| Type |
Conference Proceedings |
| Year of conference |
2018 |
| Title |
CH4 emitted by dairy cows estimated from milk MIR spectra: model based on data collected in 7 countries |
| Editor |
Claudia Heidecke, Hayden Montgomery, Hartmut Stalb, Lini Wollenberg |
| Conference name |
AgriGHG International Conference on Agricultural GHG Emissions and Food Security – Connecting research to policy and practice – |
| Conference location |
Berlin |
| Edition |
Johann Heinrich von Thünen-Institut |
| Pages |
145 |
| conference Date |
10-13 septembre 2018 |
| Abstract |
Greenhouse gases (GHG) emissions from livestock and more especially methane (CH4) emissions
from cattle related to ruminal fermentations remain the most important source of GHG within
the agricultural sector. Main levers to reduce those emissions are the diet and the genetic
selection. To study the impact of those levers and how reduce CH4 emissions, a large amount of
reference measurements are needed. However, existing techniques to measure CH4 emissions
from dairy cows are expensive, time consuming and difficult to apply on a large amount of
animals. This is why the availability of a robust proxy to estimate individual daily CH4 emissions
from dairy cows would be valuable. Estimate CH4 emissions from milk mid infrared (MIR)
spectrum present potential to meet this aim as it can be obtained routinely at reasonable cost
through milk recording process. Develop this equation is particularly challenging as the CH4
prediction equation from milk MIR spectra as CH4 is not a direct milk component but an indirect
phenotype linked to milk composition through ruminal fermentations which theoretically
influence both. To increase the variability of the calibration set, two datasets (CH4 measurements
and milk MIR spectra) have been merged: A) 532 data from 156 cows of Ireland and Belgium
using the SF6 tracer technique; B) 584 data from 147 cows of Switzerland, United Kingdom,
France, Denmark and Germany collected in respiration chambers. In addition of the calibration
using the raw reference values, a second calibration was performed with a reduction of 8 % to
CH4 values from chambers evaluate the need to correct the potential method bias in accordance
with literature. A 5-groups cross-validation was performed to test the robustness of the models.
Those equations showed a R² and a standard error of cross-validation of 0.63 and 62 g/day
respectively for the calibration on raw values and 0.65 and 59 g/day when respiration chamber
values are adjusted. The slight improvement due to adjustment of chamber measurement is not
significant. With errors around 60 g/day, the current equations does not permit to distinguish
slight variation of CH4 emissions as it is often required in nutritional context. However, more
variability is included (cows, breeds, diets, and country specific information), marked trends can
be differentiated and statistics confirming its potential as proxy especially for genetic evaluations
or life cycle analyses. |
| Fichier |
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| Authors |
Vanlierde, A., Dehareng, F., Gengler, N., Froidmont, E., Mathot, M., Kreuzer, M., Grandl, F., Khula, B., Lund, P., Olijhoek, D. ., Eugene, M., Martin, C., Bell, M., Mcparland, S., Soyeurt, H. |
