Amélie Vanlierde1, Frédéric Dehareng1, Nicolas Gengler2, Eric Froidmont3, Sinead McParland4, Michael Kreuzer5, Matthew Bell6, Peter Lund7, Cécile Martin8, Björn Kuhla9, Hélène Soyeurt2. 1. Knowledge and valorization of agricultural products Department, Walloon Agricultural Research Centre, Gembloux, Belgium. 2. AGROBIOCHEM Department and Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium. 3. Productions in agriculture Department, Walloon Agricultural Research Centre, Gembloux, Belgium. 4. Department of Animal & Grassland, Moorepark Research and Innovation Centre, Teagasc - The Agriculture and Food Development Authority, Ireland. 5. Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, Zürich, Switzerland. 6. Agri-Food and Biosciences Institute (AFBI), Hillsborough, UK. 7. Department of Animal Science, Aarhus University, AU Foulum, Tjele, Denmark. 8. UMR Herbivores, Centre de Recherches Clermont Auvergne-Rhône-Alpes - INRAe - Site de Theix, Saint-Genès-Champanelle, France. 9. Institute of Nutritional Physiology Oskar Kellner', Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Abstract
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: R2 = 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.
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: R2 = 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.
Authors: Luis Orlindo Tedeschi; Adibe Luiz Abdalla; Clementina Álvarez; Samuel Weniga Anuga; Jacobo Arango; Karen A Beauchemin; Philippe Becquet; Alexandre Berndt; Robert Burns; Camillo De Camillis; Julián Chará; Javier Martin Echazarreta; Mélynda Hassouna; David Kenny; Michael Mathot; Rogerio M Mauricio; Shelby C McClelland; Mutian Niu; Alice Anyango Onyango; Ranjan Parajuli; Luiz Gustavo Ribeiro Pereira; Agustin Del Prado; Maria Paz Tieri; Aimable Uwizeye; Ermias Kebreab Journal: J Anim Sci Date: 2022-07-01 Impact factor: 3.338
Authors: Anthony Tedde; Clément Grelet; Phuong N Ho; Jennie E Pryce; Dagnachew Hailemariam; Zhiquan Wang; Graham Plastow; Nicolas Gengler; Eric Froidmont; Frédéric Dehareng; Carlo Bertozzi; Mark A Crowe; Hélène Soyeurt Journal: Animals (Basel) Date: 2021-05-04 Impact factor: 2.752