Estimating Grass Yield on Blue Grama Range From Seasonal Rainfall and Soil Moisture Measurements
Keywordsbiomass prediction model
modified Sacramento soil moisture accounting model
rangeland net primary production (NPP)
soil water-NPP relationships
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CitationTorell, L. A., McDaniel, K. C., & Koren, V. (2011). Estimating grass yield on blue grama range from seasonal rainfall and soil moisture measurements. Rangeland Ecology & Management, 64(1), 56-66.
PublisherSociety for Range Management
JournalRangeland Ecology & Management
AbstractTo estimate annual forage production from moisture conditions it is important to consider the timing and seasonality of precipitation events as well as the past history of storm events. In this study we examined this relationship using 16 yr of annual measurements of herbaceous standing crop recorded at two study sites located on the Corona Range and Livestock Research Center in central New Mexico. Our hypothesis was that end-of-season herbaceous standing crop estimations could be improved using measured soil moisture instead of seasonal accumulations of rainfall as traditionally used for yield prediction. Daily recorded and simulated soil moisture levels were used to estimate the number of days over the growing season when soil moisture by volume was at low (< 20%), intermediate (20% to 30%), or high (> 30%) levels. Defining regression equations to include either simulated or probe-recorded measures of soil moisture improved the adjusted R2 of the regression models from 46% for the rainfall model to over 60% for various soil moisture models. Key variables for explaining annual variation in herbaceous production included seasonal moisture conditions, the amount of broom snakeweed (Gutierrezia sarothrae [Pursh] Britt. Rusby) present on the area, and the degree days of temperature accumulated over the growing season. Diurnal daily temperatures near historical averages were most advantageous for forage production. Simulated soil moisture data improved predictive grass yield estimates to a level equivalent to using onsite moisture probes to categorize daily moisture conditions. Potential exists to better predict forage conditions based on forecast information that uses soil moisture data instead of the traditional input of seasonal rainfall totals.