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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

2012 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 report addresses progress on all milestones for FY 2012. Annual meeting of the collaborators-updates and discussions were achieved through one to one meetings in Fort Collins, emails, conference calls, and a regional NP 216 customer meeting at Miles City, MT. Initial model calibration based on plant yield of the dryland till and notill wheat-fallow system at Pendleton, OR was accomplished in 2010. We worked with Dr. Long, RL Pendleton, in 2011-12 to assemble more data on soil water and soil carbon to evaluate the effects of till and no-till treatments on soil water and carbon in the model. Dr. Long is still working on assembling these data.

The results of applications of the GPFARM-Range model at the ARS Woodward location were completed and published in 2010 (Adiku et al. 2010). In the project plan, this work was a milestone slated for completion in 2011/12, but was completed earlier.

The RZWQM2 model calibrated & evaluated on experimental data for corn, wheat, and beans from Water Management Unit, Fort Collins, was used to obtain long-term average Crop Water Production Functions (CWPFs): yield vs. water use and water applied or three different soil types and six different counties in Colorado. The results have been documented in a report to the CRADA partner.

A spreadsheet based ‘Colorado Water Optimizer’ decision tool for determining optimum crop selection, water allocation, and level of limited irrigation to different crops for maximum net return on a farm has been developed and provided to the CRADA partner. The CWPFs described above feed into this optimizer. A simple drought predictor for rangelands, based on rainfall during winter and spring months, has also been nearly completed for use in the western U.S. states; this was funded by the USDA-RMA.

The RZWQM2 was evaluated for higher CO2 effects and temperatures in 2010 (Ko et al., 2010), ahead of the schedule. The GPFARM-Range model was evaluated for CO2 effects with varying temperatures over years on data from the Range Resources Research Unit in Fort Collins in 2011/12. The milestone on the evaluation of the effect of changes in extreme events between historical and projected climates on cops and rangelands could not be undertaken due to limited SY time, especially as one of the SYs took early retirement.

Early planting dates for wheat, corn, and millet in the dryland wheat-fallow, wheat-corn-fallow, and wheat-corn-millet rotations were evaluated as adaptations to climate warming. Effect of tillage vs. no-tillage was also compared for the wheat-fallow rotation. Early planting did not seem to help. No-tillage was a good adaptation up to 2050 (Ko et al., 2012). A longer duration corn variety is being tested as an adaptation for future years and initial results are encouraging. Adaptations for rangeland management could not be evaluated due to early retirement of an SY.

We have also derived new water stress functions for corn, wheat, and bean crops from experimental data at different levels of irrigation. These new functions are being tested for several datasets for corn and compared with the existing stress functions in the DSSAT crop models in RZWQM2.


4.Accomplishments
1. Crop Water Production Functions (CWPFs) for Colorado. The CWPFs estimate crop yields at different levels of irrigation, which can be used by producers to decide the most economical level of irrigation to apply for a selected crop. ARS researchers at the Agricultural Systems Research Unit in Fort Collins, CO used the previously validated cropping system model (RZWQM2) with long-term weather data to develop average CWPFs for corn, wheat, and dry beans for three soil types in six different counties of Colorado. The modeled CWPFs for drip irrigation were also extended to sprinkler and surface irrigation methods. These functions are being put into the ARS ‘Colorado Water Optimizer’ decision tool and provided to a CRADA partner. Producers use these data to optimize use of limited water for different crops.

2. ASRU cropping system model enhanced and extended research results at Sidney, MT. Application of agricultural system models to data from field research helps enhance understanding of the complex interactions among variables that determine crop yields and quantify the results. Validated models can then be extended to predict crop performance under long-term weather conditions and/or with variable soil, climate, and management conditions. Scientists at Ft. Collins, CO used the RZWQM2 model on the data for dryland spring wheat-spring wheat rotation under till and notill management with variable planting and seeding rates. Over the long-term, the model showed that wheat-wheat rotation gives higher production than the wheat-fallow rotation, and established the optimum range of planting dates and seeding rates.

3. How will climate change affect potential evapotranspiration (ET) and irrigated corn production in the Central Great Plains? Agriculture in the Central Great Plains is water limited. Recent increase in frequency and intensity of droughts and projected effects of climate warming on crop water demands are a serious concern. ARS researchers at the Systems Research Unit in Fort Collins modeled the combined effects of increases in temperatures and CO2, on on crop water demand and irrigated corn production during the 21st century. The increase in crop water demand due to temperature increases was greatly moderated by the CO2 induced closure of stomata. Modeled decreases in corn yields in future years due to rising temperatures were caused by accelerated corn maturity and direct effects of temperatures on corn growth. Possible cultivar and management adaptations are now being investigated. This information is highly valuable for planning for the warming climate.

4. A Rangeland Drought Management Tool. The western U.S. rangelands are increasingly facing droughts, and rangeland managers need a simple predictive drought management tool to adjust the size of grazing herds. ARS researchers in the Systems Research Unit in Fort Collins in collaboration with the USDA Risk Management Agency developed a tool called ‘Drought Calculator’ (DC) based on the relationship between forage growth and rainfalls during the winter and spring months. Based on the initial results, the DC has been developed for use in10 states are available from the ARS AgSoftware website.

5. An agricultural system model can help provide over-winter soil conditions for agriculture on freezing soils. Correct knowledge of over-winter conditions is important to plan and successfully grow winter and spring crops on freezing soils. In a cooperative study with ARS scientists in Fort Collins, CO, a linkage of ARS RZWQM2 and SHAW models, RZ-SHAW model was shown to simulate soil temperatures and ice contents comparable to the SHAW model in the data for two irrigation treatments from China. With this capability, the RZ-SHAW can be used to simulate the effect of winter conditions on growth of winter and early spring planted crops. Additionally, the model can be used to devise and test methods of soil management during the winter months, such as the use of mulching.


Review Publications
Ma, L., Trout, T.J., Ahuja, L.R., Bausch, W.C., Saseendran, S.A., Malone, R.W., Nielsen, D.C. 2011. Calibrating RZWQM2 model for maize responses to deficit irrigation. Agricultural Water Management. 103 (2012):140-149.

DeJonge, K.C., Ascough II, J.C., Ahmadi, M., Andales, A.A., Arabi, M. 2012. Global sensitivity analysis of a dynamic agroecosystem model under different irrigation treatments. Ecological Modeling. 231(2012):113-125.

Mcintosh, B.S., Ascough II, J.C., Twery, M., Chew, J., Elmahdi, A., Haase, D., Harou, J., Hepting, D., Cuddy, S., Jakeman, A.J., Chen, S., Kassahun, A., Lautenbach, S., Matthews, K., Merritt, W., Quinn, N.W., Rodriguez-Roda, I., Sieber, S., Stavenga, M., Sulis, A., Ticehurst, J., Volk, M., Wrobel, M., Vandelden, H., El-Sawah, S., Rizzoli, A., Voinov, A. 2011. Environmental decision support systems (EDSS)development- challenges and best practices. Journal of Environmental Modeling and Software. 26(12):1389-1402.

Ahuja, L.R., Ma, L. 2011. A synthesis of current parameterization approaches and needs for further improvements. In: Ahuja, L.R., Ma, L., editors. Methods of Introducing System Models into Agricultural Research. Madison, WI: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America. p.427-440.

Islam, A., Ahuja, L.R., Garcia, L.A., Ma, L., Anapalli, S.A., Trout, T.J. 2012. Modeling the impacts of climate change on irrigated corn production in the central Great Plains. Agricultural Water Management. 110:94-108.

McMaster, G.S., Ascough II, J.C. 2011. Crop Management to Cope with Global Change: A Systems Perspective Aided by Information Technologies. Centre for Agriculture and Biosciences International. p. 172-190.

Ma, L., Ahuja, L.R., Saseendran, S.A., Malone, R.W., Green, T.R., Nolan, B.T., Bartling, P.N., Flerchinger, G.N., Boote, K.J., Hoogenboom, G. 2011. A protocol for parameterization and calibration of RZWQM2 in field research. Soil Science Society of America Special Publication Book Chapter. p.1-64.

Last Modified: 9/10/2014
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