Assessing meteorological conditions effects on MIR predicted methane emissions of Holstein cows under a temperate environment

Assessing meteorological conditions effects on MIR

Assessing meteorological conditions effects on MIR

predicted methane emissions of Holstein cows

predicted methane emissions of Holstein cows

under a temperate environment

M.-L. Vanrobays

1

, N. Gengler

1

, H. Soyeurt

1

, P.B. Kandel

1

& H. Hammami

1,2

1 _{Department of Agricultural Science, Gembloux Agro-Bio Tech, University of Liege, B-5030 Gembloux, Belgium}

2 _{National Fund for Scientific Research (F.R.S.-FNRS), B-1000 Brussels, Belgium}

Poster 488

Contact: mlvanrobays@ulg.ac.be

Objective: To assess the impact of meteorological

Background

Need to mitigate enteric methane (

CH

₄

)

emissions produced by ruminants

Objective: To assess the impact of meteorological

conditions on CH

₄

emissions of Holstein cows

emissions produced by ruminants

CH

₄

emissions could be partly influenced by

meteorological conditions

Conclusions

meteorological conditions

(Lassey, 2007, Agr. Forest. Meteorol. 142: 120-132)

Temperature Humidity Index (

THI

)

Conclusions

Permanent environmental & year of calving x herd variances

evolve largely with THI

Temperature Humidity Index (

THI

)

– Index to assess temperature & humidity of the day

evolve largely with THI

THI seems to affect CH

₄

emissions of dairy cows

Results

Results

Material & Methods

Material & Methods

Data

Traits (N=257,635)

Mean

SD

Table 1. Descriptive statistics of the dataset

Data

Traits (N=257,635)

Mean

SD

MIR CH

₄

(g/day)

558.05

89.89

MIR CH

₄

(g/kg of FPCM)

25.64

7.76

Prediction of daily CH

₄

emissions from milk mid-infrared (

MIR

) spectra

(R² of cross-validation = 0.70)

(Vanlierde et al., 2013, Poster 433, GGAA, Dublin)

MIR CH

₄

(g/kg of FPCM)

25.64

7.76

THI

48.63

10.06

Traits (N=257,635)

YxH

PE

G

(R² of cross-validation = 0.70)

(Vanlierde et al., 2013, Poster 433, GGAA, Dublin)

257,635 milk MIR spectra & test-day (

TD

) milk yield records (kg/day)

collected between January 2007 & December 2010

Table 2. Variances of the slope relative to the variances of the intercept associated to THI

Traits (N=257,635)

YxH

PE

G

MIR CH

₄

(g/day)

3.71

1.23

0.21

collected between January 2007 & December 2010

51,782 primiparous Holstein cows from 983 herds

2 studied traits:

MIR CH

₄

(g/day)

3.71

1.23

0.21

MIR CH

₄

(g/kg of FPCM)

0.65

0.50

0.37

2 studied traits:

–

g CH

4

per day

–

g CH

4

per kg of fat and protein corrected milk (

FPCM

)

Daily meteorological data from 4 public weather stations

0.20 0.25 H e ri ta b il it ie s

Evolution of heritabilities across THI

Daily meteorological data from 4 public weather stations

Temperature Humidity Index

0.05 0.10 0.15 0.20 H e ri ta b il it ie s

Temperature Humidity Index

THI = (1.8 × T + 32 ) - [(0.55 - 0.0055 × RH) × (1.8 × T - 26)]

0.00 0.05 18 28 38 48 58 68 H e ri ta b il it ie s

Temperature Humidity Index

THI = (1.8 × T

_db

+ 32 ) - [(0.55 - 0.0055 × RH) × (1.8 × T

_db

- 26)]

where T_db = Dry bulb temperature

Temperature Humidity Index

MIR methane (g/day) MIR methane (g/kg of FPCM)

where T_db = Dry bulb temperature

where RH = Relative humidity (NRC, 1981)

600 700 /d a y )

Variances of MIR CH₄ (g/day) across THI

Model

300 400 500 600 V a ri a n ce s (g ² C H 4 /d a y

Random regression TD mixed model

resolved using REML

Model

100 200 300 V a ri a n ce s (

y = Xb + Q

₁

(Wh + Zp + Za) + e

0 18 28 38 48 58 68

Temperature Humidity Index

Year of calving x Herd variances Permanent Environmental variances Genetic variances 1

where y = Vector of observations (g MIR CH₄/day or g MIR CH₄/kg of FPCM)

where b = Vector of fixed effects

Year of calving x Herd variances Permanent Environmental variances Genetic variances

Variances of MIR CH₄ (g/kg of FPCM) across THI _{where b = Vector of fixed effects}

where h = Vector of year of calving x herd (YxH) random regression coefficients

where p = Vector of permanent environment (PE) random regression coefficients

12 14 16 o f F P C M )

Variances of MIR CH₄ (g/kg of FPCM) across THI

where p = Vector of permanent environment (PE) random regression coefficients

where a = Vector of additive genetic (G) random regression coefficients

6 8 10 g ² C H 4 /k g o f F P C M )

where Q₁ = Covariate matrix for 1st _{order Legendre polynomials related to THI}

where X, W & Z = Incidence matrices

0 2 4 18 28 38 48 58 68 V a ri a n ce s (g ² C H

where X, W & Z = Incidence matrices

where e= Error 18 28 38 48 58 68 V a ri a n ce s (

Temperature Humidity Index

Year of calving x Herd variances Permanent Environmental variances Genetic variances

The Ministry of Agriculture of Walloon Region of Belgium (Service Public de Wallonie – Direction générale de l’Agriculture, des Ressources naturelles et de l’Environnement, Direction de la Recherche) générale de l’Agriculture, des Ressources naturelles et de l’Environnement, Direction de la Recherche) is acknowledged for its financial support through the research project D31-1304. The authors gratefully acknowledge funding from the National Fund for Scientific Research (FNRS, Brussels, Belgium) and from the European Commission, “GreenHouseMilk” project (FP7 Marie Curie ITN).