Predicción de la producción diaria de leche en bovinos Gyr a través de métodos de aprendizaje supervisado

Abstract

The Asociación Colombiana de Criadores de Ganado Cebú - ASOCEBU, has interest in developing a machine to predict total daily milk yield using partial production measurements in Gyr cattle and, in particular, answering two questions: 1) can a reference predictive method be outperformed by locally developed methods? 2) which one of the two partial records (AM or PM) has a better predictive performance? Therefore, the objective of this paper was to develop a predictive machine for daily milk yield in Gyr cattle using partial records, milking interval, days in milk, and parity (n=13806), by implementing supervised learning methods. Besides the reference predictive machine, several combinations of input variables and model or learning method were considered. Arti cial neural networks, support vector machines, random forests, and linear regression with location parameters estimated via least squares, or the shrinkage methods Ridge and Lasso were used. The predictive performance (PP) was assessed through crossvalidation using the following error functions: square root of mean square error (RMSE) and mean absolute error (MAE). It was found that an arti cial neural network with a single hidden layer and the AM partial record, milking interval, parity and days in milk as input variables had the best PP (RMSE=1.5042, MAE=1.1389), but in general, the performance of the methods was similar. All machines whose parameters were learned using local data outperformed the reference method and the morning partial records showed a better PP than those from the afternoon. These results permit guiding ASOCEBU's milk control program and generate a "tailormade" method to predict total daily milk yield of Gyr cattle in Colombia, a relevant component of the genetic improvement and productivity modelling programs of this breed.