1. No category

Agricultural Systems 63 (2000) 111±121
www.elsevier.com/locate/agsy
Determinants of chemical input use in
peri-urban lowland systems: bivariate probit
analysis in Cameroon
G.B. Nkamleu a,*, A.A. Adesina b
a
International Institute of Tropical Agriculture, BP 2008, Yaounde, Cameroon
Rockfeller Foundation, 420 Fifth Avenue, New York, NY 10018-2702, USA
b
Received 29 March 1999; received in revised form 30 September 1999; accepted 13 December 1999
Abstract
Low use of chemical inputs has been cited as a major factor limiting productivity growth
of agriculture in most of sub-Saharan Africa. A wide range of variables in¯uence adoption of
such input. It is important to understand the role of these factors to ensure the development
and implementation of more e€ective programs to promote agricultural input use. This study
examines the e€ect of socio-economic factors on the likelihood of using chemical fertilizer and
pesticide in peri-urban lowland agricultural systems in Cameroon. Rather than the univariate
probit model which is commonly used, the bivariate probit model is employed to take account
of the correlation between the disturbances. Results generally indicate that lowland farmers
who are more highly educated, those with temporary land rights and those whose ®elds are
more distant from the homestead are more likely to use chemical fertilizer. In the same way,
lowland male farmers, those who have contact with extension, those who have temporary
land rights or those practising continuous cropping are more likely to use chemical pesticides.
# 2000 Elsevier Science Ltd. All rights reserved.
Keywords: Bivariate probit model; Chemical inputs; Adoption; Cameroon
1. Introduction
The ®rst thing generally discussed about sub-Saharan African agriculture is the
malthusian specter of impending disaster from population increasing faster than
agricultural output (Mensah, 1989; Sanders, 1997). All now know that over the last
* Corresponding author. Fax: +237-23-7437.
E-mail addresses: [email protected] or iita- [email protected] (G.B. Nkamleu).
0308-521X/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.
PII: S0308-521X(99)00074-8
112
G.B. Nkamleu, A.A. Adesina / Agricultural Systems 63 (2000) 111±121
two decades in sub-Saharan Africa, food production has been increasing at around
2% annually with population growing at 3% (Hailu, 1990; World Bank, 1995;
Sanders, 1997).
Many developing countries will need to double their food production by 2020 if
they are to successfully feed their burgeoning population (Hazell, 1995).
Nevertheless, as population pressure increased and the possibility of taking new
land under cultivation became limited, farming systems responded by reducing fallow periods and cultivating available land more frequently. Under these relatively
intensive systems, however, soil regeneration by leaving cultivated land under fallow
is no longer possible, and pest problems are becoming more important.
In spite of this critical problem, the use of modern agricultural inputs such as
chemical fertilizers and pesticides is generally quite low in sub-Saharan Africa
(Hailu, 1990; Pretty, 1995; Adesina, 1996; Nkamleu et al., 1999). However, the pesticide and fertilizer consumption has increased rapidly in developing countries. For
example, in the last 45 years, nitrogen consumption has risen from 2 to 75 million
tons while pesticide consumption has risen by 10±30% in the 1980s (Pretty, 1995).
Important gain realized in agricultural productivity during the last 50 years are
largely due to use of chemical pesticide and fertilizer (Wilson, 1989). Around 70 and
90% of recent increase of production is due to the rise in productivity (Pretty, 1995).
However, exclusive reliance on chemical input has adverse e€ects on the environment, such as the destruction of the natural complex. Given concerns about the
potential negative externalities of chemical input use, social scientists are actively
working with natural scientists to develop integrated pest management (IPM; Leeper and Andaloro, 1984; Delucchi, 1989) and integrated plant nutrition systems
(IPNS; FAO, 1987, 1989) that reduce reliance on chemicals.
A wide range of studies have shown that farmers can maintain or improve yields
following adoption of IPM and IPNS as well as maintain or increase pro®ts (Allen
et al., 1987; Conway and Pretty, 1991, cited by Pretty, 1995).
In most African countries, however, IPM and IPNS is not yet part of national
policy and information is lacking on the role of price and non-price factors in
in¯uencing farmers' use of pesticides and fertilizers.
The development of sustainable plant nutrition and pest management strategies
requires information on current use of pesticides and fertilizers by farmers and factors a€ecting adoption of those inputs.
The objective of this paper is to determine the factors a€ecting the adoption of
chemical fertilizers and pest control technologies within the peri-urban lowland in
Cameroon. Analysis is conducted on primary farm-level data collected in 1997 from
327 farmers in three regions of the country. Very few studies have examined the
determinants of farm-level fertilizer and pesticide use in sub-Saharan Africa (Kebede
et al., 1990). Yet, no empirical study has been carried out in Cameroon. The earlier
studies used univariate probit or logit model. Bivariate probit model, which has the
advantage of taking into account the correlation between the disturbance, is applied
in this paper.
The rest of the paper is divided into six sections. Section 2 provides overview
on data source, Section 3 outlines the bivariate probit model, its characteristics
G.B. Nkamleu, A.A. Adesina / Agricultural Systems 63 (2000) 111±121
113
and its estimation. Section 4 presents the empirical model speci®cation, while
Section 5 discusses the results. The paper ends in Section 6 with conclusions and
recommendations.
2. Data source
To capture the major socio-economic and agro-ecological factors interacting
across the peri-urban and urban lowlands of the humid forest zone of Cameroon, an
extensive national survey was conducted in 1997. The survey was the ®rst e€ort
towards bringing out the potentials, constraints and suggesting appropriate alternatives for their sustainable management.
A ®rst rapid rural appraisal was used to assess the utilization patterns of the lowlands in the peri-urban and urban zones of Yaounde, Mbalmayo and Ebolowa. This
was complemented by a detailed ®eld and household survey to identify relevant
agro-ecological factors (cropping systems, land, water and soil fertility management
strategies, pest control technologies, etc.), and socio-economic characteristics of
farmers. The data on which the empirical model is based were collected on a strati®ed random sample of 414 farmers. Some have been dropped out in the sample
estimation because of missing data. Questionnaire and check lists were used to guide
data collection. Detailed information pertaining to input, outputs, prices and other
socio-economic variables have been collected.
3. Conceptual model: the bivariate probit model
Several empirical studies have tried to capture the in¯uence of socio-economic
variables on farmers' adoption decision. In most cases, the use of probit or logit
model is applied (Rahm and Hu€man, 1984; Hailu, 1990; Kebede et al., 1990;
Adesina, 1996). In these models, farmers are assumed to make adoption decisions
based upon an objective of utility maximization. De®ne the chemical fertilizer by
``f '' and pesticide by ``p'', where f, p=1 for the adoption, and f 0 , p=0 for nonadoption. The underlying utility function which ranks the preference of the ith
farmer is assumed to be function of farmer-speci®c attributes, ``X '' (e.g. age, sex,
farm size, etc.) and a disturbance term having a zero mean:
Ui1 …X† ˆ 1 Xi ‡ "i1 for adoption and Ui0 …X† ˆ 0 Xi ‡ "i0 for non-adoption:
As the utilities are random, the ith farmer will select the alternative ``adoption'' if
and only if Ui1>Ui0.
Thus, for the farmer i, the probability of adoption is given by:
}…1† ˆ }…Ui1 > Ui0 †
}…1† ˆ }…1 Xi ‡ "i1 > 0 Xi ‡ "i0 †
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G.B. Nkamleu, A.A. Adesina / Agricultural Systems 63 (2000) 111±121
}…1† ˆ }…"i0 ÿ "i1 < 1 Xi ÿ 0 Xi †
}…1† ˆ }…"i < Xi †
}…1† ˆ …Xi †
where is the cumulative distribution function for ". The functional form for will
depend on the assumptions made about ". A probit model arises from assuming the
normal distribution for ". Thus for a farmer ``i'', the probability of the adoption of
chemical fertilizer and pesticide, respectively, is given by:
2
1
ÿt
p exp
dt
f …Xi † ˆ
2
2
ÿ1
… Xi
p …Xi † ˆ
2
1
ÿt
p exp
dt
2
2
ÿ1
… Xi
The two equations can be estimated consistently by individual single equation
probit methods. However, this is inecient in that it ignores the correlation between
the disturbances "f and "p of the underlying stochastic utilities function associated
with fertilizer and pesticide, respectively (Green, 1992, p. 465).
In this paper, the bivariate probit model is employed to circumvent inadequacies
of the single probit or logit model. The bivariate probit model is based on the joint
distribution of two normally distributed variables and is speci®ed as (Green, 1993;
Brorsen et al., 1996):
f … f; p† ˆ
2
2
2
1
p eÿ…"f ‡"p ÿ2"f "p †=…2…1ÿ ††
2
2f p 1 ÿ "f ˆ
e ÿ f
f
"p ˆ
p ÿ p
p
where is the correlation between e and p. The covariance is fp= f p. f, p, f,
and p are the means and standard deviations of the marginal distributions of f and
p, respectively. The distributions of f and p are independent if and only if =0.
The most suitable technique of estimation when using bivariate probit model is
the full information maximum likelihood. The technique requires the use of an
iterative algorithm. Through LIMDEP econometrics computer program we use the
Davidon/Fletcher/powell (DFP) algorithm.
G.B. Nkamleu, A.A. Adesina / Agricultural Systems 63 (2000) 111±121
115
4. Empirical model
The empirical speci®cation of the adoption model is employed to investigate the
pesticide and fertilizer adoption decision. A bivariate probit model is developed to
examine the relationship between socio-economic characteristics and the use of each
chemical input. Adoption behavior is a€ected by the acquisition of information
(Rodolfo and Nayga, 1996). Previous studies in sub-Saharan Africa suggest that
information acquisition, and consequently adoption behavior, is in¯uenced by various individual characteristics (Hailu, 1990; Adesina, 1996; Nkamleu et al., 1998).
Consequently, these factors are hypothesized to be important determinants of an
individuals' ability to process new information into changed behavior.
The descriptive statistics of the variables included in the empirical model are given
in Table 1. The dependent variable is whether or not the farmer used pesticide and/
or fertilizer. For chemical pesticide, this variable is given by PEST, and for fertilizer,
the variable is FERT which take on the value of 1 if the farmer is currently using the
input and 0 otherwise. The farmer-speci®c socio-economic explanatory variables are
gender of the farmer (SEX), age of the farmer (AGE), households' family size
(NADULT), marital status (MSTATUS), origin of farmer (NATIVE), level of education (EDUC), years of experience in lowland practice (EXPE), distance of the
farmers' ®eld from homestead (DISTHOME), non-farm cash income (EXTRAREV),
Table 1
Description mean and standard deviation of the independent variables used in the analyses
Variables
SEX
AGE
NADULT
Description
Gender of the farmer. 1=male, 0=female
Age of farmer in years
Households' family size. No. of adults
in the household
MSTATUS Marital status. 1=married, 0=not married
NATIVE
Is farmer a native of the village. 1=yes,
0=no
EDUC
Farmers' level of education proxy. 0=no
formal education, 1=non-formal vocational
training, 2=primary school, 3=secondary
school, 4=post secondary
EXPE
Farmer's years of experience in lowland
DISTHOME Distance of the farmer's ®eld from the
homestead (km)
EXTRAREV Whether or not farmer received cash income
from non-farm employment. 1=yes, 0=no
CONT
Contact with extension. 1=yes, 0=no
TENR
Indexes the land tenure rights. 1=farmer
has permanent tenurial rights, 0 otherwise
FPRAT
Indexes whether or not farmer practises
continuous cropping or fallow systems.
1=continuous cropping, 0=fallow systems
Continuous variables Categorical variables
Mean
S.D.
38.99
3.37
21.69
2.29
5.53
0.83
(Percentages)
1=39
0=61
1=68
1=26
0=32
0=74
0=12
2=45
4=3
1=4
3=36
1=41
0=59
1=24
1=46
0=76
0=54
1=51
0=49
6.45
1.45
116
G.B. Nkamleu, A.A. Adesina / Agricultural Systems 63 (2000) 111±121
contact with extension service (CONT), land tenure rights (TENR), and fallow
practice (FPRAT). The discussion for the expected signs for the coecients of the
independent variables included in the model is provided below.
SEX is a dummy variable that indexes the gender of the head of household, taking
on the value of 1 if the farmer is male, and 0 if female. It has been argued by some
authors that women are generally discriminated against in terms of access to external inputs and information (Dey, 1981). It is hypothesized that SEX is positively
related to dependent variables.
AGE is a variable that measures the age of the farmer. Olders farmer may be
elders in the zone and have preferential access to new information or technologies
through extension services or development projects that work in the region. Also,
with age, farmers accumulate more personal capital and, thus, show a greater likelihood of investing in innovations (Nkamleu et al., 1998). However, it may also
be that younger farmers are more likely to adopt new technologies and/or are
more likely to be early adopters (Alavalapati et al., 1995). Young people have more
energy, and it's more important for them to invest in the long-term productivity.
Therefore, the expected sign of AGE is indeterminate.
NADULT measures households' family size. A large family often has a large
number of working members. Generally, an increase in family size is likely to increase
the probability of adoption (Kebede et al., 1990; Nkamleu and Adesina, 1999).
MSTATUS is a dummy variable, which indexes marital status of farmer, taking
on the value 1 if farmer is married and 0 if not. Generally, farmers who are married
have more people to feed. Consequently, they have the obligation of high productivity. Thus they have to use more input. We hypothesize that marital status
positively in¯uences the likelihood of adoption of chemical inputs.
NATIVE is a binary variable, which indexes whether the farmer is a native of the
village or a migrant, and takes on the value of 1 if a native and 0 otherwise.
Migrants are less likely to have access to much land, and may also face restrictions
on types of land uses they can practise, because they generally acquire land either
through begging or rentals. Therefore, they are more dependent on input use. It is
hypothesized that NATIVE is positively related to adoption of chemical pesticide
and fertilizer.
EDUC measures the level of education of farmers. A commonly stated proposition is that educated farmers are more likely to adopt new technologies and/or are
more likely to be early adopters (Falusi, 1974/5; Norris and Batie, 1987; Rahm and
Singh, 1988; Kebede et al., 1990). It is hypothesized that EDUC is positively related
to PEST and FERT.
EXPE, farmer's years of experience, re¯ects number of years since farm operator
®rst began farming lowland. With increasing experience, farmers may be able to
better assess di€erential bene®ts of chemical inputs. Therefore, we expect that EXPE
is likely to be positively associated with adoption.
DISTHOME is the distance of the farmers' ®eld from the homestead. Almost all
farmers usually use organic matter like composting or animal manure to complement the use of chemical pesticides or fertilizer. Due to the highly bulky nature of
such organic matter, transportation costs for use on distant ®elds will be very high
G.B. Nkamleu, A.A. Adesina / Agricultural Systems 63 (2000) 111±121
117
and farmers are more likely to apply them on ®elds closer to the homestead. Then,
inorganic matter will be reserved for distant ®elds. It is hypothesized that DISTHOME is positively related to chemical input use.
EXTRAREV is a dummy variable that measures whether or not the farmer
received cash income from non-farm employment sources. Studies have shown that
farmers often rely on non-farm income-generating activities to buttress returns from
agriculture. Such non-farm income may in¯uence technology adoption (Kebede et
al., 1990; Adesina, 1996). It is hypothesized that EXTRAREV is positively related to
chemical input use.
CONT is a binary variable, which measures the contact of the farmer with
extension agencies. It takes the value of 1 if the farmer has contact with extension
and 0 otherwise. Farmers who have contact with extension agents tend to have
better access to information on new technologies. Access to extension or research
agents can also substitute for lower levels of education if they assist farmers
in dealing with some of the managerial complexities of chemical pesticides and
fertilizer. It is hypothesized that CONT is positively related to the adoption of
chemical input.
TENR indexes the land tenure rights. It takes the value of 1 if the farmer
has permanent tenurial rights and 0 otherwise. Permanent land rights can be
from direct land purchase or divided inheritance. Farmers with permanent
land rights often take into account the negative long-term e€ect of chemical input
use. Also, due to the fact that permanent rights enable the use of soil regeneration
and pest control alternatives such as the planting of trees, it is less likely that chemical inputs will be used on permanent land rights compared to temporary land
rights.
FPRAT indexes whether or not a farmer practises continuous cropping or fallow
systems. It takes the value of 1 for continuous cropping and 0 for fallow systems. In
®elds with continuous cropping, fertility decreases rapidly and pest development is
observed more and more chemical inputs are needed. The probability of adoption of
those inputs will be higher in those ®elds. It is hypothesized that FPRAT is positively related to the adoption.
5. Results
Using bivariate probit speci®cation mentioned with 327 observations, a full
information maximum likelihood was used for estimation. Estimates are exhibited in
Table 2. The signi®cance level chosen for this analysis was 0.10.
Rho is signi®cant at 1%, indicating that a bivariate probit model rather than two
univariate probit is more appropriate.
Globally, three variables have a signi®cant relation with the decision of whether or
not to use chemical fertilizer, while four variables have a signi®cant relationship with
the adoption of chemical pesticides. The gender of farmer is signi®cant at 1% in the
pesticide sub-model. The coecient of sex is positive, indicating that, as expected,
men are more likely to adopt chemical pesticides.
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G.B. Nkamleu, A.A. Adesina / Agricultural Systems 63 (2000) 111±121
Table 2
Bivariate probit results of adoption of fertilizer and pesticide in peri-urban lowland of Cameroon 1997a
Independant variables
Dependant variables
Constant
SEX
AGE
NADULT
MSTATUS
NATIVE
EDUC
EXPE
DISTHOME
EXTRAREV
CONT
TENR
FPRAT
Log-likelihood=ÿ372.30
Rho ()=0.72***
Sample size=327
Fertilizer
Pesticide
ÿ0.53*
(ÿ1.79)
0.71Eÿ01
(0.45)
ÿ0.22Eÿ02
(ÿ0.64)
ÿ0.45Eÿ01
(ÿ1.29)
0.22
(1.29)
0.80Eÿ01
(0.38)
0.14*
(1.63)
0.18
(1.32)
0.12*
(1.86)
ÿ0.28Eÿ01
(ÿ0.18)
ÿ0.58Eÿ01
(ÿ0.31)
ÿ0.42***
(ÿ2.39)
0.18
(1.17)
ÿ0.78**
(ÿ2.86)
0.41***
(2.57)
ÿ0.18Eÿ02
(ÿ0.28)
ÿ0.42Eÿ01
(ÿ1.13)
0.11
(0.60)
ÿ0.16
(ÿ0.74)
0.88Eÿ01
(0.97)
0.15Eÿ01
(0.97)
0.79Eÿ01
(1.49)
0.72Eÿ01
(0.45)
0.64***
(3.28)
ÿ0.30*
(ÿ1.68)
0.27*
(1.77)
a
Figures in parentheses are corresponding t-values.
* Signi®cant at 10%.
** Signi®cant at 5%.
*** Signi®cant at 1%.
Table 3
Table of actual and predicted frequency of adoption of fertilizer and pesticidea
Pesticide
Fertilizer
a
Non-adopter
Adopter
Total
Non-adopter
Adopter
Total
152 (198)
44 (42)
196 (240)
36 (43)
95 (44)
131 (87)
188 (241)
139 (86)
327 (327)
Figures in parentheses are predicted frequency. Source: model result.
G.B. Nkamleu, A.A. Adesina / Agricultural Systems 63 (2000) 111±121
119
The educational level of the head of the household has a positive relationship
to the decision to use chemical fertilizer. The coecient of EDUC is positive and
signi®cant at 10%. Although having the expected signs, it is found that the level of
education is not signi®cantly related to the adoption of pesticides. Similarly, distance from homestead is related to fertilizer use. As expected, the coecient is
positive con®rming the fact that chemical fertilizer is applied more in distant ®elds.
In contrast, contact appears to be signi®cantly related to pesticide use and not related to fertilizer use. In inland zones of Cameroon, it is observed that a part of chemical fertilizer, animal manure and/or composting, are frequently used. In contrast,
chemical pesticide does not have so many substitutes. This may explain the fact that
chemical fertilizer is used more on distant ®elds and the relative importance of
extension contact in the adoption of pesticides.
Concerning land tenure rights, results show that land tenurial rights is signi®cant
in either the chemical or pesticide sub-model. As expected, farmers with permanent
land rights have the lowest probability of fertilizer and/or pesticide use.
Another important (statistically signi®cant) variable is cropping practice (FPRAT).
This variable has the expected sign in the two sub-models, but is signi®cant only in
the pesticide sub-model. Farmers who practise continuous cropping are the most
pesticide users; this emphasizes the fact that pest problems occur more on continuous cropping ®elds.
The actual and predicted frequency of the adoption of fertilizer and pesticide is
shown in Table 3.
6. Conclusions
The growing recognition of the spatial variability in soil nutrient content and pests
within ®elds has led to a rapid expansion of research and development of technologies that can manage input applications and crop production according to in-®eld
variations.
The bene®ts of adopting these technologies as well as the cost of adoption are
likely to vary across farmers that are heterogeneous in the availability of human
capital and technical skills, and in other socio-economic characteristics. Therefore,
speci®c information on the in¯uence of socio-economic characteristics of adoption
would be helpful in the design and implementation of more e€ective programs to
promote agricultural input use.
This study examines the e€ect of socio-economic factors on the likelihood of using
chemical fertilizer and pesticides in peri-urban lowland agricultural systems. Rather
than univariate probit model which is commonly used, bivariate probit model is
employed to take account of correlation between the disturbances. Results generally
indicate:
1. lowland farmers who are more highly educated, those with temporary land
rights and those with ®elds more distant from the homestead are more likely to
use chemical fertilizer; and
120
G.B. Nkamleu, A.A. Adesina / Agricultural Systems 63 (2000) 111±121
2. lowland male farmers, those who have contact with extension, those who have
temporary land rights or those practising continuous cropping are more likely
to use chemical pesticide.
The use of chemical input is an important agricultural issue. Chemical input has
played a signi®cant role in increasing agricultural production in the developing
world over the last several decades (Mackauer, 1989; Wilson, 1989; Pretty, 1995).
Along with high-yield crop varieties, and intensive agricultural practices, chemical
inputs have formed one of the foundations of the so-called Green revolution.
Low use of chemical inputs has been cited as a major factor limiting productivity
growth of agriculture in most of sub-Saharan Africa (Hailu, 1990; Adesina, 1996).
The decrease in fertilizer and pesticide consumption is prejudicial to crop production. Therefore, there is a need of fertilizer and pesticide promotion.
Although chemical pesticide and fertilizer use in the south is apparently still on the
rise, the trend has recently come under scrutiny by the combined e€orts of grassroots watchdog group farmers' organizations, and labor unions, as well as the FAO,
the World Health Organization (WHO), the United Nations Environment Program,
and other national and international non-governmental organizations. Consequently, many developing nations are now experimenting with alternatives. However, the development of such alternatives requires information on the current use of
chemical pesticides and fertilizer, how use varies by the type of agricultural system,
and factors a€ecting the adoption.
This study has provided useful information on factors a€ecting the adoption of
chemical input in the peri-urban lowland systems of Cameroon. E€orts to promote
soil nutrient management or pest control technologies should consider the important
role that some socio-economic characteristics can play. Further research is needed to
study the dynamics of di€usion and adoption of chemical input in other cropping
systems as well as in other sub-Saharan countries.
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