Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level
The milk Fourier-transform infra-red spectrometry (FTIRS) can be used to predict the enteric methane emissions (EME) at population level. In this study, the variability in FTIRS predicted EME traits due to the breed of cow, farm, the production level of individual cows within herds, and their intera...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Italian Journal of Animal Science |
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| Online Access: | http://dx.doi.org/10.1080/1828051X.2022.2158953 |
| _version_ | 1797198464495386624 |
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| author | Gustavo Martínez-Marín Stefano Schiavon Franco Tagliapietra Alessio Cecchinato Hugo Toledo-Alvarado Giovanni Bittante |
| author_facet | Gustavo Martínez-Marín Stefano Schiavon Franco Tagliapietra Alessio Cecchinato Hugo Toledo-Alvarado Giovanni Bittante |
| author_sort | Gustavo Martínez-Marín |
| collection | DOAJ |
| description | The milk Fourier-transform infra-red spectrometry (FTIRS) can be used to predict the enteric methane emissions (EME) at population level. In this study, the variability in FTIRS predicted EME traits due to the breed of cow, farm, the production level of individual cows within herds, and their interactions were evaluated. A dataset obtained from milk recordings, which covered four breeds (Holstein, Brown Swiss, Simmental and Alpine Grey), 6,430 herds, 115,819 cows, and 1,759,706 test-day milk/spectra records was used. The herds were stratified into 5 production levels considering their average daily milk energy production; individual cows within herd were similarly stratified considering their individual production levels. The EME traits were predicted directly from milk FTIR spectra or indirectly from six informative milk FA predicted from milk spectra. The statistical model included, separately for each trait and method, breed, herd intensiveness level, cow production level, and their interactions, year, month, parity, and lactation stage. The direct and indirect methods yielded similar results in predicting CH4 yield per kg of DMI and CH4 intensity per kg of fat-protein corrected milk. The indirect method was reliable in predicting daily EME production per cow, the indirect one did not. EME was affected by the breed x herd production intensiveness interaction, and to a lesser degree, by the breed x cow production level interaction. A better understanding of the complex interactions influencing EME in dairy herds was achieved. This would be useful for the genetic improvement, the environmental certification of farms, and for setting prices in milk payment schemes.HIGHLIGHTS Causes of variation of the FTIR predicted EME were studied at population level using data from 6,430 herds and 1,759,706 milk spectra records Reliable daily EME estimates were achieved from 6 six informative fatty acids predicted from milk spectra EME was influenced by the interaction breed × level of herd intensiveness and, to a lesser extent, by the individual production level |
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| format | Article |
| id | doaj.art-d9accc78a1c747209b35553820491a3d |
| institution | Directory Open Access Journal |
| issn | 1594-4077 1828-051X |
| language | English |
| last_indexed | 2024-04-24T07:00:17Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Italian Journal of Animal Science |
| spelling | doaj.art-d9accc78a1c747209b35553820491a3d2024-04-22T08:52:28ZengTaylor & Francis GroupItalian Journal of Animal Science1594-40771828-051X2023-12-01221597510.1080/1828051X.2022.21589532158953Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population levelGustavo Martínez-Marín0Stefano Schiavon1Franco Tagliapietra2Alessio Cecchinato3Hugo Toledo-Alvarado4Giovanni Bittante5Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua)Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua)Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua)Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua)Department of Genetics and Biostatistics, Faculty of Veterinary Medicine and Zootechnics, National Autonomous University of MexicoDepartment of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua)The milk Fourier-transform infra-red spectrometry (FTIRS) can be used to predict the enteric methane emissions (EME) at population level. In this study, the variability in FTIRS predicted EME traits due to the breed of cow, farm, the production level of individual cows within herds, and their interactions were evaluated. A dataset obtained from milk recordings, which covered four breeds (Holstein, Brown Swiss, Simmental and Alpine Grey), 6,430 herds, 115,819 cows, and 1,759,706 test-day milk/spectra records was used. The herds were stratified into 5 production levels considering their average daily milk energy production; individual cows within herd were similarly stratified considering their individual production levels. The EME traits were predicted directly from milk FTIR spectra or indirectly from six informative milk FA predicted from milk spectra. The statistical model included, separately for each trait and method, breed, herd intensiveness level, cow production level, and their interactions, year, month, parity, and lactation stage. The direct and indirect methods yielded similar results in predicting CH4 yield per kg of DMI and CH4 intensity per kg of fat-protein corrected milk. The indirect method was reliable in predicting daily EME production per cow, the indirect one did not. EME was affected by the breed x herd production intensiveness interaction, and to a lesser degree, by the breed x cow production level interaction. A better understanding of the complex interactions influencing EME in dairy herds was achieved. This would be useful for the genetic improvement, the environmental certification of farms, and for setting prices in milk payment schemes.HIGHLIGHTS Causes of variation of the FTIR predicted EME were studied at population level using data from 6,430 herds and 1,759,706 milk spectra records Reliable daily EME estimates were achieved from 6 six informative fatty acids predicted from milk spectra EME was influenced by the interaction breed × level of herd intensiveness and, to a lesser extent, by the individual production levelhttp://dx.doi.org/10.1080/1828051X.2022.2158953carbon footprintenteric methane emissionglobal warmingcow milkinfra-red spectra |
| spellingShingle | Gustavo Martínez-Marín Stefano Schiavon Franco Tagliapietra Alessio Cecchinato Hugo Toledo-Alvarado Giovanni Bittante Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level Italian Journal of Animal Science carbon footprint enteric methane emission global warming cow milk infra-red spectra |
| title | Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level |
| title_full | Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level |
| title_fullStr | Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level |
| title_full_unstemmed | Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level |
| title_short | Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level |
| title_sort | interactions among breed farm intensiveness and cow productivity on predicted enteric methane emissions at the population level |
| topic | carbon footprint enteric methane emission global warming cow milk infra-red spectra |
| url | http://dx.doi.org/10.1080/1828051X.2022.2158953 |
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