Search Results (2,339)

Global policy narratives on livestock development increasingly emphasize environmental concerns, often overlooking the social dimensions of the sector. In the case of dairy, the world’s most valuable agricultural commodity, its role in social and economic development remains poorly quantified. Our study contributes to a more balanced vision of the UN SDGs thanks to the inclusion of a socio-economic dimension. Here we present a novel empirical approach to assess the socio-economic impacts of dairy development using a new global dataset and non-parametric modelling techniques (local polynomial regressions), with yield as a proxy for sectoral performance. We find that as dairy systems intensify, the number of farm households engaged in production declines, yet household incomes rise. On-farm labour productivity also increases, accompanied by a reduction in employment but higher wages. In dairy processing, employment initially grows, peaks, and then contracts, again with rising wages. The most substantial impact is observed among consumers: an increased milk supply leads to lower prices and improved affordability, expanding the access to dairy products. Additionally, dairy development is associated with greater agricultural value added, an expanding tax base, and the increased formalization of the economy. These findings suggest that dairy development, beyond its environmental footprint, plays a significant and largely positive role in social transformation, yet is having to adapt sustainably while tackling labour force relocation, and that dairy development’s social impacts mimic the general agricultural sector. These results might be of interest for the assessment of policies regarding dairy development. Full article

A unique line of Holstein cattle has been maintained without selection in Minnesota since 1964. After many generations, unselected cattle produce less milk, but have better reproductive performance and health traits when compared with contemporary cows. Comparisons between this line of unselected Holstein and those under selection provide useful insights that connect selection and complex traits in cattle. Utilizing these unique resources and sequence data, we sought to identify genome changes due to selection. We sequenced 30 unselected and 54 selected Holstein cattle and compared their sequence variants to identify selection signatures. After many years, the two populations showed completely different patterns in their genome-level population structures and linkage disequilibrium. By integrating signals from five different detection methods, we detected consensus selection signatures from at least four methods covering 14,533 SNPs and 155 protein-coding genes. An integrated analysis of selection signatures with gene annotation, pathways, and the cattle QTL database demonstrated that the genomic regions under selection are related to milk productivity, health, and reproductive efficiency. The polygenic nature of these complex traits is evident from hundreds of selection signatures and candidate genes, suggesting that long-term artificial selection has acted on the whole genome rather than a few major genes. In summary, our study identified candidate selection signatures underlying phenotypic differences between unselected and selected Holstein cows and revealed insights into the genetic basis of complex traits in cattle. Full article

Dietary tannin supplementation represents a potential strategy to modulate rumen fermentation and enhance lactation performance in dairy cows, though responses remain inconsistent. A 21-day feeding trial was conducted to evaluate the effect of chestnut tannin (CNT) extract on the enteric methane emissions (EME), blood metabolites, and milk production traits in mid-lactation dairy cows. Thirty-six Holstein cows were allocated to three homogeneous treatment groups: control (CNT₀, 0 g/d CNT), CNT₄₀ (40 g/d CNT), and CNT₈₀ (80 g/d CNT). Measurements of EME, dry matter intake (DMI), milk yield (MY), and blood and milk parameters were carried out pre- and post-21-day supplementation period. Compared with the no-additive group, the CNT extract reduced methane production, methane yield, and methane intensity in CNT₄₀ and CNT₈₀ (p < 0.001). CNT₄₀ and CNT₈₀ cows exhibited lower blood urea nitrogen (p = 0.019 and p = 0.002) and elevated serum insulin (p = 0.003 and p < 0.001) and growth hormone concentrations (p = 0.046 and p = 0.034), coinciding with reduced aspartate aminotransferase (p = 0.016 and p = 0.045), and lactate dehydrogenase (p = 0.011 and p = 0.008) activities compared to control. However, CNT₈₀ had higher circulating NEFA and BHBA than CNT₀ (p = 0.003 and p = 0.004) and CNT₄₀ (p = 0.035 and p = 0.019). The blood glucose, albumin, and total bilirubin concentrations were not affected. MY and fat- and protein-corrected milk (FPCM), MY/DMI, and FPCM/DMI were higher in both CNT₄₀ (p = 0.004, p = 0.003, p = 0.014, p = 0.010) and CNT₈₀ (p = 0.002, p = 0.003, p = 0.008, p = 0.013) cows compared with controls. Feeding CNT₈₀ resulted in higher protein content (p = 0.015) but lower fat percentage in milk (p = 0.004) compared to CNT₀. Milk urea nitrogen and somatic cell counts were significantly lower in both CNT₄₀ (p < 0.001, p = 0.009) and CNT₈₀ (p < 0.001 for both) compared to CNT₀, while milk lactose did not differ between treatments. These findings demonstrate that chestnut tannin extract effectively mitigates EME while enhancing lactation performance in mid-lactation dairy cows. Full article

The covalent cross-linking of caseins by the enzyme transglutaminase (Tgase) stabilizes the structure of casein micelles. In our study, the effects of a pretreatment of skim milk (SM) by Tgase on milk protein fractionation by microfiltration were tested. Tgase was found to induce amount-dependent modifications of all milk proteins in SM and a reduction in deposit resistance for laboratory dead-end filtrations of up to 20%. This improvement in process performance could partially be confirmed in pilot-scale cross-flow filtrations of Tgase-pretreated SM and micellar casein solutions (MCC). These comparative trials with untreated retentates under a variation of ΔpTM (0.5–2 bar) at 10 and 50° revealed distinct differences in deposit behavior and achieved the reduction in deposit resistance in a range of 0–20%. The possibility of pre-fouling with enzymatically pretreated MCC prior to SM filtration was also investigated. Under different pre-fouling conditions, practical modes of retentate change, and pre-foulant compositions, a switch to untreated SM consistently resulted in an immediate and major increase in deposit resistance by 50–150%. This was partially related to the change in the ionic environment and the protein fraction. Nevertheless, our results underline the potential of Tgase pretreatment and pre-fouling approaches to alter filtration performance for different applications. Full article

The yak (Bos grunniens) is a key species in high-altitude rangelands of Asia. Despite their ecological and economic importance, yak production faces persistent challenges, including low milk yields, vulnerability to climate changes, emerging diseases, and a lack of systematic breeding programs. This review presents the genomic, physiological, and environmental dimensions of yak biology and husbandry. Genes such as EPAS1, which encodes hypoxia-inducible transcription factors, underpin physiological adaptations, including enlarged cardiopulmonary structures, elevated erythrocyte concentrations, and specialized thermoregulatory mechanisms that enable their survival at elevations of 3000 m and above. Copy number variations (CNVs) and single nucleotide polymorphisms (SNPs) present promising markers for improving milk and meat production, disease resistance, and metabolic efficiency. F1 and F2 generations of yak–cattle hybrids show superior growth and milk yields, but reproductive barriers, such as natural mating or artificial insemination, and environmental factors limit the success of these hybrids beyond second generation. Infectious diseases, such as bovine viral diarrhea and antimicrobial-resistant and biofilm-forming Enterococcus and E. coli, pose risks to herd health and food safety. Rising ambient temperatures, declining forage biomass, and increased disease prevalence due to climate changes risk yak economic performance and welfare. Addressing these challenges by nutritional, environmental, and genetic interventions will safeguard yak pastoralism. This review describes the genes associated with different yak traits and provides an overview of the genetic adaptations of yaks (Bos grunniens) to environmental stresses at high altitudes and emphasizes the need for conservation and improvement strategies for sustainable husbandry of these yaks. Full article

Tetrabromobisphenol A (TBBPA) is a brominated flame retardant widely used in consumer products. TBBPA is often detected in soil, water, organisms, and even in human blood and breast milk. Hence, it is accessible to developing fetuses and nursing offspring after maternal exposure. The reported evidence for the endocrine disruption of TBBPA in the brain has raised concerns regarding its effects on neurodevelopmental and behavioral functions. This study investigated the effects of TBBPA exposure on neurodevelopment. A cell-based developmental neurotoxicity assay was performed to determine whether TBBPA is a developmental neurotoxicant. The assay revealed TBBPA to be a developmental neurotoxicant. C57BL/6N maternal mice were administered TBBPA at 0, 0.24, and 2.4 mg/kg during pregnancy and lactation, and their offspring underwent behavioral testing. The behavioral experiments revealed sex-specific effects. In females, only a deterioration of the motor ability was observed. In contrast, deteriorations in motor function, memory, and social interaction were noted in males. Furthermore, we validated changes in the expression of genes associated with behavioral abnormalities, confirming that perinatal exposure to TBBPA, at the administered doses, can affect neurodevelopment and behavior in offspring. These findings highlight the need for more in-depth and multifaceted research on the toxicity of TBBPA. Full article

Background/Objectives: Lactobacillus kefiranofaciens HL1, isolated from kefir, exhibits antioxidant and anti-aging activities, defined here as improved cognitive function and reductions in oxidative stress and inflammatory markers. However, its poor milk viability limits application. This study developed a novel fermented milk by co-culturing HL1 with Lactococcus lactis subsp. cremoris APL015 (APL15) to enhance fermentation and health benefits. Methods: HL1 and APL15 were co-cultured to produce fermented milk (FM), and fermentation performance, microbial viability, texture, and syneresis were evaluated. A D-galactose-induced aging BALB/c mouse model was used to assess cognitive function, oxidative stress, inflammation, antioxidant enzyme activity, and gut microbiota after 8 weeks of oral administration. Results: FM reached pH 4.6 within 16 h, with high viable counts (~10⁹ CFU/mL) for both strains. HL1 viability and texture were maintained, with smooth consistency and low syneresis. In vivo, FM improved cognitive behavior (Y-maze, Morris water maze), reduced oxidative damage (MDA), lowered IL-1β and TNF-α, and enhanced brain SOD levels. FM-fed mice exhibited increased short-chain fatty acid producers, higher cecal butyrate, and reduced Clostridium perfringens. Conclusions: The co-cultured fermented milk effectively delivers HL1 and provides antioxidant, anti-inflammatory, and anti-aging effects in vivo, likely via gut–brain axis modulation. It shows promise as a functional food for healthy aging. Full article

The authenticity of Protected Designation of Origin (PDO) Feta cheese is critical for consumer confidence and market integrity, particularly in light of widespread concerns over economically motivated adulteration. This study evaluated the potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modeling to differentiate authentic Feta from non-Feta white brined cheeses. A total of 90 cheese samples, consisting of verified Feta and cow milk cheeses, were analyzed in both freeze-dried and fresh forms. Spectral data from raw, first derivative, and second derivative spectra were analyzed using principal component analysis–linear discriminant analysis (PCA-LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish authentic Feta from non-Feta cheese samples. Derivative processing significantly improved classification accuracy. All classification models performed relatively well, but the PLS-DA model applied to second derivative spectra of freeze-dried samples achieved the best results, with 95.8% accuracy, 100% sensitivity, and 90.9% specificity. The most consistently highlighted discriminatory regions across models included ~2920 cm⁻¹ (C–H stretching in lipids), ~1650 cm⁻¹ (Amide I band, corresponding to C=O stretching in proteins), and the 1300–900 cm⁻¹ range, which is associated with carbohydrate-related bands. These findings support ATR-FTIR spectroscopy as a rapid, non-destructive tool for routine Feta authentication. The approach offers promise for enhancing traceability and quality assurance in high-value dairy products. Full article

Mycotoxins are toxic secondary metabolites produced by various fungi that contaminate livestock feed, posing serious threats to animal health, productivity, and food safety. Although historical research has often examined individual mycotoxins in isolation, real-world conditions typically involve the simultaneous presence of multiple mycotoxins, resulting in additive or synergistic toxic effects that are often more severe than those observed with single toxin exposures. This review comprehensively synthesizes recent findings on multi-mycotoxin contamination in livestock feed, highlighting their physiological effects, mechanisms of action, and implications for regulatory frameworks. Multi-mycotoxin interactions exacerbate oxidative stress, immune suppression, impaired reproduction, and organ damage across species, leading to reduced growth performance, decreased milk and egg production, compromised carcass and wool quality, and increased mortality rates. A major concern is that current international regulatory standards mainly address individual mycotoxins, overlooking the compounded risks of co-occurrence. Global surveillance studies consistently reveal high prevalence rates of mycotoxin mixtures in feedstuffs, especially combinations involving DON, ZEN, AFB₁, FB₁, and OTA. Understanding these interactions and their underlying cellular mechanisms is critical for improving risk assessment models, formulating integrated mitigation strategies, and safeguarding both livestock productivity and human food security. Full article

Milk is a nutritionally rich food and a frequent target of economically motivated adulteration, particularly through substitution with lower-cost milk types. Over the past decade, significant progress has been made in the authentication of milk using advanced proteomic and chemometric approaches, with a focus on the discovery and application of protein and peptide biomarkers for species differentiation and fraud detection. Recent innovations in both top-down and bottom-up proteomics have markedly improved the sensitivity and specificity of detecting key molecular targets, including caseins and whey proteins. Peptide-based methods are especially valuable in processed dairy products due to their thermal stability and resilience to harsh treatment, although their species specificity may be limited when sequences are conserved across related species. Robust chemometric approaches are increasingly integrated with proteomic pipelines to handle high-dimensional datasets and enhance classification performance. Multivariate techniques, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are frequently employed to extract discriminatory features and model adulteration scenarios. Despite these advances, key challenges persist, including the lack of standardized protocols, variability in sample preparation, and the need for broader validation across breeds, geographies, and production systems. Future progress will depend on the convergence of high-resolution proteomics with multi-omics integration, structured data fusion, and machine learning frameworks, enabling scalable, specific, and robust solutions for milk authentication in increasingly complex food systems. Full article

Phytochemicals are a potential alternative to antibiotic growth promoters. This study evaluated the effects of phytochemicals (curcuminoids, trans-cinnamaldehyde, and piperine) and monensin on performance and ruminal fermentation during the transition period in grazing dairy cows. In a complete randomized design, 60 Holstein cows (36 multiparous, 24 primiparous; 9 fistulated) were assigned to (1) control (CTL), (2) monensin (MON, 0.30 g/cow/day), or (3) phytochemicals (PHY, 50 g/cow/day) treatment from 30 days prepartum to 60 days postpartum. Prepartum, cows received a total mixed ration (TMR); postpartum, they grazed between a.m. and p.m. milking and were supplemented with TMR. Ruminal fermentation was evaluated at −7, 30, and 60 days postpartum. Prepartum dry matter intake was lower in MON primiparous cows than in CTL and PHY. Additives increased milk yield and lactose percentage in primiparous cows. PHY cows had lower acetate, higher propionate, and reduced acetate-to-propionate and ketogenic-to-glucogenic ratios at 60 days postpartum. MON reduced prepartum protozoa, while PHY increased prepartum branched-chain volatile fatty acids (BCVFAs). Both additives decreased BCVFA and protozoa postpartum. Additives reduced ammonia at 30 days, but only PHY persisted at 60 days. MON and PHY improved primiparous performance, enhanced ruminal fermentation, and promoted glucogenic fermentation while reducing ammonia and protozoa. Full article

Background: Cow’s milk allergy (CMA) is the most common food allergy in children, typically resolving by adolescence. In contrast, the clinical spectrum of allergies to non-cow mammalian milk and their patterns of IgE cross-reactivity are less well documented. Nutritional differences between various mammalian milks may also impact dietary management in milk-allergic patients. Objectives: To characterize clinical features, onset age, and IgE cross-reactivity patterns of non-cow mammalian milk allergies in adult patients seen at a tertiary allergy center, and to compare these findings with published cases. Methods: A retrospective analysis of patients included in the “Extended Laboratory Investigation for Rare Causes of Anaphylaxis study” with mammalian milk allergy was performed using clinical history, skin testing, and serum-specific IgE measurements. Cross-reactivity patterns were assessed in selected cases using immunoblotting, specific IgE inhibition, and basophil activation testing, and compared with published reports of non-cow mammalian milk allergy. Results: In our case series of 22 patients with mammalian milk allergy and 10 healthy control subjects, 3 patients were identified with isolated adult-onset non-cow mammalian milk allergy (n = 1 buffalo milk; n = 2 mare milk), confirmed via immunoblotting and basophil activation testing. Streptavidin-based specific IgE measurement for buffalo cheese was positive in the buffalo milk allergic patient. The literature review identified 82 cases of non-cow mammalian milk allergy. These cases typically showed late onset (mean age 8.6 years; range 1–70 years), severe reactions (CoFAR (Consortium for Food Allergy Research) grade 3 or 4 in 66%, and one fatality), and selective sensitization (affecting sheep and/or goat, camel, mare, buffalo, donkey, or combinations thereof in 56, 10, 5, 5, 4, and 2 cases, respectively). Conclusions: Non-cow mammalian milk allergies are rare but generally present later in life with selective IgE cross-reactivity, differing from the broader cross-reactivity observed in CMA. This selectivity may allow for safe dietary alternatives. These findings underscore the need for improved diagnostics and personalized dietary management in this patient population. Full article

This study analyzes the use of a lightweight image-based deep learning model to classify dairy cows into low-, medium-, and high-milk-yield categories by automatically detecting the udder region of the cow. The implemented model was based on the YOLOv11 architecture, which enables efficient object detection and classification with real-time performance. The model is trained on a public dataset of cow images labeled with 305-day milk yield records. Thresholds were established to define the three yield classes, and a balanced subset of labeled images was selected for training, validation, and testing purposes. To assess the robustness and consistency of the proposed approach, the model was trained 30 times following the same experimental protocol. The system achieves precision, recall, and mean Average Precision (mAP@50) of 0.408 ± 0.044, 0.739 ± 0.095, and 0.492 ± 0.031, respectively, across all classes. The highest precision (0.445 ± 0.055), recall (0.766 ± 0.107), and mAP@50 (0.558 ± 0.036) were observed in the low-yield class. Qualitative analysis revealed that misclassifications mainly occurred near class boundaries, emphasizing the importance of consistent image acquisition conditions. The resulting model was deployed in a mobile application designed to support field-level assessment by non-specialist users. These findings demonstrate the practical feasibility of applying vision-based models to support decision-making in dairy production systems, particularly in settings where traditional data collection methods are unavailable or impractical. Full article

Background and Objective: Large language model (LLM)–based chatbots are increasingly utilized by postpartum individuals seeking guidance on breastfeeding. However, the chatbots’ content quality, readability, and alignment with clinical guidelines remain uncertain. This study was conducted to evaluate and compare the quality, readability, and factual accuracy of responses generated by three publicly accessible AI chatbots—ChatGPT-4o Pro, Gemini 2.5 Pro, and Copilot Pro—when prompted with common maternal questions related to breast-milk supply. Methods: Twenty frequently asked breastfeeding-related questions were submitted to each chatbot in separate sessions. The responses were paraphrased to enable standardized scoring and were then evaluated using three validated tools: ensuring quality information for patients (EQIP), the simple measure of gobbledygook (SMOG), and the global quality scale (GQS). Factual accuracy was benchmarked against WHO, ACOG, CDC, and NICE guidelines using a three-point rubric. Additional user experience metrics included response time, character count, content density, and structural formatting. Statistical comparisons were performed using the Kruskal–Wallis and Wilcoxon rank-sum tests with Bonferroni correction. Results: ChatGPT-4o Pro achieved the highest overall performance across all primary outcomes: EQIP score (85.7 ± 2.4%), SMOG score (9.78 ± 0.22), and GQS rating (4.55 ± 0.50), followed by Gemini 2.5 Pro and Copilot Pro (p < 0.001 for all comparisons). ChatGPT-4o Pro also demonstrated the highest factual alignment with clinical guidelines (95%), while Copilot showed more frequent omissions or simplifications. Differences in response time and formatting quality were statistically significant, although not always clinically meaningful. Conclusions: ChatGPT-4o Pro outperforms other chatbots in delivering structured, readable, and guideline-concordant breastfeeding information. However, substantial variability persists across the platforms, and none should be considered a substitute for professional guidance. Importantly, the phenomenon of AI hallucinations—where chatbots may generate factually incorrect or fabricated information—remains a critical risk that must be addressed to ensure safe integration into maternal health communication. Future efforts should focus on improving the transparency, accuracy, and multilingual reliability of AI chatbots to ensure their safe integration into maternal health communications. Full article

A reliable estimation of protein requirements in lactating dairy cows is necessary for formulating nutritionally adequate diets, improving feed efficiency, and minimizing nitrogen excretion. This study aimed to develop machine learning-based models to predict net protein requirements for maintenance (NPm) and lactation (NPl) using random forest regression (RFR) and support vector regression (SVR). A total of 1779 observations were assembled from 436 peer-reviewed publications and open-access databases. Predictor variables included farm-ready variables such as milk yield, dry matter intake, days in milk, body weight, and dietary crude protein content. NPm was estimated based on the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) equations, while NPl was derived from milk true protein yield. The model adequacy was evaluated using 10-fold cross-validation. The RFR model demonstrated higher predictive performance than SVR for both NPm (R² = 0.82, RMSEP = 22.38 g/d, CCC = 0.89) and NPl (R² = 0.82, RMSEP = 95.17 g/d, CCC = 0.89), reflecting its capacity to model the rule-based nature of the NASEM equations. These findings suggest that RFR may provide a valuable approach for estimating protein requirements with fewer input variables. Further research should focus on validating these models under field conditions and exploring hybrid modeling frameworks that integrate mechanistic and machine learning approaches. Full article