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 scientist at Columbia, Missouri: (1) Continued study of drought tolerance of public and commercial soybean genotypes; relocated study to a center pivot irrigation system equipped with variable rate irrigation (VRI) to allow a wider range of water stress treatments to identify traits associated with improved drought tolerance in soybean (Obj. 1b). (2) Collaborated with ARS scientists at Bushland, Texas, Florence, South Carolina, and Stoneville, Mississippi, to test an ARS-developed system for VRI management to evaluate the suitability of variable-rate center pivot irrigation. A journal article is currently in review and presentations on the study are scheduled for the 6th Decennial National Irrigation Symposium. Revised treatments, planted study, and installed sensors for crop canopy temperature and soil moisture measurement for 2020 growing season, (Obj. 2a). (3) Continued observations of canopy properties in a cotton field to determine the spatial variability of crop coefficient to improve VRI management (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) Continued tests using VRI to evaluate irrigation treatments for center pivot irrigated corn, soybean, and cotton based on evapotranspiration calculated from on-site weather station data to optimize production systems for irrigated crops (Obj. 1). (3) Initiated study of optimal bedding patterns for furrow irrigated rice to refine irrigation scheduling recommendations for aerobic rice (Obj. 1a). (4) Continued 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). (5) Collected data using an unmanned aerial vehicle (UAV) to optimize production systems for irrigated cotton (Obj. 1); published articles on yield prediction and crop emergence in two journals and prepared additional article on crop emergence. (6) 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).
1. Demonstrated challenges and opportunities in irrigation scheduling in areas with variable rainfall and soil properties. Weather-based scheduling methods base irrigation timing on an estimated balance of water lost by evapotranspiration (ET) with rainfall and irrigation entering the soil. Conversely, soil-based methods measure soil water content or potential levels and attempt to recommend irrigation before there is a shortage of water for plants. Both methods are widely used, and both require accurate crop and soil information for optimum results. ARS researchers at Portageville, Missouri, and Stoneville, Mississippi, compared weather-based and soil-based methods in cotton and soybean studies to identify strengths and weaknesses of both methods. Soil crusting inhibited infiltration at the Mississippi site, impacting the weather-based estimates, while equipment failure affected the soil-based measurements. Similarly, highly variable soil texture at the Missouri site affected both methods, even when Natural Resources Conservation Service Soil Survey data were supplemented with textural analyses. Care must be taken to ensure that the recommendations are accurate regardless of the scheduling method. Accurate irrigation scheduling will optimize production while increasing the efficient use of water, benefiting producers and other users of the water resource.
2. Developed new methods to accurately document cotton plant emergence. Documenting cotton crop emergence in a timely manner is necessary to identify problem areas of the field and allow replanting if necessary. While manual methods based on counting the plants in multiple parts of the field are very time consuming, the small size of the newly emerged plants makes them difficult to identify with remote sensing. 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 developed and refined methods to process the images and provide emergence results. The method proved 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. However, efficiency of image processing was an issue, and further research is needed to shorten the processing time to allow producers to remediate problems and maintain a uniform crop. 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, feed, and fiber.
3. Developed a method for in-season estimation of cotton yield. Accurately estimating cotton crop yield during the growing season will allow producers to make informed decisions on additional inputs and marketing the crop. Current methods such as boll counts are time consuming and not very accurate. ARS researchers at Portageville and Columbia, Missouri, and University of Missouri collaborators used unmanned aerial vehicles (UAVs) to collect in-season images of cotton fields during flowering and shortly before harvest. Several measurements and combinations of measurements were evaluated for time required to collect the data and cost of the measurement along with accuracy and timeliness of the yield estimate. Data collected after defoliation could accurately predict yield but left little opportunity for the producer to take advantage of the information. However, accurate predictions were also obtained from data collected seven weeks earlier. Knowledge of yield during the growing season will allow producers and landowners to maximize profits and allow both producers and cotton gins to better prepare for the harvest season.
Feng, A., Zhou, J., Vories, E.D., Sudduth, K.A., Zhang, M. 2020. Yield estimation in cotton using UAV-based multi-sensor imagery. Biosystems Engineering. 193:101-114. https://doi.org/10.1016/j.biosystemseng.2020.02.014.
Feng, A., Zhou, J., Vories, E.D., Sudduth, K.A. 2020. Evaluation of cotton emergence using UAV-based narrow-band spectral imagery with customized image alignment and stitching algorithms. Remote Sensing. 12(11):1764. https://doi.org/10.3390/rs12111764.
Zhang, M., Zhou, J., Sudduth, K.A., Kitchen, N.R. 2019. Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery. Biosystems Engineering. 189:24-35. https://doi.org/10.1016/j.biosystemseng.2019.11.001.
Sui, R., Vories, E.D. 2020. Comparison of sensor-based and weather-based irrigation scheduling. Applied Engineering in Agriculture. 36(3):375-386. https://doi.org/10.13031/aea.13678.