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United States Department of Agriculture

Agricultural Research Service



2013 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 the 5-year life of the project. Final meeting of the collaborators-updates and discussions were achieved through one to one meetings in Fort Collins, emails, and conference calls. The RZWQM2 was enhanced based on an irrigation study in Greeley, CO. Potential maximum plant water uptake based on the Nimah-Hanks equation was found to improve plant responses to water stresses. This new enhanced model was used with historical weather data to create long-term average crop water production functions for corn, wheat, and dry beans for seven counties of Colorado. These functions were provided to the CRADA partner to be used in the ‘Colorado Deficit Irrigation Optimization Tool’. The GPFARM-Range model was enhanced with respect to the carbon dioxide concentration in the air and soil water balance. The enhanced model was applied to the published data on the effect of increased carbon dioxide on the soil water use and the growth of forage components from the Central Plains Experiment Station. The model provided good simulations of the data. During the life of the project, RZWQM2 was enhanced for crop responses to water stress, climate change (CO2 and temperature), greenhouse gas simulation, surface energy balance, crop water use, and model parameters (i.e., PEST optimization tool integration). The enhanced model was used to: (1) simulate the effects of existing and new crop rotations, tillage, and initial soil water at planting on crop production under dryland conditions in Akron, CO and Sidney, MT; (2) simulate effects of different levels of deficit irrigation on corn, wheat and dry beans in Greeley, CO; (3) evaluate the effects of past and future projected climates on dryland cropping systems and irrigated corn and wheat; (4) develop model parameters for new crops for the Great Plains (i.e., canola, millet, and triticale), for use in rotation with winter wheat; (5) extend experimental results to long-term historical weather conditions and different soil types under current and projected climate change scenarios (high CO2 and temperature, rainfall distribution); (6) identify the best summer crop in rotation with wheat based on soil moisture at planting; (7) develop Excel spreadsheet tools for use by producers to recommend summer crop selection based on initial soil water content at planting, and to evaluate canola production in the Great Plains; (8) schedule irrigation based on monthly available irrigation water. The GPFARM-Range was enhanced for effects of CO2, water, and N on range production and number of cattle per hectare. The model was tested based on experimental data from Woodward, OK, Cheyenne, WY, and Miles City, MT. A Windows/web application decision tool to predict forage growth during the upcoming grazing season in droughts to determine the herd size, the Drought Calculator, was enhanced and extended to 11 states in the U. S (AZ, CO, KS, MT, ND, NE, NM, NV, OK, UT, and WY). It has been provided to the USDA, Risk Management Agency and the USDA NRCS. NRCS produced a Webinar training for NRCS Range Managers on the Drought Calculator.

4. Accomplishments
1. Enhanced RZWQM2 model with detailed surface energy balance simulation. Knowledge of surface energy balance in a crop field is very helpful in improving understanding and prediction of seed germination, plant water stress, crop water use, and crop yield in dryland or with limited-irrigation. In order to compute energy balance in the ARS crop system model RZWQM, the ARS scientists in Fort Collins linked RZWQM with the ARS energy balance SHAW model to create RZWQM2 in previous work. In this study, the new hybrid model was further tested and improved for simulating the surface energy balance components of net radiation, latent heat (ET), sensible heat, ground heat flux. The improved RZWQM2 predicts the above components more accurately, and also has the ability to simulate canopy temperature, not found in other crop system models, which serves an index of plant water stress for guiding the time for water application.

2. Quantification of crop water stress factors in limited irrigation experiments. Dwindling water supplies for irrigated crop production is the most limiting factor facing agriculture in the world today. Correct simulation of plant responses to irrigation water is essential for model based decision support tools to help make the best use of limited water. Three plant water stress factors options were evaluated in a crop simulation model called RZWQM2 with data from an irrigation study by ARS scientists in Ft. Collins, CO. Results showed that plant water uptake and stress factor defined from the Nimah-Hanks equation provided the best plant responses to irrigation water. This improvement makes RZWQM2 a reliable irrigation planning and scheduling tool.

3. Quantification of crop water stress factors from soil water measurements in limited irrigation experiments. Quick estimation of plant water stress in the field based on soil moisture status and atmospheric demand for evapotranspiration will help improve water management. A four year irrigation study by ARS scientists in Ft. Collins, CO was analyzed for corn yield responses to different levels of average soil water content and seasonal evapotranspiration (ET) . Results showed that yield variability among irrigation treatments was explained best by the ratio of relative plant available water (plant available water/maximum soil water holding capacity) to reference crop ET. This new relationship should help the producers identify water stress in corn at different growth stages and apply water when needed.

Review Publications
Qi, Z., Bartling, P.N., Jabro, J.D., Lenssen, A.W., Iversen, W.M., Ahuja, L.R., Ma, L., Allen, B.L., Evans, R.G. 2013. Simulating dryland water availability and spring wheat production under various management practices in the Northern Great Plains. Agronomy Journal. 105:37-50.

Ko, J., Ahuja, L.R., Anapalli, S., Green, T.R., Ma, L., Nielsen, D.C., Walthall, C.L. 2011. Climate change impacts on dryland cropping systems in the central Great Plains, USA. Climatic Change. 111:445-472.

Heilman, P., Malone, R.W., Ma, L., Hatfield, J.L., Ahuja, L.R., Boyle, K., Kanwar, R. 2012. Extending results from agricultural fields with intensively monitored data to surrounding areas for water quality management. Agricultural Systems. 106:59-71.

Fang, Q.X., Malone, R.W., Ma, L., Jaynes, D.B., Thorp, K.R., Green, T.R., Ahuja, L.R. 2012. Modeling the effects of controlled drainage, N rate and weather on nitrate loss to subsurface drainage. Agricultural Water Management. 103:150-161.

Ma, L., Flerchinger, G.N., Ahuja, L.R., Sauer, T.J., Prueger, J.H., Malone, R.W., Hatfield, J.L. 2012. Simulating the surface energy balance in a soybean canopy with SHAW and RZ-SHAW models. Transactions of the ASABE. 55(1):175-179.

Qi, Z., Ma, L., Helmers, M.J., Ahuja, L.R., Malone, R.W. 2012. Simulating nitrate-nitrogen concentration from a subsurface drainage system in response to nitrogen application rates using RZWQM2. Journal of Environmental Quality. 41(1):289-295.

Anapalli, S., Nielsen, D.C., Ahuja, L.R., Ma, L., Lyon, D.J. 2012. Simulated yield and profitability of five potential crops for intensifying the dryland wheat-fallow production system. Agricultural Water Management. 116(2013):175-192.

Qi, Z., Bartling, P.N., Ahuja, L.R., Derner, J.D., Dunn, G.H., Ma, L. 2012. Development and evaluation of the carbon-nitrogen cycle module for the GPFARM-Range model. Computers and Electronics in Agriculture. 83:1-10.

Nolan, B.T., Malone, R.W., Gronberg, J., Thorp, K.R., Ma, L. 2012. Verifiable metamodels for nitrate losses to drains and groundwater in the corn belt, USA. Environmental Science and Technology. 46:901-908.

Ma, L., Ahuja, L.R., Nolan, B., Malone, R.W., Trout, T.J., Qi, Z. 2012. Root Zone Water Quality Model (RZWQM2): Model use, calibration, and validation. Transactions of the ASABE. 55(4):1425-1446.

Li, Z., Ma, L., Flerchinger, G.N., Ahuja, L.R., Wang, H. 2012. Simulation of over-winter soil water and soil temperature with SHAW and RZ-SHAW. Soil Science Society of America Journal. 76:1548-1563.

Nielsen, D.C., Saseendran, S.A., Ma, L., Ahuja, L.R. 2012. Simulating the production potential of dryland spring canola in the Central Great Plains. Agronomy Journal. 104:1182-1188.

Last Modified: 05/27/2017
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