a b s t r a c tRice farming is important for income generation in large parts of China and Asia. This paper uses detailed household, crop-and plot-level data to investigate the levels and determinants of rice producers' technical efficiency for three villages with different characteristics in a major rice-growing area of South-East China, focusing in particular on the impact of land fragmentation. Empirical results obtained by applying a stochastic frontier model showed statistically significant differences in technology level among villages, with the remotest village having the lowest technology level. Within villages average technical efficiency was generally high, ranging from 0.80 to 0.91 for the three types of rice that are grown in the region. For late-rice producers, no statistically significant variation was found in their technical efficiencies. Land fragmentation was found to be an important determinant of technical efficiency in early-rice and oneseason rice production. An increase in average plot size increased rice farmers' technical efficiency. Given average plot size, an increase in the number of plots was found to increase technical efficiency, indicating the presence of variation effects. A larger distance between homesteads and plots contributed to technical inefficiency in early-rice production. The high levels of technical efficiency found in our study support the view that to raise rice productivity in the long run, new technologies need to be introduced.
Household characteristics are commonly used to explain variation in smallholder efficiency levels. The underlying assumption is that differences in intended behavior are well described by such variables, while there is no "a priori" reason that this is the case. Moreover, heterogeneity in farmer goals and preferences, in relation to the role of the farm enterprise, are not well documented in developing countries. This article makes a contribution to fill this gap, by empirically determining heterogeneity in farmer goals and attitudes in Nigeria through a pair-wise ranking, supplemented with Likert scales. Factor analysis is used to reduce these data into behavioral factors. We estimate technical and allocative efficiency levels and analyze how these are related to farm characteristics and the identified behavioral factors. The models in which both intended behavior and farmer characteristics are included give a significantly better fit over models in which only household characteristics are included. These regression results suggest that the socioeconomic environment affects efficiency levels both directly and indirectly, through changes in goals and attitudes. Copyright (c) 2010 International Association of Agricultural Economists.
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