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.

这项美国繁荣、健康和基础设施领先工程 (LEAP-HI) 学术与工业联络资助机会 (GOALI) 项目提出了 NSF 理解生活规则和利用数据革命来满足食品供应需求的重要理念在不确定和快速变化的环境中，使用更少的资源（土地、水、农药和化肥）为不断增长的人口提供纤维和燃料。人们普遍认识到，当前的农业技术，从作物遗传改良到大田作物生产，将无法满足未来的农业需求，因为它们严重依赖昂贵、耗时、反复试验的田间试验来开发改良的植物品种。用于分析高维数据的新兴数学优化和机器学习方法为加速植物育种提供了机会，以实现作物快速有效地适应不断变化的环境。该项目中的方法将利用已用于显着提高通信、制造、运输和能源系统的效率和弹性的工程技术。 该研究需要综合多个学科，包括农学、作物建模、机器学习、运筹学、优化和植物育种，旨在展示工程在应对农业挑战方面的领导作用。三个技术问题代表了一个虽小但高度可见的问题农艺系统的子集，将解决：（1）根据遗传、农艺管理和环境数据及其相互作用准确预测植物表型； （2）设计遗传改良体系，高效培育优良表型品种； (3)设计作物管理策略，确保作物在不断变化的环境下获得优异的表型，同时在决策过程中平衡回报、时间和风险。研究团队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方法来实现目标。相应的工程目标是：（1）识别与协同效应以及附加效应相关的一小部分变量； （2）设计一套基因组选择算法，这是一种受资源可用性和生殖生物学规律约束的特殊类型的非线性、非凸、高维、动态优化问题； (3) 创建一套多目标、多层次的优化模型和算法，在遗传、环境和后勤约束下平衡奖励、时间和风险。实现这些目标将展示工程方法在提高农艺系统效率和弹性方面的力量，旨在将植物育种建立为一门工程学科。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，认为值得支持。智力价值和更广泛的影响审查标准。