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.