Abstract
This study analyses farmers’ adoption of improved rice technology, taking into account farmers’ risk preferences; the unobserved spatial heterogeneity associated with farmers’ risk preferences; farmers’ household and farm characteristics; farm locations, farmers’ access to information, and their perceptions on the rice improved varieties (i.e., high yield varieties, HYV). The study used data obtained from field experiments and a survey conducted in 2016 in Nigeria. An instrumental-variable probit model was estimated to account for potential endogenous farmers’ risk preference in the adoption decision model. Results show that risk averse (risk avoidant) farmers are less likely to adopt HYV, with the spatial lags of farmers’ risk attitudes found to be a good instrument for spatially unobserved variables (e.g., environmental and climatic factors). We conclude that studies supporting policy action aiming at the diffusion of improved rice varieties need to collect information, if possible, on farmers’ risk attitudes, local environmental and climatic conditions (e.g., climatic, topographic, soil quality, pest incidence) in order to ease the design and evaluation of policy actions on the adoption of improved agricultural technology.
Highlights
On the factors related to the access to information, 68% of the sampled rice farmers relied on their friends and relatives on information about improved agricultural technology
Socio-economic, cultural, geological, ecological, and climatic characteristics may determine farmer’s management decisions such as the adoption of a new technology along with farmers’ own perceptions of risk and the perceptions of risk of their neighbours. All these aspects may extend beyond political boundaries, suggesting that policies aiming at achieving agricultural productivity growth may need to consider these spatial aspects and be implemented not necessarily at the policy boundary level
We found an association between farmer’s risk preferences and farmer’s neighbour’s risk preferences, which may indicate a mix between social learning effects and local conditions that moderate farmers’ risk preferences
Summary
Agricultural productivity growth may be achieved through farmer’s adoption of improved agricultural technological innovation (e.g., high yield variety crops, genetically modified crops), along with a provision of good extension services, facilitating access to credit and insurance markets and irrigation facilities. All these may lead to yield, income, labour-saving, efficiency, and productivity improvements and to environmental (e.g., mitigating effects on climate change) and health benefits [2,3,4]. Agricultural growth has been identified as a solution to food insecurity problems in developing countries [11]
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