Background Rumen microbiota constitutes key determinants of productive lifespan in dairy cattle, a trait that lies at the heart of breeding program profitability. However, whether host genetic variation influences productive lifespan through its effects on the rumen microbiota remains largely unexplored. To address this, we generated and analyzed host genomic data, 16S rRNA sequences, metagenomic profiles, and longitudinal productive lifespan records from 333 dairy cows with divergent longevity. Results Rumen microbial community composition shifted significantly with increasing productive lifespan, characterized by a decline in firmicutes and an increase in proteobacteria , alongside reduced microbial diversity. We identified 28 core genera universally present across individuals and 113 keystone taxa through co-occurrence network analysis, which collectively represent functional hubs that may mediate host productive lifespan. Variance component analysis revealed substantial heritability in the rumen microbiome, with 82% of core genera and 87% of keystone taxa showing significant heritable signals. At the functional level, glycolysis/gluconeogenesis, quorum sensing, and the TCA cycle emerged as highly heritable pathways and exhibited strong genetic correlations with productive lifespan (|r_g| = 0.32–0.80). Productive lifespan itself was lowly heritable, yet the rumen microbiome explained 36.2% of its phenotypic variance-pointing to a major microbial contribution. Integrated microbiome-wide and genome-wide association studies identified four microbial genera and three host genetic variants significantly associated with productive lifespan. Multi-omics integration, combined with microbial genome assembly, further revealed that host genotypes influence productive lifespan indirectly through modulation of microbial metabolic pathways. Conclusions Integrating longitudinal multi-omics analyses, we uncovered a coordinated role for host genetics and rumen microbiota in regulating productive lifespan of dairy cattle, which lays the groundwork for genomically-informed, microbiome-targeted breeding strategies to enhance longevity, improve farm profitability, and support sustainable dairy production.

[Background]The Chinese Holstein is the primary dairy cattle breed in China,and its production performance is directly linked to the economic sustainability of the industry.Birth weight and weaning weight are key early-life traits used to evaluate calf health and future milk production potential.A systematic understanding of their genetic characteristics is essential for effective genetic improvement.However,comprehensive studies evaluating the genetic parameters,breeding values,and genetic trends of birth weight and weaning weight in large Chinese Holstein populations remain limited.[Objective]This study aimed to investigate the non-genetic factors influencing these traits,estimate their genetic parameters and breeding values,and provide a theoretical basis for efficient genetic selection in Chinese Holstein cattle.[Method]Complete production records from a large-scale Holstein farm in Hebei Province between 2017 and 2023 were collected.Following rigorous data cleaning and tracing of complete three-generation pedigrees,valid birth weight and weaning weight data were obtained from 15 672 Holstein calves,derived from 5 798 dams and 100 sires.The GLM procedure in SPSS 26.0 was used to analyze the effects of fixed factors(including birth year,birth season,parity,sex,and calving type)on birth weight and weaning weight.Variance components for both traits were estimated using a two-trait animal model with the AI-REML and EM algorithms in DMU software.Heritabilities and the genetic correlation between the two traits were subsequently calculated.Breeding values for all individuals were estimated using the BLUP method under an animal model.The effectiveness of breeding practices was evaluated by ranking sires based on estimated breeding values,comparing progeny performance,and analyzing genetic trends.[Result]Descriptive statistics indicated mean values of(40.05±4.53)kg for birth weight and(100.31±8.60)kg for weaning weight.Fixed effects analysis revealed that birth year,season,parity,sex,and calving type had highly significant effects(P＜0.01)on both traits.The direct heritability estimates were 0.57(0.04)for birth weight and 0.39(0.02)for weaning weight,indicating high heritability for both traits.A moderate-to-high positive genetic correlation(0.46±0.04)was observed between birth weight and weaning weight.Breeding value estimation demonstrated high accuracy,with progeny of the top ten sires showing significantly higher mean birth weight(40.26 kg)and weaning weight(104.41 kg)compared to those of the bottom ten sires(39.08 kg and 97.90 kg,respectively).Genetic trend analysis revealed considerable fluctuation in average breeding values for both traits over birth years,with an overall declining trend.[Conclusion]Birth weight and weaning weight in Chinese Holstein cattle are influenced by factors such as birth year and sex.The results confirm the high heritability of both traits and a moderate-to-high genetic correlation between them,supporting their simultaneous selection in breeding programs.Sires exhibiting superior performance in early growth traits were identified,and it is recommended to prioritize the use of bulls with high breeding values for weaning weight and moderate breeding values for birth weight in practical breeding.This study clarifies the genetic basis of early body weight traits in Chinese Holstein cattle,providing a theoretical foundation and data support for genetic improvement and the formulation of balanced breeding strategies for birth weight and weaning weight.

Aging is a spontaneous biological process involving intricate regulatory mechanisms over time. Studies in mice and humans indicate that the gut microbiota is closely linked to the aging process and plays an important role in it. However, the relationship between the rumen microbiota and aging in dairy cows remains unclear. In this study, we characterized rumen microbial differences across different parities in 341 dairy cows using 16S rRNA amplicon sequencing and identified microbial markers associated with productive lifespan (PL) and farm profitability through machine learning analysis. Our findings reveal that as parity increases, the rumen microbiota undergoes systematic succession: alpha diversity indices significantly decrease, microbial interactions weaken, the abundance of Proteobacteria increases, while the abundance of Bacteroidetes decreases in higher-parity cows. By integrating machine learning with 16S sequencing, we identified characteristic microbial markers predictive of PL and farm profitability. Specifically, the support vector regression model achieved a predictive performance with an area under the curve (AUC) of 0.788 and identified eight key genera associated with the PL of dairy cows. Meanwhile, the random forest (RFTEST) model attained an AUC of 0.763 and selected eight key microorganisms linked to the economic benefits of the farm, with fivefold cross-validation confirming the reliability of RFTEST. Combined with SHapley Additive exPlanations (SHAP) analysis, the genus-level taxa Eubacterium_hallii_group and Prevotella_7 can serve as indicator strains for PL and farm profitability in dairy cows. Therefore, alterations in the rumen microbiota may serve as a key driver of aging in dairy cows. This study aims to provide insights for improving PL and farm profitability through the modulation of rumen microbiota.IMPORTANCEIn the dairy industry, longevity is a critical economic trait that directly impacts overall farm profitability. Although dairy cows have a natural lifespan of approximately 20 years-with optimal productivity often extending beyond the fifth parity-their average PL is only about 2.7 parities. Identifying factors influencing PL is therefore crucial. Given the vital role of the rumen microbiota in regulating dairy performance, milk fat/protein synthesis, and other key physiological processes, elucidating its correlation with PL is essential for developing probiotic interventions to enhance longevity. Furthermore, early detection of aging-associated microbial signatures could facilitate proactive adjustments to feeding strategies. Notably, this is the first study to link parity-driven microbiome succession with PL prediction in dairy cattle. Consequently, by identifying microbial molecular markers linked to PL and potential probiotic targets, this study highlights promising opportunities to improve dairy cow health and advance sustainable dairy farming practices.

Mid-infrared spectroscopy (MIRS) is increasingly used as a rapid and effective analytical method for the quantitative prediction of detailed milk composition, such as minerals, fatty acids, and AA. These analyses require the transportation of samples to a certified laboratory. In this case, storage time may affect MIRS and its prediction results. This study aimed to determine the effect of milk storage time on MIRS and its predictions of AA content. A total of 373 individual milk samples for the development of AA content prediction equations were collected from 7 commercial dairy farms (dataset 1), and 103 individual milk samples for the analysis of the effect of storage time were collected from 2 farms (dataset 2). First, separate quantitative prediction models based on dataset 1 were developed for each AA using partial least squares regression; the accuracy of prediction was assessed using a cross-validation set. Second, repeatability and reproducibility of the predictions of AA were calculated using milk samples in dataset 2, whose spectra were measured once a day for 7 consecutive days after sampling storing at 4°C, to assess the effect of storage time on the consistency of MIRS predicted AA content. Moderate to high prediction accuracy of AA was achieved, with the ratio of performance to deviation of the cross-validation set and the R2 of the cross-validation set being in the range of 1.59 (Gly) to 2.39 (Leu), and from 0.58 (Gly) to 0.79 (Leu), respectively. Results demonstrated that the absorbance of spectral points was affected by milk storage time, especially in the absorption areas associated with fat, protein, lactose, SCC, urea, and acetone. However, the predictions of all the AA by MIRS were repeatable and reproducible across different milk storage times until 6 d after sampling, except for Tyr, His, and Phe, with repeatability and reproducibility both greater than or close to 90%. In conclusion, for milk stored at 4°C and preserved with bronopol, MIRS can provide relatively consistent and accurate predictions for AA content for 0 to 6 d after milk collection. However, a shorter storage time, such as within 3 d after collection, is recommended when conditions permit.

Gut microbiota has been established as a critical regulator of human longevity, but the mechanistic role of rumen microbiota in dairy cow productive lifespan remains unexplored. This study investigated differences in rumen microbial community structure and metabolic signatures in a longitudinal cohort of dairy cows with divergent productive lifespans, aiming to elucidate the correction and potential regulatory mechanisms governing dairy cow longevity through microbial-host metabolic reprogramming. Our longitudinal study identified critical trends linked to increasing parity in dairy cows: milk yield and rumen microbiota diversity declined progressively, with microbial communities restructuring to show 37

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.

The somatic cell count (SCC) and differential somatic cell count (DSCC) are proxies for the udder health of dairy cattle, regarded as the criterion of mastitis identification with healthy, suspicious mastitis, mastitis, and chronic/persistent mastitis. However, SCC and DSCC are tested using flow cytometry, which is expensive and time-consuming, particularly for DSCC analysis. Mid-infrared spectroscopy (MIR) enables qualitative and quantitative analysis of milk constituents with great advantages, being cheap, non-destructive, fast, and high-throughput. The objective of this study is to develop a dairy cattle udder health status diagnostic model of MIR. Data on milk composition, SCC, DSCC, and MIR from 2288 milk samples collected in dairy farms were analyzed using the CombiFoss 7 DC instrument (FOSS, Hilleroed, Denmark). Three MIR spectral preprocessing methods, six modeling algorithms, and three different sets of MIR spectral data were employed in various combinations to develop several diagnostic models for mastitis of dairy cattle. The MIR diagnostic model of effectively identifying the healthy and mastitis cattle was developed using a spectral preprocessing method of difference (DIFF), a modeling algorithm of Random Forest (RF), and 1060 wavenumbers, abbreviated as “DIFF-RF-1060 wavenumbers”, and the AUC reached 1.00 in the training set and 0.80 in the test set. The other MIR diagnostic model of effectively distinguishing mastitis and chronic/persistent mastitis cows was “DIFF-SVM-274 wavenumbers”, with an AUC of 0.87 in the training set and 0.85 in the test set. For more effective use of the model on dairy farms, it is necessary and worthwhile to gather more representative and diverse samples to improve the diagnostic precision and versatility of these models.

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.

The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

Ketosis is a common metabolic disorder in the early lactation of dairy cows. It is typically diagnosed by measuring the concentration of β-hydroxybutyrate (BHB) in the blood. This study aimed to estimate the genetic parameters of blood BHB and conducted a genome-wide association study (GWAS) based on the estimated breeding value. Phenotypic data were collected from December 2019 to August 2023, comprising blood BHB concentrations in 45,617 Holstein cows during the three weeks post-calving across seven dairy farms. Genotypic data were obtained using the Neogen Geneseek Genomic Profiler (GGP) Bovine 100 K SNP Chip and GGP Bovine SNP50 v3 (Illumina Inc., San Diego, CA, USA) for genotyping. The estimated heritability and repeatability values for blood BHB levels were 0.167 and 0.175, respectively. The GWAS result detected a total of ten genome-wide significant associations with blood BHB. Significant SNPs were distributed in Bos taurus autosomes (BTA) 2, 6, 9, 11, 13, and 23, with 48 annotated candidate genes. These potential genes included those associated with insulin regulation, such as INSIG2, and those linked to fatty acid metabolism, such as HADHB, HADHA, and PANK2. Enrichment analysis of the candidate genes for blood BHB revealed the molecular functions and biological processes involved in fatty acid and lipid metabolism in dairy cattle. The identification of novel genomic regions in this study contributes to the characterization of key genes and pathways that elucidate susceptibility to ketosis in dairy cattle.

Buffalo mastitis detection and buffalo milk quality are of great importance to the world dairy industry. Somatic cell count (SCC) can be employed to assess mammary gland health and milk quality in dairy cows, but SCC detection is costly, and the detection instrument is expensive. The purpose of this study was to develop high and low SCC identification models using Fourier-transform mid-infrared spectrum (FT-MIRS), which could be used to diagnose subclinical mastitis (SCM) in buffalo and to determine the milk quality grade at a low cost. The dataset contained 899 buffalo milk samples collected from two regions in China. Firstly, the samples were divided into positive group above the threshold (SCM or unqualified milk) and negative group below the threshold (healthy or qualified milk) with SCC = 200 x 103 cells/mL (SCM in buffalo), 400 x 103 cells/mL (EU standard for raw cow milk, ES), 500 x 103 cells/mL (Indian standard for raw buffalo milk, IS), and 750 x 103 cells/mL (US standard for raw cow milk, US) as thresholds, respectively. Then, with FT-MIRS as predictive variables, predictive models were developed using Partial Least Squares Discriminant Analysis (PLSDA), Random Forest (RF), and Gradient Boosting Machine (GBM). The main results were as follows: the AUCval of the diagnostic model of SCM in buffalo using SCM criteria as a threshold was 0.84 (PLSDA). The AUCval values of the three buffalo milk quality classification models with ES, IS, and US as thresholds were 0.76 (PLSDA), 0.78 (GBM), and 0.84 (PLSDA), respectively. The predictive models established in this paper had a weak predictive ability for positive samples, and the "stepwise discriminant analysis" was recommended to improve the model application effect: The models were applied to classify samples as positive and negative, and then the samples with higher predictive probability were selected. Finally, the remaining samples were further differentiated using the reference methods of SCC detection. To conclude, FT-MIRS has the potential to predict buffalo mammary gland health status and buffalo milk quality grade, reduce the cost of SCC detection, and improve work efficiency, which will lay the foundation for rapid buffalo milk quality classification by raw milk regulatory authorities and rapid diagnosis of SCM in buffalo by farms.

Mastitis (MAS), endometritis (MET), and ketosis (KET) are prevalent diseases in dairy cows that result in substantial economic losses for the dairy farming industry. This study gathered 26,014 records of the health and sickness of dairy cows and 99,102 data of reproduction from 13 Holstein dairy farms in Central China; the milk protein and milk fat content from 56,640 milk samples, as well as the pedigree data of 37,836 dairy cows were obtained. The logistic regression method was used to analyze the variations in the prevalence rates of MAS, MET, and KET among various parities; the mixed linear model was used to examine the effects of the three diseases on milk production, milk quality, and reproductive traits. DMU software (version 5.2) utilized the DMUAI module in conjunction with the single-trait and two-trait animal model, as well as best linear unbiased prediction (BLUP), to estimate the genetic parameters for the three diseases, milk production, milk quality, and reproductive traits in dairy cows. The primary findings of the investigation comprised the following: (1) The prevalence rates of MAS, MET, and KET in dairy farms were 20.04%, 10.68%, and 7.33%, respectively. (2) MAS and MET had a substantial impact (p < 0.01) on milk production, resulting in significant decreases of 112 kg and 372 kg in 305-d Milk Yield (305-d MY), 4 kg and 12 kg in 305-d Protein Yield (305-d PY), and 6 kg and 16 kg in 305-d Fat Yield (305-d FY). As a result of their excessive 305-d MY, some cows were diagnosed with KET due to glucose metabolism disorder. The 305-d MY of cows with KET was significantly higher than that of healthy cows (205 kg, p < 0.01). (3) All three diseases resulted in an increase in the Interval from Calving to First Service (CTFS, 0.60–1.50 d), Interval from First Service to Conception (FSTC, 0.20–16.20 d), Calving Interval (CI, 4.00–7.00 d), and Number of Services (NUMS, 0.07–0.35). (4) The heritabilities of cows with MAS, MET, and KET were found to be low, with values of 0.09, 0.01, and 0.02, respectively. The genetic correlation between these traits ranged from 0.14 to 0.44. This study offers valuable insights on the prevention and control of the three diseases, as well as feeding management and genetic breeding.

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.

Fourier transform mid-infrared spectroscopy (FT-MIRS) technique has been extensively employed for performance measurement of dairy cows and dairy herd improvement (DHI), but different milk analyzers have shown significant differences in the sensitivity, laser intensity, and stability of FT-MIRS determination, which cannot be directly integrated and applied in phenotype prediction and relevant studies. Existing literature has reported several FT-MIRS calibration methods such as piecewise direct standardization (PDS) and retroactive percentile standardization (RPS), achieving good standardization results. However, these methods require to be optimized because they take no account of the collinearity and redundancy of the spectrum. Therefore, this study established an improved agglomerative clustering piecewise direct standardization (ACPDS) method. This study used 432 standard milk samples prepared by the standard laboratory within 4 months (based on the standard sample preparation procedures in the International Dairy Federation Guidelines for the Application of Mid-infrared Spectroscopy) and carried out FT-MIRS measurements and data collection on 9 instruments in 5 DHI laboratories. Meanwhile, the new method established in this study together with the existing methods of single wavelength standardization (SWS) and PDS were adopted to standardize the spectra collected on 9 instruments. The reproducibility, computation time, memory usage, and repeatability of the milk component prediction models were verified and compared. The results revealed that ACPDS exhibited significant advantages over SWS and PDS, with a higher level of spectral reproducibility, and there was a significant advantage in the repeatability of the milk component prediction models but no significant increase in memory usage. The impact of its application across regions, months, and years was insignificant. In addition, based on the respective characteristics of ACPDS and the existing two methods, application strategies have been proposed for these three methods, providing new technologies and laying the foundation for the FT-MIRS-based milk component prediction models, widespread performance measurement of dairy cows in different instruments and at different times, and comparative analysis on the traits and phenotypes of dairy cows as well as their joint breeding in China and even the world.

Establishing a high-throughput detection technology for amino acid (AA) content in milk using mid-infrared (MIR) spectroscopy has profound implications for enhancing nutritional value of milk, identifying superior milk sources, producing specialty dairy products, and expanding Dairy Herd Improvement (DHI) metrics. The aim of this study was to evaluate the effectiveness of MIR spectroscopy in predicting the content of 15 individual total AA (TAAs) and 16 free AA (FAAs) in bovine milk as well as to investigate the major factors affecting the phenotypic variability of AA content. From March 2023 to March 2024, 513 milk samples were collected from 10 Holstein dairy farms in China and analyzed using Bentley spectrometers for MIR measurements. Their TAAs and FAAs concentrations were assessed through an AA autoanalyzer. Separate quantitative prediction models were developed for each AA using partial least squares regression; accuracy of prediction was assessed using Cow-independent external validation (CEV) and Farm-independent external validation (FEV) set. In CEV, the ratio of performance to deviation (RPD) of the TAAs models ranged from 1.45 (Ser) to 2.19 (Leu), while the FAA models ranged from 1.15 (Ser) to 2.44 (Met). In FEV, the RPD of the TAAs models ranged from 0.98 (Met) to 1.76 (Asp, Glu, and Ala), and the FAAs models ranged from 0.33 (Phe) to 1.23 (Asp and Tyr). For farms included in the calibration set, MIR spectroscopy provided a rough quantitative estimation for 4 individual TAAs (Ile, Leu, Glu, and Tyr) and 2 FAAs (Met and His), as well as a qualitative determination for high and low values in 9 individual TAAs (Phe, Met, Val, Lys, Thr, Asp, Ala, His, and Arg). For farms outside the calibration set, MIR spectroscopy could only distinguish between high and low contents for 5 individual TAAs (Glu, Asp, Ala, Leu, and Arg). Phenotypically, the variation pattern in TAAs contents mirrored that of protein, while FAAs did not show a clear trend, though mastitis led to a significant elevation of FAAs in milk (p < 0.05). Overall, the application of MIR spectroscopy can be considered very promising for a low-cost, rapid, large-scale assessment of individual TAAs and FAAs contents in milk. After refinement, some models could potentially be incorporated into DHI, which would greatly benefit the milk production and food industries.

本文分析不同DHI实验室奶牛生产性能测定数据,旨在探索不同实验室测定数据一致性评价方法.随机选取2020-2021年全国20个奶牛场的6 281头奶牛,每头采集3个平行奶样,其中1个由所在区域的DHI实验室进行检测,剩余2个样品由河南省DHI实验室检测,采用Pearson相关系数及组内相关系数对河南省DHI实验室与其他地区实验室检测的DHI数据进行一致性分析,即对各地区实验室与河南省DHI实验室测定的乳脂率、乳蛋白率、乳糖率、总固体、体细胞评分之间进行相关性和一致性分析,同时比较2020年与2021年河南省DHI实验室与各地实验室检测指标的相关性和一致性.结果表明,乳脂率、乳蛋白率、乳糖率、总固体、体细胞评分的R2分别为0.765、0.845、0.724、0.680、0.834(P＜0.01),河南省DHI实验室与其他各地DHI实验室检测结果,除总固体外,乳脂率、乳蛋白率、乳糖率、体细胞评分均存在中度相关性,且2021年各指标检测结果相关系数均高于2020年.此外,乳脂率、乳蛋白率、乳糖率、总固体、体细胞评分组内相关系数ICC值分别为0.765、0.844、0.699、0.661、0.832(P＜0.01),乳脂率、乳蛋白率、体细胞评分ICC均大于0.75,且2021年各指标ICC均高于2020年.综上,提示各地区DHI实验室乳脂率、乳蛋白率、体细胞评分ICC结果一致性较好,乳糖率、总固体一致性一般,且2021年数据一致性有所提升.

Milk spectral data on 2118 cows from nine herds located in northern China were used to access the association of days open (DO). Meanwhile, the parity and calving season of dairy cows were also studied to characterize the difference in DO between groups of these two cow-level factors. The result of the linear mixed-effects model revealed that no significant differences were observed between the parity groups. However, a significant difference in DO exists between calving season groups. The interaction between parity and calving season presented that primiparous cows always exhibit lower DO among all calving season groups, and the variation in DO among parity groups was especially clearer in winter. Survival analysis revealed that the difference in DO between calving season groups might be caused by the different P/AI at the first TAI. In addition, the summer group had a higher chance of conception in the subsequent services than other groups, implying that the micro-environment featured by season played a critical role in P/AI. A weak linkage between DO and wavenumbers ranging in the mid-infrared region was detected. In summary, our study revealed that the calving season of dairy cows can be used to optimize the reproduction management. The potential application of mid-infrared spectroscopy in dairy cows needs to be further developed.

[目的]本研究基于试验测定的中国荷斯坦牛血液和乳汁中的孕酮浓度表型,探究了影响中国荷斯坦牛体内孕酮分泌的环境因素、孕酮浓度的变化趋势、孕酮浓度与乳成分的关联程度以及血乳中孕酮浓度的预测方法.[方法]试验于2021年8月在北京、山东两个牧场采集不同胎次、泌乳阶段和妊娠状态的中国荷斯坦泌乳牛的奶样、尾根血样,测定孕酮浓度,最终获得402条乳汁孕酮浓度和298条血液孕酮浓度表型用于数据分析.对孕酮浓度进行数据转化使其近似服从正态分布后,采用固定效应模型探究胎次、泌乳阶段、妊娠状态、牧场等固定效应对奶牛孕酮表型的影响,运用R语言cor函数计算孕酮与各乳成分间的关联,并利用偏最小二乘法和个体及乳成分信息对孕酮浓度进行预测,以建立孕酮浓度表型高通量获取手段.[结果]妊娠状态对转化乳汁孕酮浓度存在极显著影响(P＜0.01),胎次、场效应对转化乳汁孕酮浓度均有显著影响(P＜0.05),而转化血液孕酮浓度只受到妊娠状态影响(P＜0.01);全乳固体、乳脂率、乳蛋白率、脂蛋比与转化乳血孕酮浓度均存在显著或极显著的正相关关系(r=0.14～0.37,P＜0.05;P＜0.01);基于本试验数据,乳成分与个体信息对转化乳血孕酮浓度的预测准确性不高(R2=0.030～0.17),但如果增加血液或乳汁的转化孕酮浓度对乳汁或血液的转化孕酮浓度进行预测,预测效果则有大幅提升(R2=0.40).[结论]影响泌乳期中国荷斯坦牛转化孕酮浓度的因素除妊娠状态外,可能还包括饲养条件与胎次.此外转化孕酮浓度与乳脂率、乳蛋白率等乳成分呈极显著相关.基于乳成分信息与转化孕酮的关系,获得了对中国荷斯坦牛乳血转化孕酮浓度预测的可用策略,为今后牧场的繁殖辅助管理、奶牛育种新性状研发以及孕酮浓度的高通量获取等提供了新思路.

为研究近交对中国荷斯坦牛泌乳性能影响,收集 2008 年 5 月至 2022 年 2 月河南 37 家奶牛场 67150头中国荷斯坦牛 1～5 胎次共 128282 条泌乳性能记录数据,筛选后用于分析的数据共 23933 头奶牛 40737 条数据.个体近交系数Fped是基于系谱信息采用R(v4.1.2)nadiv包计算,将奶牛按照近交系数分为无近交组(Fped=0)、低近交组(Fped≤6.25％)和高近交组(Fped>6.25％)3 个组.采用R(v4.1.2)方差分析(aov)函数,分析不同近交系数分组对泌乳性能指标的影响.采用DMU软件(v6)和动物模型按照总数据和分胎次对泌乳性能进行近交衰退评估,在评估模型中近交系数作为协变量,近交系数回归系数即为近交衰退效应值,并进行显著性检验.结果表明:近交对奶牛 305 d产奶量、305 d乳脂量和 305 d乳蛋白量有极显著影响;近交系数每增加1％,305 d产奶量、305 d乳脂量和305 d乳蛋白量分别减少8.55 kg(P<0.01)、0.22 kg(P<0.05)和 0.23 kg(P<0.01);近交系数每增加 1％,1 胎次和 2 胎次 305 d产奶量分别下降 6.61 kg(P<0.05)和12.74 kg(P<0.01)、1 胎和 3 胎次及以上 305 d乳蛋白量分别下降 0.21 kg和 0.34 kg(P<0.05).本研究为奶牛育种中近交控制提供数据支撑.

本研究旨在为合理利用全株玉米青贮和有针对性地提升全株玉米青贮饲料品质提供参考依据.为了综合评价河南省全株玉米青贮饲料品质,共采集104家不同规模牧场全株玉米青贮饲料,利用近红外光谱分析技术(NIRS)、行业专家感官评价对全株玉米青贮饲料营养成分、感官指标及发酵指标等进行综合评价.结果显示,2021年河南省全株玉米青贮中平均干物质(DM)、粗蛋白(CP)、淀粉、中性洗涤纤维(NDF)、乳酸含量和pH值分别为30.8%、8.2%、30.3%、42.4%、4.8%和3.8.84.0%的玉米青贮为黄绿色,74.0%的玉米青贮气味带有醇香酸味,74.0%的玉米青贮籽粒破碎良好,82.0%的玉米青贮切割均匀.河南省规模化牧场全株玉米青贮饲料质量基本处于稳定状态,符合粮改饲—优质青贮行动计划(GEAF计划)中优质全株玉米青贮推荐标准,且全株玉米青贮感官品质良好.