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Prediction of calving to conception interval (days open) in dairy cows using recurrent neural networks

Published online by Cambridge University Press:  23 December 2025

Mahdi Ravakhah ,
Mohammad Alishahi  and
Mohammad Mahdi Gheysari Gholami
Mahdi Ravakhah*
Affiliation:
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran Research Center of Smart Distribution Networks, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Mohammad Alishahi
Affiliation:
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran Research Center of Smart Distribution Networks, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Mohammad Mahdi Gheysari Gholami
Affiliation:
Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
*
Corresponding author: Mahdi Ravakhah; Email: ravakhah@iau.ac.ir
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Abstract

This research paper addresses the hypothesis that sequence-based long short-term memory (LSTM) architectures improve the prediction of the next DO (days open) relative to a feed-forward multi-layer perceptron and a Cox model under strictly temporally valid predictors. Modern dairy farming can heavily benefit from optimising ‘days open’ for profitability and animal welfare. Machine learning can forecast this metric, improving farm management, disease prevention and culling decisions. This study used a dataset of 16,472 breeding records. The study compared the performance of feed-forward neural networks and two types of recurrent neural networks (RNNs). The results showed that LSTM most accurately forecasted the next ‘days open’. This demonstrates that RNN models, due to their ability to capture temporal patterns in the data, significantly outperform feed-forward and traditional statistical methods in terms of mean absolute error and concordance.

Keywords

days opendeep learningmachine learningprecision dairy farmingrecurrent neural networks

Information

Type
Research Article
Information
Journal of Dairy Research , First View , pp. 1 - 9
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Hannah Dairy Research Foundation.

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