Abstract

The forage feed value determined by organic matter digestibility (OMD) and voluntary intake (VI) is hard and expensive. Thus, several indirect methods such as near infrared reflectance (NIR) spectroscopy have been developed for predicting the feed value of forages. In this study, NIR spectra of 1040 samples of feces from sheep fed fresh temperate forages were used to develop calibration models for the prediction of fecal crude ash (CA), fecal crude protein (CP), fresh forage OMD, and VI. Another 136 samples of feces were used to assess these models. Four calibration strategies were compared: (1) species-specific calibration; (2) family-specific calibration; (3) a global procedure; and (4) a LOCAL approach. The first three strategies were based on classical regression models developed on different sample populations, whereas the LOCAL approach is based on the development models from selected samples spectrally similar to the sample to be predicted. The first two strategies use feces-samples grouping based on the species or the family of the forage ingested. Forage calibration data sets gave value ranges of 79–327 g/kg dry matter (DM) for CA, 65–243 g/kg DM for CP, 0.52–0.85 g/g for OMD, and 34.7–100.5 g DM/kg metabolic body weight (BW^0.75) for VI. The prediction of CA and CP content in feces by species-specific fecal NIR (FNIR) spectroscopy models showed lower standard error of prediction (SEP) (CA 15.03 and CP 7.48 g/kg DM) than family-specific (CA 21.93 and CP 7.69 g/kg DM), global (CA 19.83 and CP 7.98 g/kg DM), or LOCAL (CA 30.85 and CP 8.10 g/kg DM) models. For OMD, the LOCAL procedure led to a lower SEP (0.018 g/g) than the other approaches (0.023, 0.024, and 0.023 g/g for species-specific, family-specific, and global models, respectively). For VI, the LOCAL approach again led to a lower SEP (6.15 g/kg BW^0.75) than the other approaches (7.35, 8.00, and 8.13 g/kg BW^0.75 for the species-specific, family-specific, and global models, respectively). LOCAL approach performed on FNIR spectroscopy samples gives more precise models for predicting OMD and VI than species-specific, family-specific, or global approaches.

© 2017 The Author(s)

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