Research Project: Precipitation and Irrigation Management to Optimize Profits from Crop Production

Location: Soil and Water Management Research

2020 Annual Report

Objectives
Objective 1: Develop improved methods and sensor systems for determining crop water use and stress, and integrate these into systems for water management. Sub-objective 1.1: Improve understanding of soil water status and sensing. Sub-objective 1.2: Improve determinations of evapotranspiration (ET). Sub-objective 1.3: Improve water management decisions at multiple scales by incorporating a better understanding of ET into hydrological models. Objective 2: Develop irrigation and sensor technologies, and best management practices for different irrigation application systems and technologies. Sub-objective 2.1: Compare crop water use efficiency (WUE) and partitioning of water use between evaporation (E) and transpiration (T) between subsurface drip (SDI) and sprinkler irrigation systems. Sub-objective 2.2: Develop sensors and algorithms to improve decision support for an irrigation scheduling supervisory control and data acquisition (ISSCADA) system to spatially optimize crop yields and WUE. Sub-objective 2.3: Develop irrigation application strategies that vary water application temporally for improved cotton lint yields. Objective 3: Develop and determine best management practices to maximize WUE, and long-term profitability using multi-year rotations of different crops and cropping practices, including both dryland and intermittent irrigation practices. Sub-objective 3.1: Determine if long-term weather predictions can be used to optimize irrigation strategies for increased WUE and yield. Sub-objective 3.2. Determine the effects of different conservation tillage practices on precipitation capture and harvest in relation to crop rotation phase. Sub-objective 3.3: Evaluate crop yield response to varying levels of deficit irrigation and water stress under differing management (Genetics x Environment x Management, G x E x M).

Approach
The Ogallala Aquifer region of the U.S. is one of the primary crop production areas in the country, in part because it overlays one of the country’s largest fresh water aquifers. But water availability from the aquifer has decreased significantly since the beginning of wide-spread irrigation in the 1950s, with the greatest impact on the Southern and Central High Plains of western Kansas and Texas. Responding to this will require both more efficient water use by irrigation and increased productivity with lower risk from dryland farming. Cropping practices such as rotation with fallow period for soil water recharge and irrigation practices that avoid evaporation address many of the unique needs of the Central and Southern Great Plains. However the need remains for more efficient water use in these semi-arid regions. Therefore this project will research three areas. First, a better understanding of soil water movement and evaporation, and evapotranspiration. Second, sensors that monitor soil water and crop water stress will be developed to effectively and efficiently use the remaining groundwater for irrigated crop production. Finally, the project will develop best management practices for using water more efficiently under dryland and marginal irrigation regimes. These results will enable the region to remain a competitive area for crop production, sustain farm based communities, and maintain the strength of American agriculture in world markets. Research will be conducted in laboratory and field situations from scales of small plots to regions where crop related data is extracted from remotely sensed images. New plant and soil water stresses will be developed in the laboratory, and once refined, field tested. Data will be integrated into prescriptions for dynamic site specific irrigation scheduling that account for well capacities. These will be tested under field conditions. Understanding of methods to measure evapotranspiration, like eddy covariance, COSMOS, etc., will be enhanced by comparing values from large weighing lysimeters and accurate water balance derived from neutron probe measurements for the soil profile. Measurements from microlysimeters and soil heat flux plates will be used in the field to provide better separation of measures of evaporation and transpiration components of evapotranspiration. A better understanding of evapotranspiration will be used to guide the development of best management practices for crop production and those practices will be tested under field conditions. Data will be used to refine existing hydrologic models, including AcrSWAT, Aquacrop, etc. Data bases of crop water use will be developed and made available to other scientists. This research project also leads the Ogallala Aquifer Program, a research-education consortium addressing solutions arising from decreasing water availability from the Ogallala Aquifer in western Kansa and the Texas High Plains. The consortium includes the ARS NP211 projects at Bushland and Lubbock, Texas, Kansas State University, Texas A&M AgriLife Research and Extension Service, Texas Tech University and West Texas A&M University.

Progress Report
Significant progress was made towards project’s milestones despite challenges from the dry weather during the spring and summer of 2020. Progress towards the milestones and products associated with the Objective 1 depend on a crop being grown on the large weighing lysimeter fields. Cotton was planted onto those four fields and additional instruments deployed. Measurements regarding soil evaporation and evapotranspiration (ET) were collected. Data analysis continued regarding estimates of ET from cosmic ray detection device (COSMOS), eddy covariance and scintillometers for comparisons to those measured by the large weighing lysimeters. Images from unnamed aerial systems (UAS) equipped with a variety of sensors were captured at varies times during the growing season at several heights above the field as to compare various post image analyses processes for estimating ET at various scales from a few square meters to 20 ha fields. Image processing of images collected by the UAS from previous growing seasons were analyzed also. Several experiments contributed towards progress of Sub-objective 2.1. Data comparing crop water use efficiency were collected from the large weighing lysimeter fields where fields are irrigated by either sprinklers or sub-surface drip irrigation. Crop responses to sprinkler irrigation were also compared to low energy precision application (LEPA) and mobile drip irrigation (MDI). Improvements to the irrigation scheduling system (ISSCADA) were tested using cotton. This year’s test confirmed that data from both canopy leaf thermal and soil moisture data improved the ability of the ISSCADA to schedule irrigation applications. A set of experiments extending the ISSCADA system to irrigation scheduling of potatoes was conducted this year. The potato experiments were conducted in collaboration with Texas A&M AgriLife Research and were supported by funds from Ogallala Aquifer Program, Texas Department of Agriculture and Binational Agricultural Research and Development (BARD) Fund. Research related to Sub-objectives 2.3 and 3.1, was not conducted in FY2020, because the objectives and milestones of that research had been met in prior years. Persistent dry weather from January 2020 to July 2020 will add to the location’s database of weather conditions that prevent establishment of dryland sorghum crops. These results are important aspect of research related to Sub-objective 3.2. Research characterizing the interactions between genotypes of cotton and corn and irrigation levels continued during the summer of 2020. The corn experiment utilized the location’s Soil-Plant-Environment-Research (SPER) facility which houses lysimeters of four different soils from the central Great Plains and features a rain-out shelter so that soil moisture can be strictly controlled.

Accomplishments
1. Use of soil and leaf sensors improve irrigation scheduling for water conservation. Because of limited water resources for crop production on the Texas High Plains producers are interested in growing grain sorghum, which requires less water than corn to produce maximum yields. However, precision irrigation scheduling tools are needed to optimize regional sorghum production. Thus, ARS scientists at Bushland, Texas, the Rural Development Administration, South Korea, and University of Nevada at Reno have used automated irrigation scheduling based on leaf canopy temperature with and without data from soil moisture sensors to manage grain sorghum at high, medium and low irrigation levels. The results indicate that plant and soil water sensing with multiple stress thresholds and several different irrigation volumes led to the most water efficient irrigation management for grain sorghum. The methods achieving the highest water use efficiency were readily automated. Irrigators can benefit from implementation of such sensors and control system by reduced groundwater withdrawals, reduced energy input cost, and time savings.

2. Leaf sensors mounted on a center pivot are accurate. In times of low crop prices, farmers need to produce crops as inexpensively as possible. One way farmers can decrease input costs is to apply irrigation only as needed. Crop leaf temperatures can be easily measured by sensors, which provide a real-time assessment of water stress and data for irrigation scheduling. However, users of temperature sensors have been concerned that measurements from sensors mounted on a center pivot may not be as accurate as non-moving (stationary) sensors. Therefore, ARS scientists from Bushland, Texas, compared irrigation scheduling based on data from stationary temperature sensors to those mounted on a center pivot. There were no differences in accuracy between stationary or moving temperature sensors and irrigation application governed by one type of sensor was similar to scheduling governed by the other. Center pivots are now used on 30 million acres in the U.S. Installing temperature sensors aboard center pivots and using them for irrigation scheduling could save farmers substantial water and reduce energy input costs.

3. Late planted corn required less irrigation water. Declining water levels in the Southern Ogallala Aquifer region require alternative management strategies to reduce groundwater withdrawals while maintaining profitable crop yields. Delayed planting of corn on the Texas High Plains is believed to reduce irrigation requirements by taking advantage of increased precipitation and reduced evapotranspiration demand. However, limited field data exist for corn planting dates in the region. ARS researchers at Bushland, Texas, and Texas A&M AgriLife used a calibrated Soil Water Assessment Tool (SWAT) model with long-term historical climate data to simulate corn irrigation and yields for both long and short season corn varieties. Simulation results suggested that irrigation requirements of mid-June planted corn was at least 25% less while grain yields decreased by less than 9%. Data from field experiments conducted in 2016 and 2017 with drought tolerant corn hybrids verified the trends identified from modeling experiments. These results indicate that the delayed planting of corn combined with effective irrigation management have the potential to reduce groundwater withdrawals from the Ogallala Aquifer. These results are of interest to irrigators as a means of extending their groundwater resource and decreasing their energy input costs.

4. Sub-surface drip reduces seasonal irrigation applications for corn. In the face of declining water supplies, it is important that crop farmers maximize the yield per unit of water used in crop production, the so-called crop water productivity (CWP). It is not well understood how irrigation application methods affect CWP. ARS scientists at Bushland, Texas, compared the water use and yield of grain corn and sorghum grown using sprinkler and subsurface drip irrigation (SDI) methods. Using the SDI application method, losses of water to evaporation from plant and soil surfaces were reduced by at least two inches and as much as five inches during the growing season as compared to losses that occurred with sprinkler irrigation. SDI reduced overall corn water use by up to 6 inches and increased grain yields by up to 20%. The combined effects were an increase in CWP by up to 46% compared with sprinkler irrigation. Increases in CWP are sufficient to overcome the higher installation of cost of SDI.

5. Successful irrigation scheduling must account for dry subsoils. Development of sustainable and efficient irrigation management is a priority for agricultural producers faced with water shortages. Yield and profitability of irrigated crop production depends on accurately evaluating crop water needs. However, estimates of crop water use can be inaccurate. Therefore, scientists from ARS in Bushland, Texas, University of Castilla La Mancha, Spain, and Texas A&M AgriLife Research used a corn water use model to evaluate dry subsoil conditions on corn yields. The results indicated that the irrigation strategy for optimum corn yields required deep infiltration of the irrigation water to encourage root growth at deeper soil depths. Similarly, scientists found in a field experiment with grain sorghum that maximum water use efficiency and optimum grain yields were achieved when irrigation applications favored root growth to a soil depth of 5 feet. The results indicate that irrigation applications that encourage higher soil water content in the subsoil were optimum for higher grain production. These results are of interest to irrigators, farmers, and consultants especially those irrigating summer crops after a dry spring and / or winter.

6. Crop water model identified farms that could conserve groundwater. The fresh water supply for irrigation is decreasing because of dwindling supplies and increased competition for other uses. Water use for irrigation can be reduced by matching applications to crop needs. Theoretically models can predict actual crop water needs using satellite and weather data. An advanced crop water use model was shown to accurately predict water use of corn in small experimental fields, but the model has yet to be tested on large commercial fields over several seasons. Therefore, ARS scientists at Bushland, Texas, along with researchers from Kansas State University tested the model over a large area in Northwest Kansas using data from five years. The model matched irrigation and precipitation data obtained from farmers’ fields during the irrigation season, but the model over-predicted irrigation needs after crops started to die/ senesce. The model identified fields that were irrigated more than needed for optimum crop yields. Identifying areas for improved water conservation is necessary for different water management areas in Kansas where groups of farmers are trying to meet agreed upon limits on groundwater withdrawals, thus these results are of interest to farmers, and water policy makers.

Review Publications
Marek, G.W., Chen, Y., Marek, T.H., Heflin, K.R., Oshaughnessy, S.A., Gowda, P.H., Brauer, D.K. 2019. Assessing planting date effects on seasonal water use of full- and short-season maize using SWAT in the southern Ogallala Aquifer region. Irrigation Science. 38:77-87. https://doi.org/10.1007/s00271-019-00653-3.
O'Shaughnessy, S.A., Evett, S.R., Colaizzi, P.D., Andrade, M.A., Marek, T.H., Heeren, D.M., Lamm, F.R., LaRue, J.L. 2019. Identifying advantages and disadvantages of variable rate irrigation: An updated review. Applied Engineering in Agriculture. 35(6):837-852. https://doi.org/10.13031/aea.13128.
Zhao, J., Yang, X., Liu, Z., Pullens, J.W., Chen, J., Marek, G.W., Chen, Y., Lv, S., Sun, S. 2020. Greater maize yield improvements in low/unstable yield zones through recommended nutrient and water inputs in the main cropping regions, China. Agricultural Water Management. 232:106018. https://doi.org/10.1016/j.agwat.2020.106018.
Bell, J.M., Schwartz, R.C., McInnes, K.J., Howell, T.A., Morgan, C.L. 2020. Effects of irrigation level and timing on profile soil water use by grain sorghum. Agricultural Water Management. 232(2020):106030. https://doi.org/10.1016/j.agwat.2020.106030.
O'Shaughnessy, S.A., Kim, M., Andrade, M.A., Colaizzi, P.D., Evett, S.R. 2019. Response of drought tolerant corn to varying irrigation levels in the Texas High Plains. Transactions of the ASABE. 62(5):1365-1375. https://doi.org/10.13031/trans.13234.
Evett, S.R., Marek, G.W., Colaizzi, P.D., Brauer, D.K., O'Shaughnessy, S.A. 2019. Corn and sorghum ET, E, yield and CWP affected by irrigation application method: SDI versus mid-elevation spray irrigation. Transactions of the ASABE. 62(5):1377-1393. https://doi.org/10.13031/trans.13314.
Evett, S.R., O'Shaughnessy, S.A., Andrade, M.A., Kustas, W.P., Anderson, M.C., Schomberg, H.H., Thompson, A.I. 2020. Precision agriculture and irrigation: Current U.S. perspectives. Transactions of the ASABE. 63(1):57-67. https://doi.org/10.13031/trans.13355.
Lamm, F.R., Porter, D.O., Bordovsky, J.P., Evett, S.R., O'Shaughnessy, S.A., Stone, K.C., Kranz, W.L., Rogers, D.H., Colaizzi, P.D. 2019. Targeted, precision irrigation for moving platforms: Selected papers from a center pivot technology transfer effort. Transactions of the ASABE. 62(5):1409-1415. https://doi.org/10.13031/trans.13371.
Colaizzi, P.D., O'Shaughnessy, S.A., Evett, S.R., Andrade, M.A. 2019. Comparison of stationary and moving infrared thermometer measurements aboard a center pivot. Applied Engineering in Agriculture. 35(6):853-866. https://doi.org/10.13031/aea.13443.
Schwartz, R.C., Dominguez, A., Pardo, J.J., Colaizzi, P.D., Baumhardt, R.L., Bell, J.M. 2019. A crop coefficient-based water use model with non-uniform root distribution. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2019.105892.
Evett, S.R. and Steiner, J.L. 2020. Shifting the odds of dryland agriculture: The career of B.A. Stewart. Agronomy Journal. 112:3254-3264. https://doi.org/10.1002/agj2.20260.
O'Shaughnessy, S.A., Kim, M., Andrade, M.A., Colaizzi, P.D., Evett, S.R. 2020. Site-specific irrigation of grain sorghum using plant and soil water sensing feedback - Texas High Plains. Agricultural Water Management. 240:106273. https://doi.org/10.1016/j.agwat.2020.106273.
Dhungel, R., Aiken, R., Lin, X., Kenyon, S., Colaizzi, P.D., Luhman, R., Baumhardt, R.L., O'Brien, D., Kutikoff, S., Brauer, D.K. 2019. Restricted water allocations: Landscape-scale energy balance simulations and adjustments in agricultural water applications. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2019.105854.