Seasonal forage dry matter production and quality of 29 dryland grasses in Montana

Abstract

Producers must have accurate and reliable measurements of both forage production and quality in their pastures. Previous studies with dryland grasses in Montana have mostly been limited to adaptation or yield performance of a few species at a single location. The first objective of this study was to document yield and forage quality characteristics of adapted dryland grass varieties over a three-year period at three separate locations. Another objective was to accurately predict forage quality constituents of numerous dryland forage grasses using near infrared spectroscopy (NIRS). A third objective was to generate predictive models for crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro digestible dry matter (IVDDM) using date or growing degree days. And finally, the last objective of this study was to demonstrate how forage quality information generated in this study can be useful for future improvement of animal carrying capacity predictions. Twenty-nine dryland grass varieties were established at three Montana locations. Data were collected over a three-year period. Forage production and quality data were gathered under a wide range of climatic conditions. Interactions among years, varieties, and locations illustrated the variability of the climate in Montana and biological differences of varieties at different locations. This study makes a strong case for the use of NIRS technology in estimating forage quality of dryland grasses in Montana. Compared to traditional wet chemistry procedures, NIRS proved to be much faster and generated accurate results. Predictive models using date and growing degree days generated estimates of forage quality similar to NIRS but standard errors associated with model parameters limited statistical differences among varieties for season-long forage quality. However, it was determined that the rates of forage quality decline among many of the varieties studied were different (P < 0.01). The r^2 values for predicted forage quality ranged from 0.39 (Rosana, ADF) to 0.85 (Schwendimar, ADF) for the AGGD models and all were highly significant. Strong negative correlations between yield and quality were not found in this study (-0.3<0.3). ADF and NDF were highly correlated (r>0.79). It appears that with optimal management, both forage production and quality can be. optimized. Preliminary use of this data suggests that energy becomes limiting first as the growing season progresses, followed by intake and protein. Further studies should be devoted to modeling pasture carrying capacity with forage quality data.