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ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Research Project #434747

Research Project: Stabilizing Dryland Soybean Yield and Profit in Dominant Soils Across Mississippi (MSPB Project 62-2018)

Location: Genetics and Sustainable Agriculture Research

Project Number: 6064-21660-001-29-R
Project Type: Reimbursable Cooperative Agreement

Start Date: Apr 1, 2018
End Date: Mar 31, 2019

Objective 1. Determine cost-effective management practices to stabilize dryland soybean yield and economic return in major soil types and growing environments across Mississippi. This research will evaluate and identify effective management of broiler litter, municipal biosolids, and cover crop during the fallow season for increasing soil water infiltration, soil water holding capacity and organic matter, and minimizing runoff. Objective 2. Apply agroecosystem models, in conjunction with field trials in Objective 1, to determine optimal management options for consistent dryland soybean yield across typical Mississippi weather conditions and in each of 16 dominant soil types based on 100-year daily weather records and on predicted daily weather for the future 50 years. Objective 3. Conduct economic analysis using results of field trials (Objective 1) and simulation studies (Objective 2) to compare the cost and return of using soil organic amendments or/and cover crop in comparison with conventional management practices. The goal is to help nonirrigated soybean growers in different Mississippi environments determine the long-term profit-maximizing management practices for a soil type, topography, precipitation pattern, and other climate condition found on their farm.

Soil moisture sensors and soil matric potential sensors coupled to dataloggers (Campbell Scientific Inc.) were installed in no-till field where cover crop treatments (native vegetation, wheat, cereal rye, vetch and National Resources Conservation Service mixed mustard and cereal rye were planted Oct., 2017. Soil moisture will be measured at depths of 6, 12, 18, 24 and 36 inch to determine benefit of rainfed soybean water use efficiency, growth, yield and soil health from both cover crop and poultry litter in the no-till field. We measured rainfed soybean growth and water consumption periodically in the same field in 2017 before cover crops were planted for better determining benefits in 2018. At seven locations, crop growth dynamics will be assessed from measurements of developmental growth stage, plant height, leaf area index (LAI), using Decagon, Inc., AccuPar LP-80, and dry biomass at major stages. Time Domain Reflectometer (TDR) and Watermark sensors were installed at depths of 6, 12, 18, 24 and 36 inch.. Fields at Good farm, Brooksville, Starkville, Pontotoc and Verona will be mapped for Soil Organic Mater ()SOM), and apparent Electrical Conductivity (EC) after harvest using the VERIS system (Veristech Inc.). We will also collect a 3-band spectral reflectance data from all fields using a commercial sequoia camera mounted on a drone. The Normalized Digital Vegetation Index (NDVI) along with Landsat8 measured data and other indices calculated based on these data will be used to detect soybean water stress, growth and development status and correlate with soil properties. We will measure soil health indicators of all samples, SOM, some of the major microbial groups involved in carbon and nitrogen dynamics, percentage of clay, silt and sand, aggregate stability and penetration resistance, bulk density, infiltration rate, saturated hydraulic conductivity, soil water holding capacity, plant permanent wilting point water content (Extractor pressure chamber and plates, (Soil Moisture Equipment Corp.), and nine plant available nutrients. Root Zone Water Quality Model 2, developed by USDA-ARS ( RZWQM2), Agricultural Policy/Environmental eXtender, developed by USDA-ARS (APEX), DECISION SUPPORT SYSTEM FOR AGROTECHNOLOGY TRANSFER ( DSSAT), Soil and Water Assessment Tool developed by USDA-ARS (SWAT) and Finite Difference groundwater model (MODFLOW) models will be employed in a complementary manner to evaluate impacts of different scenarios of poultry litter, municipal biosolids, and/or cover crops, and irrigation on soybean water status, growth and development, grain yield, water use and soil health. Briefly, these are process-based, daily-time step models designed for characterizing the effects of different management options on crop growth and development, yield, water use, soil and water quality, and groundwater level. Each calibrated model will be used in simulation studies to assess the most effective management practice(s) for consistent soybean yields without irrigation for each of the dominant soils in major climate regions across Mississippi under climate scenarios of past 100 years and future 50 years.