Artificial neural networks simulation to define critical temperature of Fries Holland based on physiological responses

Artificial Neural Networks (ANN) simulation for industrial engineering is used to define critical temperature of Fries Holland (FH) heifer based on physiological responses on models to predict heart rate and respiratory rate, using ambient temperature and humidity inputs. The research was conducted using six dairy cattles in Bogor and in Jakarta. The heifers were fed at 6 am and 3 pm daily. The environmental condition (Ta, Rh, THI, and Va) and physiological responses (heart rate and respiration rate) were then measured for 14 days in two months at 1 h intervals started from 5 am to 8 pm. By using this ANN simulation, the critical temperature for FH heifer were defined, from heart rate at Ta 24,5°C and Rh 78% at Bogor, and at Ta 23,5°C and Rh 88% at Jakarta, from respiratory rate at Ta 22,5°C and Rh 78% at Bogor, and at Ta 23,5°C and Rh 78% at Jakarta. The respiratory rate on FH heifer was more sensitive to stress due to Ta and Rh fluctuation than the heart rate. Key Words: Artificial Neural Network, Critical Temperature, Heifer, Physiological Respon