RESUMEN
Similar to all dairy systems internationally, pasture-based dairy systems are under increasing pressure to reduce their greenhouse gas (GHG) emissions. Ireland and New Zealand are 2 countries operating predominantly pasture-based dairy production systems where enteric CH4 contributes 23% and 36% of total national emissions, respectively. Ireland currently has a national commitment to reduce 51% of total GHG emissions by 2030 and 25% from agriculture by 2030, as well as striving to achieve climate neutrality by 2050. New Zealand's national commitment is to reduce 10% of methane emissions by 2030 and between 24% and 47% reduction in methane emissions by 2050. To achieve these reductions, factors that affect enteric methane (CH4) production in a pasture-based system need to be investigated. The objective of this study was to assess the relationship between enteric CH4 and other animal traits (feed intake, metabolic liveweight, energy corrected milk yield, milk urea concentration, and body condition score [BCS]) in a grazing dairy system. Enteric CH4 emissions were measured on 45 late lactation (213.8 ± 29 d after calving) grazing Holstein-Friesian and Holstein-Friesian × Jersey crossbred cows (lactation number 3.01 ± 1.65, 538.64 ± 59.37 kg live weight, and 3.14 ± 0.26 BCS) using GreenFeed monitoring equipment for 10 wk. There was a training period for the cows to use the GreenFeed of 3 wk before the 10-wk study period. The average enteric CH4 produced in the study was 352 g ± 45.7 g per day with an animal to animal coefficient of variation of 13%. Dry matter intake averaged 16.6 kg ± 2.23 kg per day, while milk solids (fat plus protein) averaged 1.62 kg ± 0.29 kg per day. A multiple linear regression model indicated that each one unit increase in energy corrected milk yield, metabolic liveweight and milk urea concentration, resulted in an increase in enteric CH4 production per day by 3.9, 1.74, and 1.38 g, respectively. Although each one unit increase in BCS resulted in a decrease in 39.03 g CH4 produced per day. When combined, these factors explained 47% of the variation in CH4 production, indicating that there is a large proportion of variation not included in the model. The repeatability of the CH4 measurements was 0.66 indicating that cows are relatively consistently exhibiting the same level of CH4 throughout the study. Therefore, enteric CH4 production is suitable for phenotyping.
RESUMEN
Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.