Sustainable Agricultural Land Use Practices in Large-scale Landscape Evolution

大规模景观演变中的可持续农业土地利用实践

• 批准号: 2117722
• 负责人: Jing Gao
• 金额: $ 39.52万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117722&HistoricalAwards=false
• 关键词: Sustainable; Agricultural; Land; Use; Practices

How farmers use and manage their fields impacts the environment at local, state, national, and global levels. The public and the private sectors have invested billions of dollars each year as short-term financial incentives to encourage the initial adoption of sustainable agricultural land use practices to reduce negative environmental impacts. Many agri-environmental programs promote using cover crops during the winter, a practice that reduces erosion, prevents fertilizer runoff from polluting waterways, provides pollinator services, and improves soil health. While long-term persistent use is required for sustainable agricultural land use practices to generate their public benefits, very little is known about whether short-term financial incentives can lead to long-term persistent change. This project investigates how the persistent application of cover crops varies over time and space, and assesses how this sustainable agricultural land use practice has resulted in the adoption of best management practices that promote sustainability. The findings enable scientists to better model the environmental impacts of changes in land use practices, and to provide advice on how to create landscapes that promote environmental sustainability and societal well-being by incentivizing and contracting for sustainable land use practices. Changes in local-level land use practices can accumulate to shape landscape patterns and have important lasting impacts on the environment. To study how individual-level sustainable land use decisions aggregate to form large-scale landscape patterns that yield environmental benefits, this project analyzes a large, national dataset (2010-2020) with about 374 million observations of agricultural fields that together cover approximately 95% of planted acres for major commodities, along with state-level longitudinal data and expert opinions. The project generates new insights using machine learning techniques to identify associations between characteristics of related natural and human systems and field-level persistence of the sustainable practice. The insights can be translated for practitioners at federal and state agencies to better design incentive programs to yield the most public benefits per dollar invested.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.

农民如何使用和管理他们的土地会影响地方、州、国家和全球层面的环境。公共和私营部门每年投资数十亿美元作为短期财政奖励，鼓励初步采用可持续农业土地使用做法，以减少对环境的负面影响。许多农业环境项目提倡在冬季使用覆盖作物，这种做法可以减少侵蚀，防止肥料流失污染水道，提供授粉服务，并改善土壤健康。虽然可持续农业土地使用做法需要长期持续使用，以产生公共利益，但人们对短期财政激励措施是否能导致长期持续变化知之甚少。该项目调查了覆盖作物的持续应用如何随时间和空间而变化，并评估了这种可持续的农业土地利用做法如何导致采用促进可持续性的最佳管理做法。研究结果使科学家能够更好地模拟土地利用方式变化对环境的影响，并就如何通过激励和承包可持续土地利用方式来创造促进环境可持续性和社会福祉的景观提供建议。地方一级土地使用做法的变化可累积形成景观格局，并对环境产生重要的持久影响。为了研究个人层面的可持续土地利用决策如何聚集起来形成产生环境效益的大规模景观格局，本项目分析了一个大型的全国数据集（2010-2020年），其中约3.74亿个农田观测数据，这些农田总共覆盖了约95%的主要商品种植面积，沿着国家一级的纵向数据和专家意见。该项目利用机器学习技术产生新的见解，以确定相关自然和人类系统的特征与可持续实践的实地持续性之间的关联。这些见解可以转化为从业者在联邦和州机构，以更好地设计激励计划，产生最大的公共利益每美元的投资。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。