Location: Agricultural Systems Research Unit
2013 Annual Report
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.