Prediction of the individual enteric methane emission of dairy cows from milk mid-infrared spectra

peer reviewedThe livestock sector is considered the largest producer of methane (CH4) from anthropogenic sources, world wide contributing 37% of emissions (FAO, 2006). An important step to study and develop mitigation methods for livestock emissions is to be able to measure them on a large scale. However, it is difficult to obtain a large number of individual CH4 measurements with the currently available techniques (chambers or SF6). The aim of this study was to develop a high throughput tool for determination of CH4 emissions from dairy cows. Anaerobic fermentation of food in the reticulorumen is the basis of enteric CH4 production. End-products of that enteric fermentation can be found in the milk (e.g., volatile fatty acids). Therefore individual enteric CH4 emissions could be quantified from whole milk mid-infrared (MIR) spectra which reflect milk composition and can be obtained at low cost (e.g., national milk recording). Prediction equations of individual CH4 emissions (determined using the SF6 method) from milk MIR spectra have been established (Dehareng et al., 2012; Soyeurt et al., 2013). The results presented here are the improvement of this methodology by using a multiple breed and country approach

Relations génétiques entre les caractères liés aux émissions de méthane et la composition du lait chez les vaches laitières en production

Methane (CH4) emission is one of the most important environmental traits from dairy cows. Genetic selection programs aiming to mitigate CH4 emissions require the estimation of genetic parameters, correlations with other economically important traits and predicted selection response of these traits. In first part of this thesis, CH4 emissions (g/d; PME) were predicted from several milk fatty acid based prediction equations using mid-infrared (MIR) spectra of milk samples from Holstein cows. The heritability of PME was moderate and ranged from 0.21 to 0.40. The sires genetic variability were large enough to respond selection pressure. In second part and to minimize prediction errors, genetic parameters were estimated from direct prediction of CH4 (i.e. based on SF6 measurements) from milk MIR spectra. Predicted CH4 intensity (PMI, g/kg of milk) was derived from the ratio of CH4 (g/d) value divided by the total milk yield recorded for the considered test-day which is a trait that is comparable across different production systems. The relationship between PMI and milk yield (MY) was curvilinear and the distribution of PMI being non-normal, it was log-transformed (LMI) in further analyses. The genetic analyses were performed using two genetic models with or without random within-herd lactation curve effects along with random permanent and additive genetic effects. The results showed that the model with random within-herd lactation curve effects had a better fitting. The heritability of PME was 0.26 and PMI was 0.27. The contribution of random herd-specific lactation curve effects to the total variance also suggested an impact of herd specific management on the CH4 emission traits. After confirming genetic component of CH4 traits, genetic correlations of these traits with milk production traits were explored and expanded to second lactation. The phenotypic correlations between PME and MY, fat yield and protein yield were not different than zero but with LMI, the phenotypic correlations were highly negative. The genetic correlation was low negative between PME and milk production traits but high negative with LMI. The intra-lactation heritability and correlation were changing across lactation suggested there was dynamic relationship between CH4 traits and milk production traits. After demonstrating correlation between milk production traits, the genetic correlation between CH4 traits and functional traits [fertility, body condition score (BCS), longevity], health traits (udder health) and type traits were estimated. There were positive correlations between CH4 emission traits and functional trait suggested there were tradeoffs between these traits in selection. The ingestion ability related type traits had positive genetic correlations with PME but negative genetic correlation with LMI. Finally, using the current Walloon selection index and by selecting PME and LMI, the emission traits responded by a reduction in CH4 emission, without jeopardizing in milk production traits but having negative consequences in fertility, BCS and longevity. In conclusion, this study shows the feasibility to adapt the selection index to mitigate the CH4 emitted by dairy cows

Genetic relationship between environmental impact traits and milk composition in dairy cows

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Appréciation de la variabilité d’indicateurs méthane issus de la littérature et appliqués sur une large population de vaches laitières

Description of the subject. Dairy production is identified as a major source of methane (CH4) emissions. Selective breeding represents one method for mitigating CH4 emissions but practical and cheap ways to measure this trait are not currently available. In the present study, four CH4 indicator traits based on milk fatty acid (FA) contents were referenced from the literature. Objectives. The aim of the study was to use these literature CH4 indicators for assessing the variability of methane emissions emitted by dairy cows. Method. Literature CH4 indicator traits were originally defined based on the measurements of FA content by gas chromatography. However, these measurements were not available for all the available cows in our studied population. A sample of 602 gas chromatographic analyses was therefore used to develop a calibration equation for predicting the literature CH4 indicators based on milk mid-infrared (MIR) spectra. This spectral information was available for all the studied cows. Then, in a second step, in order to predict the literature CH4 indicator traits, the developed MIR prediction equations were applied to the 604,028 recorded spectral data collected between 2007 and 2011 for 70,872 cows in their first three lactations. Genetic parameters for these traits were then estimated using single trait test-day random regression animal models. Results. The predicted MIR literature CH4 estimates were in the expected range from 350 ± 40 to 449 ± 65 g per day. The averaged predicted MIR CH4 emission (g per day) increased from the beginning of lactation, reached the highest level at the peak of lactation and then decreased towards the end of lactation. The average daily heritability values were 0.29-0.35, 0.26-0.40, and 0.22-0.37 for the different studied CH4 indicators for the first three lactations, respectively. The largest differences between the estimated breeding values of sires that had daughters in production eructing the highest and the lowest CH4 content was 24.18, 29.33 and 27.77 kg per lactation for the first three parities. Low negative correlations were observed between CH4 indicator traits and milk yield. Positive genetic correlations were estimated between CH4 indicator traits and milk fat and protein content. Conclusions. This study showed the feasibility of using MIR spectrometry results to predict fatty acid derived CH4 indicator traits developed in the literature. Moreover, the estimated genetic parameters of these traits suggested a potential phenotypic and genetic variability of the daily quantity of CH4 eructed by Holstein dairy cows.Description du sujet. La production laitière est reconnue comme une des sources majeures d’émissions de méthane (CH4). Le recours à un programme de sélection spécifique pourrait être une bonne méthode pour optimiser les émissions de méthane par les vaches laitières. Le développement d’un tel programme nécessiterait un nombre important d’enregistrements relatifs aux émissions de méthane. Malheureusement, aucune méthode pratique et bon marché n’existe actuellement pour créer une telle base de données. Cependant, quatre indicateurs CH4 basés sur les quantités en acides gras dans la matière grasse laitière ont été recensés dans la littérature. Objectifs. L’objectif de cette étude est d’utiliser ces indicateurs de la littérature afin d’apprécier la variabilité des émissions de méthane éructées par les vaches laitières. Méthode. Ces indicateurs utilisent les quantités en acides gras obtenues par chromatographie en phase gazeuse. Comme ce type de données n’est pas disponible pour toute la population laitière, un échantillon de 602 analyses chromatographiques a été créé dans cette étude afin de développer une équation de calibrage permettant de prédire les quantités de méthane émises à partir du spectre moyen infrarouge (MIR) du lait qui est disponible pour toutes les vaches étudiées. Ensuite, l’équation de calibrage ainsi obtenue a été appliquée sur 604 028 données spectrales enregistrées entre 2007 et 2011 auprès de 70 872 vaches au cours de leurs trois premières lactations afin de prédire les quantités de méthane émises. Les paramètres génétiques de ces nouveaux indicateurs méthane prédits par MIR ont également été estimés en utilisant un modèle animal de type jour de test avec régressions aléatoires. Résultats. Ces quantités prédites par MIR variaient selon une gamme attendue s’étalant entre 350 ± 40 et 449 ± 65 g par jour. L’émission prédite moyenne de CH4 en g par jour augmentait au début de la lactation, atteignait sa plus haute concentration au pic de lactation et ensuite diminuait jusqu’à la fin de la lactation. Les héritabilités journalières moyennes variaient entre 0,29-0,35 ; 0,26-0,40 et 0,22-0,37 pour les différents indicateurs méthane étudiés au cours des trois premières lactations. Les plus grandes différences entre les valeurs d’élevage estimées pour des taureaux ayant des filles en production émettant le plus et le moins de méthane étaient de 24,18 ; 29,33 et 27,77 kg par lactation pour les trois premières lactations. Des corrélations faiblement négatives ont été observées entre les indicateurs CH4 et la quantité de lait. À l’inverse, des corrélations positives ont été estimées entre ces mêmes indicateurs et les taux en matières grasses et en protéines. Conclusions. Cette étude montre la possibilité de prédire des indicateurs méthane issus de la littérature et utilisant les concentrations en acides gras dans la matière grasse laitière à partir de la spectrométrie MIR. De plus, cette étude suggère également à partir des paramètres génétiques obtenus l’existence d’une variabilité phénotypique et génétique des quantités de méthane éructées par les vaches laitières Holstein.GreenHouseMil

Estimation of genetic parameters for methane indicator traits based on milk fatty acids in dual purpose Belgian blue cattle

The genetic parameters of CH4 indicators were estimated by single trait test-day models from 16,825 records collected on Walloon Dual Purpose Belgium Blue cows in their first 3 lactations. Fatty acid based CH4 indicators published in the literature were predicted from milk mid-infrared spectra using 597 calibration samples. For the indicator showing the highest link (R2 =0.88) with SF6 CH4 data, the average daily heritability was 0.21, 0.20 and 0.10 for each lactation, respectively. The sire genetic variability was on average 2.82 kg2 of CH4 per lactation. The genetic difference between the sires having cows eructing higher and lower CH4 was 10 kg of CH4 averaged per lactation. In conclusion, CH4 indicators can be predicted by MIR and the genetic variability of these traits seems to exist.GreenHouseMil

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

MéthaMil

Genetic parameters for methane indicator traits based on milk fatty acids

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Genetic parameters for methane indicator traits based on milk fatty acids in cows

peer reviewedDairy production is pointed out for its large methane emission. Therefore, currently studies of factors affecting emission and methods to abate methane emission are numerous. However, an important issue is the development of easily obtainable indicators, because they would also allow estimating animal genetic variability of methane emission. Recently methane indicators were proposed using gas chromatrography based milk fatty acid composition. We derived these published methane indicators using 1100 calibration samples directly from mid-infrared (MIR).For the published indicator showing the highest relationship (R2 = 0.88) with Sulfur Hexafluoride 6 methane emission data, genetic parameters for this MIR based indicator were estimated by single trait random regression test-day models from 619,272 records collected from 2007 to 2011 on 71,188 Holstein cows in their first three lactations at Walloon region of Belgium. The average daily heritability was 0.35±0.01, 0.35±0.02 and 0.32±0.02 for the first three lactations, respectively. Similarly, the lactation heritability was 0.67±0.02, 0.72±0.03 and 0.62±0.03. As expected, methane production was higher during the peak milk production depicting the normal lactation curve. The largest differences between estimated breeding values (EBV) of sires having cows in production eructing the highest and the lowest methane content was 21.80, 22.75 and 24.89 kg per lactation for the first three parities, the variances of the EBV of the sires with daughters were 10.67, 12.46, 12.18 kg2. Results were similar for other indicators. This study suggested that methane indicator traits can be predicted by MIR. Genetic parameters also indicated a rather high heritability and genetic variability exist for these published indicators and consequently a potential high genetic variability of methane eructation by dairy cows. Therefore, these first finding might open new opportunities for animal selection programs that include the reduction of methane emission.GreenHouseMil

Estimation of methane and its variation across different breeds of cattle predicted from milk fatty acids

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