Location: Cropping Systems and Water Quality Research2022 Annual Report
Objective 1: Optimize production systems for irrigated cotton, corn, soybean, and rice to improve water use efficiency under variable weather conditions while considering the constraints of timing for field operations, a limited growing season, and increasingly limited water supplies. 1a: Refine irrigation scheduling recommendations for aerobic rice. 1b: Determine crop canopy traits associated with improved drought tolerance in soybean. 1c: Determine the impact of cover crop in a furrow irrigated, minimum tillage, cotton/corn rotation. Objective 2: Evaluate the suitability of variable-rate center pivot irrigation for crop production on variable soils and in varying weather conditions to determine potential costs and benefits for producers. 2a: Evaluate the potential use of the ARS Irrigation Scheduling and Supervisory Control and Data Acquisition System (ISSCADA) for variable-rate irrigation management of cotton in the sub-humid U.S. Mid-South. 2b: Determine the spatial variability of crop coefficient in a cotton field. Objective 3: Evaluate the quality of runoff from irrigated cropland to determine current and potential environmental risks and develop guidelines and BMPs to reduce impact of irrigated agriculture on water quality degradation. 3a: Determine nutrient content of runoff from a surface irrigated cotton field in the lower Mississippi River basin.
Our interdisciplinary team will evaluate systems for irrigated crop production to address key knowledge and technology gaps limiting water use efficiency (WUE) in humid and sub-humid climates where water was generally inexpensive and often considered unlimited. We will conduct field research that incorporates spatial soil, crop, and yield data to develop approaches to optimize production systems to better respond to large spatial and temporal variations in weather that are expected to increase with climate change. We will develop recommendations that take into consideration the constraints of limited timing for field operations, marginal growing seasons for cotton and rice, and water supplies facing increased scrutiny for waste and contamination. We will develop and test methods for improved management of variable-rate center pivot irrigation technology for variable crops, soils, and weather conditions to increase potential benefits for producers. We will also evaluate the quality of runoff from irrigated cropland to determine potential environmental risks and develop guidelines and BMPs to reduce water quality degradation associated with irrigated agriculture.
This is the final report for this project which terminated in February 2022. See the report for the replacement project, 5070-13610-009-000D, “Improving Irrigation Management and Water Quality for Humid and Sub-humid Climates” for additional information. This project included objectives from ARS scientists and University of Missouri (MU) scientists through a Non-assistance Cooperative Agreement collaboration. Objective 1: (1) ARS scientists in Portageville and Columbia, Missouri, collaborated with local producers to obtain measurements of water use for furrow irrigated rice for comparison with estimated amounts, together with suitability of computerized and sensor-based irrigation scheduling. (2) Identified appropriate public soybean genotypes for study of drought tolerance and obtained limited seed that was planted to increase availability for the study. The test was planted under a center pivot irrigation system equipped with variable rate irrigation (VRI) to allow a wide range of water stress treatments to identify traits associated with improved drought tolerance. However, the conventional soybean genotypes turned out to be extremely sensitive to dicamba herbicide. Delaying planting was not sufficient to avoid herbicide injury and the damage to the plants made the planned measurements meaningless. Given the high levels of dicamba use in the region, the planned approach could not work until comparable tolerant genotypes are identified. (3) Collaborated with ARS scientists in Florence, South Carolina, on long-term study of the impact of tillage practices and winter cover crops on soil health in cotton fields. Long-term production of low-residue crops like cotton negatively impacts soil health, which affects many aspects of production including irrigation. Winter cover crops can lead to more sustainable production systems and soil and crop measurements are included to better understand the process. (4) Collected data on in-season changes in soil water content. This study was expanded and included in the new project 5070-13610-009-000D “Improving Irrigated Crop Management System for Humid and Sub-humid Climates.” (5) Investigated soil sensing systems for better characterization of the high soil variability of the Upper Mississippi Delta, which greatly impacts irrigation management. The approach combined mobile sensors that provide good coverage across fields with vertical-probing sensors that provide information about how soils vary with depth. Analysis showed that the approach was able to create maps of how soil texture varies with depth across fields, which will be useful for VRI management. Presented findings at the 14th International Conference on Precision Agriculture (ICPA). 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) Conducted tests using VRI to evaluate irrigation treatments for center pivot irrigated rice, corn, cotton and soybean based on evapotranspiration calculated from on-site weather station data and observed a greater response to later irrigations for center pivot rice than for flooded rice. (3) Initiated study of optimal bedding patterns for furrow irrigated rice to refine irrigation scheduling recommendations for aerobic rice. (4) Developed and refined a smart phone app (Crop Water Use Application) for scheduling irrigation based on Missouri Mesonet data. Observed that cotton yields trended greater with increases in irrigation frequency; however, waiting to trigger irrigation saved water. Conducted field tests in conjunction with area farmers. (5) Acquired data from cotton irrigation study field that was collected by cooperator using an unmanned aerial vehicle (UAV). Presented findings at the 2018 and 2019 American Society of Agricultural and Biological Engineers Annual International Meetings (ASABE AIM). (6) Conducted long-term study of the effect of crop rotation, cover crops, reduced tillage and irrigation on corn and cotton and observed increases in corn yield associated with both irrigation and cover crop. Objective 2: (1) Collaborated with ARS scientists in Bushland, Texas, Florence, South Carolina, and Stoneville, Mississippi, to test the ARS-developed system “Irrigation Scheduling and Supervisory Control and Data Acquisition System” (ISSCADA) for VRI management. Installed sensors for crop canopy temperature and soil water content measurement. Presented findings relating soil and plant properties at the 7th Asian-Australasian Conference on Precision Agriculture. Presented findings relating soil properties and irrigation effects at 5th Global Proximal Soil Sensing Workshop. Presented findings of a three-year comparison of the ISSCADA system and traditional water balance scheduling at the 12th European Conference on Precision Agriculture. The study was revised, and findings were reported at the 6th Decennial National Irrigation Symposium (NIS). The study was continued in the new project. (2) Collected canopy spectral reflectance data multiple times each growing season to determine the spatial variability of crop coefficient in a cotton field as an alternative method to improve VRI management. The study was expanded to address differences among cotton varieties and included in the new project. (3) Because studies in the project rely on yield monitor data for harvest results, studies on cotton yield monitor performance variation among varieties were conducted. Findings were presented at 2017 ASABE AIM. Additional findings using harvest data provided by producers were presented at 14th ICPA. Through the collaboration with the University of Missouri, (1) Developed and refined guidelines for preparing VRI prescriptions to avoid runoff and expanded to include aerial and satellite-based prescriptions. Conducted simulations based on existing center pivot design and soil properties. Expanded to include field testing and presented findings at 6th Decennial NIS. Objective 3: (1) Collected baseline yield data for replicated cotton edge-of-field runoff study. Completed preparation of necessary instrumentation for monitoring and sampling to precisely measure nutrient content of runoff from surface irrigated cropland in the lower Mississippi River basin. Personnel issues delayed the study, and it was not completed; however, university collaborators plan to continue the study. Through collaboration with the University of Missouri, (1) Conducted study of Big Oak Tree State Park and the surrounding areas to better understand the quality of runoff from irrigated cropland and how it is affected by management of the park and prepare a groundwater-wetland dynamics assessment. Conducted land use and land cover characterization and compiled airplane and satellite acquired aerial images to assess the changes in vegetation over time.
1. Quantified the interactions of soil, water and weather on cotton crop development to provide producers better management strategies to optimize yields. Determining how soil properties, plant available water and weather conditions interact to impact crop development and production is the key for optimizing field management to achieve sustainable production. ARS researchers in Portageville and Columbia, Missouri, and University of Missouri collaborators used temporal aerial imagery data collected using unmanned aerial vehicles (UAVs) and soil apparent electrical conductivity (ECa), together with local weather data to investigate the soil-plant-water relationship for cotton. Soil texture estimated by linear regression from ECa was used to calculate the total plant-available water in the soil using published equations. A water stress coefficient was calculated using soil texture and weather data. Results showed that soil clay content in shallower layers affected crop development in earlier growth stages while those in deeper layers affected the later-season growth. Soil clay content had a higher impact on crop development when water inputs were not sufficient. While the crop in this study was cotton, the same methods could be applied to other crops. This information will lead to improved management of crop production systems through optimizing irrigation timing and amount, helping to ensure a stable supply of food, feed and fiber.
Feng, A., Zhou, J., Vories, E.D., Sudduth, K.A. 2022. Quantifying the effects of soil texture and weather on cotton development and yield using UAV imagery. Precision Agriculture. 23:1248–1275. https://doi.org/10.1007/s11119-022-09883-6.
Zhou, P., Sudduth, K.A., Veum, K.S., Li, M. 2022. Extraction of reflectance spectra features for estimation of surface, subsurface, and profile soil properties. Computers and Electronics in Agriculture. 196. Article 106845. https://doi.org/10.1016/j.compag.2022.106845.