CAREER: Predicting Climate Impacts on Irrigated Agriculture
CAREER: Predicting Climate Impacts on Irrigated Agriculture
批准号:
1848018
负责人:
Jonathan Winter
金额:
$49.89万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2019
资助国家:
美国
项目状态:
未结题
起止时间:
2019-06-01 至 2025-05-31
中文摘要
该项目将评估气候和供水对未来灌溉农业生产的影响。通过灌溉增加农业生产是解决全球饥饿问题的复杂办法的重要组成部分。灌溉可以使边缘土地适合农业,使现有农田更高产。然而,灌溉受到大量用水的限制。要确定灌溉在减少全球饥饿方面的潜力,需要对气候、农业供水和作物生产等多个因素有透彻的了解。该项目将通过提高对灌溉农业生产力主要制约因素的理解,改进对气候对灌溉农田影响的预测。因此,它将制定加强未来灌溉管理的战略。知识、数据和模型将直接或通过与农业模型相互比较和改进项目和堪萨斯州地质调查局的合作广泛传播。该项目还将为处于STEM关键阶段的学生增加自然地理、气候科学、数值模拟和数据分析方面的知识。此外,研究者将创建一个教学模式,使高中生能够探索气候和水资源对作物生产的影响,为教学模式开发教育者专业发展课程,并为打算主修STEM领域的不同研究生和本科生提供研究经验。直接模拟供水对产量影响的灌溉农业预测很少,而且通常对作物生长和灌溉的描述过于简单。本项目将通过四个主要努力来解决这一关键缺陷:(1)构建一个建模框架,将气候和水文数据与能够模拟有限自动灌溉的作物模型联系起来;(2)利用报告的产量和卫星衍生的蒸散发、土壤湿度和植被指数数据评估和改进建模框架;(3)用气候和供水情景强制模拟框架;(4)探索不同灌溉情景和建模假设下的作物用水和生产情况。这些努力将回答气候、水和农业交界面的三个重要问题:(1)水供应短缺对灌溉农业生产的影响是什么?现在和将来?(2)哪些灌溉管理措施可以在不损害产量的情况下减少用水量,并最终减少水资源短缺对灌溉作物生产的影响?(3)卫星数据如何改善灌溉农业生产的作物模型模拟?该项目将重点关注美国的灌溉玉米、大豆、水稻和小麦。在美国,灌溉约占耗水量的90%,农业产值达1200亿美元。然而,该项目开发的数据集、模型和方法将为改善全球灌溉农业的预测提供机会。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
This project will assess the effects of climate and water supply on future irrigated agricultural production. Increasing agricultural production through irrigation is an essential part of the complex solution to global hunger. Irrigation can make marginal land suitable for agriculture and existing croplands more productive. However, irrigation is limited by its massive use of water. Determining the potential of irrigation to reduce global hunger requires a thorough understanding of multiple factors such as climate, agricultural water supply, and crop production. This project will improve predictions of climate impacts on irrigated croplands by advancing the understanding of key constraints on irrigated agricultural productivity. As a result, it will develop strategies for enhancing future irrigation management. Knowledge, data, and models will be widely disseminated both directly and through collaborations with the Agricultural Model Intercomparison and Improvement Project and Kansas Geological Survey. This project will also increase knowledge of physical geography, climate science, numerical modeling, and data analysis for students at critical STEM stages. Additionally, the investigator will create a teaching model that enables high-school students to explore the impacts of climate and water resources on crop production, develop an educator professional development course for the teaching model, and provide research experiences for diverse graduate and undergraduate students intending to major in a STEM field.Irrigated agricultural projections that directly simulate the impacts of water supply on yield are few, and typically have simplistic representations of crop growth and irrigation. This project will address this key deficiency through four main efforts: (1) Construct a modeling framework that connects climate and hydrologic data with a crop model capable of simulating limited automatic irrigation; (2) Evaluate and improve the modeling framework using reported yields and satellite-derived evapotranspiration, soil moisture, and vegetation index data; (3) Force the modeling framework with climate and water supply scenarios; and (4) Explore crop water use and production across irrigation scenarios and modeling assumptions. These efforts will provide answers to three important questions at the interface of climate, water, and agriculture: (1) What are the effects of water supply shortages on irrigated agricultural production?currently and in the future? (2) Which irrigation management practices can reduce water use without harming yields, and ultimately decrease the impacts of water scarcity on irrigated crop production? (3) How can satellite data improve crop model simulations of irrigated agricultural production? This project will focus on irrigated corn, soybean, rice, and wheat in the United States, where irrigation is responsible for approximately 90% of consumptive water use and $120 billion of agricultural production. However, datasets, models, and methods developed by this project will provide opportunities to improve projections of irrigated agriculture globally.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.
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DOI:
10.1117/1.jrs.14.044508
发表时间:
2020-10
期刊:
Journal of Applied Remote Sensing
影响因子:
1.7
作者:
[A. Davitt;J. Winter;K. McDonald]
通讯作者:
A. Davitt;J. Winter;K. McDonald
Brazilian maize yields negatively affected by climate after land clearing
土地清理后巴西玉米产量受到气候负面影响
DOI:
10.1038/s41893-020-0560-3
发表时间:
2020
期刊:
Nature Sustainability
影响因子:
27.6
作者:
[Spera, Stephanie A., Winter, Jonathan M., Partridge, Trevor F.]
通讯作者:
Partridge, Trevor F.
DOI:
10.1029/2021ef002018
发表时间:
2021-12
期刊:
Earth's Future
影响因子:
--
作者:
[José R. López;J. Winter;J. Elliott;A. Ruane;C. Porter;G. Hoogenboom;Martha C. Anderson;C. Hain]
通讯作者:
José R. López;J. Winter;J. Elliott;A. Ruane;C. Porter;G. Hoogenboom;Martha C. Anderson;C. Hain
Cross-scale evaluation of dynamic crop growth in WRF and Noah-MP-Crop
WRF 和 Noah-MP-Crop 中作物动态生长的跨尺度评估
DOI:
10.1016/j.agrformet.2020.108217
发表时间:
2021
期刊:
Agricultural and Forest Meteorology
影响因子:
6.2
作者:
[Partridge, Trevor F., Winter, Jonathan M., Kendall, Anthony D., Hyndman, David W.]
通讯作者:
Hyndman, David W.
DOI:
10.1088/1748-9326/ab422b
发表时间:
2019-11-01
期刊:
ENVIRONMENTAL RESEARCH LETTERS
影响因子:
6.7
作者:
[Partridge, Trevor F., Winter, Jonathan M., Hyndman, David W.]
通讯作者:
Hyndman, David W.
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