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Research Project: Enhanced System Models and Decision Support Tools to Optimize Water Limited Agriculture

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Title: Using a model and forecasted weather to predict forage and livestock production for making stocking decisions in the coming growing season

Author
item Fang, Quanxiao - Qingdao Agricultural University
item Ahuja, Lajpat
item Andales, Allan - Colorad0 State University
item Derner, Justin

Submitted to: Advances in Agricultural Systems Modeling
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/13/2014
Publication Date: 12/1/2014
Citation: Fang, Q., Ahuja, L.R., Andales, A., Derner, J.D. 2014. Using a model and forecasted weather to predict forage and livestock production for making stocking decisions in the coming growing season. Advances in Agricultural Systems Modeling. 5:161-181.

Interpretive Summary: On the basis of the GPFARM-Range model simulated long-term peak standing crop (PSC) data, simple regression equations can be developed in terms of rainfall from April to June, stocking rate (SR), and soil water content on 1 April to significantly improve the PSC predictions. Combining soil water content before the growing season and SR provides an accurate tool for ranchers to predict PSC before the grazing season and make reasonable stocking decisions in the region. Including the predictions of PSC, SWG, and economic profits can be very helpful for ranchers in making better SR decisions, accounting for both economic return and environmental impact (land conservations). The regression equations can also be used to analyze the effects of weather and soil variations on the responses of PSC and SWG to SR levels. These regression equations are advantageous to previous methods, such as the Drought Calculator (Dunn et al., 2013), in that the regression equations consider both economic net return and environmental impacts and the coupled responses of PSC to SR and SWG to PSC and SR across various weather conditions. These equations can be used to develop a spreadsheet-based decisions support tool and help ranchers explore the tradeoffs between economic net return and environmental impacts (such as land degradation) as influenced by SR and weather variations and make better stocking decisions with reduced enterprise risks and land degradations on different soils across the northern mixed-grass prairie.

Technical Abstract: Forecasting peak standing crop (PSC) for the coming grazing season can help ranchers make appropriate stocking decisions to reduce enterprise risks. Previously developed PSC predictors were based on short-term experimental data (<15 yr) and limited stocking rates (SR) without including the effect of SR on PSC explicitly. Here we used long-term (30 yr) measured data of PSC and steer weight gain (SWG), extended with the help of a model for SR effect, to develop multiple-variable regression functions for predicting PSC and SWG across a wide range of SR (0.2–1.32 steers ha-1 for summer grazing season, June to mid-October) on a loam soil in a northern mixed-grass prairie. April to June rainfall was the primary weather variable influencing PSC (R2 = 0.45); inclusion of SR and soil water content on 1 April improved the accuracy in predicting PSC (R2 = 0.64). Combining the response of PSC to SR and the response of SWG to both PSC and SR enables ranchers to explore tradeoffs between economic net return and environmental impact (land conservation) as influenced by SR and weather variations. The result was further extended from the loam soil at the experimental site to the other two soil types (loam sandy and clay loam soils) by using a simple soil influence factor. A simple spreadsheet-based decision support tool can be developed to facilitate stocking decisions by ranchers in a northern mixed-grass prairie to adaptively manage rangelands in an effort to increase economic net return and reduce land degradation associated with high weather variability and SR levels.