Location: Cropping Systems and Water Quality Research
2025 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 ARS objectives and collaboration with University of Missouri (MU) scientists through a Non- Assistance Cooperative Agreement.
Research supporting Objective 1, Optimize production systems for irrigated cotton, corn, soybean, and rice to improve crop water productivity under variable weather and soil conditions:
A multi-year dataset of canopy spectral reflectance and height data for multiple cotton varieties has been assembled and validated. It will be analyzed to determine varietal differences in crop coefficient as a baseline for determining site-specific crop coefficients. Crop coefficients are used to estimate crop water use for irrigation scheduling, and coefficients developed for specific varieties will allow producers to irrigate more efficiently and profitably on their individual farms.
Through 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) Planted row rice seeding rate trials utilizing four cultivars at five seeding rates on rice fields across the Bootheel region of southeast Missouri. Results were reported to local rice producers. (3) Cover crop impacts on sediment, herbicide, and tailwater runoff were evaluated for irrigated Missouri cotton. The herbicide 2,4-D was sprayed on conventional tilled cotton and cotton planted in cover crops. Within 24 hours after application, furrow irrigation was initiated, and water was collected using samplers at the bottom end of the field. The water samples were sent for laboratory analysis, and results are forthcoming. (4) Continued a crop rotation study including corn, cotton, and soybean to address how to better design cropping systems for the conditions of the Upper Mississippi Delta. These collaborative projects address needs expressed by farmer stakeholders in the area, providing them with actionable information important for improved management of rice, cotton, corn, and soybean production.
Research supporting 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:
A multi-year dataset of canopy spectral reflectance and height data, collected in a cotton field with highly variable soils, has been assembled and validated. It will be analyzed to better understand within-field variability in the crop coefficient, which is important information for cotton farmers to successfully apply variable-rate irrigation.
Through collaboration with the University of Missouri: Continued field research to develop a data-driven decision support system for variable-rate cotton seeding under irrigated production. A third season of aerial images and ground truth data were collected in a research field with strip trials of different cotton seeding rates. Analysis is in process and manuscripts are being drafted for journal submission. Achieving a uniform cotton stand is important in optimizing production but is difficult in fields with variable soils. Predicting areas with low cotton seedling emergence in advance will allow farmers to increase seeding rates and achieve a more uniform and profitable stand.
Accomplishments
1. New method identifies field areas needing improved management. Crop yields can be optimized for many fields by varying irrigation water and other inputs from place to place within the field. This precision agriculture approach is often facilitated by dividing the field into management zones based on multiple years of mapped data, such as crop yield. There is no consensus on the best method for this process, among the many that have been used. Therefore, an ARS scientist at Columbia, Missouri, along with collaborators from multiple universities, developed a new index of yield variability, called the Yield Performance Index (YPI). The YPI identifies and separates field areas with low yields and high year-to-year variation from those with higher average yields and low year-to-year variation. Maps of the YPI show where changes in management should be considered to increase average yield and/or reduce yield variability. This research will help producers and their advisors target efforts to those portions of fields that can benefit most, helping to maximize the productivity and profitability of U.S. farmers.
Review Publications
Tian, F., Zhou, J., Ransom, C.J., Aloysius, N., Sudduth, K.A. 2025. Estimating corn leaf chlorophyll content using airborne multispectral imagery and machine learning. Smart Agricultural Technology. 10. Article 100719. https://doi.org/10.1016/j.atech.2024.100719.
De Souza, E.G., Khosla, R., Sudduth, K.A., Johann, J.A., Bazzi, C.L. 2025. Spatial and temporal variability of yield maps can localize field management -- a case study with corn and soybean. Agronomy. 15(5). Article 1179. https://doi.org/10.3390/agronomy15051179.