Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep.

As the amount of data on farms grows, it is important to evaluate the potential of artificial intelligence for making farming predictions. Considering all this, this study was undertaken to evaluate various machine learning (ML) algorithms using 52-year data for sheep. Data preparation was done before analysis.

Multivariate quantitative genetic analysis of body weight traits in Corriedale sheep.

In the present study, an attempt was made to elucidate the genetic parameters for body weight traits of lambs from Corriedale sheep population at different ages. Data were collected from 6874 lambs born over a span of 49 years from 1969 to 2017. The traits under study included body weight at birth (BW), weaning (WW), 6 months of age (6MW), 9 months of age (9MW) and yearling stage (YW).

Estimation of (Co)Variance components and genetic parameters of fibre traits in Rambouillet sheep using multi-trait analysis.

The present study aimed to estimate the genetic parameters of different fibre traits, viz., greasy fleece weight, staple length, and fibre diameter in Rambouillet sheep population using a multi-trait animal model. Data, spanning over 10 years (1998-2007) and pertaining to fibre traits at first clip, were collected for a total of 4186 Rambouillet sheep maintained at an organized farm.