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

职业：CAS-气候：用于评估作物生产系统气候适应能力的多尺度数据和模型综合知情方法

• 批准号: 2339529
• 负责人: Vaishali Sharda
• 金额: $ 50.96万
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2029-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339529&HistoricalAwards=false
• 关键词: CAREER; CAS; Climate; Multiscale; Data

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.

当今的生产者遇到了不断扩大的挑战，水资源的可持续性是主要问题之一。适应这些问题，尤其是在气候变化和越来越极端的天气条件下，必须改变农业实践。在时间和空间的不同分辨率上，正在收集大量相关数据。这些数据范围跨土壤，攀爬数据，农作物管理数据，作物健康，不同类型的压力和压力以及水的可用性和消费。现在，越来越多的粮食生产管理决策将委派给机器学习模型和参与传感器网络，这些传感器网络可在各种规模上提供和生成潜水员数据。但是，仅这些模型和方法在全面解决了广泛的量表，环境变量以及气候耐性适应和可持续性所必需的局部/区域变化方面，需要将科学和工程专业知识整合，评估一系列的作物管理方案，并延长弹性计量，以延长不可用的含量水平和非货币资源。该项目将建立研究能力，以开发和完善范围的建模能力，从特定点到区域，从一天到一个世纪。该信息对于指导适应策略至关重要，并评估其在气候变化的背景下对粮食生产和水的可持续性的影响。项目团队将通过将地面和远程感知的数据与一个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场景下为作物生产生成多种场景，以确保最佳的可持续性策略和水资源的弹性。该项目将使用现场试验，新颖的分析以及人，农场和自然系统之间的联系，以帮助改变现场作物的生长方式。目的是创建一个可以更好地维持水资源并管理养分和土壤的多合一系统。该项目方法是基于一项战略性的5年计划，该计划通过对粮食生产系统的系统研究与环境关注进行整合和教学，从而实现PI的整体职业目标，并通过研究空间 - 周期性量表与身体状况之间的联系，这些量表与有效的理解和有效的气候智能水管理实践应用于农作物生产。这项工作将需要融合多尺度，异质，多源，时变的数据，包括来自地下传感器的数据，表面数据，天气预报，作物生长和土壤养分等；了解气候 - 水作品生产循环；和弹性指标。该策略将通过以下综合目标（1）进行机器学习知识的多尺度建模，对作物生产系统的空间上变化的作物生长和水文学的反应有所不同； （2）研究气候（变化和极端事件）和作物管理方案（灌溉，营养使用，农作物选择，土地过渡）及其对粮食生产的影响； （3）量化农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与现有的本科生和研究生教学和学习的课程融合来吸引并为学生提供农业生态系统启发的基本培训，从而增强他们准备加入与STEM相关的劳动力中的数据科学，自然资源管理，自然资源管理以及环境决策支持和咨询。为该项目设计的研究活动将与早期的职业成员和学生一起通过其职业发展，并指导学生在不同的教育阶段（研究生，本科生以及高中和中学），并通过实地天数和教育外展活动与艰难的社区参与。该项目由CBET/ENG环境可持续性计划和启发竞争性研究（Eptor ant Suptive and Suptive nation Suptive and Suptive and Suptive and Suptive and Suptive and Suptive and Suptive and Suptive nations inters Scors nesf）提供了竞争性研究。认为值得通过基金会的智力优点和更广泛影响的评论标准来评估值得支持。