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.

尽管政府，捐助机构和非政府组织受到了极大的关注，但在全球人类福利的背景下，粮食安全仍然是尚未解决的挑战。 技术和概念上的限制都阻止了对富含时间频率的丰富经验数据集的收集和分析，这些数据集比大型空间范围内的时间频率高，以调查季节性降水模式的变化如何影响粮食安全。 该研究项目将改变方法和概念框架，以评估旱地农业系统的可持续性。 这项研究将对旱地农民如何适应气候季节内变异性以及这些适应如何影响其当前和未来对气候变化和气候变化的韧性。 项目发现将改善用于监测和预测水依赖农业系统的可持续性的预测模型。 通过将手机采用的简单概念与数据科学，作物预测和环境/社会监控方面的最先进研究结合在一起，该项目将进步并加速对重要的全球可持续性问题的科学理解。该项目将着重于表征对季节内持有人对适应半养殖农业农业系统气候变化的性质和影响的性质和影响。具体而言，研究解决了三个关键研究问题：（1）环境和社会系统的季节性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不确定性？ （3）如何通过增强粮食安全的方式来提高季节内数据的建模，预测和改善对气候变化的适应能力？ 研究团队将将水文和农业动态的物理模型与实时环境数据以及各个领域的每周农民决策结合在一起。 这些实时数据是从先前开发的基于细胞的新型环境传感豆荚中获得的，并结合了通过手机提交的农民决策的实时报告。 该团队将使用环境数据和社会数据的结合来开发一套建模工具，以了解气候变异性如何影响研究区域农业系统的可持续性。 研究团队还将开发建模工具，以改善对粮食安全的预测，能够对社会和环境过程的季节内动态产生新的理解。尽管这项研究的测试床是赞比亚的南部省，裂谷山谷和肯尼亚中部省的一部分集中在莱基皮亚地区，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的法定任务，并通过评估了基金会的范围，并通过评估了基金会的范围。