The Influence of Climate in Predicting Cow Milk Production Curve with Artificial Neural Network

Subject Areas : Journal of Animal Biology

Niloofar Rasti Doost ¹ , Naser Emam jomeh kashan ² , Sonia Zakizadeh ³ , Ali Asghar Sadeghi ⁴ , Reza Azizinnejad ⁵

1 - Department of Animal Science, Science and Research Unit, Islamic Azad University, Tehran, Iran
2 - Department of Animal Science, Science and Research Unit, Islamic Azad University, Tehran, Iran
3 - National Animal Science Research Institute, Agricultural Research, Education and Development Organization, Karaj, Iran
4 - Department of Animal Science, Science and Research Unit, Islamic Azad University, Tehran, Iran
5 - Department of Animal Science, Science and Research Unit, Islamic Azad University, Tehran, Iran

Received: 2024-08-02 Accepted : 2024-09-05 Published : 2025-01-12

Keywords: Milk, Neural network, Milk curve, Climate, Test day,

Abstract :

The aim of the current research is to investigate the effect of climate change on the milk production curve and fat percentage and fat percentage and the rate of milk changes of Holstein cows in different climates from seven provinces. For this purpose, 70,000 test day records related to 22,471 Holstein cows were collected during the years 2001 to 2016. Using data from the nearest synoptic station, the climate temperature humidity index was calculated and compared using temperature and relative humidity. Due to the possibility of greater impact of longer periods of heat stress compared to shorter periods, the average of 1, 2 and 3 day were also calculated. In general, the changes caused by different climates were calculated through artificial networking using Neurosolution software. In general, the changes caused by different climates were calculated through artificial networking using Neurosolution software. The goodness of fit was determined using the coefficient of explanation r and the mean square error of the MSE model. Based on the results, the temperature humidity index (THI) was determined as the best criterion to investigate the climatic conditions of the studied area. Based on the results, the humidity and temperature index was determined as the best criterion to investigate the climatic conditions of the studied area. It has also been observed that the average temperature and relative humidity 2 days before recording can explain a greater share of the changes in production performance than on the day of recording alone. The results of this study showed that increasing the THI value reduced milk production and increased milk fat percentage.

 

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