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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Agroecosystems Management Research » Research » Research Project #443545

Research Project: Managing Nutrient, Carbon, and Water Fluxes to Provide Sustainable and Resilient Cropping Systems for Midwestern Landscapes

Location: Agroecosystems Management Research

Project Number: 5030-21600-002-000-D
Project Type: In-House Appropriated

Start Date: Aug 16, 2023
End Date: Aug 15, 2028

Objective 1: Develop climate-smart frameworks for providing actionable information on both conventional and aspirational corn- and soybean-based cropping systems, including organic systems, with foci on nutrient cycling, soil water dynamics, and indicators of soil health. Sub-Objective 1.A: Determine effects of conventional and aspirational C-S based cropping systems, including organic systems, on soil nutrient dynamics, nutrient losses in subsurface drainage, crop nutrient uptake, and crop yield with a vision for developing climate resilient cropping systems for regional producers. Sub-Objective 1.B: Determine effects of conventional and aspirational C-S based cropping systems, including organic systems, on indicators of soil health. Objective 2: Provide actionable information on the influence of microclimates modified by conservation management practices (such as no-till, relay or double cropping, extended rotations) on production efficiency and resilience of corn-soybean, organic, agroforestry, and forage-based cropping systems. Sub-Objective 2.A: Quantify differences in soil temperature, plant available water, and canopy microclimate between conventional and aspirational row-crop systems and forage-based systems that impact sustainability and productivity outcomes. Sub-Objective 2.B: Utilize measurements of light interception and weather data to compare RUE and PUE between conventional and aspirational row-crop systems and forage-based systems.

A combination of controlled experiments in the field and laboratory, tile drainage monitoring, and a variety of modeling techniques and statistical analyses will quantify the effects of 4R management (Right source, Right rate, Right time, and Right place) of nitrogen on nutrient (nitrogen, phosphorus, potassium, and sulfur) cycling in a corn-soybean system (Objective 1). This same approach will be used to determine the ability of cover crops and double-cropping to reduce nitrate losses and maintain soil health in a corn-soybean system, and the efficacy of an organic cropping system with extended rotations to both reduce nitrate losses and enhance soil health. We will determine how fall-planted cover crops and no-tillage within prevailing and alternative corn-soybean rotations affect tile drainage water flow and nutrient concentrations, and how drainage water quality and soil profile water storage differ in organic systems compared with prevailing corn-soybean systems. We will also quantify how diversified systemwide management affects water- and light-use efficiency of row crops and pastures (Objective 2). The approaches we have used in corn-soybean systems will be applied to pasture systems to provide quantitative assessments of the value of silvopasture systems in the Midwest. We will use several indicators of soil health, such as aggregate stability and nitrogen mineralization potential, to compare and contrast prevailing corn-soybean based cropping systems, corn-soybean based systems that include cover crops and double crops, and organic systems that include extended rotations. These comparisons are being conducted via experimental plots with individual subsurface (tile) drains that allow robust measurements of hydrologic and nutrient balances. We are monitoring microclimate (e.g., wind speed and evaporation) in long-term plots to understand the effectiveness of silvopasture systems as a climate change adaptation strategy that may reduce either the severity of extreme events (e.g., drought) or the impact of annual or seasonal climate trends (e.g., increasing temperature). Through a well-designed series of experiments that support modeling and upscaling and by employing the LTAR infrastructure, this study will identify optimal combinations of climate-smart practices and aspirational cropping systems that increase production and offset detrimental impacts to the environment.