CAREER: Ensuring Co-Sustainability of Food Production and Environmental Quality in the U.S. Midwest Agroecosystems

在过去的200年里，美国中西部从天然草原/湿地转变为肥沃的农田，目前产量约占世界的三分之一。美国玉米和大豆。人类活动通过广泛的地下“铺瓦”（排水管道）以及化肥和其他投入物的强化使用，改变了这一景观。然而，这种转变也产生了关于环境可持续性的重要问题。随着气候变化带来的进一步压力，美国中西部能否在未来100年保持全球粮仓的地位？在这种情况下，粮食生产和环境质量如何保持？在美国中西部农业生态系统中，碳（如作物生长）、水文（水量和水质）和养分循环在各个尺度上都紧密交织在一起，从农田/水源尺度到整个河流（大密西西比河流域）网络和大陆尺度。任何人类活动和实践都不只是影响一个组成部分，而是影响一整套相互联系。因此，需要对复杂的反馈和相互作用进行“系统”分析，以评估美国中西部农业生态系统的潜在适应性。本项目采用系统视角对美国中西部农业生态系统的耦合“食物-水-营养关系”进行整体建模和量化。这些模型将极大地促进对过程的理解，并预测当前和未来气候条件下农业生态系统的行为。本研究的最终目标是在美国中西部农业生态系统的背景下，建立对不同人类管理和气候条件下作物生长、水文和营养循环如何相互作用的深入了解。该项目将开发陆地-河流耦合模型和模型-数据融合方法，并整合实地收集的数据和遥感测量。具体而言，采用基于生态水文过程的新型模型-数据融合方法，在区域尺度上对美国中西部三个关键州的排水程度和排水强度进行空间显式估算。然后，该瓦排水数据集将用于开发陆地-河流耦合网络模型（ecosis - throw），以量化该农业生态系统中跨空间尺度的水循环、氮循环和作物生产之间的反馈/相互作用，并评估有希望的人类管理实践的潜力，以实现美国中西部粮食生产和环境质量的共同可持续发展。生态系统-抛掷耦合模型的模拟结果有望被决策者和农民社区用于评估区域尺度上的农业生态系统状况和各种保护措施的潜在影响。该奖项反映了美国国家科学基金会的法定使命，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 past 200 years have seen the US Midwest be transformed from natural prairie/wetlands to fertile croplands that currently produce about one third of the world?s corn and soybean. This landscape was transformed by human activities through extensive subsurface "tiling" (drainage piping) and intensified uses of fertilizer and other inputs. However, this transformation has also created important questions about environmental sustainability. With further stress from climate change, can the US Midwest remain as the global food basket for the next 100 years? How can food production and environmental quality both be sustained in this landscape? In the US Midwest agroecosystem, carbon (e.g. crop growth), hydrology (both water quantity and quality), and nutrient cycles are all closely intertwined across scales, from the field/headwater scale to the whole river (greater Mississippi river basin) network and continental scales. Any human activities and practices do not just affect one component, rather the complete set of interconnections. Thus, a "system" analysis of the complex feedbacks and interactions is required to assess potential adaptations in the US Midwest agroecosystem. This project adopts a system view to holistically model and quantify the coupled "food-water-nutrient nexus" for the US Midwest agroecosystem. These models will significantly advance the understanding of the processes and predict agroecosystem behavior under current and future climate conditions. Two promising management practices (i.e. controlled drainage, and nutrient management) to achieve co-sustainability of food production and environmental quality will be assessedThe ultimate goal of the research is to establish an advanced understanding of how crop growth, hydrology, and nutrient cycles interact under different human management and climate conditions in the setting of the US Midwest agroecosystem. The project will develop a coupled land-river model and a model-data fusion approach, and integrate field-level collected data and remote sensing measurements. Specifically, tile drainage extent and drainage strength will be estimated in a spatially explicit manner at the regional scale for the three key states in the US Midwest, by a new model-data fusion approach based on ecohydrological processes. This dataset of tile drainage will then be used to develop a coupled land-river network model (ecosys-THREW) to quantify feedbacks/interactions among the water cycle, nitrogen cycle, and crop production across spatial scales in this agroecosystem, as well as to assess the potential of promising human management practices to allow co-sustainability of food production and environment quality in the US Midwest. Simulation results from the coupled ecosys-THREW model are expected to be used by policy makers and farmer communities to assess the agroecosystem conditions and potential impacts of various conservation practices at the regional scale.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.