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United States Department of Agriculture

Agricultural Research Service


Location: Agricultural Systems Research Unit

2010 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
A workshop on limited irrigation was held with collaborators from three universities, NRCS, and ARS scientists from 4 locations. The workshop produced critical details on the development of a limited-water Decision Support System (DSS). Individual visits to collaborator’s locations were used to facilitate data preparation and model runs. Three new crops, canola, millet, and triticale, are calibrated using CROPGRO. Crop rotation effects on crop growth at Akron are well simulated with RZWQM2 and the validated model was used to predict climate change effects on wheat and corn production based on 16 GCM scenarios. Simulations indicate that the projected negative effects of rising temperatures on crop production dominate the positive impacts of atmospheric CO2 increases in these dryland cropping systems. RZWQM2 was used to simulate the irrigation study in Greeley, CO in collaboration with the Water Management Unit in Fort Collins (2008 and 2009). The model has been enhanced to calculate reference evapotranspiration (ET). A new version of the model (version 2.0) was released on the USDA-ARS website. Focus group members were interviewed and surveyed to determine attitudes towards limited-irrigation crop production and the use of a DSS. Those surveyed included farmers, agricultural loan officers, ditch company representatives, commodity groups, and CSU faculty/extension. Weekly web conferences were held with the DSS CRADA partner to discuss design and development. The DSS under development will be a unique limited-irrigation DSS because it allows decisions at the field level, farm level, and ditch-organization level. Field data from Akron, CO, and Greeley, CO, and simulation data for both locations are available and are being added to the database. The unit is collaborating with a Conservation District (CD) to develop a queryable SQL database for use by all members of the agricultural community. The database utilizes the Silverlight Data Portal tool, an ESRI mapping application for accessing research data. The database is under evaluation by farmers in eastern Colorado, southwestern Nebraska, and north eastern Kansas. An article on the database appeared in Pathways, a popular press publication. Database evaluations are underway with the help of the CD. RZWQM2 was used to predict climate change effects based on projected climate for Colorado. GPFARM-Range model was enhanced to include climate change effects and soil carbon. Data from collaborators in Cheyenne, WY, have been used to test GPFARM-Range’s ability to predict climate change effects on forage production, species differences, and water use efficiency.

1. New crops in Root Zone Water Quality Model 2 (RZWQM2) model. Canola has been increasingly important as an alternate crop in rotations with wheat due to its potential bio-energy use. ARS Researchers at Fort Collin, CO, used RZWQM2 to simulate canola production under different cropping systems. Results indicate that the canola model is sufficient for predicting canola production under semi-arid conditions. The new model component will help extend current canola study in the Great Plains.

2. Projected effects of climate change on wheat. Using a system model to predict climate change effects on crop production is necessary for food security in the future. ARS Researchers at Fort Collins, CO, used Root Zone Water Quality Model 2, after first calibrating with a FACE (Free Air CO2 Enrichment) experiment in Maricopa, AZ, under two irrigation and two N treatments, and were able to reproduce the CO2 effects on spring wheat production under all treatments. Model simulation using projected climate conditions showed that the effects of high CO2 and increasing temperature on crop yield partially cancelled each other, and irrigation and N management might have more effects on crop yield than climate changes.

Review Publications
Fang, Q., Ma, L., Green, T.R., Yu, Q., Wang, T.D., Ahuja, L.R. 2010. Water Resources and Agricultural Water Use in the North China Plain: Current Status and Management Options. Agricultural Water Management. 97(8):1102-1116. Available: doi:10.1016/j.agwat.2010.01.008.

Fang, Q., Ma, L., Yu, Q., Ahuja, L.R., Malone, R.W., Hoogenboom, G. 2009. Irrigation Strategies To Improve Water Use Efficiency in the Wheat-Maize Double Cropping System in China. Agricultural Water Management. 97 (8):1165-1174.

Nolan, T., Puckett, L., Ma, L., Green, C.T., Bayless, E.R., Malone, R.W. 2010. Predicting Unsaturated Zone Nitrogen Mass Balances in Agricultural Settings of the United States. Journal of Environmental Quality. 39(3):1051-1065.

Last Modified: 4/20/2014
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