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 AUC_CV and AUC_HEV 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 AUC_CV and AUC_HEV 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.

Mastitis significantly impacts both the yield and quality of milk. The somatic cell count (SCC) and differential somatic cell count (DSCC), which are related to immune cells, are primary indicators for assessing mammary gland health. In this study, eight previously established mid-infrared spectroscopy models were utilized to predict the content of milk protein fractions (αs1-CN, β-CN, κ-CN, total CN, α-LA, β-LG, IgG, and LF) in milk samples from 21,388 lactating cows across 33 herds. Four linear mixed models were applied to analyze the secretion patterns of milk protein fractions by days in milk (DIM) and parity, their variations under different mastitis conditions, and their associations with the somatic cell score (SCS), DSCC, and immune cell counts (PMN + LYM score (PMN + LYMS) and MAC score (MACS)). The primary findings of the investigation comprised the following: (1) IgG was higher in early lactation, decreased with advancing lactation days, and slightly increased in late lactation, while seven other protein factions decreased from early to peak lactation and increased during mid-to-late lactation. Parity influenced all milk protein fractions except αs1-CN, with total CN, β-CN, and α-LA decreasing and κ-CN, β-LG, IgG, and LF increasing as parity increased (p < 0.05). (2) Mastitis significantly reduced the milk yield, fat percentage, protein percentage, and the contents of total CN, β-CN, κ-CN, and α-LA while increasing β-LG, IgG, and LF. (3) The SCS was negatively correlated with milk yield and α-LA but positively correlated with the fat percentage, protein percentage, κ-CN, β-LG, IgG, and LF. (4) When the DSCC increased to 50%, the milk yield decreased, while the milk protein percentage and κ-CN content significantly increased (p < 0.05). When the DSCC exceeded 50%, the fat percentage, protein percentage, total casein, αs1-CN, β-CN, κ-CN, β-LG, IgG, and LF decreased, while the α-LA content increased (p < 0.05). (5) When the PMN + LYMS increased, the milk yield and α-LA content rose, while the milk fat percentage, the milk protein percentage, and the contents of αs1-CN, β-CN, κ-CN, total CN, β-LG, IgG, and LF decreased (p < 0.05). Conversely, when the MACS increased, the milk yield and α-LA content declined, whereas the milk fat percentage, the milk protein percentage, and the contents of αs1-CN, β-CN, κ-CN, total CN, β-LG, IgG, and LF increased (p < 0.05). This study offers valuable insights into enhancing milk product quality, advancing the early diagnosis and mechanistic research of bovine mastitis, and the sustainable development of the dairy farming industry.

Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) can be used for quantitative detection of milk components. Here, milk samples of 458 Chinese Holstein cows from 11 provinces in China were collected and we established a total of 22 quantitative prediction models in milk fatty acids by FT-MIRS. The coefficient of determination of the validation set ranged from 0.59 (C18:0) to 0.76 (C4:0). The models were adopted to predict the milk fatty acids from 2138 cows and a new high-throughput computing software HiBLUP was employed to construct a multi-trait model to estimate and analyze genetic parameters in dairy cows. Finally, genome-wide association analysis was performed and seven novel SNPs significantly associated with fatty acid content were selected, investigated, and verified with the FarmCPU method, which stands for “Fixed and random model Circulating Probability Unification”. The findings of this study lay a foundation and offer technical support for the study of fatty acid trait breeding and the screening and grouping of characteristic dairy cows in China with rich, high-quality fatty acids. It is hoped that in the future, the method established in this study will be able to screen milk sources rich in high-quality fatty acids.

Fatty acid is an important factor affecting the nutritional quality of milk. In this study, we collected and assessed 78,086 milk samples from 12,065 Chinese Holstein cows from 11 farms in Northern China from November 2019 to September 2022. The contents of eight fatty acid groups were predicted using FT-MIRS-based models. The contents of TFAs, SFAs, UFAs, MUFAs, PUFAs, and LCFAs in milk reached the highest at 96–125 DIM, and SCFA and MCFA contents reached the highest at 276–305 DIM. With the increase in somatic cell score, the contents of various fatty acid groups in milk gradually decreased, and the nutritional value of milk and flavor of dairy products gradually deteriorated. The contents of high-quality fatty acids in milk, particularly UFAs and MUFAs, were significantly higher in the non-pregnant state than in the pregnant state. However, SCFA and MCFA contents exhibited the opposite pattern. Our findings provided valuable information on the content and distribution range of fatty acid groups in milk from Chinese Holstein cows. Further analysis is warranted to explore the breeding of Chinese Holstein cows providing milk with abundant beneficial fatty acids.

Mineral content is an important nutrient component in milk. At present, there is not much research and application on the ecological mineral profiles of milk, especially in the development and utilization of the dominant milk source in Xinjiang. This study uses a mid-infrared spectroscopy (MIRS) model to predict the mineral content of seven key minerals in milk, explores the secretion patterns and characteristics of mineral profiles in milk, provides production methods for the efficient utilization of high-calcium milk sources, and analyzes the possible economic benefits. The results indicate that the mineral content of milk in Xinjiang has advantages over that of other regions of China. Mineral profiles’ characteristics in milk are influenced by the parity, days of lactation, sampling season, calving season, and breast health status. Moreover, there are correlations between different minerals. Milk with higher calcium content also has elevated levels of other minerals and regular milk components (milk protein and milk fat). Therefore, such milk may serve as a reference for producing season-dependent high-calcium milk sources. If native calcium content above 1300 mg/kg, as identified in this study, was used to produce high-calcium pasteurized fresh milk and premium high-calcium pasteurized fresh milk, the dairy industry could see a significant increase in economic benefits. This study provides a foundation for the production of characteristic milk sources and diversified dairy products in Xinjiang. It also lays the groundwork for understanding the secretion patterns and mechanisms of minerals in milk.

This study was performed to evaluate the effects of maternal 25-hydroxycholecalciferol (25-OH-D₃) supplementation on sow performance, as well as the dynamic alterations of both the amino acid and fatty acid profiles in milk. On day 85 of gestation, twenty primiparous hybrid sows were allocated into two groups (10 sows/group) and fed a basal diet (3200 IU/kg vitamin D₃) containing either 0 or 50 μg/kg 25-OH-D₃ until weaning on d 21 of lactation. Milk was collected at 1, 3, 7, 14, and 21 d of lactation. The results showed that dietary 25-OH-D₃ supplementation notably decreased the score of tear stain at 101 d of gestation when compared to the Ctrl group (p = 0.030). No significant difference was found in terms of the gestation day, litter size, and litter weight at birth, whereas maternal 25-OH-D₃ intervention notably increased weaning weight and weight gain of the piglet (p < 0.05), while dietary 25-OH-D₃ supplementation contributed to a 16.4% body gain during lactation. The concentration of all amino acids in milk was higher in colostrum, following a dramatic drop. The rate of reduction for all amino acids was increased by dietary 25-OH-D₃ supplementation. The contents of saturated fatty acids and polyunsaturated fatty acids were increased and decreased linearly throughout lactation (both p < 0.05). Dietary 25-OH-D₃ supplementation initially suppressed both saturated and unsaturated fatty acid levels from d 1 to 7, while prompting a recovery of specific fatty acids from d 14 to 21 of lactation, particularly oleic acid, linoleic acid, and arachidonic acid. These findings indicate that maternal 25-OH-D₃ supplementation alters the pattern of milk fatty acid and amino acid composition, which may be associated with the observed improvement in piglet outcomes.