farmer will improve efficiency greatly.
The present study is an empirical investigation on the impact of health
on agricultural productivity. The research findings bring to light the
importance of health capital as an indispensable production input in agriculture
and the economic development of the nation as a whole.
The coefficient associated with health variable of the principal farm
operator in the model is positive, large and statistically significant;
thus, the study proposes that, achieving self-sufficiency in food production
and the much desired growth in agricultural sector of the economy, will
continue to elude Nigeria, if health issues in agriculture are not properly
Policy actions to train farmers in work related risk reduction geared
at curbing infections and incapacitations occasioned by diseases, accident
and strains, may impact farmers health and agricultural productivity much
as past policies, that improved farmers access to inputs (like machinery,
land etc.). Health capital expenditure is a justified basis of promoting
development through large increases in productivity.