Comparison of methane production, intensity, and yield throughout lactation in Holstein cows.

Genetic selection to reduce methane (CH4) emissions from dairy cows is an attractive means of reducing the impact of agricultural production on climate change. In this study, we investigated the feasibility of such an approach by characterizing the interactions between CH4 and several traits of interest in dairy cows. We measured CH4, dry matter intake (DMI), fat- and protein-corrected milk (FPCM), body weight (BW), and body condition score (BCS) from 107 first- and second-parity Holstein cows from December 2019 to November 2021. Methane emissions were measured using a GreenFeed device and expressed in terms of production (MeP, in g/d), yield (MeY, in g/kg DMI), and intensity (MeI, in g/kg FPCM). Because of the limited number of cows, only animal parameters were estimated. Both MeP and MeI were moderately repeatable (>0.45), whereas MeY presented low repeatability, especially in early lactation. Mid lactation was the most stable and representative period of CH4 emissions throughout lactation, with animal correlations above 0.9. The average animal correlations of MeP with DMI, FPCM, and BW were 0.62, 0.48, and 0.36, respectively. The MeI was negatively correlated with FCPM (<-0.5) and DMI (>-0.25), and positively correlated with BW and BCS. The MeY presented stable and weakly positive correlations with the 4 other traits throughout lactation, with the exception of slightly negative animal correlations with FPCM and DMI after the 35th week. The MeP, MeI, and MeY were positively correlated at all lactation stages and, assuming animal and genetic correlations do not strongly differ, selection on one trait should lead to improvements in all. Overall, selection for MeI is probably not optimal as its change would result more from CH4 dilution in increased milk yield than from real decrease in methane emission. Instead, MeY is related to rumen function and is only weakly associated with DMI, FPCM, BW, and BCS; it thus appears to be the most promising CH4 trait for selection, provided that this would not deteriorate feed efficiency and that a system of large-scale phenotyping is developed. The MeP is easier to measure and thus may represent an acceptable alternative, although care would need to be taken to avoid undesirable changes in FPCM and BW.

Short communication: Correlation of methane production, intensity, and yield with residual feed intake throughout lactation in Holstein cows.

The environmental impact of dairy production can be reduced in several ways, including increasing feed efficiency and reducing methane (CH4) emissions. There is no consensus on their relationship. This study aimed at estimating the correlations between residual feed intake (RFI) and CH4 emissions expressed in g/d methane production (MeP), g/kg of fat- and protein-corrected milk methane intensity (MeI), or g/kg of DM intake methane yield (MeY) throughout lactation. We collected CH4 data using GreenFeed devices from 107 Holstein cows, as well as production and intake phenotypes. RFI was predicted from DM intake, fat- and protein-corrected milk, BW, and body condition score. Five-trait random regression models were used to estimate the individual variance components of the CH4 and production traits, which were used to calculate the correlations between RFI and CH4 traits throughout lactation. We found positive correlations of RFI with MeP and MeI ranging from 0.05 to 0.47 throughout the lactation. Correlations between RFI and MeY are low and vary from positive to negative, ranging from -0.18 to 0.17. Both MeP and MeI are favorably correlated with RFI, as is MeY during the first half of lactation. These correlations are mostly favorable for genetic selection, but the confirmation of these results is needed with genetic correlations over a larger dataset.

Combining short-term breath measurements to develop methane prediction equations from cow milk mid-infrared spectra.

Predicting methane (CH4) emission from milk mid-infrared (MIR) spectra provides large amounts of data which is necessary for genomic selection. Recent prediction equations were developed using the GreenFeed system, which required averaging multiple CH4 measurements to obtain an accurate estimate, resulting in large data loss when animals unfrequently visit the GreenFeed. This study aimed to determine if calibrating equations on CH4 emissions corrected for diurnal variations or modeled throughout lactation would improve the accuracy of the predictions by reducing data loss compared with standard averaging methods used with GreenFeed data. The calibration dataset included 1 822 spectra from 235 cows (Holstein, Montbéliarde, and Abondance), and the validation dataset included 104 spectra from 46 (Holstein and Montbéliarde). The predictive ability of the equations calibrated on MIR spectra only was low to moderate (R2v = 0.22-0.36, RMSE = 57-70 g/d). Equations using CH4 averages that had been pre-corrected for diurnal variations tended to perform better, especially with respect to the error of prediction. Furthermore, pre-correcting CH4 values allowed to use all the data available without requiring a minimum number of spot measures at the GreenFeed device for calculating averages. This study provides advice for developing new prediction equations, in addition to a new set of equations based on a large and diverse population.

Codependency: a disorder separate from chemical dependency.

This study examined the relationship between codependency (an excessive preoccupation with the lives, feelings, and problems of others), chemical dependency of a significant other, and depression. The Significant Others' Drug Use Survey (SODS) determined whether the subject was in a relationship with a significant other at risk of being chemically dependent. Beck's Depression Inventory (BDI) was used to assess depression. Two hypotheses were tested: first, that codependency exists independently of chemical dependency and, second, that codependent people tend to be more depressed than non-codependents. Results supported the first hypothesis, but not the second. A significant correlation between depression and having a significant other likely to be chemically dependent was observed. The usefulness of the concept of codependency is discussed with proposals for subsequent research.