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Medium and maximum air temperatures around the milk cowsheds were measured and these empirical data were used to create a neural prediction model evaluating the cows’ milk yield under varying thermal conditions. We found out that artificial neural networks were an effective tool supporting the process of short-term milk yield forecasting. An analysis of sensitivity to input variables performed for the generated neural model allowed for identifying the dominant input variable for the proposed neural model. The dominant variable was the maximum temperature of the day, a key risk factor of the heat stress in cows. Keywords : Neural modeling, milk yield, cows, heat stress, prediction. African Journal of Biotechnology Vol. 12(29), pp. 4707-4712

Neural prediction of cows milk yield according to environment temperature