WSC-Category 2 Collaborative: Impacts of Agricultural Decision Making and Adaptive Management on Food Security

WSC-2 类协作：农业决策和适应性管理对粮食安全的影响

• 批准号: 1801251
• 负责人: Kelly Caylor
• 金额: $ 52.61万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1801251&HistoricalAwards=false
• 关键词: WSC; Category; Collaborative; Impacts; Agricultural

Despite significant attention from governments, donor agencies, and NGOs, food security remains an unresolved challenge in the context of global human welfare. Both technical and conceptual limits have prevented the collection and analysis of rich empirical datasets with high temporal frequency over large spatial extents necessary to investigate how changes to seasonal precipitation patterns are affecting food security. This research project will transform both methodological and conceptual frameworks for assessing the sustainability of dryland agricultural systems. The research will bring new understanding of how dryland farmers adapt to within-season variability in climate and how those adaptations affect their current and future resilience to climate variability and climate change. Project findings will improve forecast models used to monitor and predict the sustainability of water-dependent agricultural systems. By marrying the simple idea of cell phone adoption with state-of-art research in data science, crop prediction, and environmental/social monitoring, the project will advance and accelerate scientific understanding of an important global sustainability problem.This project will focus on characterizing the nature and impact of intra-seasonal smallholder decision making on adaptation to climate variability in semi-arid agricultural systems. Specifically, the research addresses three critical research questions: (1) How do intra-seasonal dynamics of both the environment and social systems shape farmer adaptive capacity? (2) To what extent does intra-seasonal decision making enable farmers to adapt to climate uncertainty? and (3) How can intra-seasonal data improve the ability to model, predict, and improve adaptation to climate variability in ways that enhance food security? The research team will integrate physical models of hydrological and agricultural dynamics with real-time environmental data and weekly farmer decision making in individual fields. These real-time data are obtained from previously-developed novel cellular-based environmental sensing pods coupled to real-time reports of farmer decision making submitted via cell phones. The team will use a combination of environmental and social data to develop a suite of modeling tools for understanding how climate variability impacts the sustainability of agricultural systems in the study regions. The research team also will develop modeling tools for improved forecasts of food security capable of producing new understandings of the intra-seasonal dynamics of both social and environmental processes. Although the test bed for this research is the Southern Province of Zambia and portions of the Rift Valley and Central Provinces of Kenya centered around the Laikipia District, the results may well be broadly applicable to other semi-arid and arid regions of the world.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.

尽管各国政府、捐助机构和非政府组织给予高度关注，但粮食安全在全球人类福祉方面仍然是一个尚未解决的挑战。 技术和概念上的限制都阻碍了在大空间范围内收集和分析丰富的经验数据集，这些数据集是研究季节性降水模式的变化如何影响粮食安全所必需的。 该研究项目将改变评估旱地农业系统可持续性的方法和概念框架。 该研究将为旱地农民如何适应季节内气候变化以及这些适应如何影响他们当前和未来对气候变化和气候变化的适应能力带来新的认识。 项目研究结果将改进用于监测和预测依赖水的农业系统可持续性的预测模型。 通过将手机采用的简单理念与数据科学、作物预测和环境/社会监测领域的最先进研究相结合，该项目将促进和加速对重要的全球可持续发展问题的科学理解。该项目将重点关注表征半干旱农业系统季节性小农决策对适应气候变化的性质和影响。具体来说，该研究解决了三个关键的研究问题：（1）环境和社会系统的季节内动态如何塑造农民的适应能力？ (2) 季节内决策在多大程度上使农民能够适应气候的不确定性？ (3) 季节内数据如何提高建模、预测和适应气候变化的能力，从而增强粮食安全？ 研究团队将把水文和农业动态的物理模型与实时环境数据和每周农民在各个田地的决策相结合。 这些实时数据是从先前开发的新型基于蜂窝的环境传感吊舱中获得的，并与通过手机提交的农民决策的实时报告相结合。 该团队将结合环境和社会数据开发一套建模工具，以了解气候变化如何影响研究地区农业系统的可持续性。 研究团队还将开发建模工具，以改进粮食安全预测，从而对社会和环境过程的季节内动态产生新的理解。尽管这项研究的试验台是赞比亚南部省以及以莱基皮亚地区为中心的肯尼亚裂谷和中部省份的部分地区，但结果很可能广泛适用于世界其他半干旱和干旱地区。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。