1a. Objectives (from AD-416):
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.
1b. Approach (from AD-416):
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.
3. Progress Report:
Project Number 5070-13610-007-00D was initiated during FY17. It includes objectives from Agricultural Research Service (ARS) and a Non-Assistance Cooperative Agreement (NACA) with university cooperators. Under ARS leadership: (1) Identified appropriate soybean varieties for drought tolerance study; obtained limited seed and planted to increase availability for study. (2) Collaborated with ARS scientists in Bushland, Texas, Florence, South Carolina, and Stoneville, Mississippi, to test ARS-developed system for variable rate irrigation (VRI) management. Installed additional sensors for soil moisture measurement and collected data on in-season changes in soil properties. Prepared article relating soil and plant properties for presentation in FY18. (3) Expanded field study to determine the spatial variability of crop coefficient in a cotton field to improve VRI management. Prepared article from previous year’s results and presented at 2017 American Society of Agricultural and Biological Engineers Annual International Meeting. (4) Continued study to collect baseline yield data for replicated cotton edge-of-field study. Completed preparation of necessary instrumentation for monitoring and sampling. When installation is complete, the study will precisely measure nutrient content of runoff from surface irrigated cropland in the lower Mississippi River basin. (5) Obtained measurements of spatially referenced cotton canopy properties in ongoing studies of irrigation practice and cultivar effects. Through a Non Assistance Cooperative Agreement with the University of Missouri (MU) (5070-13610-007-01S): (1) Maintained three real-time weather stations at research facilities in southeast Missouri with web access to the information. (2) Continued tests using VRI to evaluate irrigation treatments for center pivot irrigated rice, corn, and soybean based on evapotranspiration calculated from on-site weather station data; continued to refine smart phone app for scheduling irrigation and continued field testing on several farms. (3) Continued study to evaluate effectiveness of controlled release nitrogen (CRN) fertilizers relative to traditional nitrogen programs for furrow irrigated cotton. (5) Continued long-term study of effect of cover crops and reduced tillage on irrigated corn and cotton. (6) Refined guidelines for preparing VRI prescriptions to avoid runoff; expanded to include aerial and satellite based prescriptions and began field testing.
1. Demonstrated that a crop simulation model could be used to supplement soybean field test data. Dynamic crop models that incorporate the effect of environmental variables can potentially explain observed yield differences. ARS researchers in Portageville, Missouri, and university collaborators calibrated soybean growth models using yield, seed weight, and seed oil and protein concentration from 58 irrigated environments during two years. During the subsequent season with 33 environments, the model was effective for predicting yield but not efficient predicting seed number and seed weight. Similarly, the model was able to simulate differences in seed oil concentration across environments but not protein concentration. This research is essential for ensuring a stable supply of an important food and feed crop by successfully supplementing time-consuming field experimentation results with computer simulation.
Cho, Y., Sudduth, K.A., Chung, S. 2016. Soil physical property estimation from soil strength and apparent electrical conductivity sensor data. Biosystems Engineering. 152:68-78. doi: 10.1016/j.biosystemseng.2016.07.003.
Salmeron, M., Purcell, L.C., Vories, E.D., Shannon, G. 2016. Simulation of genotype-by-environment interactions on irrigated soybean yields in the U.S. Midsouth. Agricultural Systems. 150:120-129. doi: 10.1016/j.agsy.2016.10.008.