HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Parsons, D. Articles by Gauch, H. G. Search for Related Content PubMed Articles by Parsons, D. Articles by Gauch, H. G. Agricola Articles by Parsons, D. Articles by Gauch, H. G. Related Collections Forage Management Best Management Practices

Forages

Estimation of Preharvest Fiber Content of Mixed Alfalfa–Grass Stands in New York

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

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

Received for publication December 5, 2005. Regression equations can be used to estimate the neutral detergent fiber (NDF) of alfalfa (Medicago sativa L.), assisting producers in decision making at harvest time. In New York State, where most alfalfa is grown in mixed stands with grass, there are no available models to estimate NDF. The objectives of this experiment were to develop equations for estimating total mixed stand NDF with an emphasis on producer useable equations based on easily obtainable data. Stands of first-cut alfalfa and grass (0.1–0.9 fraction 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 and used to develop prediction equations. For selection of two to five variable models using 899 data points, R² ranged from 0.89 to 0.94 and root mean square error (RMSE) ranged from 21.2 to 30.1 g kg^–1 dry matter (DM). The most important explanatory variables were the fraction of grass and alfalfa height. Growing degree days and day of the year improved goodness of fit but were biased between years. Categorization of the grass fraction into 0.2, 0.4, 0.6, or 0.8 allows estimation without requiring species separations. Categorization decreased R² and increased RMSE but is a variable that could be more easily used by producers. Model validation found significant biases with some model estimates; however biases and prediction errors were small enough to suggest that the results are practically applicable to New York farms.

Abbreviations: DM, dry matter • LC, lack of correlation • 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

This article has been cited by other articles:

				D. Parsons, J. H. Cherney, and P. R. Peterson Preharvest Neutral Detergent Fiber Concentration of Alfalfa as Influenced by Stubble Height Agron. J., July 1, 2009; 101(4): 769 - 774. [Abstract] [Full Text] [PDF]

				D. Parsons, J. H. Cherney, and H. G. Gauch Jr. Alfalfa Fiber Estimation in Mixed Stands and Its Relationship to Plant Morphology Crop Sci., October 2, 2006; 46(6): 2446 - 2452. [Abstract] [Full Text] [PDF]