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Factors affecting the technical efficiency among smallholder farmers in the slash and burn agriculture zone of Cameroon
Food Policy 29 (2004) 531–545 www.elsevier.com/locate/foodpol
Factors affecting the technical efficiency among smallholder farmers in the slash and burn agriculture zone of Cameroon Joachim Nyemeck Binam a,*, Jean Tonye` a, Njankoua wandji b,1, Gwendoline Nyambi a, Mireille Akoa
a
a
b
Institute of Agricultural Research for Development (IRAD), Farming Systems, Economy and Rural Sociology Division, P.O. Box 2067, Yaounde´-Messa (Republic of Cameroon) International Institute of Tropical Agriculture, Humid forest Ecoregional Center, P.O. Box 2008, Yaounde´-Messa (Republic of Cameroon)
Abstract This paper estimates technical efficiency among small holder farmers in the slash and burn agriculture zone of Cameroon and identifies sources of inefficiency using detailed survey data obtained from 450 farmers over 15 villages throughout 2001/2002 growing season. We find that the mean levels of technical efficiency are 77%, 73% and 75%, respectively, for groundnut monocrop, maize monocrop and maize–groundnut farming systems suggesting existence of substantial gains in output and/or decreases in cost with available technology and resources. The efficiency differences are explained significantly by credit, soil fertility, social capital, distance of the plot from the access road and extension services. Policy recommendations are drawn from these findings. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Technical efficiency; Slash and burn agriculture; Stochastic frontiers
*
Corresponding author. Tel.: +237 223 89 63; fax: +237 223 74 37. E-mail address: [email protected] (J.N. Binam). 1 Tel.: +237 223 74 34.
0306-9192/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodpol.2004.07.013
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Introduction Agriculture is an important sector in the Cameroonian economy. Recent studies show that, it accounted for as much as 30% of the gross domestic product (GDP), 70% of overall employment, and over 40% of total foreign exchange earnings (DSCN, 2002). A significant feature of the agriculture in the slash and burn zone of Cameroon is the dual structure of the farming system, composed of farming practices that produce perennial export crops (coffee, cocao, bananas, oil-palm), and small peasants farms that produce annual foods for subsistence and local markets. For both systems, the productivity is low. The poor performance of the forest zone agriculture is evidenced by low standard of living in rural areas compared to cities; thus, the largest concentration of absolute poverty, illetaracy, and infant mortality is found in countryside (DSCN, 2002). There is a general agreement that a sustainable economic development depends on promoting productivity and output growth in the agricultural sector, particularly among small-scale producers. Empirical evidence suggests that small farms are desirable not only because they reduce unemployment, but also because they provide a more equitable distribution of income as well as an effective demand structure for other sectors of the economy (Bravo-Ureta and Pinheiro, 1993, 1997). Consequently, many researchers and policymakers have focused their attention on the impact the adoption of new technologies can have on increasing farm productivity and income (Hayami and Ruttan, 1985; Kuznets, 1966). However, during the last decade, major technological gains stemming from the green revolution have been effective across the developing world. This suggest that attention to productivity gains arising from a more efficient use of existing technology is justified (Bravo-Ureta and Pinheiro, 1993, 1997; Squires and Tabor, 1991). The presence of shortfalls in efficiency means that output can be increased without requiring additional conventional inputs and without the need for new technologies. If this is the case, then empirical measures of efficiency are necessary in order to determine the magnitude of the gain that could be obtained by improving performance in production with a given technology. policy implication stemming from significant levels of inefficiency is that it might be more cost effective to achieve short run increases in farm output, and thus income, by concentrating on improving efficiency rather than introducing new technologies (Belbase and Grabowski, 1985; Shapiro and Mu¨ller, 1977). In the slash and burn agriculture zone of Cameroon, groundnut and maize are the second and third most important food crops after cassava. They are planted either in monocrop or in association. Improving technical efficiency (TE) of these crops based systems will contribute enormously to improving the overall agricultural productivity in the slash and burn zone of Cameroon. From 2000 to 2003, Agricultural Projects supported not only the introduction of improved groundnut and maize varieties in the slash and burn zone of Cameroon,
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but also it supported the study of the technical efficiency of these crops-based systems. The present study sets out to analyse technical efficiency of groundnut and maizebased systems farmers in the slash and burn agriculture zone of Cameroon, and to identify farm-specific characteristics that explain variation in efficiency of individual farmers. An understanding of these relationship could provide the policymakers with information to design programmes that can contribute to measures needed to expand the food production potential of the nation. The analysis performed this way goes beyond much of the published literature concerning efficiency because much research in this area of productivity analysis focuses exclusively on the measurement of technical efficiency (Bravo-Ureta and Pinheiro, 1993; Coelli, 1995). The paper is organized as follows, the next section presents the analytical framework. The data and empirical procedure are presented in the third section. In the fourth section, the results are presented and discussed and the last section draws conclusion and implications for policy.
Analytical framework Since Farrell (1957) seminal paper, there has been a growing interest in methodologies and their applications to efficiency measurement. While early methodologies were based on deterministic models that attribute all deviations from the maximum production to efficiency, recent advances have made it possible to separately account for factors beyond and within the control of firms such that only the latter will cause inefficiency. The popular approach to measure the technical efficiency component, is the use of frontier production function (Tzouvelekas et al., 2001; Wadud and White, 2000; Sharma et al., 1999; Battese and Coelli, 1995). Aigner et al. (1977), and Meeusen and Van Den Broeck (1977) independently proposed the stochastic frontier production function to account for the presence of measurement errors and other noise in the data, which are beyond the control of firms. Stochastic frontiers have two error terms. The first accounts for the presence of technical inefficiencies in production and the second accounts for measurement errors in output, weather, etc and the combined effects of unobserved inputs in production. Also, in a number of studies on efficiency measurement (Nkamleu, 2004; Nyemeck et al., 2003; Bravo-Ureta and Pinheiro, 1997), the predicted efficiency indices were regressed against a number of household/farm characteristics, in an attempt to explain the observed differences in efficiency among farms, using a two-stage procedure. Although this exercise has been recognized as a useful one, the two-stage estimation procedure utilized for this exercise has also been recognized as one which is inconsistent in its assumptions regarding the independence of the inefficiency effects in the two-stage estimations. Coelli (1996) and Battese and Coelli (1995) extended the stochastic production frontier model by suggesting that the inefficiency effects can be expressed as a linear function of explanatory variables, reflecting farm-specific characteristics. The
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advantage of Battese and Coelli (1995) model is that it allows estimation of the farm specific efficiency scores and the factors explaining efficiency differentials among farmers in a single stage estimation procedure (Rahman, 2003). The present paper utilises this model.
Data and empirical model Data Primary data for the study pertains to an intensive farm-survey of three sample of peasant farmers, each representing distinct farming systems. Samples were collected from five villages of the Makak sub-district, five villages of the Nkometou sub-district and five villages of the Biamo sub-district representing the slash and burn agriculture zone of Cameroon. A total of 500 farm households from these 15 villages were selected following a multi-stage stratified random sampling procedure. The systems chosen for the analysis are maize monocrop, groundnut monocrop and maize/groundnut intercrop. These three systems are used by 72% of the farmers in the slash and burn zone. In some areas such as Nkometou and other villages close to the cities, these three systems are practiced by 87% of farmers. Among these 500 farms, 450 farms practice these systems and therefore taken as the final sample size. Data were collected through frequent visits to the sample farm householdsÕ crop fields to carry out interviews and to take plot-level measurements and observations throughout 2001/2002 growing season. Input data were collected on a fortnight basis by asking the farmer to recall his/her activities during the past two weeks. Data included labour time disaggregated by source, gender, age, and field operation. The quantities and the price of all purchased inputs were also collected during this time. Output data on all the quantities of groundnut, and maize harvested were collected. A separate survey was conducted to collect output price information from local market during planting and harvesting times. A summary of the values of the variables used in the analysis is presented in Table 1. A number of points can be noted from Table 1. First, we note that these farms are small, with an average size less than one hectare in all farming systems. Farmers practicing the maize–groundnut farming system obtained higher average crop output per hectare of land cultivated in view of average yield. Both groups of farmers have comparably the average level of education less than five years. Farmers practicing maize and groundnut farming systems have had more contacts with extension officers and access to credit than those practicing maize– groundnut farming system. Empirical model Despite its well-known limitations, we use a Cobb-Douglas functional form to specify the stochastic production frontier. In fact, Taylor et al. (1986) argued that as long as interest rests on efficiency measurement and not on the analysis of the
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Table 1 Summary statistics of the variables used in the analysis Variables
Farming systems
Groundnut output (kg) Maize and groundnut output (kg) Maize output (kg) Cultivated land (ha) Labor (Man-days/ha) Farm capital (CFAF) Education of the farmer (years) Age (years) Distance (kilometres) Soil fertility index (number) Club (%) Extension contact (%) Credit access (%)
Groundnut Mean (SD)
Maize–groundnut Mean (SD)
Maize Mean (SD)
720 (367.2) – – 0.73 (0.67) 56 (25.2) 19,180 (8,735) 3.31 (1.84) 39.7 (10) 2.32(1.26) 1.39 (0.37) 52.4 (38.1) 31 (16) 15 (17)
– 1260 (617.4) – 0.65 (0.34) 60 (22.8) 20,095 (11,980) 3.05 (2.37) 37 (10) 1.97 (1.06) 1.59 (0.48) 67.1 (37.6) 27 (13) 9 (11)
– – 992 (426.56) 0.71 (0.68) 55 (23.65) 19,590 (13,075) 4.89 (3.48) 48.7 (12) 2.08 (1.53) 1.11 (0.33) 69 (23) 40 (11) 21 (10)
Note. SD, standard deviation; 1 US dollar, 550 CFAF.
general structure of the production technology, the Cobb-Douglas production function provides an adequate representation of the production technology. Moreover, in one of the very few studies examining the impact of functional form on efficiency, Kopp and Smith (1980) concluded ‘‘ that functional specification has a discernible but rather small impact on estimated efficiency’’. That is why the Cobb-Douglas functional form has been widely used in farm efficiency analyses both in developing and developed countries (Battese, 1992; Bravo-Ureta and Pinheiro, 1993). The specific model estimated is given by: ln Y ¼ b0 þ
3 X
bi ln X i þ vi ui
ð1aÞ
i¼1
and ui ¼ d0 þ
7 X
dm zmi ;
ð1bÞ
m¼1
where Y denotes the quantity of maize and groundnut cultivated of the ith farmer measured in kilograms. Xi are inputs used. 2 I = 1, denotes the total land under maize/groundnut cultivation in hectares. 2 The explanatory variables included in this model have been commonly used in estimating agricultural production frontiers for developing countries (Kalirajan and Flinn, 1985; Phillips and Marble, 1986; Taylor and Shonkwiler, 1986; Bravo-Ureta and Pinheiro, 1997; Nyemeck et al., 2003) and particularly in the slash and burn agriculture zone of Cameroon (Adesina et al., 2000; Nkamleu and Adesina, 2000).
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I = 2, denotes the total of family labour, exchange labour, and hired labour in mandays. I = 3, denotes farm capital used (total expenditures on seeds and small farm tools) measured in CFA francs. vis are assumed to be independently and identically distributed N ð0; r2v Þ two sided random errors, independently of the uis; and the uis are non-negative random variables, associated with the inefficiency in production, with are assumed to be independentlyPdistributed as truncations at zero of the normal distribution with mean, li = d0 + mdmzmi and variance r2u ðjN ðl; r2u Þj ln = natural logarithm. zmi = variables representing socio-economic characteristics of the farm to explain inefficiency, m. m = 1, education (number of completed years of schooling for the farmer). m = 2, age (number of years of the farmer). m = 3, Distance of the plot from the main access road (kilometres). m = 4, a soil fertility index. 3 m = 5, club (a dummy variable to measure if the farmer is a member to a peasant club or association. Value is 1 if yes, and 0 otherwise). m = 6, extension contact (dummy variable to measure the influence of agricultural extension on efficiency. Value is 1 if the farmer has had permanent contact with an agricultural extension officer in the past year, 0 otherwise). m = 7, access to cash credit (dummy variable to measure the influence of credit access on efficiency. Value is 1 if the farmer has had cash credit in the past year, 0 otherwise). b0, bi, d0, and dmi are the parameters to be estimated. We follow Battese and Corra (1977) in replacing the variance parameters, r2v and r2u , with c ¼ r2u =ðr2v þ r2u Þ; and r2s ¼ ðr2v þ r2u Þ in the estimating model. This is done so that a grid search over value of c between 0 and 1 can be used to obtain good starting values for the iterative search routine used to obtain the maximum likelihood estimates. Following Coelli and Perelman (1996) the farm-specific estimates of technical efficiency is defined by: " # M X EFF i ¼ E½expðui Þjni ¼ E expðd0 dm zmi Þjni ; ð2Þ m¼1
where ni = vi ui, E is the expectation operator. This is achieved by obtaining the expressions for the conditional expectation ui upon the observed value of ni. The method of maximum likelihood is used to estimates the unknown parameters, with the stochastic frontier and the inefficiency effects functions estimated simultaneously. 3 The sol fertility index is constructed from test results of soil samples collected from the study villages during the field survey. Eight soil fertility parameters were tested. These are: soil pH, available nitrogen, available potassium, available phosphorus, available sulphur, available zinc, soil texture, and soil organic matter content. A high index value refers to better soil fertility.
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Empirical results The maximum-likelihood estimates (MLE) of the parameters of Cobb-Douglas stochastic frontier function defined by Eq. (1a), given the specifications for the inefficiency effects defined by (1b), estimates of the model were obtained using maximum-likelihood procedures, detailed by Coelli and Perelman (1996), by using FRONTIER 4.1 (Coelli, 1996). The results are presented in the upper part of Table 2. The first section of Table 2 gives the function coefficient for the three equations, which measures the proportional change in output when all inputs included in the model are changed in the same proportion. The function coefficients for the MLE are approximately 0.90, 0.84 and 0.94, which indicates that returns to scale are decreasing. Restricted least squares regression was used to formally test the null hypothesis of constant return to scale. The computed F statistic are, respectively, 4.34, 5.31 and 3.96, which exceed the critical F value of 3.84 at 5 percent level of significance. Consequently, the null hypothesis of constant return to scale was rejected. The lower section of Table 2 reports the results of testing the hypothesis that the efficiency effects jointly estimated with the production frontier function are not simply random errors. The key parameter is c ¼ r2u =ðr2v þ r2u Þ, which is the ratio of the errors in Eq. (1a) and is bounded between zero and one, where if c = 0, inefficiency is not present, and if c = 1, there is not random noise (Battese and Coelli, 1995). The estimated value of c is close to 1 in all model and is significantly different from zero, thereby, establishing the fact that a high level of inefficiencies exists in these farming systems of the slash and burn agriculture zone of Cameroon. Moreover, the corresponding variance- ratio parameters. 4 c* imply 70.84%, 87.34% and 92.15% of the differences between observed and the maximum production frontier for groundnut, maize–groundnut and maize farming systems, respectively, is due to the existing differences in efficiency levels among farmers. Further, a set of hypothesis on different inefficiency specifications using Likelihood Ratio (LR) test statistic was tested. The null hypothesis that c = 0 is rejected at the 5% level of significance in all cases confirming that inefficiency exit and are indeed stochastic (LR statistics 10.75; 19:2 and 11:6 > v2ð1;0:95Þ ¼ 2:71). 5 The results also indicate that technical efficiency (TE) indices range from 48% to 95% for the pure groundnut farming system, with an average of 71% (Table 3). This indicates that, if the average farmer in the sample was to achieve the TE level of its most efficient counterpart, then the average farmer could realize an 25% cost savings (i.e., 1 [71/95]). A similar calculation for the most technically inefficient farmer reveals cost savings of 49% (i.e., 1 [48/95]). Table 3 also shows that, TE range from 53% to 97% 4
The parameter c is not equal to the ratio of the variance of the efficiency effects to the total residual variance because the variance of ui is equal to [(p2)/p ]r2 not r2. The relative contribution of the inefficiency effect to the total variance term (c*) is equal to c/[c +(1c)p /(p2)] (Coelli et al., 1998; Rahman, 2003). 5 See Coelli et al. (1998).
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Table 2 Maximum likelihood estimates of stochastic frontier production function Independent variables
Coefficient
Farming systems Groundnut N = 150
Production function Intercept Ln (land) Ln (Labor) Ln (Capital)
b0 b1 b2 b3
8.09 0.38 0.23 0.29
(10.38)*** (4.23)*** (2.54)*** (2.16)**
Maize–groundnut N = 150
Maize N = 150
10.01 (23.02)*** 0.28 (2.57)*** 0.31 (3.05)*** 0.25 (2.77)***
16.42 (44.28)*** 0.36 (3.58)*** 0.28 (5.21)*** 0.30 (2.78)***
Sum elasticities F statistic CRTSa
0.90 4.34**
0.84 5.31**
0.94 3.96***
Variance parameters r2s ¼ ðr2v þ r2u Þ c ¼ r2u =ðr2v þ r2u Þ Log likelihood v2ð1Þ
0.56 (6.11)*** 0.87 (4.78)*** 13.97 10.75
0.51 (4.33)*** 0.95 (7.65)*** 15.68 19.2
0.49(3.71)*** 0.97 (6.21)*** 19.4 11.6
0.49 (4.7)*** 0.004 (1.07) 0.2 (1.09) 0.007 (2.33)** 0.61 (2.53)** 0.23 (4.23)*** 0.05 (1.5) 0.11 (1.85)*
0.42 (2.17)** 0.002 (1.36) 0.06 (0.85) 0.034 (2.42)** 0.66 (3.74)*** 0.10 (4.65)*** 0.0117 (1.21) 0.08 (1.63)*
0.76 (9.4)*** 0.10 (2.0)** 0.07 (1.44) 0.12 (2.40)** 0.57 (2.03)** 0.09 (3.34)*** 0.06 (1.4) 0.205 (2.18)**
Inefficiency effects Constant Education Age Distance Soil fertility Club Extension Credit a * ** ***
d0 d1 d2 d3 d4 d5 d6 d7
CRTS = constant return to size and figures in parentheses are asymptotict ratios. Significant at 10% level (P < 0.10). Significant at 5% level (P < 0.05). Significant at 1% level (P < 0.01).
and 55% to 99%, respectively, for maize–groundnut intercrop and maize monocrop farming systems, with an average of 73% and 75%. This means that, if the average farmer mixed farming system was to achieve the TE level of its most efficient counterpart, then the average farmer could realize a 25% cost savings (i.e., 1 [73/97]). The same farming system reveals cost savings of 45% for the most technically inefficient farmer. A similar calculation indicates that if the average farmer in the maize monocrop farming system was to achieve the TE level of its most efficient counterpart in the sample, then the average farmer could realize a 24% cost savings. Moreover, the most technically inefficient farmer in that farming system could realize a 44% cost savings. No significant differences were found between the means of TE indices among cropping systems (Table 4). This means that, TE indices are independent of the cropping practices in humid and forest zone in Cameroon.
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Table 3 Frequency distribution and summary statistics of efficiency estimates for farmers in the slash and burn agricultural zone of Cameroon Efficiency (%)
Farming systems Groundnut
>90 >80 6 90 >70 6 80 >60 6 70 >50 6 60 >40 6 50 Mean Min Mean
Maize–groundnut
Maize
Number
Percent farms
Number
Percent farms
Number
Percent farms
9 43 52 23 15 8
6 29 35 15 10 5
17 33 61 29 10 0
11 22 41 19 7 0
26 33 54 30 7 0
17 22 36 20 5 0
71 48 95
73 53 97
75 55 99
This may indicate that the small holder farmers in the slash and burn agriculture zone of Cameroon can gain at least an average crop output growth of 25% through full improvements in technical efficiency. As existing empirical studies in Africa show, the 71%, 73% and 75% means technical efficiency found in this study is in the line with the finding reported by Seyoum et al. (1998), Weir (1999) and Mochebelele and Winter-Nelson (2000). Factors explaining inefficiency The parameter estimates of the inefficiency effects stochastic production frontier model employed to identify the factors influencing farmersÕ levels of technical inefficiency are listed in the lower panel of Table 2. The results show that, the distance of the plot from the main access road, the soil fertility index, the credit access and the variable club have a significant impact on technical inefficiency of farmers among farming systems in the slash and burn agriculture zone, while the educational level has only a significant impact on the technical inefficiency of the farmers practicing the maize mono cropping system. Variable education has a positive and highly significant impact on efficiency in maize mono cropping system, while its effect on farmersÕ inefficiencies for both mono groundnut and mixed maize–groundnut farming systems is also positive but not significant. These results indicate that farmers with four or more years of schooling exhibited higher levels of technical efficiency. These results are similar to the findings of Weir (1999), and of Weir and Knight (2000). Weir (1999) found substantial internal benefits of schooling for farmer productivity of cereal crops in rural Ethiopia in terms of efficiency gains but found a threshold effect that implies that at least four years of schooling are required to lead to significant effects on farm level technical efficiency. In the study area, the mono groundnut and mixed maize–groundnut systems are specially practiced by women that are generally less educated than men. In
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Table 4 ANOVA test of TE indices across farming systems Test
Distribution
Computed value
Critique value 5%
Null hypothesis H0 (a)
BARTLETT ANOVA
v2ð2Þ F(2,447)
2.89 0.125
5.99 3.11
Accepted Accepted
a
H 0 : r21 ¼ r22 ¼ r23 for Bartlett test; H0: TE1 = TE2 = TE3 for ANOVA test.
our sample set, farmers practicing the mono maize system have at mean, more than four years of education. Although maize and groundnut in the sample areas is produced mainly to meet subsistence needs, the distance of the plot from the main access road is statistically significant at 5% level. In this case, the results imply that technical inefficiency increases with the distance of the plot from the main access road and underscores the importance of better infrastructure in agricultural development. The quality of soils is negatively associated with technical inefficiency. Thus, farmers located at more fertile regions perform significantly better than their peers in less fertile regions, thereby reinforcing the argument that improvement in soil fertility is a crucial element in increasing productivity. The role of social capital in providing incentives for efficient production is revealed by the negative and statistically significant relationship between club membership of a household member and technical inefficiency. The sharing of information on farming practices at club or association level tend to filter to other members of the households that are not members or through demonstration effects of farming practices on club or association membersÕ plot. Thus club membership has some external effects on family members that are not members of the farming clubs. This finding is consistent with GloverÕs argument (1984) that farmers that are club members can be very valuable for small-scale operations, because it facilitates access to markets and increases income and employment for growers. In addition club members provide farmers with a secure market for their crops as well as some technical assistance: source of farmer technical efficiency. The role of credit cannot be overemphasized in the agricultural productivity of the poor rural farmers of Cameroon. The serious shortage of cash facing the farmers due to deteriorating product prices and demands of new inputs for adequate knowledge of proper utilization have undesirable impact on timely operation and optimal input applications, thereby influencing farmersÕ level of technical inefficiencies. Furthermore, the negative and significant impact of credit and technical inefficiency implies that access to cash credit is likely to enhance the technical efficiency of farmers in the slash and burn agriculture zone of Cameroon through the alleviation of capital constraints on agricultural households. In fact, expenditures on agricultural inputs and on food and essential nonfood items are incurred during the planting and vegetable growth periods of crops, whereas returns are received only after the crop are harvested several months later. Most farm households show a negative cash flow during
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the planting season. Therefore, to finance the purchase of essential consumption and production inputs, the farm household must either dip into its savings or obtain credit. Access to credit can therefore significantly increase the ability of poor households with little or no savings to acquire agricultural inputs. Furthermore, easing potential capital constraints through the granting of credit reduces the opportunity costs of capital-intensive assets relative to family labour, thus encouraging the adoption of labour-saving, higher-yielding technologies and therefore increasing land and labor productivity, a crucial factor in encouraging development, in particular in many African countries (Delgado, 1995; Zeller et al., 1997a,b). The poor effect of agricultural extension programs in farming systems is not surprising. Similar results have been reported in past analyses of the productivity of agriculture in developing countries (Feder et al., 2004). Although agricultural extension and farmer-education programs are key policy instruments for government seeking to improve the productivity of agriculture, while protecting environment, yet, many observers document poor performance in the operation of extension and informal education systems, due to bureaucratic inefficiency, deficient program design, and some generic weaknesses inherent in publicly operated, staff-intensive, information delivery systems. One deficiency highlighted by researchers and practitioners is the tendency of many public officers dealing with the transmission of knowledge to conduct their assignment in a ‘‘top-down’’ manner. Often, the information conveyed is presented as a technological package comprising recommended practices. This is perceived as a less effective method for improving knowledge. In this case, more participatory approaches are suggested to extend science-based knowledge and practices are suggested (Braun et al., 2002).
Conclusion and implications for policy This study used stochastic production frontier functions to analyze technical efficiency of smallholder farmers in the slash and burn agriculture zone of Cameroon. Using detailed survey data obtained from 450 farmers over 15 villages throughout 2001/2002 growing season we obtain measures of technical inefficiency among farmers. The mean levels of technical efficiency are 77%, 73% and 75%, respectively, for groundnut monocrop, maize monocrop and maize–groundnut farming systems. These results suggest that substantial gains in output and/or decreases in cost can be attained given existing technology. The farm-specific variables used to explain inefficiencies indicate that those farmers who have more than four years of schooling, better access to credit, located in fertile regions and those who are members to farmersÕ club or association tend to be more efficient. The distance of the plot from the main access road and the extension services have a negative influence in increasing technical efficiency in different farming systems. The policy implications are clear. Inefficiency in farming systems can be reduced significantly by:
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Providing farmers with greater access to credit Given that the necessary complementary resources and economic environment are not yet in place for access to formal credit for CameroonianÕs rural population, and considering that the formation of sustainable rural financial institutions is such a difficult task in poor rural economies, the study recommends a cautious and gradual strategy for expansion of the rural financial institutions in the study area. This strategy would require direct support by the government, through an adequate legal and regulatory framework, of institutional innovations and pilot programs in rural areas that may have the potential to reduce transaction costs in providing savings, credit, and insurance services to the rural clientele. Strengthening extension services and improving rural infrastructures In recent years, a number of development agencies, including the world bank, have promoted farmer field schools (FFS) as a more effective approach to extend science-based knowledge and practices. The FFS training program utilizes participatory methods ‘‘to help farmers develop their analytical skills, critical thinking, and creativity, and help them learn to make better decisions’’ (Kenmore, 1997). Such an approach, in which the trainer is more of a facilitator than instructor, reflects a paradigm shift in extension work (Roling and van de Fliert, 1994). As an extension approach, the FFS concept does not require that all farmers attend FFS training. Rather, only a selected number within a village or local farmersÕ group are trained in these informal schools. However, in order to disseminate new knowledge more rapidly, selected farmers receive additional training to become farmer-trainers, and are expected to organize field school replications within the community, with some support from public sources. These farmer-to-farmer diffusion effects are expected to bring about cost-effective knowledge dissemination and financial sustainability, issues that have hampered many public extension systems in developed and developing countries (Quizon et al., 2001; Hanson and Just, 2001). FFS who were introduced in Cameroon in 2002 by the STCP 6 Pilot Project, for disseminating Integrated Pest Management (IPM) technology among cocoa farmers in the slash and burn agriculture zone of Cameroon, must be extended on food crops such as maize and groundnut for promoting local innovative cropping systems. Furthermore, public investment in physical infrastructure (road, transportation, communication) is crucial to improving smallholder farmersÕ efficiency, and then, earnings. Improving soil fertility According to our results, farmers located in fertile soil regions exhibit higher level of efficiency than those in less fertile regions. Improving soil conditions in different land-use systems practised in the slash and burn agriculture zone is of importance 6
Sustainable Tree Crops Program.
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to improving and maintaining soil productivity. This calls for a coordinated effort to promote effective soil fertility management, for example through erosion control, liming and fertilizer application. To avoid erosion due to exposure to heavy rains, residue from harvests must be used to cover the soil surface, particularly in foodcrop fields, since this would not increase the number and the complexity of the farm management tasks facing the producer. For nutrients that persist in the soil, commercial fertilizer can compensate for nutrients taken up by crops or lost by runoffs and leaching. Since farmers cannot afford to buy lime and mineral fertilizers and agroforestery is hardly practised, the utilization of green manure and compost is a promising option.
Acknowledgements The authors gratefully acknowledge the constructive comments and suggestions given by two anonymous referees of this Journal which helped improving an earlier draft of this article.
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Factors affecting the technical efficiency among smallholder farmers in the slash and burn agriculture zone of Cameroon Joachim Nyemeck Binam a,*, Jean Tonye` a, Njankoua wandji b,1, Gwendoline Nyambi a, Mireille Akoa
a
a
b
Institute of Agricultural Research for Development (IRAD), Farming Systems, Economy and Rural Sociology Division, P.O. Box 2067, Yaounde´-Messa (Republic of Cameroon) International Institute of Tropical Agriculture, Humid forest Ecoregional Center, P.O. Box 2008, Yaounde´-Messa (Republic of Cameroon)
Abstract This paper estimates technical efficiency among small holder farmers in the slash and burn agriculture zone of Cameroon and identifies sources of inefficiency using detailed survey data obtained from 450 farmers over 15 villages throughout 2001/2002 growing season. We find that the mean levels of technical efficiency are 77%, 73% and 75%, respectively, for groundnut monocrop, maize monocrop and maize–groundnut farming systems suggesting existence of substantial gains in output and/or decreases in cost with available technology and resources. The efficiency differences are explained significantly by credit, soil fertility, social capital, distance of the plot from the access road and extension services. Policy recommendations are drawn from these findings. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Technical efficiency; Slash and burn agriculture; Stochastic frontiers
*
Corresponding author. Tel.: +237 223 89 63; fax: +237 223 74 37. E-mail address: [email protected] (J.N. Binam). 1 Tel.: +237 223 74 34.
0306-9192/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodpol.2004.07.013
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Introduction Agriculture is an important sector in the Cameroonian economy. Recent studies show that, it accounted for as much as 30% of the gross domestic product (GDP), 70% of overall employment, and over 40% of total foreign exchange earnings (DSCN, 2002). A significant feature of the agriculture in the slash and burn zone of Cameroon is the dual structure of the farming system, composed of farming practices that produce perennial export crops (coffee, cocao, bananas, oil-palm), and small peasants farms that produce annual foods for subsistence and local markets. For both systems, the productivity is low. The poor performance of the forest zone agriculture is evidenced by low standard of living in rural areas compared to cities; thus, the largest concentration of absolute poverty, illetaracy, and infant mortality is found in countryside (DSCN, 2002). There is a general agreement that a sustainable economic development depends on promoting productivity and output growth in the agricultural sector, particularly among small-scale producers. Empirical evidence suggests that small farms are desirable not only because they reduce unemployment, but also because they provide a more equitable distribution of income as well as an effective demand structure for other sectors of the economy (Bravo-Ureta and Pinheiro, 1993, 1997). Consequently, many researchers and policymakers have focused their attention on the impact the adoption of new technologies can have on increasing farm productivity and income (Hayami and Ruttan, 1985; Kuznets, 1966). However, during the last decade, major technological gains stemming from the green revolution have been effective across the developing world. This suggest that attention to productivity gains arising from a more efficient use of existing technology is justified (Bravo-Ureta and Pinheiro, 1993, 1997; Squires and Tabor, 1991). The presence of shortfalls in efficiency means that output can be increased without requiring additional conventional inputs and without the need for new technologies. If this is the case, then empirical measures of efficiency are necessary in order to determine the magnitude of the gain that could be obtained by improving performance in production with a given technology. policy implication stemming from significant levels of inefficiency is that it might be more cost effective to achieve short run increases in farm output, and thus income, by concentrating on improving efficiency rather than introducing new technologies (Belbase and Grabowski, 1985; Shapiro and Mu¨ller, 1977). In the slash and burn agriculture zone of Cameroon, groundnut and maize are the second and third most important food crops after cassava. They are planted either in monocrop or in association. Improving technical efficiency (TE) of these crops based systems will contribute enormously to improving the overall agricultural productivity in the slash and burn zone of Cameroon. From 2000 to 2003, Agricultural Projects supported not only the introduction of improved groundnut and maize varieties in the slash and burn zone of Cameroon,
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but also it supported the study of the technical efficiency of these crops-based systems. The present study sets out to analyse technical efficiency of groundnut and maizebased systems farmers in the slash and burn agriculture zone of Cameroon, and to identify farm-specific characteristics that explain variation in efficiency of individual farmers. An understanding of these relationship could provide the policymakers with information to design programmes that can contribute to measures needed to expand the food production potential of the nation. The analysis performed this way goes beyond much of the published literature concerning efficiency because much research in this area of productivity analysis focuses exclusively on the measurement of technical efficiency (Bravo-Ureta and Pinheiro, 1993; Coelli, 1995). The paper is organized as follows, the next section presents the analytical framework. The data and empirical procedure are presented in the third section. In the fourth section, the results are presented and discussed and the last section draws conclusion and implications for policy.
Analytical framework Since Farrell (1957) seminal paper, there has been a growing interest in methodologies and their applications to efficiency measurement. While early methodologies were based on deterministic models that attribute all deviations from the maximum production to efficiency, recent advances have made it possible to separately account for factors beyond and within the control of firms such that only the latter will cause inefficiency. The popular approach to measure the technical efficiency component, is the use of frontier production function (Tzouvelekas et al., 2001; Wadud and White, 2000; Sharma et al., 1999; Battese and Coelli, 1995). Aigner et al. (1977), and Meeusen and Van Den Broeck (1977) independently proposed the stochastic frontier production function to account for the presence of measurement errors and other noise in the data, which are beyond the control of firms. Stochastic frontiers have two error terms. The first accounts for the presence of technical inefficiencies in production and the second accounts for measurement errors in output, weather, etc and the combined effects of unobserved inputs in production. Also, in a number of studies on efficiency measurement (Nkamleu, 2004; Nyemeck et al., 2003; Bravo-Ureta and Pinheiro, 1997), the predicted efficiency indices were regressed against a number of household/farm characteristics, in an attempt to explain the observed differences in efficiency among farms, using a two-stage procedure. Although this exercise has been recognized as a useful one, the two-stage estimation procedure utilized for this exercise has also been recognized as one which is inconsistent in its assumptions regarding the independence of the inefficiency effects in the two-stage estimations. Coelli (1996) and Battese and Coelli (1995) extended the stochastic production frontier model by suggesting that the inefficiency effects can be expressed as a linear function of explanatory variables, reflecting farm-specific characteristics. The
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advantage of Battese and Coelli (1995) model is that it allows estimation of the farm specific efficiency scores and the factors explaining efficiency differentials among farmers in a single stage estimation procedure (Rahman, 2003). The present paper utilises this model.
Data and empirical model Data Primary data for the study pertains to an intensive farm-survey of three sample of peasant farmers, each representing distinct farming systems. Samples were collected from five villages of the Makak sub-district, five villages of the Nkometou sub-district and five villages of the Biamo sub-district representing the slash and burn agriculture zone of Cameroon. A total of 500 farm households from these 15 villages were selected following a multi-stage stratified random sampling procedure. The systems chosen for the analysis are maize monocrop, groundnut monocrop and maize/groundnut intercrop. These three systems are used by 72% of the farmers in the slash and burn zone. In some areas such as Nkometou and other villages close to the cities, these three systems are practiced by 87% of farmers. Among these 500 farms, 450 farms practice these systems and therefore taken as the final sample size. Data were collected through frequent visits to the sample farm householdsÕ crop fields to carry out interviews and to take plot-level measurements and observations throughout 2001/2002 growing season. Input data were collected on a fortnight basis by asking the farmer to recall his/her activities during the past two weeks. Data included labour time disaggregated by source, gender, age, and field operation. The quantities and the price of all purchased inputs were also collected during this time. Output data on all the quantities of groundnut, and maize harvested were collected. A separate survey was conducted to collect output price information from local market during planting and harvesting times. A summary of the values of the variables used in the analysis is presented in Table 1. A number of points can be noted from Table 1. First, we note that these farms are small, with an average size less than one hectare in all farming systems. Farmers practicing the maize–groundnut farming system obtained higher average crop output per hectare of land cultivated in view of average yield. Both groups of farmers have comparably the average level of education less than five years. Farmers practicing maize and groundnut farming systems have had more contacts with extension officers and access to credit than those practicing maize– groundnut farming system. Empirical model Despite its well-known limitations, we use a Cobb-Douglas functional form to specify the stochastic production frontier. In fact, Taylor et al. (1986) argued that as long as interest rests on efficiency measurement and not on the analysis of the
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Table 1 Summary statistics of the variables used in the analysis Variables
Farming systems
Groundnut output (kg) Maize and groundnut output (kg) Maize output (kg) Cultivated land (ha) Labor (Man-days/ha) Farm capital (CFAF) Education of the farmer (years) Age (years) Distance (kilometres) Soil fertility index (number) Club (%) Extension contact (%) Credit access (%)
Groundnut Mean (SD)
Maize–groundnut Mean (SD)
Maize Mean (SD)
720 (367.2) – – 0.73 (0.67) 56 (25.2) 19,180 (8,735) 3.31 (1.84) 39.7 (10) 2.32(1.26) 1.39 (0.37) 52.4 (38.1) 31 (16) 15 (17)
– 1260 (617.4) – 0.65 (0.34) 60 (22.8) 20,095 (11,980) 3.05 (2.37) 37 (10) 1.97 (1.06) 1.59 (0.48) 67.1 (37.6) 27 (13) 9 (11)
– – 992 (426.56) 0.71 (0.68) 55 (23.65) 19,590 (13,075) 4.89 (3.48) 48.7 (12) 2.08 (1.53) 1.11 (0.33) 69 (23) 40 (11) 21 (10)
Note. SD, standard deviation; 1 US dollar, 550 CFAF.
general structure of the production technology, the Cobb-Douglas production function provides an adequate representation of the production technology. Moreover, in one of the very few studies examining the impact of functional form on efficiency, Kopp and Smith (1980) concluded ‘‘ that functional specification has a discernible but rather small impact on estimated efficiency’’. That is why the Cobb-Douglas functional form has been widely used in farm efficiency analyses both in developing and developed countries (Battese, 1992; Bravo-Ureta and Pinheiro, 1993). The specific model estimated is given by: ln Y ¼ b0 þ
3 X
bi ln X i þ vi ui
ð1aÞ
i¼1
and ui ¼ d0 þ
7 X
dm zmi ;
ð1bÞ
m¼1
where Y denotes the quantity of maize and groundnut cultivated of the ith farmer measured in kilograms. Xi are inputs used. 2 I = 1, denotes the total land under maize/groundnut cultivation in hectares. 2 The explanatory variables included in this model have been commonly used in estimating agricultural production frontiers for developing countries (Kalirajan and Flinn, 1985; Phillips and Marble, 1986; Taylor and Shonkwiler, 1986; Bravo-Ureta and Pinheiro, 1997; Nyemeck et al., 2003) and particularly in the slash and burn agriculture zone of Cameroon (Adesina et al., 2000; Nkamleu and Adesina, 2000).
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I = 2, denotes the total of family labour, exchange labour, and hired labour in mandays. I = 3, denotes farm capital used (total expenditures on seeds and small farm tools) measured in CFA francs. vis are assumed to be independently and identically distributed N ð0; r2v Þ two sided random errors, independently of the uis; and the uis are non-negative random variables, associated with the inefficiency in production, with are assumed to be independentlyPdistributed as truncations at zero of the normal distribution with mean, li = d0 + mdmzmi and variance r2u ðjN ðl; r2u Þj ln = natural logarithm. zmi = variables representing socio-economic characteristics of the farm to explain inefficiency, m. m = 1, education (number of completed years of schooling for the farmer). m = 2, age (number of years of the farmer). m = 3, Distance of the plot from the main access road (kilometres). m = 4, a soil fertility index. 3 m = 5, club (a dummy variable to measure if the farmer is a member to a peasant club or association. Value is 1 if yes, and 0 otherwise). m = 6, extension contact (dummy variable to measure the influence of agricultural extension on efficiency. Value is 1 if the farmer has had permanent contact with an agricultural extension officer in the past year, 0 otherwise). m = 7, access to cash credit (dummy variable to measure the influence of credit access on efficiency. Value is 1 if the farmer has had cash credit in the past year, 0 otherwise). b0, bi, d0, and dmi are the parameters to be estimated. We follow Battese and Corra (1977) in replacing the variance parameters, r2v and r2u , with c ¼ r2u =ðr2v þ r2u Þ; and r2s ¼ ðr2v þ r2u Þ in the estimating model. This is done so that a grid search over value of c between 0 and 1 can be used to obtain good starting values for the iterative search routine used to obtain the maximum likelihood estimates. Following Coelli and Perelman (1996) the farm-specific estimates of technical efficiency is defined by: " # M X EFF i ¼ E½expðui Þjni ¼ E expðd0 dm zmi Þjni ; ð2Þ m¼1
where ni = vi ui, E is the expectation operator. This is achieved by obtaining the expressions for the conditional expectation ui upon the observed value of ni. The method of maximum likelihood is used to estimates the unknown parameters, with the stochastic frontier and the inefficiency effects functions estimated simultaneously. 3 The sol fertility index is constructed from test results of soil samples collected from the study villages during the field survey. Eight soil fertility parameters were tested. These are: soil pH, available nitrogen, available potassium, available phosphorus, available sulphur, available zinc, soil texture, and soil organic matter content. A high index value refers to better soil fertility.
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Empirical results The maximum-likelihood estimates (MLE) of the parameters of Cobb-Douglas stochastic frontier function defined by Eq. (1a), given the specifications for the inefficiency effects defined by (1b), estimates of the model were obtained using maximum-likelihood procedures, detailed by Coelli and Perelman (1996), by using FRONTIER 4.1 (Coelli, 1996). The results are presented in the upper part of Table 2. The first section of Table 2 gives the function coefficient for the three equations, which measures the proportional change in output when all inputs included in the model are changed in the same proportion. The function coefficients for the MLE are approximately 0.90, 0.84 and 0.94, which indicates that returns to scale are decreasing. Restricted least squares regression was used to formally test the null hypothesis of constant return to scale. The computed F statistic are, respectively, 4.34, 5.31 and 3.96, which exceed the critical F value of 3.84 at 5 percent level of significance. Consequently, the null hypothesis of constant return to scale was rejected. The lower section of Table 2 reports the results of testing the hypothesis that the efficiency effects jointly estimated with the production frontier function are not simply random errors. The key parameter is c ¼ r2u =ðr2v þ r2u Þ, which is the ratio of the errors in Eq. (1a) and is bounded between zero and one, where if c = 0, inefficiency is not present, and if c = 1, there is not random noise (Battese and Coelli, 1995). The estimated value of c is close to 1 in all model and is significantly different from zero, thereby, establishing the fact that a high level of inefficiencies exists in these farming systems of the slash and burn agriculture zone of Cameroon. Moreover, the corresponding variance- ratio parameters. 4 c* imply 70.84%, 87.34% and 92.15% of the differences between observed and the maximum production frontier for groundnut, maize–groundnut and maize farming systems, respectively, is due to the existing differences in efficiency levels among farmers. Further, a set of hypothesis on different inefficiency specifications using Likelihood Ratio (LR) test statistic was tested. The null hypothesis that c = 0 is rejected at the 5% level of significance in all cases confirming that inefficiency exit and are indeed stochastic (LR statistics 10.75; 19:2 and 11:6 > v2ð1;0:95Þ ¼ 2:71). 5 The results also indicate that technical efficiency (TE) indices range from 48% to 95% for the pure groundnut farming system, with an average of 71% (Table 3). This indicates that, if the average farmer in the sample was to achieve the TE level of its most efficient counterpart, then the average farmer could realize an 25% cost savings (i.e., 1 [71/95]). A similar calculation for the most technically inefficient farmer reveals cost savings of 49% (i.e., 1 [48/95]). Table 3 also shows that, TE range from 53% to 97% 4
The parameter c is not equal to the ratio of the variance of the efficiency effects to the total residual variance because the variance of ui is equal to [(p2)/p ]r2 not r2. The relative contribution of the inefficiency effect to the total variance term (c*) is equal to c/[c +(1c)p /(p2)] (Coelli et al., 1998; Rahman, 2003). 5 See Coelli et al. (1998).
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Table 2 Maximum likelihood estimates of stochastic frontier production function Independent variables
Coefficient
Farming systems Groundnut N = 150
Production function Intercept Ln (land) Ln (Labor) Ln (Capital)
b0 b1 b2 b3
8.09 0.38 0.23 0.29
(10.38)*** (4.23)*** (2.54)*** (2.16)**
Maize–groundnut N = 150
Maize N = 150
10.01 (23.02)*** 0.28 (2.57)*** 0.31 (3.05)*** 0.25 (2.77)***
16.42 (44.28)*** 0.36 (3.58)*** 0.28 (5.21)*** 0.30 (2.78)***
Sum elasticities F statistic CRTSa
0.90 4.34**
0.84 5.31**
0.94 3.96***
Variance parameters r2s ¼ ðr2v þ r2u Þ c ¼ r2u =ðr2v þ r2u Þ Log likelihood v2ð1Þ
0.56 (6.11)*** 0.87 (4.78)*** 13.97 10.75
0.51 (4.33)*** 0.95 (7.65)*** 15.68 19.2
0.49(3.71)*** 0.97 (6.21)*** 19.4 11.6
0.49 (4.7)*** 0.004 (1.07) 0.2 (1.09) 0.007 (2.33)** 0.61 (2.53)** 0.23 (4.23)*** 0.05 (1.5) 0.11 (1.85)*
0.42 (2.17)** 0.002 (1.36) 0.06 (0.85) 0.034 (2.42)** 0.66 (3.74)*** 0.10 (4.65)*** 0.0117 (1.21) 0.08 (1.63)*
0.76 (9.4)*** 0.10 (2.0)** 0.07 (1.44) 0.12 (2.40)** 0.57 (2.03)** 0.09 (3.34)*** 0.06 (1.4) 0.205 (2.18)**
Inefficiency effects Constant Education Age Distance Soil fertility Club Extension Credit a * ** ***
d0 d1 d2 d3 d4 d5 d6 d7
CRTS = constant return to size and figures in parentheses are asymptotict ratios. Significant at 10% level (P < 0.10). Significant at 5% level (P < 0.05). Significant at 1% level (P < 0.01).
and 55% to 99%, respectively, for maize–groundnut intercrop and maize monocrop farming systems, with an average of 73% and 75%. This means that, if the average farmer mixed farming system was to achieve the TE level of its most efficient counterpart, then the average farmer could realize a 25% cost savings (i.e., 1 [73/97]). The same farming system reveals cost savings of 45% for the most technically inefficient farmer. A similar calculation indicates that if the average farmer in the maize monocrop farming system was to achieve the TE level of its most efficient counterpart in the sample, then the average farmer could realize a 24% cost savings. Moreover, the most technically inefficient farmer in that farming system could realize a 44% cost savings. No significant differences were found between the means of TE indices among cropping systems (Table 4). This means that, TE indices are independent of the cropping practices in humid and forest zone in Cameroon.
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Table 3 Frequency distribution and summary statistics of efficiency estimates for farmers in the slash and burn agricultural zone of Cameroon Efficiency (%)
Farming systems Groundnut
>90 >80 6 90 >70 6 80 >60 6 70 >50 6 60 >40 6 50 Mean Min Mean
Maize–groundnut
Maize
Number
Percent farms
Number
Percent farms
Number
Percent farms
9 43 52 23 15 8
6 29 35 15 10 5
17 33 61 29 10 0
11 22 41 19 7 0
26 33 54 30 7 0
17 22 36 20 5 0
71 48 95
73 53 97
75 55 99
This may indicate that the small holder farmers in the slash and burn agriculture zone of Cameroon can gain at least an average crop output growth of 25% through full improvements in technical efficiency. As existing empirical studies in Africa show, the 71%, 73% and 75% means technical efficiency found in this study is in the line with the finding reported by Seyoum et al. (1998), Weir (1999) and Mochebelele and Winter-Nelson (2000). Factors explaining inefficiency The parameter estimates of the inefficiency effects stochastic production frontier model employed to identify the factors influencing farmersÕ levels of technical inefficiency are listed in the lower panel of Table 2. The results show that, the distance of the plot from the main access road, the soil fertility index, the credit access and the variable club have a significant impact on technical inefficiency of farmers among farming systems in the slash and burn agriculture zone, while the educational level has only a significant impact on the technical inefficiency of the farmers practicing the maize mono cropping system. Variable education has a positive and highly significant impact on efficiency in maize mono cropping system, while its effect on farmersÕ inefficiencies for both mono groundnut and mixed maize–groundnut farming systems is also positive but not significant. These results indicate that farmers with four or more years of schooling exhibited higher levels of technical efficiency. These results are similar to the findings of Weir (1999), and of Weir and Knight (2000). Weir (1999) found substantial internal benefits of schooling for farmer productivity of cereal crops in rural Ethiopia in terms of efficiency gains but found a threshold effect that implies that at least four years of schooling are required to lead to significant effects on farm level technical efficiency. In the study area, the mono groundnut and mixed maize–groundnut systems are specially practiced by women that are generally less educated than men. In
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Table 4 ANOVA test of TE indices across farming systems Test
Distribution
Computed value
Critique value 5%
Null hypothesis H0 (a)
BARTLETT ANOVA
v2ð2Þ F(2,447)
2.89 0.125
5.99 3.11
Accepted Accepted
a
H 0 : r21 ¼ r22 ¼ r23 for Bartlett test; H0: TE1 = TE2 = TE3 for ANOVA test.
our sample set, farmers practicing the mono maize system have at mean, more than four years of education. Although maize and groundnut in the sample areas is produced mainly to meet subsistence needs, the distance of the plot from the main access road is statistically significant at 5% level. In this case, the results imply that technical inefficiency increases with the distance of the plot from the main access road and underscores the importance of better infrastructure in agricultural development. The quality of soils is negatively associated with technical inefficiency. Thus, farmers located at more fertile regions perform significantly better than their peers in less fertile regions, thereby reinforcing the argument that improvement in soil fertility is a crucial element in increasing productivity. The role of social capital in providing incentives for efficient production is revealed by the negative and statistically significant relationship between club membership of a household member and technical inefficiency. The sharing of information on farming practices at club or association level tend to filter to other members of the households that are not members or through demonstration effects of farming practices on club or association membersÕ plot. Thus club membership has some external effects on family members that are not members of the farming clubs. This finding is consistent with GloverÕs argument (1984) that farmers that are club members can be very valuable for small-scale operations, because it facilitates access to markets and increases income and employment for growers. In addition club members provide farmers with a secure market for their crops as well as some technical assistance: source of farmer technical efficiency. The role of credit cannot be overemphasized in the agricultural productivity of the poor rural farmers of Cameroon. The serious shortage of cash facing the farmers due to deteriorating product prices and demands of new inputs for adequate knowledge of proper utilization have undesirable impact on timely operation and optimal input applications, thereby influencing farmersÕ level of technical inefficiencies. Furthermore, the negative and significant impact of credit and technical inefficiency implies that access to cash credit is likely to enhance the technical efficiency of farmers in the slash and burn agriculture zone of Cameroon through the alleviation of capital constraints on agricultural households. In fact, expenditures on agricultural inputs and on food and essential nonfood items are incurred during the planting and vegetable growth periods of crops, whereas returns are received only after the crop are harvested several months later. Most farm households show a negative cash flow during
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the planting season. Therefore, to finance the purchase of essential consumption and production inputs, the farm household must either dip into its savings or obtain credit. Access to credit can therefore significantly increase the ability of poor households with little or no savings to acquire agricultural inputs. Furthermore, easing potential capital constraints through the granting of credit reduces the opportunity costs of capital-intensive assets relative to family labour, thus encouraging the adoption of labour-saving, higher-yielding technologies and therefore increasing land and labor productivity, a crucial factor in encouraging development, in particular in many African countries (Delgado, 1995; Zeller et al., 1997a,b). The poor effect of agricultural extension programs in farming systems is not surprising. Similar results have been reported in past analyses of the productivity of agriculture in developing countries (Feder et al., 2004). Although agricultural extension and farmer-education programs are key policy instruments for government seeking to improve the productivity of agriculture, while protecting environment, yet, many observers document poor performance in the operation of extension and informal education systems, due to bureaucratic inefficiency, deficient program design, and some generic weaknesses inherent in publicly operated, staff-intensive, information delivery systems. One deficiency highlighted by researchers and practitioners is the tendency of many public officers dealing with the transmission of knowledge to conduct their assignment in a ‘‘top-down’’ manner. Often, the information conveyed is presented as a technological package comprising recommended practices. This is perceived as a less effective method for improving knowledge. In this case, more participatory approaches are suggested to extend science-based knowledge and practices are suggested (Braun et al., 2002).
Conclusion and implications for policy This study used stochastic production frontier functions to analyze technical efficiency of smallholder farmers in the slash and burn agriculture zone of Cameroon. Using detailed survey data obtained from 450 farmers over 15 villages throughout 2001/2002 growing season we obtain measures of technical inefficiency among farmers. The mean levels of technical efficiency are 77%, 73% and 75%, respectively, for groundnut monocrop, maize monocrop and maize–groundnut farming systems. These results suggest that substantial gains in output and/or decreases in cost can be attained given existing technology. The farm-specific variables used to explain inefficiencies indicate that those farmers who have more than four years of schooling, better access to credit, located in fertile regions and those who are members to farmersÕ club or association tend to be more efficient. The distance of the plot from the main access road and the extension services have a negative influence in increasing technical efficiency in different farming systems. The policy implications are clear. Inefficiency in farming systems can be reduced significantly by:
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Providing farmers with greater access to credit Given that the necessary complementary resources and economic environment are not yet in place for access to formal credit for CameroonianÕs rural population, and considering that the formation of sustainable rural financial institutions is such a difficult task in poor rural economies, the study recommends a cautious and gradual strategy for expansion of the rural financial institutions in the study area. This strategy would require direct support by the government, through an adequate legal and regulatory framework, of institutional innovations and pilot programs in rural areas that may have the potential to reduce transaction costs in providing savings, credit, and insurance services to the rural clientele. Strengthening extension services and improving rural infrastructures In recent years, a number of development agencies, including the world bank, have promoted farmer field schools (FFS) as a more effective approach to extend science-based knowledge and practices. The FFS training program utilizes participatory methods ‘‘to help farmers develop their analytical skills, critical thinking, and creativity, and help them learn to make better decisions’’ (Kenmore, 1997). Such an approach, in which the trainer is more of a facilitator than instructor, reflects a paradigm shift in extension work (Roling and van de Fliert, 1994). As an extension approach, the FFS concept does not require that all farmers attend FFS training. Rather, only a selected number within a village or local farmersÕ group are trained in these informal schools. However, in order to disseminate new knowledge more rapidly, selected farmers receive additional training to become farmer-trainers, and are expected to organize field school replications within the community, with some support from public sources. These farmer-to-farmer diffusion effects are expected to bring about cost-effective knowledge dissemination and financial sustainability, issues that have hampered many public extension systems in developed and developing countries (Quizon et al., 2001; Hanson and Just, 2001). FFS who were introduced in Cameroon in 2002 by the STCP 6 Pilot Project, for disseminating Integrated Pest Management (IPM) technology among cocoa farmers in the slash and burn agriculture zone of Cameroon, must be extended on food crops such as maize and groundnut for promoting local innovative cropping systems. Furthermore, public investment in physical infrastructure (road, transportation, communication) is crucial to improving smallholder farmersÕ efficiency, and then, earnings. Improving soil fertility According to our results, farmers located in fertile soil regions exhibit higher level of efficiency than those in less fertile regions. Improving soil conditions in different land-use systems practised in the slash and burn agriculture zone is of importance 6
Sustainable Tree Crops Program.
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to improving and maintaining soil productivity. This calls for a coordinated effort to promote effective soil fertility management, for example through erosion control, liming and fertilizer application. To avoid erosion due to exposure to heavy rains, residue from harvests must be used to cover the soil surface, particularly in foodcrop fields, since this would not increase the number and the complexity of the farm management tasks facing the producer. For nutrients that persist in the soil, commercial fertilizer can compensate for nutrients taken up by crops or lost by runoffs and leaching. Since farmers cannot afford to buy lime and mineral fertilizers and agroforestery is hardly practised, the utilization of green manure and compost is a promising option.
Acknowledgements The authors gratefully acknowledge the constructive comments and suggestions given by two anonymous referees of this Journal which helped improving an earlier draft of this article.
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