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: 2327138
• 负责人: Wei Ren
• 金额: $ 51万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327138&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.

密西西比河拥有第三大流域，是世界上生产力最高的农业地区之一，其产量占美国玉米和大豆总产量的 80%，占全国农产品出口的 92%。大规模工业化农业给该地区带来了显着的社会经济收益，但也付出了环境代价（水土流失、养分污染和水体酸化）。气候智能型农业（CSA）管理实践已被提议作为这些成本的解决方案，因为它们不仅可以提高作物产量，还可以减少温室气体排放，并维持土壤和水质。然而，CSA 实践的有效性在不同的气候和土地利用条件下会有所不同，并且涉及紧密耦合的碳、水和养分循环。这些相互作用尚未得到充分研究，而且这种知识差距阻碍了对 CSA 实践的理解和有效应用，以实现提高粮食生产、减缓气候变化和环境可持续性的效益。该项目的总体目标是开发一个综合生态系统监测、建模和机器学习框架（EcoM3），该框架结合了实地观测、卫星遥感数据、基于过程的建模和深度学习方法，系统地研究CSA实践（免耕和覆盖作物）对关键农业生态系统指标（作物产量、土壤碳储存、温室气体和碳/氮淋滤）的具体影响 在多个尺度上。该项目将利用肯塔基州的一个长期实地站点（连续观测超过50年）作为一个测试站点，以调查从日常到季节、每年、十年尺度的CSA实践效果；检查密西西比河流域多个气候和土壤条件不同的地点的不同 CSA 效应；并预测 CSA 实践对整个流域规模的潜在影响。多尺度数据和模型结果将被整合到 EcoM3 框架的学习平台中，以便与不同的利益相关者和政策制定者交流 CSA 的时空有效性。这项研究解决了一个具有挑战性的问题：增强的系统方法能否充分促进我们对农业生态系统、气候和环境系统之间相互关联关系的理解，使我们能够同时管理多个目标（粮食安全、碳固存和 环境可持续性）？这项研究代表了一种系统方法，用于研究在异质气候和土壤条件下，在场地和区域尺度上 CSA 实践对农业系统的综合影响。拟议的 EcoM3 框架纳入了 CSA 管理，旨在促进对气候、土地利用/管理和生态系统之间的相互作用和反馈循环的概念和操作理解。这项研究的产品将改善环境系统模型中农业生态系统的机械表征，从而更准确地预测生物地球化学循环和未来气候变化，并将为农民提供可行的建议，并为制定有关建设可持续和气候适应型农业的循证政策提供科学依据。研究结果将通过当地推广会议和多州农民峰会（密西西比河流域各地区的代表）与农民进行沟通。项目产品将提高人们对 CSA 管理在建设气候适应型农业生态系统和保护土壤和水健康方面重要性的认识。多尺度数据集将公开用于研究和教育。该项目由 CBET 环境可持续发展计划和刺激竞争研究既定计划 (EPSCoR) 联合资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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

Differentiable modelling to unify machine learning and physical models for geosciences

• DOI: 10.1038/s43017-023-00450-9
• 发表时间: 2023-07
• 期刊: Nature Reviews Earth & Environment
• 影响因子: 42.1
• 作者: Chaopeng Shen;A. Appling;P. Gentine;Toshiyuki Bandai;H. Gupta;A. Tartakovsky;M. Baity-Jesi;F. Fenicia;Daniel Kifer;Li Li-Li;Xiaofeng Liu;Wei Ren;Y. Zheng;C. Harman;M. Clark;M. Farthing;D. Feng;Praveen Kumar;Doaa Aboelyazeed;F. Rahmani;Yalan Song;H. Beck;Tadd Bindas;D. Dwivedi;K. Fang;Marvin Höge;Christopher Rackauckas;B. Mohanty;Tirthankar Roy;Chonggang Xu;K. Lawson

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

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

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.