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United States Department of Agriculture

Agricultural Research Service

Research Project: ENHANCED SYSTEM MODELS AND DECISION SUPPORT TOOLS TO OPTIMIZE WATER LIMITED AGRICULTURE

Location: Agricultural Systems Research Unit

2011 Annual Report


1a.Objectives (from AD-416)
The long-term research objective of this project is to help evaluate current and proposed alternative practices for limited-water and projected global change conditions in the arid western United States. This modeling research will support a broad USDA-ARS effort of integrating and extending temporal and spatial experimental research across environments, and the transfer of recommendation to producers and decision-makers via decision support tools.

Objective 1: Extend RZWQM2 and GPFARM-Range model applications to limited-water agricultural systems in the arid western U.S. by evaluating current experimental results for long-term weather and different soil conditions, and derive alternative optimal management strategies for limited-water with respect to biomass production (including selected bio-energy crops), soil water usage, soil carbon and nitrogen status, and yield in crop and rangeland systems. • Sub-objective 1A. Assess and improve the utility of RZWQM2 and GPFARM-Range applications and their components using existing and new datasets of crop and range-livestock systems from selected locations in the arid western United States for the purpose of evaluating existing and alternative crop and range-livestock management systems. • Sub-objective 1B. Develop relational databases and simple Decision Support System (DSS) tools using the above modeling and experimental results to help agricultural decision makers better cope with drought and water-limited conditions by selecting alternative crop sequences/rotations, improving irrigation scheduling, using reduced tillage practices, crop residue management, and range-livestock drought management tools. Assess the DSS tools under real-world situations by analyzing their economic feasibility and uncertainty/risk under drought and limited-water conditions.

Objective 2: Use RZWQM2 and GPFARM-Range models to evaluate the effects of projected climate change on current and alternative production systems. • Sub-objective 2A. Evaluate and improve RZWQM2/GPFARM-Range models for response to higher CO2 and temperature and extreme rainfall patterns on crop and range growth, water use, and productivity. • Sub-objective 2B. Under the projected climatic conditions, re-evaluate management systems for crop rotation, plant species, irrigation, tillage, and crop residue management, and propose alternative management practices for typical crop and rangeland systems at the selected locations in the arid western U.S.


1b.Approach (from AD-416)
RZWQM2 (a RZWQM-DSSAT4.0 hybrid) and GPFARM-Range process-level models will be used in this study. Typical crop and range livestock management systems will be selected at cooperating ARS locations in the arid western U.S.: Fort Collins, CO, Akron, CO, Bushland, TX, Sidney, MT, Pendleton, OR and Pullman, WA for cropping systems, and Cheyenne-WY, Miles City-MT, and Woodward-OK for range-livestock systems. The work will be done with cooperating scientists at each location. Scientists at the selected locations will collect minimum datasets (e.g., weather, soil, and crop information) needed for RZWQM2 or GPFARM-Range models, and then work with ASRU scientists to calibrate and evaluate the models. Calibrated model parameters should be transferable from location to location, except for site-specific inputs (e.g., soil, weather, crop variety). The models will then be validated by comparing the model predictions (e.g., crop production, evapo-transpiration, N uptake, soil moisture, and etc.) against measured data not used in the calibration or in another location. Failure of satisfactory validation will require more accurate measurement of the input data for site-specific parameters or enhancement of a model component’s science code for the location. Once the model has been satisfactorily validated for available experimental data at a location, it will be used to extend results for a longer duration using historical and projected climate-change weather conditions (down-scaled from climate change model) and for other important soil types in the surrounding area of the location. Biomass production, soil water usage, soil C/N status, and yield in different crop sequences or rangeland plant species over both the long and short term periods will be analyzed and interpreted. The model will then be applied to propose alternative crop and grazing management scenarios. Promising alternative management scenarios derived from the models will be the subject of future field testing. Synthesizing all simulation results across locations will give confidence in applying the model outside the test locations and will result in a comprehensive set of guidelines for management and policy in areas around the locations. The effects of high CO2 and high temperature on plant growth under possible global change conditions will also be examined for interactions and indirect effects on water and nitrogen uptake, carbon and nitrogen allocations in plants.

Simulated and experimental results will be used to populate a database with querying ability, which will provide information for crop selection, plant species composition, and management effects on crop production, forage-livestock production, water use efficiency, soil C sequestration, and soil water and N losses in different environments, under current and projected climate conditions. Simple regression-based decision tools will be developed for guiding planning and management.


3.Progress Report
The effects of till versus no-till systems and changes in planting date and seeding rate within the dryland Spring Wheat – Spring Wheat rotation at the Rasmussen Site, Sidney, MT for 2004-2010 were simulated with the RZWQM2 Model. Issues in the observed soil water dataset required additional sampling in fall 2010 to resolve neutron probe calibration equation performance for the Sidney location. Previous model calibration for the Reeder Spring Wheat variety was redone and extended through 2010 with new data. Model performance is still being evaluated. However, within any given climate year both modeled and observed data showed later planting dates and higher seeding rates had lower crop yields than other treatments. The same soil and weather data are also used to simulate corn production under different planting density from 2007-2010. The work will be completed in FY2012. To develop crop water production functions in Colorado, we have calibrated the RZWQM2 model to simulate corn at three locations. The calibrated model was run with long-term weather records at these locations to develop Crop Water Response Functions (CWRF) for use in irrigation water management decision making in the region. Work is in progress to extend the CWRF’s to other soils and climates at various counties in Colorado for use in a limited-irrigation optimizer decision tool being developed. Work on the limited-irrigation optimizer proceeded steadily this year. In addition to the project plan milestone, this decision support system (DSS) is being developed as part of a CRADA. An Excel based 4-field version has been built that runs off the standard Excel solver. An Excel based 10-field version was also built that requires the purchase of add-on software. The 10-field version was built primarily to assist the CRADA partner in the development of a commercial version optimizer. The CRADA partner has applied for a patent of the commercial version. The 4-field version is in beta testing which includes sensitivity, risk, and break-even analysis. These tests and the possible changes to the optimizer will be completed this FY with an anticipated release of version 1.0 either before the end of FY2011 or early in FY2012. Work on a decision support system (DSS) for estimating range forage production early in the growing season is also proceeding steadily. An earlier version of the DSS, referred to as the Drought Calculator (DC), was built under a grant from USDA-Risk Management Agency (USDA-RMA). USDA-RMA provided additional funding for the improvement and development for more states. A meeting with users of the original DC was held in Bismarck, ND, in May. Several improvements in the design and layout of the Excel based tool were requested. It was also decided that the version would be web based instead of being Excel based. Work on the web based version has begun. A post-doc has been hired to evaluate the improvement in predictive ability of the DC if spring time temperature was included with the existing weather parameter, total monthly precipitation. The web based version without the additional weather parameter should be completed by early FY2012.


4.Accomplishments
1. Soil water based summer crop selections in the Central Great Plains. Water is a critical and limiting resource, especially in arid and semi-arid regions of the world. Owing to the uncertain water availability through precipitation, selection of a summer crop in a wheat-summer crop-fallow rotation is a challenge. ARS researchers in Fort Collins, CO, examined soil water content at planting as a criterion for selecting summer crops. They simulated long-term probability of yield and net return from five summer crops (corn, canola and proso millet for grain; and spring triticale and foxtail millet for forage) at low to high levels of water availability to the crop at planting. Under the current price levels at Akron, CO, and Sydney, NE, the two forage crops gave better net returns than the three grain crops at all initial levels of water availability. Among the grain crops, proso millet provided the best net return, followed by canola or corn. In general, for making crop choice decisions based on initial levels of water availability at planting, information on water status in the whole soil profile is most useful. Optimizing crop selection in relation to initial water availability at planting will enhance net returns to U.S. farmers and ranchers in arid and semi-arid regions, and especially where water for irrigation is scarce or as competition for irrigation water increases.

2. Corn model adopted for managing limited irrigation water. Irrigation is the key to major food production in the semiarid regions of the U.S. and the world. However, the water available for irrigation is increasingly being limited due to increasing urban demands; optimal management of this limited water under variable rainfall and soil conditions requires a systems approach via a validated system model. The ARS RZWQM2 model was calibrated and evaluated against three years of corn data in eastern Colorado at six irrigation levels based on meeting varying degrees of crop evapo-transpiration (ET) requirements. The model was capable of reproducing the observed corn responses to irrigation amounts under varying rainfall conditions. This model can now help the farmers and farm advisors in making the best use of limited water in the Great Plains.

3. A rangeland management model will also estimate soil carbon storage. Rangelands cover approximately 50% of the terrestrial surface of the earth. Understanding the role of rangelands in carbon and nitrogen cycling is imperative to both sustained grazing and carbon sequestration both in the U.S and internationally, especially under climate change. The ARS researchers in Fort Collins, CO, added a carbon cycling module from Nitrogen Leaching and Environmental Analysis Package (NLEAP) into the ARS GPFARM-Range forage-livestock model. This linkage was tested against a 14-year forage data set (1993-2006) with measurements of C carbon in 1993, 2003, and 2006 near Cheyenne, WY, to ensure that that the changes in soil organic carbon, as well as the forage production, were reasonably simulated by the new GPFARM-Range model. Carbon sequestration is an important strategy to reduce carbon based greenhouse gases in the atmosphere and to slow or reverse global climate change. The GPFARM-Range model with the new carbon module can be used to evaluate different rangeland management practices with the goal of sequestering more carbon.

4. Forage growth in the ARS GPFARM-Range model now responds to carbon dioxide. Most beef cattle in the U.S. are started on native rangeland and pastures; in developing countries the percentage is even higher. The immediate and long-term effects of increasing levels of carbon dioxide in the atmosphere on forage quality and quantity are as yet unknown. ARS researchers in Fort Collins, CO, incorporated forage growth responses to carbon dioxide into the GPFARM-Range model, involving changes in potential growth rate and stomatal resistance for each plant group. The model was calibrated and evaluated using the forage growth data from a confined growth chamber and an open top chamber at varied carbon dioxide concentrations. The model can now be used to project the effects of increased carbon dioxide under future climate change and devise grazing management strategies to sustain livestock productivity in the U.S. and the world.


Review Publications
DeJonge, K.C., Andales, A.A., Ascough II, J.C., Hansen, N.C. 2011. Modeling of full and limited irrigation scenarios for corn in a semiarid environment. Transactions of the ASABE. 54(2):481-492.

Ko, J., Ahuja, L.R., Kimball, B.A., Anapalli, S., Ma, L., Green, T.R., Ruane, A., Wall, G.W., Pinter Jr, P.J., Bader, D. 2010. Simulation of free air CO2 enriched wheat growth and interaction with water, nitrogen, and temperature. Agricultural and Forest Meteorology. 150:1331-1346.

Powers, S.E., Ascough II, J.C., Nelson, R.G., Larocque, G.R. 2011. Modeling water and soil quality environmental impacts associated with bioenergy crop production and biomass removal in the midwest usa. Ecological Modeling. 222(14):2430-2447.

Saseendran, S.A., D.C. Nielsen, Liwang Ma, L.R. Ahuja, M.F. Vigil, 2010. Simulating Alternative Dryland Rotational Cropping Systems in the Central Great Plains with RZWQM2. Agronomy Journal. 102:1521-1534. doi:10.2134/agronj2010.0141.

Saseendran, S.A., Nielsen, D.C., Ma, L., Ahuja, L.R. 2010. Adapting CROPGRO for Simulating Spring Canola Growth with Both RZWQM2 and DSSAT 4.0. Agronomy Journal 102:1606-1621. Doi:10.2134/agronj2010.0277.

Last Modified: 7/28/2014
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