Bridging the spatial and cognitive dimensions of farmer climate adaptation

弥合农民气候适应的空间和认知维度

• 批准号: 2341278
• 负责人: Kurt Waldman
• 金额: $ 40万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341278&HistoricalAwards=false
• 关键词: Bridging; spatial; cognitive; dimensions; farmer

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.

该项目考察了农民在评估转向适应异质环境和气候的新型种子的可能性时的决策过程。这些杂交主粮作物品种是大量努力的产物，许多地区的技术进步集中在开发成熟期较早的作物品种上。随着这些技术的进步，政府对杂交种子的大规模补贴和种子市场的自由化相结合，产生了大量多样的种子选择。因此，许多私营农民在气候变化和可获得的种子技术变化带来不确定性的情况下，做出了关键的生计决定，包括种植哪种种子品种和何时播种。该项目的研究人员考察了农民在多大程度上依靠过去的经验和认知启发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下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。研究人员使用多尺度建模方法，将基于物理的高分辨率陆地表面建模与卫星数据和机器学习相结合，确定了土壤湿度变化和极端降水的热点。这组科学家还利用这些热点地区观测到的玉米生长期的卫星数据，绘制了气候适应性玉米品种的播种日期、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，我们可以使用因果推理模型来检验农民为什么选择不同的种子品种和播种日期。该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影响农民决策的方式。这项研究对人类与环境相互作用的研究做出了广泛贡献，为小农面临的空间和时间不确定性以及关键粮食安全决策背后的认知过程提供了新的见解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。