Background India is the largest milk producer globally, with the largest proportion of cattle milk production coming from smallholder farms with an average herd size of less than two milking cows. These cows are mainly undefined multi-generation crosses between exotic dairy breeds and indigenous Indian cattle, with no performance or pedigree recording. Therefore, implementing genetic improvement based on genetic evaluation has not yet been possible. We present the first results from a large smallholder performance recording program in India, using single nucleotide polymorphism (SNP) genotypes to estimate genetic parameters for monthly test-day (TD) milk records and to obtain and validate genomic estimated breeding values (GEBV). Results The average TD milk yield under the high, medium, and low production environments were 9.64, 6.88, and 4.61 kg, respectively. In the high production environment, the usual profile of a lactation curve was evident, whereas it was less evident in low and medium production environments. There was a clear trend of an increasing milk yield with an increasing Holstein Friesian (HF) proportion in the high production environment, but no increase above intermediate grades in the medium and low production environments. Trends for Jersey were small but yield estimates had a higher standard error than HF. Heritability estimates for TD yield across the lactation ranged from 0.193 to 0.250, with an average of 0.230. The additive genetic correlations between TD yield at different times in lactation were high, ranging from 0.846 to 0.998. The accuracy of phenotypic validation of GEBV from the method that is believed to be the least biased was 0.420, which was very similar to the accuracy obtained from the average prediction error variance of the GEBV. Conclusions The results indicate strong potential for genomic selection to improve milk production of smallholder crossbred cows in India. The performance of cows with different breed compositions can be determined in different Indian environments, which makes it possible to provide better advice to smallholder farmers on optimum breed composition for their environment.
Dairy farming plays a very important role in improving the economy of rural India. The study was conducted to explore the socio-economic profile of dairy farmers and farmers feedback about dairy development project. The survey was conducted to study the education status, family structure, education status and management of animals, different patterns of rearing of dairy animals and status of milk production. Data was collected from the 3000 dairy farmers of three states namely Maharashtra, Bihar and Uttar Pradesh during year 2016 .Concentration of poor farmers was relatively high in Bihar (35.5%), followed by Uttar Pradesh (30.9%) and 16.3% in case of Maharashtra. Average family size show 8.74 members per household in Bihar, 6.76 members in Uttar Pradesh and 6.17 members in Maharashtra. Results revealed that majority of the families were nuclear families. Main source of income was agriculture which includes livestock farming. As regards to the size of land owned, nearly 56% of the landowners were Marginal farmers (owning 0.1 -1 ha of land), 23% were small (1.1-2 ha) landowners while about 12% farmers owned above 2 ha of land. Literacy level was higher among farmers of Maharashtra (71.6%) as compared to Uttar Pradesh (65.8%) and Bihar (65.4%). Majority of the farmers followed mixed cropping system, Maximum number of cows and buffaloes were owned by the farmers of Maharashtra i.e. 3.37 cows and 1.42 buffaloes, followed by Uttar Pradesh (1.60 cows & 1.42 buffaloes) and Bihar (1.75 cows & 0.24 buffaloes).Farmers of Maharashtra owned maximum percentage of crossbred cows (90.97%), followed by Uttar Pradesh (83.4%) and Bihar (75.9%).Maharashtra farmers possessed maximum number of upgraded buffaloes (79.4%), followed by Bihar (55.7%) and Uttar Pradesh (51.5%). In study of average quantity of milk produced by cows was higher among the crossbred cows (10.18 litres), in indigenous cows it was (4.47 litres) and 4.23 liters in Non-Descript cows. The data shows the same pattern of milk produced across the three states with slight variation. In buffaloes, the average quantity of milk produced was observed to be higher among the upgraded buffaloes (8.42 litres) as compared to Non-Descript buffaloes (5.17 litres). Respondents appreciated the fact that due to dairy development project by BAIF their family and social status have increased.
The objective of the study was to identify the factors affecting variation in conception rate of buffaloes inseminated by Murrah bulls’ frozen semen under field conditions. Total of 18,396 insemination records pertaining to 11,793 buffaloes that were inseminated artificially at BAIF’s field AI centers during the period of June 2010 to December 2014 in 3 states. Logistic regression analysis was used to compute the odds ratio and probability of conception rate. Records were classified according to agroclimatic zones, lactation order, season of insemination and body condition score. Agroclimatic zones, lactation order and body condition score showed significant variation. The overall conception rate was 48.01%. Conception rate of western plain zone of Uttar Pradesh was higher than other zones under study. Body condition score 3 was favourable where probability of conception was 0.51. Conception rate of first parity buffaloes was lower than other parities with the probability of 0.46. Conception rate increased in second parity with probability 0.52. Highest conception rate was found in fourth parity with probability 0.53. There was marginal difference between second to fifth parity. Season of insemination did not affect conception rate, however, the distribution of artificial inseminations was higher during the favourable season than that during lean-season. It could be inferred that the factors like agroclimatic zones, lactation order and body condition score should be considered while evaluating the conception rates in buffaloes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.