Location: Cropping Systems and Water Quality Research
2023 Annual Report
Objectives
Objective 1: Optimize production systems for irrigated cotton, corn, soybean, and rice to improve crop water productivity under variable weather and soil conditions.
1A: Develop improved methods for determining the appropriate values of field capacity for use in irrigation scheduling.
1B: Develop a database of crop canopy sensing data for calculating crop coefficient in fields with uniform soil to serve as baseline for determining site-specific crop coefficients.
Objective 2: Evaluate and/or develop site-specific best management irrigation practices based on localized soil and environmental conditions to optimize crop production while minimizing water usage.
2A: Evaluate the potential for use of the ARSPivot program for variable-rate irrigation management in the sub-humid U.S. Mid-South.
2B: Document the spatial variability of crop water coefficient and other crop and soil properties in a field and how they interact to affect crop water productivity.
Approach
Our team will address impediments to the overall goal of improving performance, profitability, and sustainability of irrigated agriculture in humid and sub-humid climates. We will develop and refine tools to improve irrigation scheduling and develop improved methods for determining appropriate values for a specific soil’s field capacity, information which is essential for optimal water management. Building on our previous research and as part of a multi-location, multi-disciplinary team, we will investigate how best to achieve site-specific irrigation management through use of the ARSPivot computer program to manage mechanized irrigation systems, and observations of soil and crop variability within the field.
Progress Report
This project includes objectives from ARS scientists and University of Missouri (MU) scientists through a Non-assistance Cooperative Agreement collaboration.
Objective 1: (1) Collected canopy spectral reflectance and height data multiple times during the growing season to develop a database of crop canopy sensing data for calculating crop coefficient in fields with uniform soil and management to serve as baseline for determining site-specific crop coefficients. (2) Implemented the second year of a crop rotation study including corn, cotton, soybean and peanut to address how to better design cropping systems to fit the climate of the Upper Mississippi Delta.
Through a collaboration with the University of Missouri: (1) Maintained three real-time weather stations at research facilities in southeast Missouri with free web access to the information as part of the Missouri Mesonet statewide network of weather stations (mesonet.missouri.edu). (2) Conducted studies to improve management practices for producing furrow irrigated rice. (3) Investigated sources and transport pathways of nitrate in Big Oak Tree State Park and surrounding agricultural areas through determining the annual and seasonal variations in total surface and groundwater inputs and nitrate loads within the park area, determining nitrate concentrations and transport pathways within the wetland areas in the park, and determining the water table fluctuations in the adjacent croplands and pathways of nitrates.
Objective 2: (1) Collected canopy spectral reflectance and height data multiple times during the growing season with ground-based mobile sensors and a cooperator-operated unmanned aerial vehicle (UAV) in a cotton field. The goal was to observe differences between two cotton varieties in this field with highly variable soils, to better understand within-field variability in the cotton crop coefficient.
Accomplishments
1. Maximizing soybean oil yield and meal protein concentration by strategic combinations of planting dates and cultivar maturity. Planting date and cultivar maturity are major management factors affecting soybean productivity, but their effect on oil concentration and meal protein concentration is less understood. U.S. soybean yields increased from 2000 to 2020, but seed and meal protein concentrations declined, and a minimum protein concentration is required for proper development of poultry and livestock fed from soybean meal. An ARS researcher at Portageville, Missouri, cooperating with researchers from multiple universities, determined the interactive effect of planting date and cultivar maturity on soybean yield, seed oil and protein concentrations. The results showed that maximizing total oil yield for soybeans planted in May and June required earlier maturing cultivars than those recommended to maximize seed yield. Short-season cultivars also reduced the risk of low meal protein concentration. This study provides cultivar and sowing date strategies for maximizing oil yield, as well as for increasing meal protein concentration without sacrificing seed yield. This research will benefit producers by helping them make the best choices regarding cultivar selection and planting dates.
Review Publications
Salmeron, M., Bourland, F., Buehring, N., Earnest, L., Fritschi, F., Gbur, E., Golden, B., Hathcoat, D., Lofton, J., McClure, A., Miller, T.D., Neely, C., Shannon, G., Udeigwe, T., Verbree, D., Vories, E.D., Wiebold, W.J., Purcell, L.C. 2022. Regional analysis of planting date and cultivar maturity recommendations that improve soybean oil yield and meal protein concentration. Frontiers in Plant Science. 13. Article 95411. https://doi.org/10.3389/fpls.2022.954111.
Feng, A., Vong, C., Zhou, J., Conway, L., Zhou, J., Vories, E.D., Sudduth, K.A., Kitchen, N.R. 2023. Developing an image processing pipeline to improve the position accuracy of single UAV images. Computers and Electronics in Agriculture. 206. Article 107650. https://doi.org/10.1016/j.compag.2023.107650
Conway, L.S., Sudduth, K.A., Kitchen, N.R., Anderson, S.H., Veum, K.S., Myers, B.D. 2022. Soil organic matter prediction with benchtop and implement-mounted optical reflectance sensing approaches. Soil Science Society of America Journal. 86(6):1652-1664. https://doi.org/10.1002/saj2.20475.
