Location: Location not imported yet.Title: Modeling weather and stocking rate threshold effects on forage and steer production in northern mixed-grass prairie Author
|Fang, Q - Qingdao Agricultural University|
|Andales, A - Colorado State University|
|Bartling, Patricia - Pat|
Submitted to: Agricultural Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/22/2014
Publication Date: 6/16/2014
Publication URL: http://handle.nal.usda.gov/10113/59710
Citation: Fang, Q.X., Andales, A.A., Derner, J.D., Ahuja, L.R., Ma, L., Bartling, P.N., Reeves, J.L. 2014. Modeling weather and stocking rate threshold effects on forage and steer production in northern mixed-grass prairie. Agricultural Systems. 129:103-114.
Interpretive Summary: In the revised Great Plains Framework for Agricultural Resource Management-Range (GPFARM-range) model, grazing effects on forage growth and cattle weight gain were improved by incorporating an index of utilization based on previous studies. The improved-model predicted the effects of SD on PSC and SWG adequately across seasons (index of agreement (d) from 0.80 to 0.88 for PSC and from 0.81 to 0.83 for steer weight gain), and can be used to predict forage production and cattle weight gain under different SD across various weather conditions on the Northern Mixed-grass Prairie. The long-term simulations extended the previous results on weather effects on PSC and SWG under experimental SD levels to coupled effects of weather and grazing management on PSC and SWG for a wider range of SD levels. The long-term simulation results showed maximum SWG per area at the SD of about 0.88 steer ha-1, and the intersection for the two response curves (SWG per area or per steer) to PSC at about 939 kg ha-1 with SD of 0.88 steers ha-1, but the two response curves (SWG per area or per steer) to SD intersected at about 1.03 steers ha-1 which was associated lower PSC and higher seasonal variations (risks). The quadratic relationship between SWG per area and SD also suggested a continuous decline in the net return (SWG per area) with the increase of SD. Therefore, a SD level at about 0.88 can produce higher SWG per area, which can be reduced to between 0.44 and 0.88 steer ha-1 accounting for the lower yearly variations (lower enterprise risks) associated with weather conditions and less rangeland degradation (higher PSC level), compared to other SD.
Technical Abstract: Model evaluations of forage production and yearling steer weight gain (SWG) responses to stocking density (SD) and seasonal weather patterns are presented for semi-arid northern mixed-grass prairie. We used the improved Great Plains Framework for Agricultural Resource Management-Range (GPFARM-Range) model to simulate peak standing crop (PSC, kg ha-1) and SWG (kg ha-1) for three experimental SD treatments (low, moderate and high; 0.20, 0.33 and 0.44 steers ha-1, respectively) from 1982 to 2012 at Cheyenne, Wyoming, U.S.A. The improved-model accurately predicted the effects of SD on PSC and SWG across seasons (index of agreement [d] from 0.80 to 0.88 for PSC and from 0.84 to 0.86 for SWG). We also used the model with long-term weather data and 50% to 300% higher stocking densities (0.66 to 1.76 steers ha-1) than the high SD experimental treatment to assess if thresholds exist for responses of PSC and SWG. The quadratic relationships between SWG per area with PSC or with SD showed the threshold response of SWG per area to both SD (0.88-1.10 steers ha-1) and PSC (936-904 kg ha-1) and a continuous decline in the net return (SWG per area) with the increase of SD. Combining the response of SWG to both SD and PSC and the response of PSC to SD can be more effective for selecting the seasonable SD to achieve high SWG and lower rangeland degradation and economic risks. PSC was not identified as a significant factor influencing SWG at the experimental SD levels (P > 0.19), but was significant (P<0.0001) for the simulated data at heavier SD levels. These results can be helpful for ranchers to determine better SD decisions considering both PSC and SWG responses to SD and reduce enterprise risk.