Methodological guidelines: Cow milk mid-infrared spectra to predict reference enteric methane data collected by an automated head-chamber system.

Abstract

Various methodological protocols were tested on milk samples from cows fed diets affecting both methanogenesis and milk synthesis to identify the best approach for the prediction of GreenFeed system (GF) measured methane (CH4) emissions by milk mid-infrared (MIR) spectroscopy. The models developed were also tested on a data set from cows fed chemical inhibitors of CH4 emission [3-nitrooxypropanol (3NOP)] that just marginally affect milk composition. A total of 129 primiparous and multiparous Holstein cows fed diets with different methanogenic potential were considered. Individual milk yield (MY) and dry matter intake were recorded daily, whereas fat- and protein-corrected milk (FPCM) was recorded twice a week. The MIR spectra from 2 consecutive milkings were collected twice a week. Twenty CH4 spot measurements with GF were taken as the basic measurement unit (BMU) of CH4. The equations were built using partial least squares regression by splitting the database into calibration and validation data sets (excluding 3NOP samples). Models were developed for milk MIR spectra by milking and on day spectra obtained by averaging spectra from 2 consecutive milkings. Models based on day spectra were calibrated by using CH4 reference data for a measurement duration of 1, 2, 3, or 4 BMU. Models built from the average of the day spectra collected during the corresponding CH4 measurement periods were developed. Corrections of spectra by days in milk (DIM) and the inclusion of parity, MY, and FPCM as explanatory variables were tested as tools to improve model performance. Models built on day milk MIR spectra gave slightly better performances that those developed using spectra from a single milking. Long duration of CH4 measurement by GF performed better than short duration: the coefficient of determination of validation (R2V) for CH4 emissions expressed in grams per day were 0.60 vs. 0.52 for 4 and 1 BMU, respectively. When CH4 emissions were expressed as grams per kilogram of dry of matter intake, grams per kilogram of MY, or grams per kilogram of FPCM, performance with a long duration also improved. Coupling GF reference data with the average of milk MIR spectra collected throughout the corresponding CH4 measurement period gave better predictions than using day spectra (R2V = 0.70 vs. 0.60 for CH4 as g/d on 4 BMU). Correcting the day spectra by DIM improved R2V compared with the equivalent DIM-uncorrected models (R2V = 0.67 vs. 0.60 for CH4 as g/d on 4 BMU). Adding other phenotypic information as explanatory variables did not further improve the performance of models built on single day DIM-corrected spectra, whereas including MY (or FPCM) improved the performance of models built on the average of spectra (uncorrected by DIM) recorded during the CH4 measurement period (R2V = 0.73 vs. 0.70 for CH4 as g/d on 4 BMU). When validating the models on the 3NOP data set, predictions were poor without (R2V = 0.13 for CH4 as g/d on 1 BMU) or with (R2V = 0.31 for CH4 as g/d on 1 BMU) integration of 3NOP data in the models. Thus, specific models would be required for CH4 prediction when cows receive chemical inhibitors of CH4 emissions not affecting milk composition.