Sustainable Agricultural Land Use Practices in Large-scale Landscape Evolution
Sustainable Agricultural Land Use Practices in Large-scale Landscape Evolution
批准号:
2117722
负责人:
Jing Gao
金额:
$39.52万
依托单位:
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
项目状态:
未结题
起止时间:
2021-08-01 至 2025-01-31
中文摘要
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英文摘要
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.
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