资源环境科技发展态势分析平台

Conservation Agriculture (CA) has been widely promoted as an agro-ecological approach to sustainable production intensification. Across Sub-Saharan Africa, however, there have been low rates of adoption with fierce debate over its attractiveness for resource-poor farmers. Farm-level economics has been a key component of this debate with several authors questioning whether short-term benefits can occur with CA and advocating the need for more sophisticated economic analysis when comparing CA and conventional agriculture. This has included the importance placed upon more detailed farm-level data gathering as opposed to on-farm/on-station research. This study uses farm-level budget data gathered from a cross-sectional survey of 197 farmers, for the 2013/2014 season, within a district situated in Cabo Delgado Mozambique, to compare the underlying economics of CA and conventional agriculture. The study is enriched by having observations reflecting each year of CA use i.e. first, second and third year. Probabilistic cash flow analysis is used to compare the net present value of CA compared to conventional cropping over the short and longer term for differing crop mixes. Benefits are found in the short-term under CA but these are largely dependent on crop mix and the opportunity cost of labour assumed. We further employ Monte-Carlo simulations to compare the poorest farmers' net returns under different crop mixes and risk tolerance levels. Contrary to previous research, which has mostly suggested that better-off farmers are more likely to find CA useful, we find evidence that for the cohort of farmers under study the poorest are likely to find CA beneficial for a variety of crop mixes and risk-levels including under extreme risk aversion with the full opportunity cost of labour and mulch accounted for. These findings suggest that CA can be an attractive option for a wide variety of resource levels and crop mixes including those of the very poor in similar farming systems elsewhere in Sub-Saharan Africa. (C) 2017 Elsevier B.V. All rights reserved.