Artificial neural networks for predicting first-lactation 305-day milk yield in crossbred cattle

2025-01-27T18:10:59+00:00

This study was conducted using the first-lactation records of 1092 Vrindavani crossbred cattle to compare the relative efficiency of an artificial neural network (ANN) versus multiple linear regression for predicting the first-lactation 305-day milk yield (FL305DMY). The two input sets used for predicting FL305DMY in the study were input set-1: first four monthly test-day milk yields, age at first calving, and peak milk yield; and input set-2: first four monthly milk yields, age at first calving, and peak milk yield. The ANN was trained using a backpropagation algorithm based on Bayesian regularisation, and the algorithm was tested using four sets of training and test data at ratios of 66.67:33.33, 75:25, 80:20, and 90:10. The results revealed that the coefficient of determination showed no regular trend with decreasing the test dataset. Nevertheless, the observed values were highest for the 90:10 ratio of training-test data for both input sets, with the lowest root mean square error. The ANN model outperformed the multiple linear regression model when predicting FL305DMY, with an accuracy of 79.09% for input set-1 and 83.67% for input set-2, with the lowest root mean square error values for both input sets. Therefore, the ANN model can be used as an alternative technique to predict FL305DMY in Vrindavani cows.

Heat stress in dairy cows: A review of abiotic and biotic factors, with reference to the subtropics

2025-01-27T18:43:26+00:00

Heat stress has been identified as one of the major challenges for livestock production. Global temperatures are steadily increasing, with South African temperatures increasing at nearly twice the global rate. Of the livestock used for food production, dairy cows are the most sensitive to thermal changes, which have detrimental effects on their health, welfare, and overall productivity. Several abiotic factors that influence the heat load experienced by the cow are not commonly included in thermal indices used to measure heat stress; these include solar radiation, wind speed, and soil quality. Furthermore, the thermal comfort zone of cows has been altered by years of intense selection for increased milk yield, causing cows to become heat stressed at lower temperatures. Considering the abiotic and biotic factors affecting the cow’s heat load, it can be argued that dairy cows in tropical and subtropical climates are experiencing constant heat stress. In this review, the abiotic and biotic factors influencing the heat load experienced by dairy cows are reviewed, along with the available thermal indices that can be utilised at farm level.

South African Journal of Animal Science

Artificial neural networks for predicting first-lactation 305-day milk yield in crossbred cattle

Heat stress in dairy cows: A review of abiotic and biotic factors, with reference to the subtropics