Use of early-lactation milk Fourier-transform mid-infrared spectroscopy and farm data to predict the calving-to-conception interval in Chinese dairy cows

The potential of milk Fourier-transform mid-infrared (FTIR) spectroscopy in predicting cow fertility has been extensively examined, but largely based on the number of services per conception (NSC). Compared with NSC, Calving-to-conception interval (CCI) may be a more critical factor influencing the profitability, productivity, and sustainability of dairy herds, as it reflects days to first breeding, voluntary waiting period, NSC, and service intervals. Our objectives were to evaluate the ability of FTIR spectroscopy and farm data collected from early lactation to predict CCI length postcalving in Holstein cows from a highly productive TMR system. We also sought to identify the most informative milk sampling periods for CCI prediction. From January 2019 to December 2023, FTIR spectra records, cow information, milk recording information, and fertility information were collected from 28,434 Holstein cows within 13 dairy farms in China. First, cows were classified into long calving-to-conception interval (LCCI) and short calving-to-conception interval (SCCI) groups based on 2 strategies. Strategy 1 defined LCCI as cows with a CCI longer than 150 d and SCCI as cows with a CCI shorter than 150 d. Strategy 2 employed a similar method but with a CCI threshold of 90 d. Second, partial least squares discriminant analysis was employed to develop prediction models for the classification of LCCI and SCCI cows. The performance of models was assessed using herd-independent cross-validation. These analyses were conducted separately for the complete dataset as well as for each of the 9 subsets stratified based on postcalving time windows. The results showed that the area under the receiver operating characteristic curve of cross-validation (AUC_CV) varied from 0.461 to 0.675 across different predictors, strategies, and time windows. Across all strategies, prediction accuracy was highest for models developed using data from time windows 22 to 30 days postpartum (dpp) and >60 dpp. The classification model, developed using standard normal variate preprocessed spectra, cow information, milk yield, SCS, and fat-to-protein ratio (FPR) data collected from the time window 22 to 30 dpp, demonstrated the best performance in strategy 1. The values of AUC_CV, sensitivity of cross-validation (SENS_CV), and specificity of cross-validation (SPEC_CV) were 0.650, 0.519, and 0.706, respectively. The best-performing classification model based on strategy 2 was developed using Savitzky-Golay preprocessed spectra, cow information, milk yield, SCS, and FPR data collected from the time window >60 dpp, with AUC_CV, SENS_CV, and SPEC_CV values of 0.675, 0.552, and 0.712, respectively. In conclusion, FTIR and farm data collected from early lactation could distinguish between cows with different CCI lengths with moderate accuracy. The time window 22 to 30 dpp could provide more effective and accurate predictions for the future fertility of dairy cows, and its use and implementation should be considered in practical farm production. Our study highlights the future application of high-throughput phenotyping technologies in precision livestock farming and offers novel insights into alternate methods for assessing cow fertility.