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Fang, Q. X.; Andales, A. A.; Derner, J. D.; Ahuja, L. R.; Ma, L.; Bartling, P. N. S.; Reeves, J. L.; and Qi, Z., "Modeling weather and stocking rate effects on forage and steer production in northern mixed-grass prairie" (2014 Stocking rate (SR) is the primary management factor that influences livestock gains, plant community changes and forage production, as well as economic returns for livestock producers. More effective stocking decision making by ranchers in the semi-arid northern mixed-grass prairie requires clearly understanding forage production and yearling steer weight gain (SWG) responses to SR and high weather variability. The objectives of this study were to: (1) test the Great Plains Framework for Agricultural Resource Management-Range (GPFARM-Range) model for predicting forage production and SWG under three experimental SR treatments and long-term weather conditions on semiarid northern mixed-grass prairie in southeast Wyoming, USA; and (2) quantify the threshold responses of forage production and SWG to SR and the yearly weather variability across years using long-term simulations with SR higher than those experimentally evaluated. We improved upon the GPFARM-Range model to simulate peak standing crop (PSC) and SWG for three experimental SR treatments (low, moderate and high; 0.20, 0.33 and 0.44 steer ha À1, respectively) from 1982 to 2012 at Cheyenne, Wyoming, USA. The improved model accurately predicted the effects of SR on PSC and SWG across years (root mean square errors from 355 to 387 kg ha À1 for PSC and from 12.8 to 14.2 kg head À1 for SWG). We ran the model with long-term weather data and 50-300% higher SR (0.66-1.76 steer ha À1 ) than the high SR experimental treatment.Differential responses of predicted total intake of digestible nutrients (quadratic increase) and metabolic maintenance (linear increase) to these higher SR resulted in a quadratic increase of predicted SWG with SR and high yearly variability at high SR levels. The financially-optimum SR with highest profits was reduced to 0.33 steer ha À1 for dry or normal seasons and 0.44 steer ha À1 for wet seasons. Such reduced SR can also benefit land conservation with high PSC and low harvest efficiency. The moderate SR with 25% harvest efficiency was determined between 0.22 and 0.33 steer ha À1 for dry or normal seasons, or between 0.33 and 0.44 steer ha À1 for wet seasons. These results provide useful direction for selecting an effective SR to achieve high economic net return with lower yearly variability (risk) and reduced likelihood of rangeland degradation.

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The ability to predict climate change effects on crop yield through field experiments and crop modeling is essential for developing mitigation strategies. The objective of this study was to compare two different crop modules (CROPGRO and HERMES) in the Root Zone Water Quality Model 2 (RZWQM2) for predicting climate change effects on soybean [Glycine max (L.) Merr.] production. The modules were previously calibrated for measured temperature responses using data from a 4-yr open-top chamber experiment (2015)(2016)(2017)(2018) in North Carolina. Both crop modules simulated similar climate change effects in terms of yield and biomass by the end of Year 2100 (2083-2099) using 40 general circulation model (GCM) projections and two Representative Concentration Pathways (RCP4.5 and RCP8.5), compared with the simulations using current baseline (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018). For both modules, much greater reductions in biomass and seed yield were simulated under RCP8.5 than under RCP4.5 due to higher air temperature. In addition, both modules predicted lower variability of biomass and seed yield across these GCMs under irrigated than under rainfed conditions. CROPGRO predicted a greater positive climate change effect in response to the projected higher precipitation and increased atmospheric CO 2 (compared with baseline conditions) than HERMES. Soybean production will likely benefit more from the projected high precipitation and elevated CO 2 under rainfed conditions than under irrigated conditions. Due to much higher simulated yield under irrigation than under rainfed conditions, supplementary irrigation may be an effective mitigation strategy to maintain soybean yield; however, adjusting sowing dates appear to have little effect on soybean production. INTRODUCTIONCrop models have been used for more than two decades to simulate climate change effects on soybean [Glycine max (L.