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

密西西比河流域是世界上第三大流域，也是世界上最高产的农业区之一，玉米和大豆产量占美国总产量的80%，农产品出口占全国的92%。大规模工业化农业带来了显著的社会经济收益，但在该地区付出了环境代价（土壤侵蚀，营养污染和水酸化）。气候智能型农业管理做法已被提议作为这些成本的解决方案，因为它们不仅增加作物产量，而且减少温室气体排放，并保持土壤和水质。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，并涉及紧密耦合的碳，水和养分循环。这些相互作用尚未得到很好的研究，这种知识差距阻碍了对CSA做法的理解和有效应用，以实现提高粮食生产，减缓气候变化和环境可持续性的好处。该项目的总体目标是开发一个集成的生态系统监测、建模和机器学习框架（EcoM 3），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，以系统地调查CSA实践的具体影响。（免耕和覆盖作物）对关键农业生态系统指标（作物产量、土壤碳储量、温室气体和碳/氮淋失）的影响。该项目将使用一个长期的现场在肯塔基州（连续观察超过50年）作为一个测试站点，以调查CSA实践的影响，从日常到季节，年度，十年尺度;检查不同的CSA影响在多个站点不同的气候和土壤条件在整个密西西比河流域;并预测CSA的做法在整个流域尺度的潜在影响。多尺度数据和模型结果将被整合到EcoM 3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的时间和空间有效性。一种增强的系统方法是否能促进我们对农业生态系统、气候、和环境系统足以让我们同时管理多个目标（粮食安全，碳封存和环境可持续性）？本研究代表了一个系统的方法，调查CSA措施在农业系统中的综合影响，在站点和区域尺度下异质气候和土壤条件。拟议的EcoM 3框架纳入了CSA管理，旨在促进对气候，土地使用/管理和生态系统之间相互作用和反馈回路的概念和操作理解。从这项研究中获得的产品将改善环境系统模型中农业生态系统的机械表示，以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性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）与农民进行交流。项目产品将提高对CSA管理在建设具有气候抗御力的农业生态系统和保持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。该项目由CBET环境可持续性计划和刺激竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF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。

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.