UBC Theses and Dissertations
NIRS analysis of intact grass silage and fresh grass for the prediction of dry matter, crude protein and acid detergent fibre Kennedy, Carol Ann
Diverse fresh grass and grass silage samples (n = 292) were collected from 138 dairy farms in southwestern British Columbia, Canada. Sources of variation within the forage sample population included different crop years, harvests, grass species, ensiling additives and storage facilities. These samples were used to determine the accuracy of near infrared reflectance spectroscopy (MRS) for predicting the feeding value of undried, unground fresh grass and grass silage. NIRS spectra were collected from intact samples with a NIRSystems 6500 scanning monochromator instrument followed by analyses for dry matter (DM), crude protein (CP), acid detergent fibre (ADF), and fermentation end-products (lactic, acetic, propionic, isobutyric, butyric, isovaleric, and valeric acids) by conventional chemical laboratory procedures. The means and standard deviations of the sample population were 34.7 and 13.0% for DM, 17.1 and 3.5% for CP, and 34.3 and 3.9% for ADF when corrected to a moisture-free basis. The calibration set (n =216) was selected by spectral variation using the "neighbourhood Ff" method and the spectral duplicates were assigned to the validation set (n = 67). Prediction equations were developed with modified partial least square regression and cross validation utilizing different scatter treatments, derivatives and wavelength segments. The calibration R2 and standard errors of cross validation (SECV) for DM, CP, and ADF corrected to a moisture-free basis were 1.00 and 1.15%, 0.95 and 1.05%, and 0.95 and 1.16%, respectively. Standard errors of performance (SEP), means, and coefficients of variability (SEP* 100 ⌯ mean) for the validation set were 0.73, 27.2 and 2.69% for DM, 0.79, 15.7 and 5.03% for CP, and 0.95, 34.5 and 2.75% for ADF. The errors associated with the equations developed for the short chain organic acids were unacceptably high. Prediction equations were also developed on reference values calculated on an "as received" basis. Different procedures for calibration and validation set selection were compared with no one common method producing the lowest error on all constituents. It was concluded that the NIRS prediction equation for DM produced excellent accuracy as indicated by the low SECV and SEP. The prediction equations for CP and ADF had acceptable accuracy for monitoring forage nutrient quality for livestock ration balancing programs. The NIRS method of analysis will provide forage quality information faster and at a reduced cost compared to conventional chemical procedures.
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