Accuracy of methane emissions predicted from milk mid-infrared spectra and measured by laser methane detectors in Brown Swiss dairy cows

Lactation modeling and the effects of rotational crossbreeding on milk production traits and milk-spectra-predicted enteric methane emissions

Rotational crossbreeding has not been widely studied in relation to the enteric methane emissions of dairy cows, nor has the variation in emissions during lactation been modeled. Milk infrared spectra could be used to predict proxies of methane emissions in dairy cows. Therefore, the objective of this work was to study the effects of crossbreeding on the predicted infrared proxies of methane emissions and the variation in the latter during lactation. Milk samples were taken once from 1,059 cows reared in 2 herds, and infrared spectra of the milk were used to predict milk fat (mean ± SD; 3.79 ± 0.81%) and protein (3.68 ± 0.36%) concentrations, yield (21.4 ± 1.5 g/kg dry matter intake), methane intensity (14.2 ± 2.0 g/kg corrected milk), and daily methane production (358 ± 108 g/d). Of these cows, 620 were obtained from a 3-breed (Holstein, Montbéliarde, and Viking Red) rotational mating system, and the rest were purebred Holsteins. Milk production data and methane traits were analyzed using a nonlinear model that included the fixed effects of herd, genetic group, and parity, and the 4 parameters (a, b, c, and k) of a lactation curve modeled using the Wilmink function. Milk infrared spectra were found to be useful for direct prediction of qualitative proxies, such as methane yield and intensity, but not quantitative traits, such as daily methane production, which appears to be better estimated (450 ± 125 g/d) by multiplying a measured daily milk yield by infrared-predicted methane intensity. Lactation modeling of methane traits showed daily methane production to have a zenith curve, similar to that of milk yield but with a delayed peak (53 vs. 37 d in milk), whereas methane intensity is characterized by an upward curve that increases rapidly during the first third of lactation and then slowly till the end of lactation (10.5 g/kg at 1 d in milk to 15.2 g/kg at 300 d in milk). However, lactation modeling was not useful in explaining methane yield, which is almost constant during lactation. Lastly, the methane yield and intensity of cows from 3-breed rotational crossbreeding are not greater, and their methane production is lower than that of purebred Holsteins (452 vs. 477 g/d). Given the greater longevity of crossbred cows, and their lower replacement rate, rotational crossbreeding could be a way of mitigating the environmental impact of milk production.

Microbial composition, rumen fermentation parameters, enteric methane emissions, and lactational performance of phenotypically high and low methane-emitting dairy cows

This experiment was designed to investigate the relation of high and low methane-yield phenotypes with body weight (BW), dry matter intake (DMI), lactation performance, enteric CH4 emissions, and rumen fermentation parameters in lactating dairy cows. A total of 130 multi- and primiparous Holstein cows were screened for enteric CH4 emissions using the GreenFeed system (C-Lock Inc.). Out of these 130 cows, 5 were identified as phenotypically high (HM) and 5 as phenotypically low (LM) CH4 emitters. Cows in the LM group had lower daily enteric CH4 emissions than cows in the HM group (on average 346 vs. 439 g/d, respectively), lower CH4 yield (15.5 vs. 20.4 g of CH4/kg of DMI), and CH4 intensity (13.2 vs. 17.0 g of CH4/ kg of energy-corrected milk yield). Enteric emissions of CO2 and H2 did not differ between HM and LM cows. These 10 cows were blocked by parity, days in milk, and milk production, and were used in a 5-wk randomized complete block design experiment. Milk composition, production, and BW were also not different between LM and HM cows. The concentration of total volatile fatty acids in ruminal contents did not differ between CH4 phenotypes, but LM cows had a lower molar proportion of acetate (57 vs. 62.1%), a higher proportion of propionate (27.5 vs. 21.6%, respectively), and therefore a lower acetate-to-propionate ratio than HM cows. Consistently, the 16S cDNA analysis revealed the abundance of Succinivibrionaceae and unclassified Veillonellaceae to be higher in LM cows compared with HM cows, bacteria that were positively correlated with ruminal propionate concentration. Notably, Succinivibrionaceae trigger the formation of propionate via oxaloacetate pathway from phosphoenolpyruvate via Enzyme Commission: 4.1.1.49, which showed a trend to be higher in LM cows compared with HM cows. Additionally, LM cows possessed fewer transcripts of a gene encoding for methyl-CoM reductase enzyme compared with HM. In this study, low and high CH4-yield cows have similar production performance and milk composition, but total-tract apparent digestibility of organic matter and fiber fractions was lower in the former group of animals.

Genetic Analysis of Methane Emission Traits in Holstein Dairy Cattle

Genetic selection can be a feasible method to help mitigate enteric methane emissions from dairy cattle, as methane emission-related traits are heritable and genetic gains are persistent and cumulative over time. The objective of this study was to estimate heritability of methane emission phenotypes and the genetic and phenotypic correlations between them in Holstein cattle. We used 1765 individual records of methane emission obtained from 330 Holstein cattle from two Canadian herds. Methane emissions were measured using the GreenFeed system, and three methane traits were analyzed: the amount of daily methane produced (g/d), methane yield (g methane/kg dry matter intake), and methane intensity (g methane/kg milk). Genetic parameters were estimated using univariate and bivariate repeatability animal models. Heritability estimates (±SE) of 0.16 (±0.10), 0.27 (±0.12), and 0.21 (±0.14) were obtained for daily methane production, methane yield, and methane intensity, respectively. A high genetic correlation (rg = 0.94 ± 0.23) between daily methane production and methane intensity indicates that selecting for daily methane production would result in lower methane per unit of milk produced. This study provides preliminary estimates of genetic parameters for methane emission traits, suggesting that there is potential to mitigate methane emission in Holstein cattle through genetic selection.

Association between Days Open and Parity, Calving Season or Milk Spectral Data

Milk spectral data on 2118 cows from nine herds located in northern China were used to access the association of days open (DO). Meanwhile, the parity and calving season of dairy cows were also studied to characterize the difference in DO between groups of these two cow-level factors. The result of the linear mixed-effects model revealed that no significant differences were observed between the parity groups. However, a significant difference in DO exists between calving season groups. The interaction between parity and calving season presented that primiparous cows always exhibit lower DO among all calving season groups, and the variation in DO among parity groups was especially clearer in winter. Survival analysis revealed that the difference in DO between calving season groups might be caused by the different P/AI at the first TAI. In addition, the summer group had a higher chance of conception in the subsequent services than other groups, implying that the micro-environment featured by season played a critical role in P/AI. A weak linkage between DO and wavenumbers ranging in the mid-infrared region was detected. In summary, our study revealed that the calving season of dairy cows can be used to optimize the reproduction management. The potential application of mid-infrared spectroscopy in dairy cows needs to be further developed.

Relationship between Reticulorumen Parameters Measured in Real Time and Methane Emission and Heat Stress Risk in Dairy Cows

The objective of this study was to investigate a connection between CH4 emissions and reticulorumen pH and temperature. During the experiment, we registered the following parameters: reticulorumen pH (pH), reticulorumen temperature (RR temp.), reticulorumen temperature without drinking cycles, ambient temperature, ambient relative humidity, cow activity, heat index, temperature−humidity index (THI), and methane emissions (CH4). The experimental animals were divided into two groups based on the reticulorumen pH: 1. pH < 6.22 and 2. pH 6.22−6.42. We found that cows assigned to the second pH class had higher (46.18%) average values for methane emissions (p < 0.01). For the other indicators, higher average values were detected in cows of the first pH class, RR temperature (2.80%), relative humidity (20.96%), temperature−humidity index (2.47%) (p < 0.01), and temperature (3.93%) (p < 0.05), which were higher compared to cows of the second pH class. Reticulorumen pH was highly negatively correlated with THI and temperature (r = −0.667 to 0.717, p < 0.001) and somewhat negatively with heat index, relative humidity, and RR temperature (r = −0.536, p < 0.001; r = −0.471 to 0.456, p < 0.01). Cows with a higher risk of heat stress had a higher risk of lower reticulorumen pH.

Application of a hand-held laser methane detector for measuring enteric methane emissions from cattle in intensive farming

The hand-held laser methane detector (LMD) technique has been suggested as an alternative method for measuring methane (CH4) emissions from enteric fermentation of ruminants in the field. This study aimed to establish a standard procedure for using LMD to assess CH4 production in cattle and evaluate the efficacy of the protocol to detect differences in CH4 emissions from cattle fed with diets of different forage-to-concentrate (FC) ratios. Experiment 1 was conducted with four Hanwoo steers (584 ± 57.4 kg body weight (BW)) individually housed in metabolic cages. The LMD was installed on a tripod aimed at the animal's nostril, and the CH4 concentration in the exhaled gas was measured for 6 min every hour for two consecutive days. For the data processing, the CH4 concentration peaks were identified by the automatic multi-scale peak detection algorithm. The peaks were then separated into those from respiration and eructation by fitting combinations of two of the four distribution functions (normal, log-normal, gamma, and Weibull) using the mixdist R package. In addition, the most appropriate time and number of consecutive measurements to represent the daily average CH4 concentration were determined. In Experiment 2, 30 Hanwoo growing steers (343 ± 24.6 kg BW), blocked by body weight, were randomly divided into three groups. Three different diets were provided to each group: high FC ratio (35:65) with low energy concentrate (HFC-LEC), high FC ratio with high energy concentrate (HFC-HEC), and low FC ratio (25:75) with high energy concentrate (LFC-HEC). After ten days of feeding the diets, the CH4 concentrations for all steers were measured and analyzed in duplicate according to the protocol established in Experiment 1. In Experiment 1, the mean correlation coefficient between the CH4 concentration from respiration and eructation was highest when a combination of two normal distributions was assumed (r = 0.79). The most appropriate measurement times were four times, two hours and one hour before, and one hour and two hours after morning feeding. Compared with LFC-HEC, HFC-LEC showed 49% and 57% higher CH4 concentrations in exhaled gas from respiration and eructation, respectively (P < 0.01). In conclusion, the LMD method can be applied to evaluate differences in CH4 emissions in cattle using the protocol established in this study.

In Vitro and In Vivo Evaluation of the Effects of a Compound Based on Plants, Yeast and Trace Elements on the Ruminal Function of Dairy Cows

The high production levels reached by the dairy sector need adjustment in nutritional inputs and efficient feed conversion. In this context, we evaluated a compound (QY—Qualix Yellow) combining optimized inputs in trace elements and 20% MIX 3.0. In a first step, the effects of MIX 3.0 on ruminal function were assessed in vitro by incubating ruminal fluid with the mixture at a ratio of 20:1. The results obtained encouraged us to test QY in vivo, on a herd of dairy cows. The herd was divided into one group of 19 dairy cows receiving the compound and a control group of 20 animals conducted in the same conditions, but which did not received the compound; the production performance and feed efficiency of the two groups were compared. In vitro experiments showed improved digestion of acid and neutral detergent fibres by 10%. The propionate production was enhanced by 14.5% after 6 h incubation with MIX 3.0. The plant mixture decreased the production of methane and ammonia by 37% and 52%, respectively, and reduced the number of protozoa by 50%. An increase in milk yield by 2.4 kg/cow/d (p < 0.1), combined with a decrease in concentrate consumption of 0.27 kg DM/cow/d (p < 0.001), was observed in vivo after consumption of the compound. Sixty-six days after the beginning of the trial, methane emissions per kg of milk were significantly lower in the group receiving QY. In conclusion, MIX 3.0 induced change in ruminal function in vitro and, when it entered into the composition of the QY, it appeared to improve feed efficiency and production performance in vivo.

Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions

Methods and technologies enabling the estimation at large scale of important traits for the dairy sector are of great interest. Those phenotypes are necessary to improve herd management, animal genetic evaluation, and milk quality control. In the recent years, the research was very active to predict new phenotypes from the mid-infrared (MIR) analysis of milk. Models were developed to predict phenotypes such as fine milk composition, milk technological properties or traits related to cow health, fertility and environmental impact. Most of models were developed within research contexts and often not designed for routine use. The implementation of models at a large scale to predict new traits of interest brings new challenges as the factors influencing the robustness of models are poorly documented. The first objective of this work is to highlight the impact on prediction accuracy of factors such as the variability of the spectral and reference data, the spectral regions used and the complexity of models. The second objective is to emphasize methods and indicators to evaluate the quality of models and the quality of predictions generated under routine conditions. The last objective is to outline the issues and the solutions linked with the use and transfer of models on large number of instruments. Based on partial least square regression and 10 datasets including milk MIR spectra and reference quantitative values for 57 traits of interest, the impact of the different factors is illustrated by evaluating the influence on the validation root mean square error of prediction (RMSEP). In the displayed examples, all factors, when well set up, increase the quality of predictions, with an improvement of the RMSEP ranging from 12% to 43%. This work also aims to underline the need for and the complementarity between different validation procedures, statistical parameters and quality assurance methods. Finally, when using and transferring models, the impact of the spectral standardization on the prediction reproducibility is highlighted with an improvement up to 86% with the tested models, and the monitoring of individual spectrometer stability over time appears essential. This list inspired from our experience is of course not exhaustive. The displayed results are only examples and not general rules and other aspects play a role in the quality of final predictions. However, this work highlights good practices, methods and indicators to increase and evaluate quality of phenotypes predicted at a large scale. The results obtained argue for the development of guidelines at international levels, as well as international collaborations in order to constitute large and robust datasets and enable the use of models in routine conditions.