LEAP-HI/GOALI: Engineering Crops for Genetic Adaptation to Changing Enviroments

LEAP-HI/GOALI：基因改造作物以适应不断变化的环境

• 批准号: 1830478
• 负责人: Lizhi Wang
• 金额: $ 200万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830478&HistoricalAwards=false
• 关键词: LEAP; HI; GOALI; Engineering; Crops

This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) Grant Opportunities for Academic Liaison with Industry (GOALI) project addresses the NSF Big Ideas of Understanding the Rules of Life and Harnessing the Data Revolution in targeting the need to provide food, fiber and fuel for a growing population using fewer resources (land, water, pesticides and fertilizers) in uncertain and rapidly changing environments. It is widely recognized that current agricultural technologies, from crop genetic improvement to field crop production, will not meet future agricultural demands, due to their heavy reliance on expensive, time-consuming, trail and error field trials to develop improved plant breeds. Emerging mathematical optimization and machine learning methods for analyzing high-dimensional data provide opportunities to speed up plant breeding to achieve rapid and efficient adaptation of crops to changing environments. The approaches in this project will take advantage of engineering techniques that have been used to remarkably improve the efficiency and resiliency of communication, manufacturing, transportation and energy systems. The research requires the synthesis of multiple disciplines, including agronomy, crop modeling, machine learning, operations research, optimization and plant breeding and aims to demonstrate the leadership role of engineering in addressing agricultural challenges.Three technical issues, which represent a small but highly visible subset of agronomic systems, will be addressed: (1) accurately predicting plant phenotypes based on genetic, agronomic management and environmental data and their interactions; (2) design of genetic improvement systems to efficiently develop cultivars with superior phenotypes; and (3) design of crop management strategies to assure that crops achieve superior phenotypes under changing environments, while balancing reward, time, and risk in the decision-making process. The research team will first translate the technical issues into engineering objectives and then identify existing methods and design new ones to achieve the objectives. The corresponding engineering objectives are: (1) identify a small subset of variables associated with synergistic effects in addition to their additive effects; (2) design a set of algorithms for genomic selection, which is a special type of nonlinear, non-convex, high-dimensional, and dynamic optimization problem constrained by resource availability and laws of reproductive biology; and (3) create a set of multi-objective and multi-level optimization models and algorithms for balancing reward, time, and risk, subject to genetic, environmental, and logistical constraints. Achieving these objectives will demonstrate the power of engineering approaches in improving the efficiency and resiliency of agronomic systems, with the aim of establishing plant breeding as an engineering discipline.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.

这项针对美国繁荣，健康和基础设施（LEAP-HI）的领先工程赋予了与行业联络的机会（Goari）项目，该项目涉及NSF理解生活规则并利用数据革命的巨大思想，目的是针对目标的需求，为不断增长的人群提供较少资源（水，水，水，水，农药和生育和快速的环境），为不断增长的人群提供粮食，纤维和燃料。人们普遍认识到，从作物遗传改善到现场农作物的产量，当前的农业技术将由于对昂贵，耗时，耗时，越野和错误的现场试验的严重依赖而无法满足未来的农业需求，从而发展了改善的植物品种。新兴的数学优化和机器学习方法用于分析高维数据提供了加快植物育种的机会，以实现农作物对不断变化的环境的快速有效调整。该项目中的方法将利用工程技术的优势，这些技术可显着提高沟通，制造，运输和能源系统的效率和弹性。 这项研究需要综合多个学科，包括农艺建模，机器学习，操作研究，优化和植物育种，并旨在证明工程在应对农业挑战中的领导作用。 （2）设计遗传改善系统以有效地开发具有优质表型的品种； （3）设计作物管理策略，以确保在不断变化的环境下实现卓越的表型，同时在决策过程中平衡奖励，时间和风险。研究团队将首先将技术问题转化为工程目标，然后确定现有方法并设计新的方法以实现目标。相应的工程目标是：（1）确定与协同效应相关的一小部分变量，除了它们的添加效果； （2）设计一组用于基因组选择的算法，这是一种非线性，非凸，高维和动态优化问题的特殊类型，受到资源可用性和生殖生物学定律的约束； （3）创建一组多目标和多级优化模型以及算法，以平衡奖励，时间和风险，但要遵守遗传，环境和后勤约束。实现这些目标将证明工程方法在提高农艺系统的效率和弹性方面的力量，目的是建立植物育种作为工程学科。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来获得支持的。