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

尽管各国政府、捐助机构和非政府组织给予了极大的关注，但在全球人类福祉的背景下，粮食安全仍然是一个尚未解决的挑战。由于技术和概念上的限制，无法在大范围内收集和分析具有高时间频率的丰富经验数据集，这是调查季节性降水模式变化如何影响粮食安全所必需的。这一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。这项研究将为旱地农民如何适应气候的季节内变化以及这些适应如何影响他们目前和未来对气候变化和气候变化的适应能力带来新的理解。项目成果将改进用于监测和预测依赖水的农业系统的可持续性的预测模型。通过将手机采用的简单想法与数据科学、作物预测和环境/社会监测方面的最新研究相结合，该项目将促进和加速对一个重要的全球可持续问题的科学理解。该项目将重点描述季节内小农决策的性质和影响，以适应半干旱农业系统的气候变化。具体地说，这项研究涉及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？以及(3)季节内数据如何以增强粮食安全的方式提高建模、预测和改进适应气候变化的能力？研究小组将把水文和农业动态的物理模型与实时环境数据和每周一次的农民个别田地决策相结合。这些实时数据来自以前开发的基于蜂窝的新型环境传感吊舱，与通过手机提交的农民决策的实时报告相结合。该小组将利用环境和社会数据的组合来开发一套建模工具，以了解气候变化如何影响研究地区农业系统的可持续性。研究小组还将开发改进粮食安全预测的建模工具，能够对社会和环境过程的季节内动态产生新的理解。虽然这项研究的试验台是赞比亚南部省份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，但其结果很可能广泛适用于世界其他半干旱和干旱地区。

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.