An algorithm-based approach for identification of most relevant linear traits for selecting high producing Murrah buffaloes

Abstract

Selection of high producing dairy animals is important for dairy profitability and future breeding stock. Thefarmers have relied on physical characters for identification of milk producing ability in animals. In the presentstudy feature selection algorithm were implemented to identify most relevant traits for prediction of peak milk yieldin buffaloes. Based on data recorded from 259 lactating Murrah buffaloes, 14 different body and udder conformation traits, viz. Body Length (BL), Height at Wither (HW), Heart Girth (HG), Body Depth (BD), Paunch Girth (PG), Naval-Udder Distance (NUD), Udder Depth (UD), Rear Udder Height (RUH), Fore Teat Distance (FTD), RearTeat Distance (RTD), Fore Rear Teat Distance (FRTD), Teat Length (TL), Rump Width (RW) and Rear UdderWidth (RUW) were selected. Descriptive statistical analysis revealed that the correlation with peak yield is highestfor RUH, followed RUW, lactation number (LN), NUD, FRTD, HG, RW, RTD, UD, TL, PG, BL, BD, HW andFTD. Correlation-based feature selection in ‘WEKA’ software platform suggested that nine parameters have highcorrelation with peak yield – UD, NUD, RTD, FRTD, TL, RW, RUW, RUH and TL. The Multiple linear regression(MLR) was implemented using the linear regression function available under function classifier in WEKA. TwoRegression models (Model 1 and Model 2) were developed using all fifteen input parameters and with subset of 9input parameters suggested in ‘feature selection’. All models were trained and validated with 10-fold cross validation method. The performance of models developed for prediction peak milk yield was evaluated using the metrics correlation coefficient and root mean squared error (RMSE). Comparison of the performance evaluation matrices revealed that the Model 2 requiring lesser number of inputs is good enough in predicting peak yield with 0.8429 correlation coefficient and 2.16 root mean squared error.

Serum exosomes of pregnant and non-pregnant buffaloes revealed protein signatures of conceptus development and events of the non-fertile cycle

“…The few up-regulated proteins one day after delivery were conagglutinin, apolipoprotein A-II, deoxyhemoglobin, and ECM1, and down-regulated proteins were haptoglobin and lipopolysaccharide-binding proteins when compared to day 21 before delivery. Up-regulation of at least 65 protein spots has been reported in major proteins depleted serum proteome analysis during different stages of early pregnancy in buffaloes 34 . This study claimed some of the identi ed spots as promising pregnancy biomarkers, especially synaptojanin-1, apolipoprotein A-1, apolipoprotein B, keratin 10, and Von Willebrand factors, that have a known role in embryogenesis and early pregnancy maintenance 34 .…”

“…Up-regulation of at least 65 protein spots has been reported in major proteins depleted serum proteome analysis during different stages of early pregnancy in buffaloes 34 . This study claimed some of the identi ed spots as promising pregnancy biomarkers, especially synaptojanin-1, apolipoprotein A-1, apolipoprotein B, keratin 10, and Von Willebrand factors, that have a known role in embryogenesis and early pregnancy maintenance 34 . The differences in protein pro les in the serum exosome fraction at days 30 and 60 of early pregnancy has signi cance as during this period de nitive attachment of placenta occur.…”

“…The differences in protein pro les in the serum exosome fraction at days 30 and 60 of early pregnancy has signi cance as during this period de nitive attachment of placenta occur. We preferred isolation of protein from serum rather than depletion of the most abundant serum albumin and immunoglobulins proteins using an immuno-a nity matrix to improve the signal of low abundant proteins 34 . The major protein depletion approach as followed in human, rodent, and cattle studies 35 has enormous drawbacks.…”

Effect of prostaglandin treatment on the estrus behaviour, follicular and luteal morphometry and serum hormone profile in sub‐estrus buffaloes during non‐breeding season

“…Buffaloes have significant economic importance in the tropical and subtropical regions, primarily for their contributions to milk, meat, leather, and draught power (Balhara et al., 2017). In India, a total of 109.85 million buffaloes contribute to 44.81% of total milk and 17.94% of total meat produced annually and play a key role in the national economy (20th Livestock Census, 2019).…”

Influence of additive and maternal effects on production and reproduction traits in Murrah buffaloes: Insights from Bayesian analysis

“…Murrah, nevertheless, being crowned as the ‘black gold’, the buffaloes in the country form the backbone of the dairy sector. Buffaloes with inherited advantages such as better feed conversion efficiency, sustainable on poor feed and forage quality, rich nutritive value of milk, better adaptability to harsh environment and high disease resistance in comparison with crossbred cows are preferred by livestock farmers in India (Balhara et al., 2017). The well appreciated performance of the breed under various climatic conditions has paved way for the breeders to explore the possible ways to bring an overall genetic improvement in Murrah buffaloes.…”