Methodological guidelines: Cow milk mid-infrared spectra to predict GreenFeed enteric methane emissions
Résumé
Various methodological protocols were tested on milk
samples from cows fed diets affecting both methano-
genesis and milk synthesis to identify the best approach
for the prediction of GreenFeed system (GF) measured
methane (CH 4) 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 spec-
tra from 2 consecutive milkings were collected twice a
week. Twenty CH 4 spot measurements with GF were
taken as the basic measurement unit (BMU) of CH 4.
The equations were built using partial least squares re-
gression 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 mea-
surement duration of 1, 2, 3, or 4 BMU. Models built
from the average of the day spectra collected during
the corresponding CH 4 measurement periods were de-
veloped. Corrections of spectra by days in milk (DIM)
and the inclusion of parity, MY, and FPCM as explana-
tory 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 (R 2V
= 0.70 vs. 0.60 for CH 4 as g/d on 4 BMU). Correct-
ing the day spectra by DIM improved R 2V compared
with the equivalent DIM-uncorrected models (R 2V =
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 CH 4 measurement period (R2V =
0.73 vs. 0.70 for CH4 as g/d on 4 BMU). When validat-
ing the models on the 3NOP data set, predictions were
poor without (R 2V = 0.13 for CH 4 as g/d on 1 BMU)
or with (R2V = 0.31 for CH4 as g/d on 1 BMU) integra-
tion 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.
Domaines
Sciences du Vivant [q-bio]Origine | Fichiers éditeurs autorisés sur une archive ouverte |
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