XXXX -specific diet digestibility predictions based on near-infrared reflectance spectroscopy scans of faecal samples.

@entity62 @entity291
This study was designed to obtain information on prediction of diet digestibility from near-infrared reflectance spectroscopy (NIRS) scans of faecal spot samples from dairy @entity291 at different stages of lactation and to develop a faecal sampling protocol. NIRS was used to predict diet @entity62 ( @entity62 ) and indigestible neutral detergent fibre content (iNDF) from faecal samples, and @entity62 ( @entity62 ) using iNDF in feed and faecal samples as an internal marker. Acid-insoluble ash (AIA) as an internal digestibility marker was used as a reference method to evaluate the reliability of NIRS predictions. Feed and composite faecal samples were collected from 44 @entity291 at approximately 50, 150 and 250  days in milk (DIM). The estimated standard deviation for @entity291 -specific @entity62 analysed by AIA was 12.3  g/kg, which is small considering that the average was 724  g/kg. The phenotypic correlation between direct faecal @entity62 prediction by NIRS and @entity62 by AIA over the lactation was 0.51. The low repeatability and small variability estimates for direct @entity62 predictions by NIRS were not accurate enough to quantify small differences in @entity62 between @entity291 . In contrast to @entity62 , the repeatability estimates for @entity62 by iNDF and especially for direct faecal iNDF predictions were 0.32 and 0.46, respectively, indicating that developing of NIRS predictions for @entity291 -specific digestibility is possible. A data subset of 20 @entity291 with daily individual faecal samples was used to develop an on-farm sampling protocol. Based on the assessment of correlations between individual sample combinations and composite samples as well as repeatability estimates for individual sample combinations, we found that collecting up to three individual samples yields a representative composite sample. Collection of samples from all the @entity291 of a herd every third month might be a good choice, because it would yield a better accuracy.
