Journal of Dairy Science最新文献

India is home to more than 525 million ruminants, which are major contributors to global warming via enteric methane (Ent_CH4) emissions. Various mitigation strategies exist to reduce Ent_CH4 emissions but accurate emission estimates are needed to establish the true potential of these strategies. Measuring Ent_CH4 emissions is expensive and unrealistic at such a large scale, so an urgent need exists for accurate Ent_CH4 prediction models. The present study evaluated the accuracy of various existing models and developed a new model to predict Ent_CH4 emissions from cattle in India. Six Ent_CH4 prediction models based on either DMI or gross energy intake (GEI) were identified as applicable to and suitable for the Indian context. Models based on DMI and GEI were derived from various works, including those of the Intergovernmental Panel on Climate Change and others (designated as IPCC_DMI, IPCC_GEI, Ribeiro_DMI, Ribeiro_GEI, Patra_DMI, and Patra_GEI). These were evaluated using 2 independent datasets characterizing 528 lactating (dairy) and 122 nonlactating (nondairy) cattle from 15 and 13 studies, respectively, under different management practices across 13 Indian states. Furthermore, the same datasets were combined to develop an empirical Ent_CH4 prediction model using a linear mixed-effects framework. The relative prediction error (RPE) and mean bias error (MBE) were used to evaluate model accuracy. A model's prediction was considered acceptable when RPE was <20%. None of the 6 models predicted Ent_CH4 for nondairy cattle with an RPE <20%. None of the 6 models predicted Ent_CH4 for both dairy and nondairy cattle with an RPE <20%. For dairy cattle, only the Ribeiro_DMI and Patra_DMI models approached this threshold, producing RPE values of 22.6% and 22.9%, respectively. The linear mixed-effects model (Alam's model, described herein: Ent_CH4 [g/d per cow] = 15.45 + 1.91 × DMI [kg/d], conditional R² = 0.94), developed for both dairy and nondairy cattle, achieved a substantially lower RPE (9.48%) than any of the 6 previously tested models. Therefore, the Ribeiro_DMI and Patra_DMI models could acceptably predict Ent_CH4 emissions from dairy cattle in India, but none of the models evaluated in our study was suitable for nondairy cattle. Our linear mixed-effects model provides more accuracy than the latter 2 in estimating emissions for dairy cattle and also offers a suitable option for nondairy cattle in India.

Sensory quality plays a key role in consumer acceptance and can contribute to the growing market share of private-label dairy products. In this study, the sensory quality of private-label dairy products in Slovenia was investigated over a 4-year period (2020-2023). A total of 2,188 samples from 5 major Slovenian retailers were analyzed using a modified sensory evaluation method (ISO 22935). Each retailer conducted about 4 samplings per year. The products were categorized into 11 groups and evaluated by trained panelists according to appearance, color, odor, taste, and product-specific characteristics. The results showed a continuous improvement in overall sensory quality, particularly taste, across most retailers and product categories. We observed an overall reduction in deviation from target sensory profiles. Sensory and taste scores improved significantly for ice creams, flavored fermented milk, creams, and cheeses, with no significant change for pasteurized and UHT milk. Trends in cheese taste defects showed a decrease in major technological defects, such as bitter and unclean flavors, and indicated emerging issues such as flat taste. We observed a high incidence of flat taste in butters (38.5%-76.0%) and too sweet ice creams (12.5%-48.8%). The proportion of inharmoniously sour fermented milk products increased linearly from 18.3% in 2020 to 60.7% in 2023. These results show that private-label products can achieve high sensory standards through systematic monitoring of sensory quality and targeted improvements.

Sorghum, a drought-tolerant crop, may serve as a promising forage alternative to corn in water-scarce regions. The study objective was to assess the effect of replacing conventional (nonbrown midrib) corn silage (CCS) with brown midrib male-sterile sorghum silage (BMR-MS-SS) at replacement rates of 0% (control), 25% (25%-BMR-MS-SS), and 50% (50%-BMR-MS-SS) on apparent total-tract nutrient digestibility (TTD), DMI, and ECM production. Compared with the control, protein concentrates and grass hay were decreased and dry ground corn increased in 25%-BMR-MS-SS and 50%-BMR-MS-SS to maintain similar nutrient composition among diets. Dietary treatments were randomly assigned to 48 cows and a subsample of 30 cows was randomly selected for TTD measurements. The 50%-BMR-MS-SS diet had the lowest TTD of DM, CP, starch, ADF, and NDF, which may be partially explained by its increased DMI. Compared with the control, the DMI of 25%-BMR-MS-SS and 50%-BMR-MS-SS diets were 12.7% and 11.4% greater, resulting in higher DMI as a percentage of BW, as well as increased milk and ECM yields. Dry matter intake and ECM were greater for cows fed 25%-BMR-MS-SS (26.6 and 44.4 kg/d) and 50%-BMR-MS-SS (26.3 and 43.7 kg/d) compared with the control cows (23.6 and 40.4 kg/d, respectively). We concluded that BMR-MS-SS can be a viable forage alternative to partially replace CCS in lactating dairy cow diets when diets are balanced for similar nutrient composition.

Consumer preferences for food products serve as a fundamental factor that influences purchasing and utilization decisions. Consumer preferences for infant formula (IF) are predominantly determined by its aroma characteristics. This study examined the volatile flavor compounds (VOC), aroma characteristics of IF detected via GC-ion mobility spectrometry (IMS), quantitative descriptive analysis, and consumer testing, respectively, hoping to provide a novel perspective for enterprise quality control. The results indicate that GC-IMS can be used for the rapid analysis of VOC in IF. Correlation analyses between consumer preferences and sensory profiles revealed that overall liking (OL) displayed a significant positive association with milky and creamy flavors, and a highly negative correlation with fishy and oxidation aromas. Of the 4 key sensory profiles, fishy and creamy flavors had a notably stronger effect on OL than oxidation and milky aromas. The results of the 2-way orthogonal partial least squares analyses indicated that acetoin (B6), (Z)-4-heptenal (A12), β-pinene (G1), o-xylene (G6), 3-octanone (B9), 1-hexanal (A7), 2-methylpropanol (C6), nonanal (A2), and 2-methyl butanal (A4) could be used to predict the sensory intensity associated with IF, and consequently consumer preference. This study aimed to provide a foundational data source for IF quality control and consumer preference maintenance, as well as a stable, rapid aroma analysis process for IF-related products.

Automated milking systems (AMS) enable the generation of objective measurements of teat placement, a key factor influencing milking efficiency and udder health. In this context, we performed GWAS to investigate the genetic background of 2 teat placement traits, i.e., rear teat distance (RTD) and front teat distance (FTD), derived from Cartesian coordinates recorded by AMS in American Holstein cows. Phenotypic data were collected from 36 AMS robots, resulting in 4,232,026 records from 4,118 cows genotyped for 57,598 SNPs. The GWAS was performed using the POSTGSF90 software, with SNP effects estimated by back-solving genomic EBV, followed by calculation of approximate P-values. For RTD, we identified 7 chromosome-wise significant SNPs located on chromosomes BTA8, BTA9, and BTA26. These genomic regions overlap with strong candidate genes, including HTR1B, PRLHR, EMX2, and GRK5, which have been previously associated with milk production, growth, and muscular development. A total of 203 previously reported QTL were found in the BTA9 and BTA26 regions identified for RTD, indicating the complex genetic background of this trait. For FTD, 8 significant SNPs were identified on BTA2, BTA8, BTA18, and BTA28, encompassing key genes such as UBE2R2, UBAP2, and NLRP12. We identified 46 QTL overlapping with these regions, which were previously associated with traits such as susceptibility to bovine respiratory disease, length of productive life, and stayability. These results suggest a genetic link between teat spacing and cow health and longevity. Overall, our findings indicate a polygenic basis for both FTD and RTD, with numerous small-effect variants associated with teat placement. The identified genomic regions and candidate genes associated with these traits contribute to enhancing our understanding of the biological mechanisms underlying teat placement traits in American Holstein cattle.

Goat milk production has increased notably in recent years. Because a large portion of this production is used for cheese manufacturing, the study of goat milk technological traits becomes particularly important. Traditionally, milk technological traits are measured using reference analytical techniques, which limit large-scale data collection due to their high costs, technical demands, and time requirements. This study aimed to evaluate mid-infrared spectroscopy (MIRS) as a rapid and cost-effective alternative for predicting technological traits of individual milk from Saanen and Alpine goat breeds. Mid-infrared spectra were collected from 388 individual goat milk samples, which were also analyzed for rennet coagulation time (RCT), curd-firming time (k₂₀), curd firmness (a₃₀, mm), pH, and the milk aptitude to coagulate index (IAC) using reference analytical methods. Prediction models were developed using partial least squares regression algorithm. The goodness of fit was evaluated through the coefficient of determination in calibration (R²_C), the coefficient of determination in validation (R²_V), and the ratio of performance to deviation in validation (RPD_V). Calibration models exhibited R²_C ranging from 0.68 to 0.83 for a₃₀ and pH, respectively. The best predicted trait in validation was pH (R²_V = 0.82), followed by IAC (R²_V = 0.70), RCT and a₃₀ (R²_V = 0.68), and k₂₀ (R²_V = 0.62). Milk pH exhibited also the highest RPD_V (2.36), being confirmed as the best predicted trait. Findings of the present study suggest that the prediction models for RCT, k₂₀, a₃₀, and IAC could help to compare groups or to discriminate between high and low values, whereas the prediction accuracy for milk pH supports their use in rough screening. Predicted milk technological properties, and in particular IAC, could support milk payment systems and breeding programs aimed at discriminating between milk samples with superior or inferior coagulation performance, thereby contributing to improved coagulation aptitude and processing efficiency.

The objective of this randomized controlled experiment was to evaluate the effect on first-service reproductive outcomes for lactating Holstein cows of a targeted reproductive management program including insemination in estrus (AIE) after a modified Double-Ovsynch protocol and re-enrollment of nonestrus cows in a synchronization of ovulation protocol. Lactating dairy cows (n = 2,101) from a commercial dairy farm fitted with neck-attached sensors for automated detection of estrus were synchronized with a Double-Ovsynch protocol up to the first PGF_2α (PGF-L) of the Breeding-Ovsynch portion of the protocol (GnRH, 7 d later PGF_2α, 3 d later GnRH, 7 d later GnRH, 7 d later PGF_2α, 1 d later PGF_2α). At PGF-L, cows were blocked by parity and type of semen used for first service and then randomly assigned to the DO-G80 (n = 1,048) and the DO-ED-P4Ov (n = 1,053) treatments. Cows in the DO-G80 treatment received GnRH for induction of ovulation at 80 h after induction of luteolysis (GnRH 80 h after PGF-L, 16 h later timed artificial insemination [TAI]). All cows with automated estrus alerts (AEA) after PGF-L and before the GnRH treatment received AIE. Cows in the DO-ED-P4Ov treatment were eligible to receive AIE for 7 d after PGF-L and if not AIE were submitted to a modified Ovsynch protocol with progesterone (P4) supplementation (P4-Ov: GnRH + P4 device in, 7 d later PGF_2α and P4 device removal, 1 d later PGF_2α, 32 h later GnRH, 16 h later TAI). Data were analyzed by logistic regression and ANOVA. The DO-ED-P4Ov treatment increased the proportion of cows AIE (+41%; DO-G80 46.7% and DO-ED-P4Ov 88.1%) and first-service pregnancy per artificial insemination (P/AI; +8%) compared with the DO-G80 treatment through enhanced P/AI of primiparous cows (primiparous DO-G80 50.5% and DO-ED-P4Ov 63.6%; multiparous DO-G80 45.3% and DO-ED-P4Ov 48.5%) and P/AI in the range of 45% for nonestrus cows targeted with the P4-Ovsynch protocol. Despite increased costs of individual artificial insemination (AI) services, the DO-ED-P4Ov treatment also had reduced costs per pregnancy. In conclusion, a targeted reproductive management program designed to increase fertility through insemination of cows in estrus after a modified Double-Ovsynch protocol and a targeted intervention for nonestrus cows might be an alternative to increase first-service fertility and reduce costs per pregnancy primarily through better performance of primiparous cows. This program could also have other herd management benefits including identifying cows of different fertility potential and the distribution of AI services over more days of the week compared with TAI.

Dairy cow grazing is widely appreciated by society due to its perceived benefits for animal welfare and health. However, concerns about dairy cow health are also cited as reasons for adopting year-round confinement systems. Comprehensive assessments on the relation of grazing intensity and duration (GID) with dairy cow production and health under practical conditions are lacking. This exploratory, longitudinal observational field study assessed the association between grazing parameters and production and health indicators in 70 commercial Dutch dairy herds during the 2023 and 2024 grazing seasons. Studied GID parameters included grazing hours, grazing days, average daily grazing hours (ADGH), restricted versus unrestricted grazing, and cow-hours per hectare available for grazing (CH/ha). Collected farm data included indicators of production (standardized milk yield, fat and protein content), udder health (bulk milk SCC), parasitic infections (antibodies to Dictyocaulus viviparus and Ostertagia ostertagi), and claw health (antibodies to Treponema spp.). Compared with the pregrazing season (∼6 wk before turnout), the grazing season was associated with slightly lower milk production and slightly poorer udder health, whereas fewer farms tested positive for Treponema spp. antibodies. Standardized milk yield showed a small negative association with GID, whereas fat and protein content were positively associated. More CH/ha was linked to lower bulk milk SCC. Grazing hours and ADGH were associated with increased odds of infection with D. viviparus and O. ostertagi. Unrestricted grazing (pasture access for ≥17.5 h/d and ≥30 consecutive days) was associated with higher odds for D. viviparus antibodies and more CH/ha with O. ostertagi antibodies. No GID parameters were associated with Treponema spp. antibodies. Although associations were modest and based on univariable analyses, the findings offer novel insights into how different grazing practices may affect production and health in commercial dairy herds with seasonal pasture access.

This study examined national patterns in Taiwan's frozen dairy cattle semen imports from 2020 to 2024 using official import clearance records issued by the Bureau of Animal and Plant Health Inspection and Quarantine. A total of 643 individual Holstein sires were identified across all shipments during the 5-yr period. Annual import volumes, semen type distributions, and sire-specific usage patterns were analyzed to characterize changes in breeding strategies. Genetic indices for imported sires were obtained from the Council on Dairy Cattle Breeding August 2025 evaluation and linked with import data to assess how sire characteristics corresponded to importer preferences. Descriptive analyses summarized temporal trends, and annual diversity patterns were quantified using the Shannon diversity index. A linear mixed-effects model compared import volumes between semen types, with year included as a fixed effect, and a fixed-effects model incorporating sire identity quantified the extent to which individual bulls contributed to variation in import demand. Principal component analysis and clustering were used to identify groups of sires with similar genetic and usage profiles. For the most heavily used sires, age-related usage patterns were evaluated by comparing recently released younger sires with older sires possessing longer semen-production histories. Overall, total semen imports peaked in 2021 before declining in subsequent years, accompanied by a substantial reduction in the proportion of sexed semen. Although more than 150 sires were imported annually, usage concentrated on a subset of bulls whose genetic profiles reflected different breeding priorities, including production-oriented, durability-focused, or intermediate strategies. Cluster patterns further indicated that Taiwan's import decisions are shaped by heterogeneous breeding goals and evolving market conditions. These findings provide the first national-scale assessment of Taiwan's semen importation dynamics and offer evidence-based guidance for refining sire selection, improving cost-effectiveness, and supporting sustainable genetic progress in subtropical dairy production systems.

With increasing consumer demand for goat milk, accelerating the breeding of elite dairy goats through livestock embryo engineering has become imperative. Elucidating estrogen's molecular mechanism in primordial follicle activation is crucial for improving in vitro embryo production to enhance breeding efficiency. This study aimed to elucidate the molecular mechanisms by which estrogen regulates the activation of primordial follicles in goats. By supplementing the ovarian cortical fragment culture system with estrogen and small molecular reagents, and utilizing experimental techniques including hematoxylin and eosin staining, immunohistochemical staining, immunofluorescence staining, RT quantitative PCR, Western blot analysis, and ELISA, we demonstrated that the protein levels of phosphorylated (p)-mTOR, p-rpS6, and KITL were significantly higher in granulosa cells of growing follicles than in those of primordial follicles in goat ovaries. Supplementing 17β-estradiol (E₂) in cultured goat ovarian cortex fragments markedly increased activated follicle proportion. Mechanistically, E₂ activated the mTOR/KITL/FOXO3a signaling cascade, wherein mTOR/KITL mediated E₂-induced FOXO3a phosphorylation and primordial follicle activation. Furthermore, nitric oxide synthase (NOS) mRNA and protein were detected in goat ovaries, and inhibition of NOS/nitric oxide (NO) signaling reversed E₂-driven follicle activation. We found that E₂ upregulated protein expression and phosphorylation of endothelial NOS (eNOS) and neuronal NOS (nNOS) through the nuclear estrogen receptor pathway, triggering NO/cyclic guanosine monophosphate (cGMP)/PKG signaling to induce mTOR/KITL/FOXO3a activation. Specific activation of estrogen receptor α (ERα) by the agonist propyl pyrazole triol (PPT) induced NO/cGMP/PKG signaling, which mediated PPT promotion of mTOR/KITL/FOXO3a activation and follicle development. Collectively, estrogen activates primordial follicles in dairy goats through ERα-dependent upregulation of NOS, initiating NO/cGMP/PKG → mTOR/KITL/FOXO3a signaling cascades. This study deciphers estrogen's regulatory mechanism in follicular activation, offering novel insights for increasing oocyte yield via in vitro culture in goat breeding.