Bridging the spatial and cognitive dimensions of farmer climate adaptation

这个项目考察了农民在评估转向适应不同环境和气候的新型种子的可能性时的决策过程。这些杂交主要作物品种是大量努力的产物，许多地区的技术进步侧重于开发成熟期较早的作物品种。伴随着这些技术进步，政府对杂交种子的大规模补贴和种子市场自由化相结合，导致了大量和多样化的种子选择。因此，许多私营农民在气候多变性和他们可获得的种子技术不断变化的不确定性背景下，作出关键的生计决定，包括种植哪些种子品种以及何时播种。该项目的研究人员调查了农民在多大程度上依赖过去的经验和认知启发式方法来做出促进粮食安全的选择。该项目还有助于培训和教育多名学生，同时向私营农民和在不同环境中支持农学家的组织提出建议。该项目开发了一种方法，以弥合围绕私营农民生计决策的空间和认知维度。使用多尺度建模方法，将基于物理的高分辨率陆面建模与卫星数据和机器学习相结合，研究人员确定了土壤水分变异性和极端降雨量的热点。研究人员还利用在这些热点地区观测到的玉米生育期的卫星数据，为适应气候变化的玉米品种创建了播种日期、生长季节长度和产量结果的地图。将这些数据产品与对小农的调查相结合，使我们能够使用因果推理模型来检查农民为什么选择不同的种子品种和播种日期。这项研究特别关注与极端天气事件相关的常见启发式和认知偏差对农民决策的影响。这项研究对人与环境相互作用的学术做出了广泛的贡献，为小农户面临的空间和时间不确定性以及关键食品安全决策背后的认知过程提供了新的见解。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project examines the decision-making process of farmers as they evaluate the possibilities of switching to new types of seeds that are adaptable to heterogeneous environments and climates. These hybrid staple crop varieties are the product of substantial effort, and technological advances in many regions have focused on developing crop varieties with earlier maturity periods. A combination of large-scale government subsidization of hybrid seeds and seed market liberalization have accompanied these technological advances, which results in a large and diverse array of seed choices. Many private farmers therefore make critical livelihood decisions, including both which seed varieties to plant and when to sow them, in a context characterized by uncertainty arising from both climate variability and changing seed technology available to them. The researchers on this project examine the extent to which farmers rely on past experiences and cognitive heuristics to make choices that promote food security. The project also contributes to the training and education of multiple students while advancing recommendations for private farmers and organizations that support agriculturists in diverse settings.This project develops a methodology to bridge the spatial and cognitive dimensions around the livelihood decisions of private farmers. Using a multi-scale modeling approach that combines physically based high-resolution land surface modeling with satellite data and machine learning, the researchers identify hotspots of soil moisture variability and precipitation extremes. The researchers also use satellite data of observed maize growth periods in these hotspots to create maps of sowing dates, growing season length, and yield outcomes for climate adaptive maize varieties. Combining these data products with surveys of smallholder farmers allows us to employ causal inference models to examine why farmers choose different seed varieties and sowing dates. The study devotes attention in particular to the ways that common heuristics and cognitive biases related to extreme weather events influence the decision-making of farmers. Contributing broadly to scholarship on human-environment interactions, this research provides new insight into the spatial and temporal uncertainty that smallholder farmers face and the cognitive processes that underlie critical food security decisions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.