Milk fatty acids estimated by mid-infrared spectroscopy and milk yield can predict methane emissions in dairy cows

Engelke, Stefanie W.; Daş, Gürbüz; Derno, M.; Tuchscherer, Armin; Berg, Werner; Kuhla, Björn; Metges, C. C.

doi:10.1007/s13593-018-0502-x

Cited by 12 publications

(8 citation statements)

References 24 publications

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“…This has to do with the fact that milk yield has a much greater variability than CH 4 /CM IR , and it was used both in GC reference and IR-predicted traits. Similar good estimates of daily methane production have been obtained by Engelke et al [ 48 ] using FTIR predicted fatty acids and daily corrected milk yield. It makes no practical sense to try to predict a cow’s milk production from an infrared spectrum of its milk when the measured yield is available.…”

Section: Discussionsupporting

confidence: 87%

Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle

Bittante

Cipolat‐Gotet

Cecchinato

2020

Animals

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This study aimed to infer the genetic parameters of five enteric methane emissions (EME) predicted from milk infrared spectra (13 models). The reference values were estimated from milk fatty acid profiles (chromatography), individual model-cheese, and daily milk yield of 1158 Brown Swiss cows (85 farms). Genetic parameters were estimated, under a Bayesian framework, for EME reference traits and their infrared predictions. Heritability of predicted EME traits were similar to EME reference values for methane yield (CH4/DM: 0.232–0.317) and methane intensity per kg of corrected milk (CH4/CM: 0.177–0.279), smaller per kg cheese solids (CH4/SO: 0.093–0.165), but greater per kg fresh cheese (CH4/CU: 0.203–0.267) and for methane production (dCH4: 0.195–0.232). We found good additive genetic correlations between infrared-predicted methane intensities and the reference values (0.73 to 0.93), less favorable values for CH4/DM (0.45–0.60), and very variable for dCH4 according to the prediction method (0.22 to 0.98). Easy-to-measure milk infrared-predicted EME traits, particularly CH4/CM, CH4/CU and dCH4, could be considered in breeding programs aimed at the improvement of milk ecological footprint.

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Section: Discussionsupporting

confidence: 87%

Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle

Bittante

Cipolat‐Gotet

Cecchinato

2020

Animals

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“…Consequently, milk yield could be a potential alternative for DMI as a variable in prediction equations (Hristov et al, 2013;Negussie et al, 2017). We have reported that a prediction equation based on MFA, determined by mid-infrared spectroscopy, which contained DMI as an additional explanatory variable, showed a similar coefficient of determination when DMI was replaced by ECM (Engelke et al, 2018).…”

Section: Introductionmentioning

confidence: 90%

Methane prediction based on individual or groups of milk fatty acids for dairy cows fed rations with or without linseed

Engelke

Daş

Derno

et al. 2019

Journal of Dairy Science

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Milk fatty acids (MFA) are a proxy for the prediction of CH 4 emission from cows, and prediction differs with diet. Our objectives were (1) to compare the effect of diets on the relation between MFA profile and measured CH 4 production, (2) to predict CH 4 production based on 6 data sets differing in the number and type of MFA, and (3) to test whether additional inclusion of energy-corrected milk (ECM) yield or dry matter intake (DMI) as explanatory variables improves predictions. Twenty dairy cows were used. Four diets were used based on corn silage (CS) or grass silage (GS) without (L0) or with linseed (LS) supplementation. Ten cows were fed CS-L0 and CS-LS and the other 10 cows were fed GS-L0 and GS-LS in random order. In feeding wk 5 of each diet, CH 4 production (L/d) was measured in respiration chambers for 48 h and milk was analyzed for MFA concentrations by gas chromatography. Specific CH 4 prediction equations were obtained for L0-, LS-, GS-, and CS-based diets and for all 4 diets collectively and validated by an internal cross-validation. Models were developed containing either 43 identified MFA or a reduced set of 7 groups of biochemically related MFA plus C16:0 and C18:0. The CS and LS diets reduced CH 4 production compared with GS and L0 diets, respectively. Methane yield (L/ kg of DMI) reduction by LS was higher with CS than GS diets. The concentrations of C18:1 trans and n-3 MFA differed among GS and CS diets. The LS diets resulted in a higher proportion of unsaturated MFA at the expense of saturated MFA. When using the data set of 43 individual MFA to predict CH 4 production (L/d), the cross-validation coefficient of determination (R 2 CV ) ranged from 0.47 to 0.92. When using groups of MFA variables, the R 2 CV ranged from 0.31 to 0.84. The fit parameters of the latter models were improved by inclusion of ECM or DMI, but not when added to the data set of 43 MFA for all diets pooled. Models based on GS diets always had a lower prediction potential (R 2 CV = 0.31 to 0.71) compared with data from CS diets (R 2 CV = 0.56 to 0.92). Models based on LS diets produced lower prediction with data sets with reduced MFA variables (R 2 CV = 0.62 to 0.68) compared with L0 diets (R 2 CV = 0.67 to 0.80). The MFA C18:1 cis-9 and C24:0 and the monounsaturated FA occurred most often in models. In conclusion, models with a reduced number of MFA variables and ECM or DMI are suitable for CH 4 prediction, and CH 4 prediction equations based on diets containing linseed resulted in lower prediction accuracy.

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“…In dairy science, machine learning has been used successfully to predict a whole range of different traits, such as mastitis (Kamphuis et al, 2010;Ebrahimie et al, 2018), methane production (Zheng et al, 2016), and milk production (Gianola et al, 2011). However, despite the advantages of machine learning, other recent studies have used traditional linear methods to predict disease risk (Moretti et al, 2017), methane production (Engelke et al, 2018), and milk production (Wallén et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle

Heide

Veerkamp

Pelt

et al. 2019

Journal of Dairy Science

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In this study, we compared multiple logistic regression, a linear method, to naive Bayes and random forest, 2 nonlinear machine-learning methods. We used all 3 methods to predict individual survival to second lactation in dairy heifers. The data set used for prediction contained 6,847 heifers born between January 2012 and June 2013, and had known survival outcomes. Each animal had 50 genomic estimated breeding values available at birth and up to 65 phenotypic variables that accumulated over time. Survival was predicted at 5 moments in life: at birth, at 18 mo, at first calving, at 6 wk after first calving, and at 200 d after first calving. The data sets were randomly split into 70% training and 30% testing sets to evaluate model performance for 20-fold validation. The methods were compared for accuracy, sensitivity, specificity, area under the curve (AUC) value, contrasts between groups for the prediction outcomes, and increase in surviving animals in a practical scenario. At birth and 18 mo, all methods had overlapping performance; no method significantly outperformed the other. At first calving, 6 wk after first calving, and 200 d after first calving, random forest and naive Bayes had overlapping performance, and both machine-learning methods outperformed multiple logistic regression. Overall, naive Bayes has the highest average AUC at all decision points up to 200 d after first calving. Random forest had the highest AUC at 200 d after first calving. All methods obtained similar increases in survival in the practical scenario. Despite this, the methods appeared to predict the survival of individual heifers differently. All methods improved over time, but the changes in mean model outcomes for surviving and non-surviving animals differed by method. Furthermore, the correlations of individual predictions between methods ranged from r = 0.417 to r = 0.700; the lowest correlations were at first calving for all methods. In short, all 3 methods were able to predict survival at a population level, because all methods improved survival in a practical scenario. However, depending on the method used, predictions for individual animals were quite different between methods.

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Milk fatty acids estimated by mid-infrared spectroscopy and milk yield can predict methane emissions in dairy cows

Cited by 12 publications

References 24 publications

Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle

Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle

Methane prediction based on individual or groups of milk fatty acids for dairy cows fed rations with or without linseed

Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle

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