Resumen

ROSERO-NOGUERA, Ricardo; BEDOYA-MAZO, Sebastián  y  POSADA-OCHOA, Sandra L.. Predicting forage dry matter intake in dairy cows by using accelerometers. Inf. tecnol. [online]. 2022, vol.33, n.4, pp.63-72. ISSN 0718-0764.  http://dx.doi.org/10.4067/S0718-07642022000400063.

This research study evaluated the use of accelerometers to determine grass dry matter intake (DMIg) in dairy cows. Knowing dry matter intake is essential to balance rations. Data from fifteen Holstein cows fitted with an accelerometer in the atlanto-occipital region were used to build and validate a predictive model. An artificial neural network (ANN) was constructed to predict DMIg based on the angular position of the head. Validation of ANN predictions was determined using statistical criteria. The results showed that ANN’s intake time estimate was 480.4(73 minutes/day. The DMIg observed and predicted by ANN was 13.1(1.8 and 13.8(2.1 kg/day, respectively. In conclusion, the residual distance between the observed and predicted values showed that by using accelerometers it was possible to predict the DMIg with a high degree of accuracy.

Palabras clave : cattle food; dry grass intake; ration balance; modeling; use of accelerometers.

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