CAREER: CAS-Climate: Multiscale Data and Model Synthesis Informed Approach for Assessing Climate Resilience of Crop Production Systems

今天的生产者面临着不断扩大的挑战，水资源的可持续性是主要问题之一。要适应这些问题，特别是在气候变化和日益极端的天气条件下，就必须改变耕作方式。大量相关数据正在以不同的时间和空间分辨率收集。这些数据包括土壤的性质和条件、气候数据、作物管理数据、作物健康、不同类型的压力和压力源以及水的可用性和消耗。现在，越来越多的食品生产管理决策被委托给机器学习模型和相应的传感器网络，这些模型和传感器网络提供并生成各种规模的各种数据。但是，这些模型和方法本身不足以全面解决广泛的尺度，环境变量和地方/区域变化所需的气候适应能力和可持续集约化的作物生产系统。有必要整合科学和工程专业知识，评估一系列作物管理方案，并制定复原力指标，以延长不可再生和有限水资源的可行性。该项目将建立研究能力，以开发和完善跨尺度的建模能力，从特定点到区域，从一天到世纪。这一信息对于指导适应战略和评估其在气候变化背景下对粮食生产和水可持续性的影响至关重要。项目团队将通过将地面和遥感数据与新的建模框架联系起来来应对这一挑战，该框架能够在不同的未来气候情景下生成作物生产的多种情景，以确保水资源可持续性和复原力的最佳战略组合。 该项目将使用田间试验，新颖的分析以及人，农场和自然系统之间的联系，以帮助改变田间作物的种植方式。目标是创建一个一体化系统，可以更好地维持水资源并管理养分和土壤。 该项目的方法是基于一个战略性的5年计划，以实现PI的整体职业目标，通过研究时空尺度和物理条件之间的联系，阻碍了对作物生产的气候智能水管理实践的理解和有效应用，通过系统地调查粮食生产系统与环境问题，整合她的研究和教学。这项工作将需要融合多尺度、异质、多来源、随时间变化的数据，包括来自地下传感器的数据、地面数据、天气预报、作物生长和土壤养分等;了解气候-水-作物生产循环;以及复原力指标。该战略将通过以下综合目标来实现：（1）对作物生产系统对作物生长和水文的时空变化响应进行机器学习知情的多尺度建模;（2）调查气候（变化和极端事件）和作物管理情景（灌溉、养分使用、作物选择、土地过渡）及其对粮食生产的影响;以及（3）量化作物生产系统可持续性的弹性指标，以指导未来气候下管理措施的优先级。该项目的教育目标是通过整合现有的本科和研究生教学课程，让学生参与并获得农业生态系统启发的基础培训，从而加强他们加入数据科学，自然资源管理和环境决策支持和咨询的STEM相关劳动力的准备。为该项目设计的研究活动将使早期职业教师和学生在职业生涯中取得进步，并指导不同教育阶段的学生（研究生，本科生，和高中和初中），并通过实地考察日和教育外展活动与农村社区接触。该项目由CBET/ENG环境可持续发展计划和刺激竞争力研究的既定计划（EPSCoR）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评估被认为值得支持。影响审查标准。

Today’s producers encounter a continuously expanding array of challenges with the sustainability of water resources being one of the major issues. Adapting to these issues, especially under a changing climate and increasingly extreme weather conditions, necessitates a shift in farming practices. A large amount of related data is being collected at different resolutions in time and space. These data range across properties and condition of soils, climate data, crop management data, crop health, different types of stresses and stressors, and water availability and consumption. More and more food production management decisions are now delegated to machine learning models and accompanying sensor networks that provide and generate diverse data across various scales. But these models and methods alone fall short in comprehensively addressing the wide range of scales, environmental variables, and local/regional variations necessary for climate-resilient adaptation and sustainable intensification of crop production systems. There is a need to integrate scientific and engineering expertise, assess a range of crop management scenarios, and develop resilience metrics to prolong the viability of non-renewable and finite water resources. This project will build research capacity for developing and refining modeling capabilities across scales, ranging from specific points to regions and from one day to a century. This information is crucial to steer adaptation strategies and assess their effects on both food production and water sustainability in the context of climate change. The project team will address this challenge by linking on- ground and remotely sensed data with a new modeling framework that is capable of generating multiple scenarios for crop production under different future climate scenarios to ensure the best set of strategies for sustainability and resilience of water resources. The project will use field trials, novel analytics, and links between people, farms, and natural systems to help change how field crops are grown for the better. The goal is to create an all-in-one system that can better sustain water resources and manage nutrients and soils. The project approach is based on a strategic 5-year plan for achieving the PI’s overall career goal of integrating her research and teaching through systematic investigations of food production systems with environmental concerns by studying the connections between spatial-temporal scales and physical conditions that have impeded understanding and effective application of climate smart water management practices for crop production. This effort will require a fusion of multiscale, heterogeneous, multi-sourced, time-varying data including data from sub-surface sensors, surface data, weather forecasts, crop growth, and soil nutrients, etc.; understanding of the climate-water-crop production loop; and resilience metrics. The strategy will be pursued through the following integrated objectives (1) conduct machine learning-informed multiscale modeling of crop production systems’ spatio-temporally varying responses of crop growth and hydrology; (2) investigate climate (change and extreme events) and crop management scenarios (irrigation, nutrient use, crop choice, land transition) and their impact on food production; and (3) quantify resilience metrics for the sustainability of crop production systems to guide prioritization of management measures under future climate. The education goal of this project is to engage and equip students with agroecosystem-inspired fundamental training through integration with the existing curriculum of undergraduate and graduate teaching and learning, thus strengthening their readiness to join a STEM- related workforce in data science, natural resource management, and environmental decision support and consulting. The research activities designed for the project will engage an early career faculty member and students in advancing through their careers and guiding students at different stages of education (graduate, undergraduate, and high and middle school) and engaging with rural communities through field days and educational outreach activities.This project is jointly funded by the CBET/ENG Environmental sustainability program and the Established Program to Stimulate Competitive Research (EPSCoR).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.