Agricultural decision-making processes occur in complex and dynamic environments and are highly contextual. Despite evidence to the contrary, utility maximization is often the implicit theoretical assumption underlying agricultural decision-making processes. This study undertakes an exploratory approach to test alternative theories of human decision-making on the process of agricultural adaptation of farmers in India by synthesizing multiple sources of social and environmental data. We developed an empirical agent-based model (ABM) to simulate past adoption decisions of six agricultural adaptation strategies of 959 farmers in northern India. The model assessed the fit of four major decision-making rules – utility maximization, self-satisficing, social norms, and random choice for farmers differentiated by farm size. Scenario analysis was conducted to test whether (and which) alternative decision-making rules offered a better explanation of the adoption of (which) adaptation strategies. Results demonstrated that the utility-maximizing decision rule had a higher fit for productivity-enhancing adaptation strategies, such as adopting high yield varieties and enhanced fertilizer use, with model performance increasing, generally, with farm size. The adoption of climate tolerant varieties by farmers was most closely guided by self-satisficing and social norms decision-rules, with the model performance, under both scenarios, highest for marginal landholders. Marginal farmers are more likely to use these heuristics to adopt climate tolerant varieties as their decisions may not necessarily be geared towards increasing profit, unlike larger farmers. Social norms had a higher fit for the adoption of climate-related strategies, including enhanced irrigation, with model fit increasing, generally, with farm size. Agricultural policy and extension efforts that incorporate the varied motivations and heuristics of agricultural decision-making, rather than assuming adaptation as a utility maximization exercise, can better design, develop, and disseminate solutions to support the adaptive capacity of farmers.

农业决策过程发生在复杂和动态的环境中，并且是高度情境化的。尽管有相反的证据，效用最大化通常是农业决策过程背后的隐含理论假设。本研究采用探索性方法，通过综合多种社会和环境数据来源，测试有关印度农民农业适应过程的人类决策替代理论。我们开发了一个基于经验代理的模型 (ABM) 来模拟印度北部 959 名农民过去采用的六种农业适应策略的决策。该模型评估了四种主要决策规则的拟合度——效用最大化、自我满足、社会规范和针对不同农场规模的农民的随机选择。进行情景分析以测试是否（以及哪些）替代决策规则为采用（哪些）适应策略提供了更好的解释。结果表明，效用最大化决策规则更适合提高生产力的适应策略，例如采用高产品种和增加肥料使用，模型性能通常随着农场规模的增加而增加。农民采用耐气候品种最受自我满足和社会规范决策规则的指导，在两种情况下，模型性能对于边缘土地所有者来说最高。与较大的农民不同，边缘农民更有可能使用这些启发式方法来采用耐气候品种，因为他们的决定可能不一定是为了增加利润。社会规范更适合采用与气候相关的策略，包括加强灌溉，模型的拟合度通常随着农场规模的增加而增加。结合农业决策的各种动机和启发式的农业政策和推广工作，而不是将适应假设为效用最大化练习，可以更好地设计、开发和传播支持农民适应能力的解决方案。