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The effect of land fragmentation on the productivity and technical efficiency of smallholder maize farms in Southern Rwanda( Télécharger le fichier original )par Karangwa Mathias Makerere University - M.sc Agricultural and Applied Economics; Bachelors in Economics(Money and Banking) 2007 |
4.2 Analysis of productivity and technical efficiency of farms in Gisagara districtAs expected, results in table 4.12 show that each of the inputs; maize area, seed and hhsize (a proxy for labor) has a significant positive effect on maize production. Table 4.12: Results of the stochastic Cobb-Douglas production function
Source: Survey data, 2009; ***, **, * imply significance at 1%, 5% and 10% respectively. Seed is significant at 1 percent while maize area (land) and household size are significant at 10 percent. Our results were consistent with empirical studies. Byiringiro and Reardon (1996) estimated a Translog production function to analyze the determinants of production in Rwanda. They found out that land and labor had positive significant effects on production. Msuya et al. (2008) found out that land, expenditure on materials (including maize seed) and family labor positively affected maize productivity in Tanzania. The coefficients in the stochastic Cobb-Douglas production model show partial elasticities and seed has the highest output elasticity. The total elasticity of production (Returns to scale) is close to 1, implying constant returns to scale. 4.2.1 Marginal physical productsMaize seed had the highest elasticity, followed by area under maize (land) and then household size (a proxy for labor). Therefore, maize seed and land were very important determinants of maize productivity (Table 4.13). Maize seed has the highest marginal physical product. Household size has the lowest marginal physical product. This is perhaps due to the fact that a larger household may have many of its members as children or very old and therefore not very productive. The low marginal physical product for land could be due to the fact that land in Rwanda is scarce and therefore farmed intensively. Land has lost fertility due to over-cultivation and yet farmers have limited access to fertilizers (Musahara, 2006). Table 4.13: Productivity analysis
Source: calculated from survey data, 2009 Following Debertin (2002), marginal physical products were computed (table 4.13) for the inputs used in maize production as follows:
.............................. (12) Equation 12 can be manipulated to give marginal physical product as shown in equation 13 ; ............................................................................... (13) Whereby and are arithmetic means of maize output and the ith input respectively. In the technical inefficiency model, only four variables were significant. A negative sign in the inefficiency model implies negative effect on inefficiency or positive effect on efficiency. Variables that reduce inefficiency increase productivity/technical efficiency. Results show that possession of land titles, education and age reduce inefficiency while average number of plots increase inefficiency (Table 4.14). A likelihood ratio test of hypothesis 1 was carried out. Using the likelihood ratio test, hypothesis 1 was not rejected. The likelihood ratio test of the null hypothesis gave us the following results: chibar2 (01) = 0.66 and Prob>=chibar2 = 0.66 and it was concluded that smallholder maize farms in Gisagara district were technically efficient. This study was mainly interested in finding out the effects of the various dimensions of land fragmentation on the productivity and technical efficiency of smallholder farms and not to assess the technical efficiency differentials among households with respect to farm-specific and household-specific characteristics. The study therefore did not assess technical efficiency differentials among households with respect to the various dimensions of land fragmentation. Table 4.14: Results of the technical inefficiency model
Source: Survey data, 2009; ***, **, * imply significance at 1%, 5% and 10% respectively. The number of plots per household (land fragmentation) increases inefficiency of smallholder maize farms. So, hypothesis 4 was not rejected. Similar findings were obtained in Rwanda by Bizimana (2004). The inverse relationship between the number of plots and technical efficiency could have been due to the problems involved in managing many plots such as supervision of workers (Shuhao, 2005; Raghbendra, 2005). Average distance from household residences to plots is not statistically significant and thus, hypothesis 2 was rejected and concluded that distance from residences to plots did not individually reduce the efficiency of farms. This is consistent with the findings of Shuhao (2005) and Msuya et al. (2008). Distance to reach plots was insignificant perhaps due to the fact that distances are very short (see Table 4.9) Plot size was also statistically insignificant and therefore did not individually reduce efficiency of farms. Therefore hypothesis 3 was rejected. The implication is that households who operated smaller plots were as efficient as those who operated larger plots. This finding is consistent with the work of Kalaitzadonakes et al. (1992). The joint test of the significance of all the indicators of land fragmentation gave the following results: chi2 (3) =5.14 and Prob > chi2 = 0.01617, implying that the indicators were jointly significant and therefore jointly increased inefficiency of farms. Given this finding, hypothesis 5 was not rejected and it was concluded that all indicators of land fragmentation reduced the productivity and technical efficiency of smallholder maize farms in southern Rwanda. The dummy variable for land title was significant at 10% and reduced inefficiency. This implies that farmers who had land titles were more efficient than those without titles. This could have been due to the fact that land ownership rights (possession of land titles) encourage soil conservation investments and may therefore increase productivity and efficiency (Musahara, 2006). Age of the household head was significant at 5% and reduced inefficiency, implying that households headed by old people were more efficient than those headed by young ones. This was perhaps due to the fact that older household heads had farming experience and adopted new technologies than young ones (Amos, 2007; Ahmad et al., 2002; Kibaara, 2005). Education level of the household head was significant at 5% and reduced inefficiency of farms. The same result was obtained in Rwanda by Bizimana (2004). This could have been due to the fact that educated farmers made better assessments of the importance and complexities of production decisions and/or learned faster and utilized well extension information, resulting in better farm management (Basnayake and Gunaratne, 2002).
This study did not estimate the sub-samples (sector samples) due to the fact that the sectors had smaller samples and thus estimating them by use of maximum likelihood (ML) could not have satisfied the asymptotic property of the ML estimator. Table 4.15: The effect of land fragmentation on the productivity of smallholder farms
Source: Survey data, 2009; ***, **, * imply significance at 1%, 5% and 10% respectively. This study found out that farm size positively affected the productivity of farms; that is, the larger the farm size, the more the productivity (Table 4.14). This is consistent with some empirical evidence which show that larger farms are economically productive (Kelly and Murekezi, 2000; Mosley, 2004). Conversely, having many plots reduced productivity. Since distances between plots were short, distance between plots did not have a significant effect on productivity and the interaction term also had no significant effect. Some empirical studies show that having many plots may not be beneficial due to the difficulty of supervising workers, carrying farm inputs to different plots, among other reasons ( Blarel Benoit, Peter Hazell, Frank Place and John Quiggin, 1992; Marara and Takeuchi, 2003). The insignificance of the interaction term () suggests that land fragmentation is probably not a big problem as long as plots are close to homes. The summary statistics for the variables used in the productivity and technical efficiency analysis are presented in table 3.3 Table 4.16: Summary statistics for the variables used in the productivity and technical efficiency analysis
Source: Survey data, 2009; *SD stands for standard deviation
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