Location: Cropping Systems and Water Quality Research2021 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.
ARS scientists at Portageville and Columbia, Missouri: (1) Collaborated with local producer 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 (Obj. 1a). (2) Collaborated with ARS scientists at Bushland, Texas, Florence, South Carolina, and Stoneville, Mississippi, to test an ARS-developed system for variable rate irrigation (VRI) management to evaluate the suitability of variable rate center pivot irrigation. Two journal articles were published that included the Missouri effort and presentations on the research are scheduled for the 6th Decennial National Irrigation Symposium. Prepared follow-up study with revised treatments and additional measurements. Planted study with uniform management for 2021 growing season in preparation for follow-up study (Obj. 2a). (3) Continued observations of canopy properties in a cotton field to determine the spatial variability of crop coefficient to improve VRI management. Prepared follow-up study with revised treatments and additional measurements (Obj. 2b). (4) Collaborated with ARS scientists at Florence, South Carolina, on long-term study of the impact of tillage and cover crops on soil health (Obj. 1c). (5) Prepared field for replicated edge-of-field runoff quantity and quality study to determine nutrient content of water from agricultural fields (Obj. 3). Through a collaboration with the University of Missouri: (1) Maintained three real-time weather stations at research facilities in southeast Missouri with web access to the information as part of the Missouri Mesonet. (2) Collected data using an unmanned aerial vehicle (UAV) to optimize production systems for irrigated cotton (Obj. 1); published journal article on crop emergence and prepared additional articles on the effects of soil texture and weather on cotton development and yield and using deep learning. (3) Completed long-term study of irrigated corn and cotton to determine the impact of cover crops in a furrow irrigated, minimum tillage, cotton/corn rotation (Obj. 1c). (4) Completed land use and land cover characterization of Big Oak Tree State Park and the surrounding areas to better understand the quality of runoff from irrigated cropland (Obj. 3); preparing journal article based on findings.
1. Demonstrated negative effect of sand content on irrigated cotton yield. Soil textural variability diminishes the effectiveness of conventional irrigation management and variable rate irrigation (VRI) can address soil variability. ARS scientists at Bushland, Texas, patented an Irrigation Scheduling Supervisory Control And Data Acquisition (ISSCADA) system to provide guidance to prepare VRI prescriptions for optimal water application. To test the system under a range of environments, ARS researchers at Portageville and Columbia, Missouri, compared uniform irrigation scheduling based on water balance estimates, a standard practice, with VRI scheduled with the ISSCADA system for two years. Sand content in the top 0.5-m soil layer, estimated from soil apparent electrical conductivity, had a negative effect on cotton yield in both seasons. The ISSCADA system tended to have a higher irrigation crop water productivity in both seasons, although the difference was not significant in 2016 when adequate rainfall for most of the season led to low total irrigation applications. Research at Portageville, together with other locations, will enhance the ISSCADA system for increased crop water productivity to better meet the needs of agricultural producers.
2. Soil water field capacity values determined in-situ to aid irrigation management. Information about the soil’s field capacity (FC), or the water content after a saturated soil has drained for at least 24 hours, is needed to determine the amount of water that can be safely removed by plants without excessive water deficit stress. Using incorrect FC values can result in inefficient use of water by crops due to waterlogging or water deficit stress. ARS researchers at Portageville and Columbia, Missouri, calculated soil FC from soil water content sensor measurements included in ongoing cotton irrigation studies during the 2017 through 2019 growing seasons. Sensor-based FC values were compared among multiple commercially available sensors and published information. Observed values for moisture content at FC for coarser textured soils were much wetter than expected based on published values. The findings demonstrated that the application of soil moisture sensors for irrigation management is site specific, and differences in in-situ FC can be observed over short distances within a field. The research will better meet the needs of agricultural producers, consultants, research and extension personnel, and others for information to improve irrigation management.
3. Developed faster methods to accurately document cotton plant emergence. Quickly and accurately documenting cotton crop emergence can identify problem areas of the field and allow replanting if necessary. The small size of the newly emerged plants makes them difficult to identify with remote sensing and the time required for extensive data processing to accurately detect them often prevents remediation based on the measurements. ARS researchers at Portageville and Columbia, Missouri, and University of Missouri collaborators used unmanned aerial vehicles (UAVs) to collect early-season images of cotton fields and then developed and refined methods to quickly process the images and provide emergence results. The method proved more than 90% accurate in identifying the numbers of plant seedlings in the field and in recognizing weeds and other extraneous material to avoid including them in the seedling count. The near-real-time processing with the new method was much faster than traditional image processing methods that take days or longer. This technology will allow cotton producers throughout the world to better manage their crops for more efficient production systems to ensure a stable supply of food (cottonseed oil), feed (cottonseed meal), and fiber.
Vories, E.D., Sudduth, K.A. 2021. Determining sensor-based field capacity for irrigation scheduling. Agricultural Water Management. 250. Article 106860. https://doi.org/10.1016/j.agwat.2021.106860.
Feng, A., Zhou, J., Vories, E.D., Sudduth, K.A. 2020. Evaluation of cotton emergence using UAV-based imagery and deep learning. Computers and Electronics in Agriculture. 177. Article 105711. https://doi.org/10.1016/j.compag.2020.105711.
Vories, E.D., O'Shaughnessy, S.A., Sudduth, K.A., Evett, S.R., Andrade, A., Drummond, S.T. 2020. Comparison of precision and conventional irrigation management of cotton and impact of soil texture. Precision Agriculture. 22(2):413-431. https://doi.org/10.1007/s11119-020-09741-3.
Evett, S.R., O'Shaughnessy, S.A., Andrade, M.A., Colaizzi, P.D., Schwartz, R.C., Schomberg, H.H., Stone, K.C., Vories, E.D., Sui, R. 2020. Theory and development of a VRI decision support system: The USDA-ARS ISSCADA approach. Transactions of the ASABE. 63(5):1507-1519. https://doi.org/10.13031/trans.13922.