| Summary: | 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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