Optimizing the Genomic Evaluation Model in Crossbred Cattle for Smallholder Production Systems in India
Implementing genomic selection in smallholder dairy systems is challenging due to limited genetic connectedness and diverse management practices. This study aimed to optimize genomic evaluation models for crossbred cattle in South India. Data included 305-day first lactation milk yield (FLMY) record...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/9/945 |
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| Summary: | Implementing genomic selection in smallholder dairy systems is challenging due to limited genetic connectedness and diverse management practices. This study aimed to optimize genomic evaluation models for crossbred cattle in South India. Data included 305-day first lactation milk yield (FLMY) records from 17,650 cows (1984–2021), with partial pedigree and genotypes for 1004 bulls and 1568 cows. Non-genetic factors such as geography, season and period of calving, and age at first calving were significant sources of variation. The average milk yield was 2875 ± 123.54 kg. Genetic evaluation models used a female-only reference. Heritability estimates using different approaches were 0.32 ± 0.03 (REML), 0.40 ± 0.03 (ssGREML), and 0.25 ± 0.08 (GREML). Bayesian estimates (Bayes A, B, C, Cπ, and ssBR) ranged from 0.20 ± 0.02 to 0.43 ± 0.04. Genomic-only models showed reduced variance due to the Bulmer effect, as genomic data belonged to recent generations. Breeding value prediction accuracies were 0.60 (PBLUP), 0.45 (GBLUP), and 0.65 (ssGBLUP). Using the LR method, the estimates of bias, dispersion, and ratio of accuracies for ssGBLUP were −39.83, 1.09, and 0.69; for ssBR, they were 71.83, 0.83, and 0.76. ssGBLUP resulted in more accurate and less biased GEBVs than ssBR. We recommend ssGBLUP for genomic evaluation of crossbred cattle for milk production under smallholder systems. |
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| ISSN: | 2077-0472 |