期刊
JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
卷 101, 期 8, 页码 3394-3403出版社
WILEY
DOI: 10.1002/jsfa.10969
关键词
methane; milk; MIR spectra; dairy; phenotype; reference method
资金
- European Union's Seventh Framework Programme: GreenHouseMilk [238562]
- GplusE [613689]
- OptiMIR (INTERREG IVB North-West Europe) European project
- COST Methagene (COST-Horizon 2020) European project
- optiKuh project - German Federal Ministry of Food and Agriculture (BMBL) through the Federal Office for Agriculture and Food (BLE)
- French National Research Agency (ANR) through the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI) Global Network programme [ANR-13-JFAC-0003-01]
- Danish Milk Levy Fund
- Aarhus University
- Agence Nationale de la Recherche (ANR) [ANR-13-JFAC-0003] Funding Source: Agence Nationale de la Recherche (ANR)
This study aimed to improve the robustness and accuracy of prediction models for estimating daily CH4 emissions from dairy cows using milk FT-MIR spectra. The models developed based on a combined dataset showed the expected pattern in CH4 values during a lactation cycle. The inclusion of MY, P, and B information in the model provided the best prediction results.
BACKGROUND A robust proxy for estimating methane (CH4) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH4 emissions from milk Fourier transform mid-infrared (FT-MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH4 were developed using a combined dataset including daily CH4 measurements (n = 1089; g d(-1)) collected using the SF6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT-MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables. RESULTS Models developed based on a combined RC and SF6 dataset predicted the expected pattern in CH4 values (in g d(-1)) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross-validation statistics: R-2 = 0.68 and standard error = 57 g CH4 d(-1)). CONCLUSIONS The models developed accounted for more of the observed variability in CH4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large-scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. (c) 2020 Society of Chemical Industry
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