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FORAGE & GRAZINGLANDS

Alfalfa Fiber Estimation in Mixed Stands and Its Relationship to Plant Morphology

Dep. of Crop and Soil Sci., Cornell Univ., Ithaca, NY 14853

^* Corresponding author (jhc5{at}cornell.edu)

In New York, most alfalfa (Medicago sativa L.) is grown in mixed stands with grass, and models for estimating neutral detergent fiber (NDF) are not used. The objectives of this experiment were to test the suitability of existing equations for estimating NDF of the alfalfa component of mixed stands, and to better understand how alfalfa is affected by the presence of grass. Stands of first-cut alfalfa and grass (10–90% grass) were sampled at two experimental sites and producers' fields in 19 New York counties during May and June 2004 and 2005. A range of plant measurements and environmental characteristics were recorded. The predictive equations for alfalfa quality (PEAQ) and other models were examined for applicability to mixed stand alfalfa NDF estimation. The R² values ranged from 0.80 to 0.87 and RMSE ranged from 20.5 to 25.2 g kg^–1. The most biased model was PEAQ, possibly due to the lower cutting height used to generate the PEAQ equation than the cutting height used in this study. For producer fields, a model based on alfalfa height was the best one-variable model, with R² of 0.84 and RMSE of 22.7 g kg^–1. Presence of grass increases the number of nodes and increases alfalfa height; however, the relationship between alfalfa height and NDF is not changed, suggesting that the predictive ability of models based on alfalfa height is not affected by the percentage of grass in the sward.

Abbreviations: GCANOPY, height of the grass canopy in the sample area • GDD, growing degree day • GRASS%, actual percentage of grass in the sample • LC, lack of correlation • MAXHT, height of the tallest alfalfa stem in the sample area • MSD, mean squared deviation • NDF, neutral detergent fiber • NU, nonunity slope • PEAQ, predictive equations for alfalfa quality • RMSE, root mean square error • SB, squared bias • SBC, Schwarz Bayesian criterion