Variance components estimation for methane emission in smallholders’ dairy farms
Abstract
Enteric methane (CH4) emissions from cattle account for 70% of livestock GHG emissions in Sub-Saharan Africa. Also, climate change has impact on smallholder livestock-based food systems in terms of feed resources and emergence of new diseases. Direct selection for CH4 is one of the approaches to mitigate the effects of climate change and this requires estimation of genetic parameters. Moreover, the amount of CH4 emitted is influenced by the activity status (ACTs) of the cow such as feeding, ruminating, sleeping, and standing idle during time of measurement. The aim of this study was to evaluate CH4 emissions under different activities, estimate variance components and compare accuracies of predicting CH4 emissions using MIR information.The data consistent of over 14500 point-measurement of methane emissions measured by laser methane detectors with minimum duration of 3 minutes from 940 cows in 29 small-holders dairy farms in Ethiopia under various cow activities from July 2023 to March 2025. Records obtained under different ACTs for feeding, ruminating, sleeping, and standing idle were 2382, 7885, 660, and 3494 respectively. Pedigree information was also available for 435 cows with observation for CH4 and the remaining 459 cows were genotyped using a 90k SNP chip. Overall average CH4 production was 341 g/day. CH4 production in feeding status was highest with 517 g/day on average. Pedigree BLUP (PBLUP), and single step combining both pedigree and genomic information (HBLUP) were applied to estimate variance components (VCs) using different modelling approaches. A repeatability animal model (full model (FM)) was fitted with ACTs, year-season, and average farm milk yield as fixed factors and permanent environmental effects a random effect in addition to animal. Also, records averaged within year-season subclasses (average model) were also analyzed with fixed effects of year-season and average farm milk yield and random effects of animal and permanent environmental effects. Heritability estimates for the FM were 0.09 (0.03), and 0.10(0.02) for PBLUP and HBLUP, respectively. The corresponding estimates for the average model were 0.14 (0.06), and 0.19 (0.04). For the indirect prediction of CH4, a partial least square modelling approach was applied using milk mid-infrared data obtained in one week period around the CH4 measurements. The model with data restricted only to cows feeding gave higher prediction accuracy of 0.41 compared to 0.28 when using all data. In summary, heritabilities were low and consistent with published estimates, indirect predictions accuracy of CH4 were moderate. In general, feeding status not only had the highest production average but also highest prediction accuracy and has influence on genetic parameters.
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