Determinants of chemical input use in
peri-urban lowland systems: bivariate probit
analysis in Cameroon
G.B. Nkamleu
a,*, A.A. Adesina
baInternational Institute of Tropical Agriculture, BP 2008, Yaounde, Cameroon bRockfeller Foundation, 420 Fifth Avenue, New York, NY 10018-2702, USA
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 eective programs to promote agricultural input use. This study examines the eect 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
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* Corresponding author. Fax: +237-23-7437.
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 fal-low 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 pes-ticide 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 eects on the environ-ment, 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; Lee-per 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 fac-tors aecting adoption of those inputs.
The objective of this paper is to determine the factors aecting 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.
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 eort towards bringing out the potentials, constraints and suggesting appropriate alter-natives for their sustainable management.
A ®rst rapid rural appraisal was used to assess the utilization patterns of the low-lands 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 Human, 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 f0, p=0 for
non-adoption. 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 1Xi"i1for adoption andUi0 X 0Xi"i0for non-adoption:
As the utilities are random, theith farmer will select the alternative ``adoption'' if and only ifUi1>Ui0.
Thus, for the farmeri, the probability of adoption is given by:
} 1 } Ui1>Ui0
} 1 } "i0ÿ"i1< 1Xiÿ0Xi
} 1 } "i< Xi
} 1 Xi
whereis the cumulative distribution function for". The functional form forwill 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:
f Xi
The two equations can be estimated consistently by individual single equation probit methods. However, this is inecient in that it ignores the correlation between the disturbances "fand "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): andpare the means and standard deviations of the marginal distributions offand p, respectively. The distributions offandpare independent if and only if=0.
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 aected 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 var-ious 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 edu-cation (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 Description Continuous variables Categorical variables Mean S.D. (Percentages) SEX Gender of the farmer. 1=male, 0=female 1=39 0=61 AGE Age of farmer in years 38.99 21.69
NADULT Households' family size. No. of adults in the household
3.37 2.29
MSTATUS Marital status. 1=married, 0=not married 1=68 0=32 NATIVE Is farmer a native of the village. 1=yes,
0=no
1=26 0=74 EDUC Farmers' level of education proxy. 0=no
formal education, 1=non-formal vocational
EXPE Farmer's years of experience in lowland 5.53 6.45 DISTHOME Distance of the farmer's ®eld from the
homestead (km)
0.83 1.45 EXTRAREV Whether or not farmer received cash income
from non-farm employment. 1=yes, 0=no
1=41 0=59 CONT Contact with extension. 1=yes, 0=no 1=24 0=76 TENR Indexes the land tenure rights. 1=farmer
has permanent tenurial rights, 0 otherwise
1=46 0=54 FPRAT Indexes whether or not farmer practises
continuous cropping or fallow systems. 1=continuous cropping, 0=fallow systems
contact with extension service (CONT), land tenure rights (TENR), and fallow practice (FPRAT). The discussion for the expected signs for the coecients 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 exter-nal 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 like-lihood 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 pro-ductivity. 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 proposi-tion 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 dierential bene®ts of chemical inputs. Therefore, we expect that EXPE is likely to be positively associated with adoption.
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 DIST-HOME 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 eect 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 che-mical 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 posi-tively 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.
Table 2
Bivariate probit results of adoption of fertilizer and pesticide in peri-urban lowland of Cameroon 1997a
Independant variables Dependant variables
Fertilizer Pesticide
Constant ÿ0.53* ÿ0.78**
(ÿ1.79) (ÿ2.86)
SEX 0.71Eÿ01 0.41***
(0.45) (2.57)
AGE ÿ0.22Eÿ02 ÿ0.18Eÿ02
(ÿ0.64) (ÿ0.28) NADULT ÿ0.45Eÿ01 ÿ0.42Eÿ01
(ÿ1.29) (ÿ1.13)
MSTATUS 0.22 0.11
(1.29) (0.60)
NATIVE 0.80Eÿ01 ÿ0.16
(0.38) (ÿ0.74)
EDUC 0.14* 0.88Eÿ01
(1.63) (0.97)
EXPE 0.18 0.15Eÿ01
(1.32) (0.97)
DISTHOME 0.12* 0.79Eÿ01
(1.86) (1.49) EXTRAREV ÿ0.28Eÿ01 0.72Eÿ01
(ÿ0.18) (0.45)
CONT ÿ0.58Eÿ01 0.64***
(ÿ0.31) (3.28)
TENR ÿ0.42*** ÿ0.30*
(ÿ2.39) (ÿ1.68)
FPRAT 0.18 0.27*
(1.17) (1.77) Log-likelihood=ÿ372.30
Rho ()=0.72*** Sample size=327
a Figures in parentheses are correspondingt-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
Non-adopter Adopter Total Fertilizer Non-adopter 152 (198) 36 (43) 188 (241)
Adopter 44 (42) 95 (44) 139 (86) Total 196 (240) 131 (87) 327 (327)
The educational level of the head of the household has a positive relationship to the decision to use chemical fertilizer. The coecient 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, dis-tance from homestead is related to fertilizer use. As expected, the coecient 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 rela-ted to fertilizer use. In inland zones of Cameroon, it is observed that a part of che-mical 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 con-tinuous 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 technolo-gies 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 eective programs to promote agricultural input use.
This study examines the eect 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:
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 produc-tion. Therefore, there is a need of fertilizer and pesticide promoproduc-tion.
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 eorts of grass-roots 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. Conse-quently, many developing nations are now experimenting with alternatives. How-ever, 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 aecting the adoption.
This study has provided useful information on factors aecting the adoption of chemical input in the peri-urban lowland systems of Cameroon. Eorts 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 diusion and adoption of chemical input in other cropping systems as well as in other sub-Saharan countries.
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