CAREER: Quantifying Multi-Scale Climate-Smart-Agriculture Management for Triple Wins in Food production, Climate Mitigation, and Environmental Sustainability

职业：量化多尺度气候智能农业管理，实现粮食生产、气候减缓和环境可持续性三赢

• 批准号: 2045235
• 负责人: Wei Ren
• 金额: $ 51万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045235&HistoricalAwards=false
• 关键词: CAREER; Quantifying; Multi; Scale; Climate

The Mississippi River has the third-largest drainage basin and represents one of the most productive agricultural regions in the world, yielding 80% of US total corn and soybean production and 92% of the nation’s agricultural exports. Large-scale industrial agriculture has led to significant socio-economic gains, but at environmental costs (soil erosion, nutrient pollution, and aquatic acidification) in this region. Climate-smart agriculture (CSA) management practices have been proposed as solutions to these costs, as they not only increase crop yield, but also reduce greenhouse gas emissions, and sustain soil and water quality. However, the effectiveness of CSA practices varies under diverse climate and land use conditions and involves tightly coupled carbon, water, and nutrient cycles. These interactions have not been well studied, and this knowledge gap has hindered understanding and efficient application of CSA practices to achieve the benefits of enhancing food production, climate mitigation, and environmental sustainability. The overall goal of this project is to develop an integrated ecosystem monitoring, modeling, and machine learning framework (EcoM3) that incorporates field observations, satellite remote sensing data, process-based modeling, and a deep-learning approach to systematically investigate specific effects of CSA practice (no-tillage and cover crops) on key agroecosystem indicators (crop yield, soil carbon storage, greenhouse gases, and carbon/nitrogen leaching) at multiple scales. This project will use a long-term field site in Kentucky (continuous observations over 50 years) as one testing site to investigate CSA practice effects from daily to seasonal, annual, decadal scales; examine varied CSA effects at multiple sites with diverse climate and soil conditions across the Mississippi River basin; and predict the potential impacts of CSA practices at the entire river basin scale. Multi-scale data and model results will be integrated into the learning platform of the EcoM3 framework to communicate temporal and spatial CSA effectiveness with diverse stakeholders and policy-makers.This study addresses a challenging question: Will an enhanced systems approach advance our understanding of the interconnected relationships among agroecosystems, climate, and environment systems sufficiently to allow us to simultaneously manage multiple goals (food security, carbon sequestration, and environmental sustainability)? This study represents a systematic method to investigate the comprehensive effects of CSA practices in agricultural systems at both site and regional scales under heterogeneous climate and soil conditions. The proposed EcoM3 framework incorporates CSA management that is targeted to advance conceptual and operational understanding of interactions and feedback loops among climate, land use/management, and ecosystems. Products derived from this study will improve the mechanistic representation of the agroecosystem in Environmental System Models toward a more accurate prediction of biogeochemical cycles and future climate change and will provide viable recommendations for farmers and a scientific basis for making evidence-informed policy about building sustainable and climate-resilient agriculture. Research findings will be communicated with farmers through local extension meetings and the Multi-state Farmer Summit (representatives across regions in Mississippi River basin). Project products will enhance awareness about the importance of CSA management in building climate-resilient agroecosystems and preserving soil and water health. Multi-scale datasets will be made publicly available for research and education.This project is jointly funded by the CBET Environmental Sustainability program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

密西西比河是第三大排水盆地，代表了世界上生产力最高的农业地区之一，占美国玉米和大豆总产量的80％，占全国农业出口的92％。大规模的工业农业已导致了显着的社会经济增长，但以环境成本（土壤侵蚀，养分污染和水生酸化）。已经提出了气候智能农业（CSA）管理实践作为解决这些成本的解决方案，因为它们不仅增加了农作物的产量，而且还会减少温室气体排放，并维持土壤和水质。但是，在多样化的气候和土地使用条件下，CSA实践的有效性各不相同，涉及紧密耦合的碳，水和营养周期。这些相互作用并不是很好的研究，并且这些知识差距阻碍了CSA实践的理解和有效应用，以实现增强粮食生产，缓解气候和环境可持续性的好处。 The overall goal of this project is to develop an integrated ecosystem monitoring, modeling, and machine learning framework (EcoM3) that incorporates field observations, satellite remote sensing data, process-based modeling, and a deep-learning approach to systematically investigate specific effects of CSA practice (no-tillage and cover crops) on key agroecosystem indicators (crop yield, soil carbon storage, greenhouse gases, and carbon/nitrogen walking) at multiple秤。该项目将在肯塔基州使用长期的现场（在50年内连续观察）作为一个测试地点，以研究CSA实践的效果，从日常到季节性，年度衰老量表；在密西西比河盆地的多个地点检查多个地点的CSA效应；并预测CSA实践在整个河流量表上的潜在影响。 Multi-scale data and model results will be integrated into the learning platform of the EcoM3 framework to communicate temporary and spatial CSA effectiveness with divers stakeholders and policy-makers.This study addresses a challenge question: Will an enhanced systems approach advance our understanding of the interconnected relationships among agroecosystems, climate, and environment systems sufficiently to allow us to simply manage multiple goals (food security, carbon session, and environmental sustainability)?这项研究代表了一种系统的方法，可以研究在异质气候和土壤条件下，在现场和区域尺度上，CSA实践在农业系统中的全面影响。拟议的ECOM3框架结合了CSA管理，该管理旨在提高对气候，土地使用/管理和生态系统之间相互作用和反馈循环的概念和操作的理解。从这项研究中得出的产品将改善环境系统模型中农业生态系统的机理表示，以更准确地预测生物地球化学周期和未来的气候变化，并将为农民提供可行的建议，并为制定有关可持续和气候耐候农业的证据政策提供科学基础。研究发现将通过当地的推广会议和多州农民峰会（密西西比河河流域的代表）与农民进行交流。项目产品将提高人们对CSA管理在建设气候富度农业生态系统以及保护土壤和水健康方面的重要性的认识。多尺度数据集将公开用于研究和教育。本项目由CBET环境可持续性计划共同资助，既定的竞争研究（EPSCOR）的既定计划（EPSCOR）。该奖项反映了NSF的法定任务，并通过使用该基金会的智力功能和广泛影响来评估CRITERIA CRITERIA CRITERIA。

项目成果

Simulating no-tillage effects on crop yield and greenhouse gas emissions in Kentucky corn and soybean cropping systems: 1980–2018

• DOI: 10.1016/j.agsy.2021.103355
• 发表时间: 2022-03
• 期刊: Agricultural Systems
• 影响因子: 6.6
• 作者: Yawen Huang;B. Tao;Yanjun Yang;Xiaochen Zhu;Xiaojuan Yang;J. Grove;W. Ren

A global synthesis of biochar's sustainability in climate-smart agriculture - Evidence from field and laboratory experiments

• DOI: 10.1016/j.rser.2022.113042
• 发表时间: 2023-02
• 期刊: Renewable and Sustainable Energy Reviews
• 影响因子: 15.9
• 作者: Yawen Huang;B. Tao;R. Lal;Klaus E. Lorenz;P. Jacinthe;R. Shrestha;Xiongxiong Bai;M. Singh;L. Lindsey;W. Ren

Conservation tillage increases corn and soybean water productivity across the Ohio River Basin

• DOI: 10.1016/j.agwat.2021.106962
• 发表时间: 2021-05-12
• 期刊: AGRICULTURAL WATER MANAGEMENT
• 影响因子: 6.7
• 作者: Huang, Yawen;Tao, Bo;Ren, Wei
• 通讯作者: Ren, Wei

Instream sensor results suggest soil–plant processes produce three distinct seasonal patterns of nitrate concentrations in the Ohio River Basin

河内传感器结果表明，土壤植物过程在俄亥俄河流域产生了三种不同的硝酸盐浓度季节性模式

• DOI: 10.1111/1752-1688.13107
• 发表时间: 2023
• 期刊: JAWRA Journal of the American Water Resources Association
• 作者: Gerlitz, Morgan;Fox, Jimmy;Ford, William;Husic, Admin;Mahoney, Tyler;Armstead, Mindy;Hendricks, Susan;Crain, Angela;Backus, Jason;Pollock, Erik
• 通讯作者: Pollock, Erik

Biochar as a negative emission technology: A synthesis of field research on greenhouse gas emissions

生物炭作为负排放技术：温室气体排放实地研究综合

• DOI: 10.1002/jeq2.20475
• 发表时间: 2023
• 期刊: Journal of Environmental Quality
• 影响因子: 2.4
• 作者: Shrestha, Raj K.;Jacinthe, Pierre‐Andre;Lal, Rattan;Lorenz, Klaus;Singh, Maninder P.;Demyan, Scott M.;Ren, Wei;Lindsey, Laura E.
• 通讯作者: Lindsey, Laura E.