中文摘要
关于农业技术采用的研究通常聚焦于单一投入、单项实践或某一技术包, 这在分析上有其用处, 但可能忽视了众多农业决策所需的多维学习所涉及的复杂性。在肯尼亚, 我们在随机邀请农户参与农艺研究试验 (该试验在连续三个季节中比较不同的投入组合) 之后, 考察了他们在随后六个季节中的动态学习 (向自身和他人学习) 与采用决策。作为对试验的反应, 尽管在接触试验后的多个季节里并未获得正利润, 采用仍稳步上升。技术诀窍 (know-how) 迅速增加, 且在试验最多的高技能农户中增加更快, 但代价是产生新的错误。上述发现与一个理论模型相一致: 该模型刻画了投入与实践决策的多维性, 以及不同技能农户从自身经验中学习的差异; 在该模型中, 互补性意味着采用一种投入需要找到如何重新优化其他维度, 这增加了采用的成本。
Abstract
Studies on agricultural technology adoption often focus on one input, practice, or package, which is analytically useful, but may overlook the complexities involved with multidimensional learning needed for a lot of agricultural decisions. In Kenya, we study farmers' dynamic learning (from oneself and others) and adoption decisions over six seasons after randomly inviting them to participate in agronomic research trials, comparing different combinations of inputs during three consecutive seasons. As a response to the trials, adoption increases steadily despite the absence of positive profits multiple seasons after exposure to the trials. Know‐how increases rapidly and faster for high skill farmers who experiment the most, at the cost of making new mistakes. The findings are consistent with a theoretical model with multidimensionality of input and practice decisions and differential learning from one's own experience by skills, where complementarities imply that adoption of an input requires finding how to re‐optimize other dimensions, which adds to the cost of adoption.