Modeling lactation curve for genetic evaluation of Murrah buffaloes

Abstract

The present study aimed at the detection of suitable Lactobacillus species from dogs for usage as probiotic. A total of 67 rectal swabs from healthy pups were analyzed and 49 (73.1%) Lactobacillus isolates were identified based on morphological, biochemical characteristics and confirmed by genus specific PCR targeting 16S rRNA gene. A total of 20 isolates that showed strong aggregation, high cell surface hydrophobicity, acid and bile tolerance were screened for in-vitro antibacterial activity by agar well diffusion assay, where prominent inhibitory zones were observed against majority of the test pathogens. Reduction in antibacterial activity was noticed after neutralization, proteinase K and heat treatment of supernatants. Partial 16S rRNA nucleotide sequence analysis of seven Lactobacillus isolates that showed prominent in-vitro antibacterial activity revealed maximum sequence homology with Lactobacillus fermentum (for six isolates) and Lactobacillus agilis (for one isolate). The in-vitro antibacterial activity results highlighted the need for in-vivo studies in India to establish probiotic potential of canine faecal Lactobacillus species in the near future.

Modeling of lactation curves for prediction of standard lactation milk yield in Marathwadi buffaloes

“…However, Kumar (2007) and Singh et al (2015) observed 99.16 and 98.65% R 2 value for parabolic exponential model in Murrah buffaloes. Similar results were observed by Sahoo et al (2017) in Murrah buffaloes.…”

“…The average total lactation milk yield across different lactations is 1,118 kg and average milk fat is 8.8% ranging from 6.25 to 10.50% (Kataria et al 2012, NBAGR 2016. Modeling of lactation curves in dairy animals is important for prediction of milk yield, nutritional interventions, breeding management etc (Sahoo et al 2017). This property of the model can prove beneficial in case of incomplete lactation records.…”

Comparative efficiency of different multiple linear regression prediction equations of first lactation 305-day milk yield for sire evaluation in Murrah buffaloes

“…have made test-day milk yields a suitable alternative option to daily milk recording in buffaloes (Chakraborty et al, 2010;Singh and Tailor, 2013). The different methods of sire evaluation viz., least squares (LSQ), simple regressed least squares (SRLS), best linear unbiased prediction (BLUP) and restricted maximum likelihood method (REML) have been used by different workers (Kumar et al 2015, Sahoo et al 2017. For sire evaluation, a test-day milk yield model reduces residual variance by providing more information per sire by using all available test day records of sire's daughters and by detecting or adjusting for more environmental effects in the genetic evaluation model.…”

Assessment of lactation curve parameters for test-day milk yield, Fat% and SNF% in Murrah Buffalo

“…A dairy bovine with a stable or standard lactation curve (non-linear) are considered to be more persistent, compared to those animals who are having steep or skewed lactation curves. Recently, lactation curves are being used for genetic evaluations (Strucken et al 2011;Jamuna, 2015;Sahoo et al 2018) as the peak yield and days to attain peak yield in combination with shape of lactation curve can help us in deciding superior dairy animals, as area under a curve represents the yield. Lactation persistency is an another important dairy trait and is defined as the ability of a dairy bovine to maintain its milk at a higher level after she has attained the peak production during the lactation, in other words, if the rate of decline in yield is slow for a dairy animal, such an animal is considered to be a persistent producer.…”

“…Several parametric models, such as exponential decline function, inverse polynomial function, gamma type function, exponential function, Legendre polynomial function, and mixed log function have been developed to describe the lactation curve in dairy animals (Woods, 1967;Wilminks, 1987;Ali and Schaeffer, 1987;Kirkpatrick et al 1990;Beever et al 1991;Sherchand et al 1995;Grossman and Koops, 1999;Macciotta et al 2005;Vohra, 2010;Macciotta et al 2011;Vohra et al 2011;Prakash et al 2015;Jamuna 2015, Vohra et al 2017Sahoo et al 2018), differing mainly in the type of regression, in the number of parameters and in their degree of relationships with the main features of a typical lactation pattern, such as peak yield, days to attain peak yield, days in milk and persistency. The shape of lactation curve depicted by individual dairy animal is subjected to random variation and different functions vary in their goodness of fit to individual lactations in different species.…”