Accurate identification of cows' likelihood of conception during the period from recent calving to the first artificial insemination (AI) will provide assistance in managing the fertility of dairy cows and contribute to the economic prosperity and sustainability of the farm. The purpose of this study was to use FTIR spectroscopy collected from recent calving to the first artificial insemination (AI) to predict the cow's likelihood of conception to first AI, first or second AI. This study specifically focused on the role of FTIR spectral and farm data collected at different time windows in improving the accuracy of model for predicting the cow's likelihood of conception to first AI, first or second AI. From 2019 to 2023, fertility information of 10,873 Holstein dairy cows in China were collected, coupled with 21,928 spectral data. First, cows were classified into "good" and "poor." Strategy 1 (S1) defined "good" as cows conceived at first AI and "poor" as the others. Strategy 2 (S2) defined "good" as cows conceived at first or second AI and "poor" as the others. Second, the partial least squares discriminant analysis was used to establish models for predicting the likelihood of conception to first AI, first or second AI. The model was assessed using a cross-validation (CV) set and herd-independent-external validation (HEV) set. The study also focused on examining the potential correlation between the accuracy of prediction and the period of spectral and farm data collection by analyzing the diagnostic performance of the model at 8 different time windows: '0 to 7 d postpartum (dpp)', '8 to 14 dpp', '15 to 21 dpp', '22 to 30 dpp', '31 to 45 dpp', '46 to 60 dpp', ' ≥ 61 dpp', and '0 to 7 d before the first AI'. The results showed that the model based on S1 performed better while in proximity to the first AI with AUCCV and AUCHEV of 0.621 and 0.633, respectively. Models based on S2 exhibited superior performance throughout the late phase of uterine involution. The optimal model was developed by using spectral data collected from '22 to 30 dpp'. The AUCCV and AUCHEV were 0.644 and 0.660, respectively, which was higher than that of S1. This study demonstrates the potential of using FTIR spectral data to predict the cow's ability to conceive. The model developed from data collected within a certain time window exhibited better prediction accuracy, particularly during 22 to 30 dpp and 0 to 7 d before the first AI. This study offers novel perspectives on alternate approaches for assessing the fertility of cows, which will contribute to the regularization and sustainability of farms, as well as to the precision management of agriculture.
Keywords: Dairy cattle; fertility; fourier transformed infrared spectroscopy; likelihood of conception; machine learning.
© 2025, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).