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The present investigation was carried out on 216 records of Osmanabadi goat and 208 records of Deccani sheep. The relative efficiency of three different methods, viz. Shaffer's formula method, multiple linear regression and artificial neural network for prediction of live body weight were investigated. Individual animals were weighed on electronic weighing balance along with their body measurements like lengths, height and girths were measured. The explanatory variables were body lengths, body height and chest girths while dependant variable was body weight. A multilayer feed forward neural network with back propagation of error learning mechanism was developed using artificial neural network using bayesian regularization algorithms. It was observed that artificial neural network was best fitted with in goat and sheep, with the adjusted R2 of 0.93, explained by its linear relationship with the explanatory variables in goat. However, the prediction accuracy (R2 value) was observed as 94.21% with 2.35 kg error. While in sheep, the adjusted R2 was 0.82 and the prediction accuracy (R2 value) was observed as 85.29% with 3.48 kg error. The multiple linear regressions observed the adjusted R2 of 0.894, and the prediction accuracy (R2 value) as 90.03% with 4.73 kg error. The correlation coefficients for different body measurements using three different methods were ranged from 0.952 to almost one.

The present study was carried out on 538 monthly milk yield records of Marathwadi buffaloes maintained at Cattle Breeding Farm, College of Veterinary and Animal Sciences, Udgir Dist-Latur Maharashtra spread over a period of 9 years. The monthly milk yield was recorded at 6th, 36th, 66th and so on till standard lactation with 30 days interval. The goodness of fit of 3 lactation curve models, viz. parabolic exponential function, inverse polynomial function and mixed log function were used on monthly milk yields in Marathwadi buffaloes. The highest coefficient of determination for fitting of lactation curve models was with mixed log function (97.63%), followed by parabolic exponential function (95%) and inverse polynomial function (82.70%). Further, the root mean square error was found least for mixed log function (1.70), followed by parabolic exponential function (3.55) and inverse polynomial function (4.61). It is therefore inferred that mixed log function fitted best for prediction and modeling of lactation curves based upon monthly milk yields.