Short communication: Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers

Evaluation and mitigation of enteric methane (CH4) emissions from ruminant livestock, in particular from dairy cows, have acquired global importance for sustainable, climate-smart cattle production. Based on CH4 reference measurements obtained with the SF6 tracer technique to determine ruminal CH4 production, a current equation permits evaluation of individual daily CH4 emissions of dairy cows based on milk Fourier transform mid-infrared (FT-MIR) spectra. However, the respiration chamber (RC) technique is considered to be more accurate than SF6 to measure CH4 production from cattle. This study aimed to develop an equation that allows estimating CH4 emissions of lactating cows recorded in an RC from corresponding milk FT-MIR spectra and to challenge its robustness and relevance through validation processes and its application on a milk spectral database. This would permit confirming the conclusions drawn with the existing equation based on SF6 reference measurements regarding the potential to estimate daily CH4 emissions of dairy cows from milk FT-MIR spectra. A total of 584 RC reference CH4 measurements (mean ± standard deviation of 400 ± 72 g of CH4/d) and corresponding standardized milk mid-infrared spectra were obtained from 148 individual lactating cows between 7 and 321 d in milk in 5 European countries (Germany, Switzerland, Denmark, France, and Northern Ireland). The developed equation based on RC measurements showed calibration and cross-validation coefficients of determination of 0.65 and 0.57, respectively, which is lower than those obtained earlier by the equation based on 532 SF6 measurements (0.74 and 0.70, respectively). This means that the RC-based model is unable to explain the variability observed in the corresponding reference data as well as the SF6-based model. The standard errors of calibration and cross-validation were lower for the RC model (43 and 47 g/d vs. 66 and 70 g/d for the SF6 version, respectively), indicating that the model based on RC data was closer to actual values. The root mean squared error (RMSE) of calibration of 42 g/d represents only 10% of the overall daily CH4 production, which is 23 g/d lower than the RMSE for the SF6-based equation. During the external validation step an RMSE of 62 g/d was observed. When the RC equation was applied to a standardized spectral database of milk recordings collected in the Walloon region of Belgium between January 2012 and December 2017 (1,515,137 spectra from 132,658 lactating cows in 1,176 different herds), an average ± standard deviation of 446 ± 51 g of CH4/d was estimated, which is consistent with the range of the values measured using both RC and SF6 techniques. This study confirmed that milk FT-MIR spectra could be used as a potential proxy to estimate daily CH4 emissions from dairy cows provided that the variability to predict is covered by the model

Ranking cows’ methane emissions under commercial conditions with sniffers versus respiration chambers

This study assessed the ranking of dairy cows using individual-level correlations for methane (CH 4 ) emission on-farm using sniffers and in respiration chambers. In total 20 lactating dairy cows, ten Holstein and ten Jerseys were recorded using sniffers installed in milking robots for three weeks of lactation and subsequently in respiration chambers (RC) where they were each recorded on three occasions within the RC. Bivariate linear mixed models were used to determine the individual-level correlations (r I ) between sniffer and RC phenotypes as proxies for genetic correlations. Despite differences in feeding and management, the predicted CH 4 production from sniffers correlated highly with RC CH 4 production r I = 0.77 ± 0.18 and CH 4 breath concentration correlated nearly as well with RC CH 4 production r I = 0.75 ± 0.20. These correlations between sniffers on-farm and RC demonstrate the potential of sniffers measurements as large-scale indicator traits for CH 4 emissions in dairy cattle. </p

Effect of dietary nitrate level on enteric methane production, hydrogen emission, rumen fermentation, and nutrient digestibility in dairy cows

AbstractNitrate may lower methane production in ruminants by competing with methanogenesis for available hydrogen in the rumen. This study evaluated the effect of 4 levels of dietary nitrate addition on enteric methane production, hydrogen emission, feed intake, rumen fermentation, nutrient digestibility, microbial protein synthesis, and blood methemoglobin. In a 4×4 Latin square design 4 lactating Danish Holstein dairy cows fitted with rumen, duodenal, and ileal cannulas were assigned to 4 calcium ammonium nitrate addition levels: control, low, medium, and high [0, 5.3, 13.6, and 21.1g of nitrate/kg of dry matter (DM), respectively]. Diets were made isonitrogenous by replacing urea. Cows were fed ad libitum and, after a 6-d period of gradual introduction of nitrate, adapted to the corn-silage-based total mixed ration (forage:concentrate ratio 50:50 on DM basis) for 16d before sampling. Digesta content from duodenum, ileum, and feces, and rumen liquid were collected, after which methane production and hydrogen emissions were measured in respiration chambers. Methane production [L/kg of dry matter intake (DMI)] linearly decreased with increasing nitrate concentrations compared with the control, corresponding to a reduction of 6, 13, and 23% for the low, medium, and high diets, respectively. Methane production was lowered with apparent efficiencies (measured methane reduction relative to potential methane reduction) of 82.3, 71.9, and 79.4% for the low, medium, and high diets, respectively. Addition of nitrate increased hydrogen emissions (L/kg of DMI) quadratically by a factor of 2.5, 3.4, and 3.0 (as L/kg of DMI) for the low, medium, and high diets, respectively, compared with the control. Blood methemoglobin levels and nitrate concentrations in milk and urine increased with increasing nitrate intake, but did not constitute a threat for animal health and human food safety. Microbial crude protein synthesis and efficiency were unaffected. Total volatile fatty acid concentration and molar proportions of acetate, butyrate, and propionate were unaffected, whereas molar proportions of formate increased. Milk yield, milk composition, DMI and digestibility of DM, organic matter, crude protein, and neutral detergent fiber in rumen, small intestine, hindgut, and total tract were unaffected by addition of nitrate. In conclusion, nitrate lowered methane production linearly with minor effects on rumen fermentation and no effects on nutrient digestibility

CH4 estimated from milk MIR spectra: model on data from 7 countries and 2 measurement techniques

peer reviewedAvailability of a robust proxy to estimate individual daily methane (CH4) emissions from dairy cows would be valuable especially for large scale studies, for instance with genetic purpose. Milk mid infrared (MIR) spectroscopy presents potential to meet this aim as spectra can be obtained routinely at reasonable cost through milk recording process. Development of a prediction equation requires as much variability as possible in the reference data set to improve the accuracy and ensure the robustness of the model. So, two datasets including CH4 measurements and corresponding milk MIR spectra have been merged: the first contained 532 data from 156 cows of Ireland and Belgium with CH4 measurements obtained with SF6 tracer technique; the second reached 584 data from 147 cows of Switzerland, United Kingdom, France, Denmark and Germany with CH4 measurements obtained with respiration chambers. In addition to the Partial Least Squares (PLS) equation using the raw CH4 values, a second equation was performed with a reduction of 8% to CH4 values from chambers to evaluate the need to correct the potential method bias in accordance with literature. A 5-group cross-validation was performed to test the robustness of the models. R2 and the standard error of cross-validation were 0.63 and 62 g/day from raw data and 0.65 and 59 g/day when CH4 respiration chamber values were adjusted. This slight improvement due to the adjustment of chamber measurement does not permit to conclude that this correction is needed. The study of residuals showed a non-significant effect due to the CH4 measurement technique. In conclusion this new equation combining CH4 from 2 different techniques covered more variability (cows, diets and country specific information) and shows potential as a proxy especially for genetic evaluation