Hasil untuk "Dairying"

Nawazish Ali, Rachael Shaw, Karl Mason

Dairy farming is an energy intensive sector that relies heavily on grid electricity. With increasing renewable energy integration, sustainable energy management has become essential for reducing grid dependence and supporting the United Nations Sustainable Development Goal 7 on affordable and clean energy. However, the intermittent nature of renewables poses challenges in balancing supply and demand in real time. Intelligent load scheduling is therefore crucial to minimize operational costs while maintaining reliability. Reinforcement Learning has shown promise in improving energy efficiency and reducing costs. However, most RL-based scheduling methods assume complete knowledge of future prices or generation, which is unrealistic in dynamic environments. Moreover, standard PPO variants rely on fixed clipping or KL divergence thresholds, often leading to unstable training under variable tariffs. To address these challenges, this study proposes a Deep Reinforcement Learning framework for efficient load scheduling in dairy farms, focusing on battery storage and water heating under realistic operational constraints. The proposed Forecast Aware PPO incorporates short term forecasts of demand and renewable generation using hour of day and month based residual calibration, while the PID KL PPO variant employs a proportional integral derivative controller to regulate KL divergence for stable policy updates adaptively. Trained on real world dairy farm data, the method achieves up to 1% lower electricity cost than PPO, 4.8% than DQN, and 1.5% than SAC. For battery scheduling, PPO reduces grid imports by 13.1%, demonstrating scalability and effectiveness for sustainable energy management in modern dairy farming.

Ruixue Pan, Yuexia Ding, Jinju Peng et al.

ABSTRACT: The emergence of methicillin-resistant Staphylococcus aureus (MRSA) and its robust biofilm-forming capability pose severe threats to public health, livestock production, and food safety, and underscores the urgent need for novel antibacterial and anti-biofilm agents. In this study, we identified and characterized a novel bacteriocin, PFB252, derived from Bacillus velezensis through a multistep purification process involving acid precipitation, TA-GF75 gel column chromatography, Tiderose Q HP anion-exchange chromatography (TRUKING, Changsha, China), and reversed-phase HPLC. PFB252 exhibited remarkable thermal stability, pH tolerance, and resistance to enzymatic degradation, and demonstrated potent antibacterial activity against MRSA. At subinhibitory concentrations (1/32× minimum inhibitory concentration [MIC] and 1/16× MIC), PFB252 significantly disrupted biofilm formation and impaired the metabolic viability of embedded bacteria, and it drastically reduced extracellular polysaccharide, the key component of the biofilm matrix. Transcriptional analysis further revealed that PFB252 at subinhibitory concentrations downregulated critical biofilm-associated genes. PFB252 exhibited strong antimicrobial efficacy in dairy applications and could reduce MRSA counts in milk from 103 to <10 cfu/mL within 4 d at MIC and maintain suppression in cheese below 102 cfu/g over 7 d. These properties highlight PFB252's potential as a natural biopreservative for combating MRSA in food systems and offer a promising solution for food safety applications.

H.M. Hammon, L. Bachmann, L. Vogel et al.

ABSTRACT: Long-chain fatty acids such as essential fatty acids (EFA) and CLA have the potential to affect glucose metabolism and milk synthesis in the mammary gland. The present study tested the hypothesis that EFA, CLA, or a combined EFA and CLA treatment influences the formation of milk constituents from glucose and regulation of milk synthesis during early lactation. For this purpose, the carbon flow from glucose and important enzymes and regulatory factors of milk synthesis were measured in the mammary gland. Rumen-cannulated German Holstein cows (n = 38) were investigated from wk 9 antepartum until wk 9 postpartum. The cows were abomasally infused with coconut oil (CTRL, 76 g/d; n = 9), 78 g/d linseed and 4 g/d safflower oil (EFA treatment; n = 9), Lutalin (CLA treatment, isomers cis-9,trans-11 and trans-10,cis-12 CLA, each 10 g/d; n = 10), or the combination of EFA+CLA (n = 10). In wk 3 postpartum, [13C6]-glucose was infused intravenously for 4 h, and 13C-enrichment was measured in milk ingredients (lactose, casein, and fat) before and several times after tracer infusion. Tissue from the mammary gland was collected after tracer infusion in wk 4 by biopsy and in wk 9 after slaughtering the cows. Tissue was used for measuring gene and protein (only wk 9) expression of parameters related to milk synthesis. Milk FCM and ECM decreased with CLA treatment. After [13C6]-glucose infusion, 13C-enrichment increased about 30-fold higher in lactose than in casein and milk fat. 13C-Enrichment in milk fat increased more, but in casein increased less in CLA-treated than non-CLA-treated cows after infusion of [13C6]-glucose. In milk fat, 13C-enrichment of triglycerides was closely related to glycerol. The mRNA abundance of key factors and enzymes associated with milk fat synthesis, such as SREBF1, ACACA, FASN, ELOVL2, and EEF1A1, was reduced by CLA treatment. Protein expression of FAS and SREBP (68 kD) was reduced, but FABP4 was increased in cows infused with CLA. The reduced 13C-enrichment in casein of CLA-treated cows was partly combined with lower urea and protein concentrations in milk but no clear changes in gene and protein expression of parameters associated with milk protein synthesis. The EFA treatment did not affect 13C-enrichment of milk ingredients after [13C6]-glucose infusion, but protein expression of SREBP (68 kD) was reduced. Results indicated a coordinated inhibition of parameters related to milk fat synthesis in the mammary gland in CLA and EFA+CLA cows. The inhibitory effect on milk fat synthesis was associated with an elevated carbon flux of glucose in glycerol of the triglycerides in milk fat. Reduction of glucose flux in casein by CLA treatment and less milk urea pointed at less nonessential AA synthesized from glucose, but regulation of milk protein synthesis was barely affected by CLA and EFA+CLA. The EFA treatment had no influence on glucose flux into milk ingredients and seems to barely affect milk fat and protein synthesis in the present study.

Padmanabhan Jagannathan Prajesh, Kaliaperumal Ragunath, Miriam Gordon et al.

Methane (CH4) emissions from dairy farming are a significant but under-quantified component of agricultural greenhouse gases. This study provides a satellite-based assessment of dairy-specific methane emissions across Canada using high-resolution Sentinel-5P TROPOMI data. By integrating spatial clustering of 1,701 dairy farms and processors, a quasi-experimental design with paired non-dairy reference regions, and seasonal pattern decomposition, we analyzed national and regional spatiotemporal emission trends. Results show persistently higher methane levels in dairy regions (mean difference: 16.99 ppb), with consistent fall-winter peaks. Notably, the dairy-specific methane anomaly, defined as the concentration difference between dairy and non-dairy regions declined by 62.25% from 2019 to 2024, with a sharp drop during 2022-2023 (-41.11%). Meanwhile, national methane levels rose by 3.83%, with increasing spatial heterogeneity across provinces. An inverse relationship between baseline methane levels and growth rates suggests a convergence effect. Seasonal analysis revealed universal spring minima and fall-winter maxima, offering distinct temporal signatures for source attribution. This study demonstrates the value of satellite-based monitoring for policy-relevant methane assessments and introduces a scalable framework applicable to other regions. The observed narrowing of dairy methane anomaly indicates evolving emission dynamics, potentially reflecting rising baseline methane rather than a definitive reduction in dairy source emissions. This highlights the need for integrated satellite and ground-based approaches to enhance understanding and guide mitigation efforts.

Mengisti Berihu Girmay, Felix Möhrle

This paper examines the views of software providers in the German dairy industry with regard to dairy farmers' needs for explanation of digital decision support systems. The study is based on mastitis detection in dairy cows using a hypothetical herd management system. We designed four exemplary explanation formats for mastitis assessments with different types of presentation (textual, rule-based, herd comparison, and time series). In our previous study, 14 dairy farmers in Germany had rated these formats in terms of comprehensibility and the trust they would have in a system providing each format. In this study, we repeat the survey with 13 software providers active in the German dairy industry. We ask them how well they think the formats would be received by farmers. We hypothesized that there may be discrepancies between the views of both groups that are worth investigating, partly to find reasons for the reluctance to adopt digital systems. A comparison of the feedback from both groups supports the hypothesis and calls for further investigation. The results show that software providers tend to make assumptions about farmers' preferences that are not necessarily accurate. Our study, although not representative due to the small sample size, highlights the potential benefits of a thorough user requirements analysis (farmers' needs) to improve software adaptation and user acceptance.

Celia Julliot, Gabriel M. Dallago, Amir Nejati et al.

Detecting walking pattern abnormalities in dairy cows early on holds the potential to reduce the occurrence of clinical lameness. This study aimed to predict gait scores in non-clinically lame dairy cows by using gait attributes based on kinematic data. Markers were placed on 20 anatomical landmarks on 12 dairy cows. The cows were walked multiple times through a corridor while recorded by six cameras, representing 69 passages. Specific gait attributes were computed from the 3D coordinates of the hoof markers. Gait was visually assessed using a 5-point numerical rating system (NRS). Due to the limited number of observations with NRS lower than 2 (n = 1) and higher than 3 (n = 6), the NRS labels were combined into three groups, representing NRS <= 2, NRS = 2.5, and NRS >= 3. The dataset was split into training and testing sets (70:30 ratio), stratified by the distribution of the NRS categories. Random forest (RF), gradient boosting machine (GBM), extreme gradient boosting machine (XGBM), and support vector machine (SVM) with a radial basis kernel models were trained using k-fold repeated cross-validation with hyperparameters defined using a Bayesian optimization. Accuracy, sensitivity, specificity, F1 score, and balanced accuracy were calculated to measure model performance. The GBM model performed best, achieving an overall accuracy and F1 score of 0.65 in the testing set. The findings of this study contribute to the development of an automated monitoring system for early identification of gait abnormalities, thereby enhancing the welfare and longevity of dairy cows.

Mahdi Saki, Justin Lipman

Dairy farmers should decide to keep or cull a cow based on an objective assessment of her likely performance in the herd. For this purpose, farmers need to identify more resilient cows, which can cope better with farm conditions and complete more lactations. This decision-making process is inherently complex, with significant environmental and economic implications. In this study, we develop an AI-driven model to predict cow longevity using historical multivariate time-series data recorded from birth. Leveraging advanced AI techniques, specifically Multi-Head Attention Transformers, we analysed approximately 780,000 records from 19,000 unique cows across 7 farms in Australia. The results demonstrate that our model achieves an overall determination coefficient of 83% in predicting herd life across the studied farms, highlighting its potential for practical application in dairy herd management.

Mengisti Berihu Girmay, Jakob Droste, Hannah Deters et al.

Artificial Intelligence (AI) promises new opportunities across many domains, including agriculture. However, the adoption of AI systems in this sector faces several challenges. System complexity can impede trust, as farmers' livelihoods depend on their decision-making and they may reject opaque or hard-to-understand recommendations. Data privacy concerns also pose a barrier, especially when farmers lack transparency regarding who can access their data and for what purposes. This paper examines dairy farmers' explainability requirements for technical recommendations and data privacy, along with the influence of socio-demographic factors. Based on a mixed-methods study involving 40 German dairy farmers, we identify five user personas through k-means clustering. Our findings reveal varying requirements, with some farmers preferring little detail while others seek full transparency across different aspects. Age, technology experience, and confidence in using digital systems were found to correlate with these explainability requirements. The resulting user personas offer practical guidance for requirements engineers aiming to tailor digital systems more effectively to the diverse requirements of farmers.

C.L. van Zyl, H.K. Eriksson, E.A.M. Bokkers et al.

ABSTRACT: In cow-calf contact (CCC) systems breaking the maternal bond may induce stress for the cow, thereby affecting feed intake, milk yield, milk flow rate, and milk electrical conductivity. This study aimed to determine the consequences of weaning and separation strategies in CCC systems for feed intake and milking characteristics of the cow. In 2 experiments, Swedish Holstein and Swedish Red cows either had (experiment 1) whole-day CCC (CCC1, n = 12) for 8.5 ± 1.2 wk (mean ± SD) followed by 12 h of daytime CCC for 8 wk, before abrupt weaning and separation at 16.4 ± 1.2 wk, or (experiment 2) whole-day CCC for 16 ± 1.0 wk; thereafter half of the calves were weaned via nose flaps for 2 wk (NF, n = 10) before physical separation and half via nose flaps for 1 wk and fence-line contact for 1 wk (NFFL, n = 9). Cows were compared with conventionally managed cows (CONV1 or CONV2 in experiment 1 or 2) separated from their calves within 12 h postpartum. In experiment 1, the study period included the week before and after the system switch from whole-day to daytime CCC, and the week before and after separation. In experiment 2, the study period included the week before the start of weaning, during weaning, and 1 week after separation. All cows were milked in the same automatic milking unit. In experiment 1, feed intake of CCC1 cows at separation tended to be lower than CONV1 cows. In experiment 2, roughage intake of NF, NFFL, and CONV2 cows did not differ, but the concentrate intake of NF cows was lower than that of CONV2 cows. In experiment 1, the system switch did not affect milking characteristics. However, after separation, machine milk yield and milk electrical conductivity of CCC1 cows increased, remaining lower than CONV1 cows. In experiment 2, machine milk yield of NF and NFFL cows increased when calves were fitted with nose flaps, but remained lower than CONV2 cows. In the week after separation, milk yield of NFFL cows was similar to that of CONV2 cows, and the NF cows remained lower. In the week before weaning, milk flow rates of NF cows were lower than those of CONV2 cows, and the NFFL cows did not differ. Before weaning, milk electrical conductivity of NF and NFFL cows was lower than that of CONV2 cows, but not thereafter. In conclusion, machine milk yield of CCC cows remained lower either until the week of separation, for NFFL cows, or until 3 or 11 wk after weaning and separation for CCC1 and NF cows of experiments 1 and 2, respectively. Cow-calf contact reduced milk electrical conductivity, and milk and peak milk flow rates increased the week after separation of cow and calf. Not for experiment 2, but for experiment 1, cow roughage and concentrate intake decreased at separation and recovered within a week, indicating that abrupt separation exerted a greater impact on the cow than separation after nose flap weaning or fence-line contact. Future studies should compare both weaning strategies within the same experimental setup, also focusing on the consequences for calves.

Qing-Ao Xiao, Lin Chen, Xiao-Long Li et al.

ABSTRACT: There is currently a lack of research examining the association between the consumption of different dairy products and type 2 diabetes (T2D) in East Asian populations. To address this gap, the present study employs Mendelian randomization to investigate the potential effects of 3 different types of milk consumption (including whole milk, semi-skim milk, and skim milk) on the risk of developing T2D. The results indicate that both whole milk and skim milk are associated with an increased risk of T2D (whole milk: odds ratio [OR] = 1.022, 95% CI: 1.001–1.044; skim milk: OR = 1.023, 95% CI: 1.007–1.039). Mediation analysis revealed that asparagine acts as a mediator between skim milk consumption and T2D, with a mediation effect of 0.003 (95% CI: 0.000 to 0.008), accounting for 14.269% of the total effect.

L.A. Jiménez-Maroto, S. Govindasamy-Lucey, J.J. Jaeggi et al.

ABSTRACT: High-pressure processing (HPP) and low-temperature storage (0°C) were explored as alternatives to freezing for extending the performance shelf life of low-moisture, part-skim (LMPS) mozzarella intended for export. Batches (n = 5) of reduced Na LMPS mozzarella were manufactured using camel chymosin as a lower proteolytic type of rennet. Cheeses were stored for 2 wk at 4°C, divided into control (non-HPP) and HPP (600 MPa for 3 min) groups, and stored at 3 different temperatures (4, 0, and −18°C) for 365 d. Analyses were performed at 0, 90, 150, 210, 270, and 365 d of storage. Frozen and 0°C samples (∼2.3 kg) were thawed/tempered at 4°C for 1 wk before analysis. Urea PAGE and quantification of the pH 4.6 soluble N over time were used to monitor primary proteolysis. Body and rheological properties were monitored using texture profile analysis (TPA) and dynamic low-amplitude oscillatory rheology. Changes in flavor, body, shred properties, and pizza performance were evaluated using quantitative descriptive analysis with 12 trained panelists using a 15-point scale. High-pressure processing treatment caused ∼5 log cfu/mL reduction in starter counts, partial solubilization of the insoluble Ca, and a small pH increase (from ∼5.2 to 5.3). The rate of primary proteolysis was reduced by HPP and low-temperature storage. High-pressure processing treatment reduced initial cheese hardness, but no further significant decrease was observed over storage time, whereas the hardness of non-HPP samples decreased over the 365 d of storage, apart from the frozen samples. In pizza applications, blister quantity development and loss of strand thickness were limited by storage at −18°C. Freezing LMPS mozzarella to −18°C gave the least changes in proteolysis and pizza performance over the 365 d of study, storage of cheese at 0°C slowed the loss of hardness and the deterioration of pizza performance attributes. The combination of HPP and 0°C storage of cheese resulted in little change in blistering quantity of pizza during the 365 d of study, whereas cheese stored at 0°C had blisters covering much of the pizza after this extended storage time. Combining HPP with low-temperature storage is a promising alternative approach to freezing for the extension of the functionality shelf life of LMPS mozzarella.

Nawazish Ali, Abdul Wahid, Rachael Shaw et al.

Dairy farming consumes a significant amount of energy, making it an energy-intensive sector within agriculture. Integrating renewable energy generation into dairy farming could help address this challenge. Effective battery management is important for integrating renewable energy generation. Managing battery charging and discharging poses significant challenges because of fluctuations in electrical consumption, the intermittent nature of renewable energy generation, and fluctuations in energy prices. Artificial Intelligence (AI) has the potential to significantly improve the use of renewable energy in dairy farming, however, there is limited research conducted in this particular domain. This research considers Ireland as a case study as it works towards attaining its 2030 energy strategy centered on the utilization of renewable sources. This study proposes a Q-learning-based algorithm for scheduling battery charging and discharging in a dairy farm setting. This research also explores the effect of the proposed algorithm by adding wind generation data and considering additional case studies. The proposed algorithm reduces the cost of imported electricity from the grid by 13.41%, peak demand by 2%, and 24.49% when utilizing wind generation. These results underline how reinforcement learning is highly effective in managing batteries in the dairy farming sector.

Shahid Ismail, Moises Diaz, Cristina Carmona-Duarte et al.

Lameness is one of the costliest pathological problems affecting dairy animals. It is usually assessed by trained veterinary clinicians who observe features such as gait symmetry or gait parameters as step counts in real-time. With the development of artificial intelligence, various modular systems have been proposed to minimize subjectivity in lameness assessment. However, the major limitation in their development is the unavailability of a public dataset which is currently either commercial or privately held. To tackle this limitation, we have introduced CowScreeningDB which was created using sensory data. This dataset was sourced from 43 cows at a dairy located in Gran Canaria, Spain. It consists of a multi-sensor dataset built on data collected using an Apple Watch 6 during the normal daily routine of a dairy cow. Thanks to the collection environment, sampling technique, information regarding the sensors, the applications used for data conversion and storage make the dataset a transparent one. This transparency of data can thus be used for further development of techniques for lameness detection for dairy cows which can be objectively compared. Aside from the public sharing of the dataset, we have also shared a machine-learning technique which classifies the caws in healthy and lame by using the raw sensory data. Hence validating the major objective which is to establish the relationship between sensor data and lameness.

Yuexing Hao, Tiancheng Yuan, Yuting Yang et al.

Dairy owners spend significant effort to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce these costs, yet obstacles arise when adapting advanced tools to farming environments. In this work, we adapt AI tools to dairy cow teat localization, teat shape, and teat skin condition classifications. We also curate a data collection and analysis methodology for a Machine Learning (ML) pipeline. The resulting teat shape prediction model achieves a mean Average Precision (mAP) of 0.783, and the teat skin condition model achieves a mean average precision of 0.828. Our work leverages existing ML vision models to facilitate the individualized identification of teat health and skin conditions, applying AI to the dairy management industry.

Nawazish Ali, Rachael Shaw, Karl Mason

Dairy farms consume a significant amount of electricity for their operations, and this research focuses on enhancing energy efficiency and minimizing the impact on the environment in the sector by maximizing the utilization of renewable energy sources. This research investigates the application of Proximal Policy Optimization (PPO), a deep reinforcement learning algorithm (DRL), to enhance dairy farming battery management. We evaluate the algorithm's effectiveness based on its ability to reduce reliance on the electricity grid, highlighting the potential of DRL to enhance energy management in dairy farming. Using real-world data our results demonstrate how the PPO approach outperforms Q-learning by 1.62% for reducing electricity import from the grid. This significant improvement highlights the potential of the Deep Reinforcement Learning algorithm for improving energy efficiency and sustainability in dairy farms.

Iias Faiud, Michael Schukat, Karl Mason

The agricultural sector is facing mounting demands to enhance energy efficiency within farm enterprises, concurrent with a steady escalation in electricity costs. This paper focuses on modelling the adoption rate of photovoltaic (PV) energy within the dairy sector in Ireland. An agent-based modelling approach is introduced to estimate the adoption rate. The model considers grid energy prices, revenue, costs, and maintenance expenses to calculate the probability of PV adoption. The ABM outputs estimate that by year 2022, 2.45% of dairy farmers have installed PV. This is a 0.45% difference to the actual PV adoption rate in year 2022. This validates the proposed ABM. The paper demonstrates the increasing interest in PV systems as evidenced by the rate of adoption, shedding light on the potential advantages of PV energy adoption in agriculture. This study possesses the potential to forecast future rates of PV energy adoption among dairy farmers. It establishes a groundwork for further research on predicting and understanding the factors influencing the adoption of renewable energy.

Mian Ibad Ali Shah, Enda Barrett, Karl Mason

Farm businesses are increasingly adopting renewables to enhance energy efficiency and reduce reliance on fossil fuels and the grid. This shift aims to decrease dairy farms' dependence on traditional electricity grids by enabling the sale of surplus renewable energy in Peer-to-Peer markets. However, the dynamic nature of farm communities poses challenges, requiring specialized algorithms for P2P energy trading. To address this, the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES) has been developed, providing a platform to experiment with Reinforcement Learning techniques. The simulations demonstrate significant cost savings, including a 43% reduction in electricity expenses, a 42% decrease in peak demand, and a 1.91% increase in energy sales compared to baseline scenarios lacking peer-to-peer energy trading or renewable energy sources.

Hanqing Bi, Suresh Neethirajan

This study investigates the correlation between dairy farm characteristics and methane concentrations as derived from satellite observations in Eastern Canada. Utilizing data from 11 dairy farms collected between January 2020 and December 2022, we integrated Sentinel-5P satellite methane data with critical farm-level attributes, including herd genetics, feeding practices, and management strategies. Initial analyses revealed significant correlations with methane concentrations, leading to the application of Variance Inflation Factor (VIF) and Principal Component Analysis (PCA) to address multicollinearity and enhance model stability. Subsequently, machine learning models - specifically Random Forest and Neural Networks - were employed to evaluate feature importance and predict methane emissions. Our findings indicate a strong negative correlation between the Estimated Breeding Value (EBV) for protein percentage and methane concentrations, suggesting that genetic selection for higher milk protein content could be an effective strategy for emissions reduction. The integration of atmospheric transport models with satellite data further refined our emission estimates, significantly enhancing accuracy and spatial resolution. This research underscores the potential of advanced satellite monitoring, machine learning techniques, and atmospheric modeling in improving methane emission assessments within the dairy sector. It emphasizes the critical role of farm-specific characteristics in developing effective mitigation strategies. Future investigations should focus on expanding the dataset and incorporating inversion modeling for more precise emission quantification. Balancing ecological impacts with economic viability will be essential for fostering sustainable dairy farming practices.

B. Meghzili, F.A. Benyahia, K. Szkolnicka et al.

Background: Buttermilk, a significant by-product of the dairy industry, is acknowledged as a beneficial food due to its content of water-soluble vitamins, polar lipids, and milk fat globule membranes. This research is focused on investigating the potential of buttermilk as a substitute in the production of a novel soft cheese type ‘‘camembert’’. Methods: A total of 12 cheese samples of camembert cheese, both with and without buttermilk, were prepared and subjected to a series of physico-chemical analyses in October 2023 to measure protein, fat, total solids, pH, and production yield. Texture Profile Analysis was applied to evaluate textural characteristics, and the microstructure was examined using Scanning Electron Microscopy. A hedonic scale was employed in sensory evaluation to measure taste intensity. Results: The sample containing 90% cow's milk and 10% buttermilk exhibited the most significant (p≤0.05) physico-chemical characteristics as production yield of 45.33%±0.710, protein content of 28.9%±0.58, fat content of 24.88%±0.026, total solids of 54.62±0.23, and a pH of 6.42±0.58. Sensory evaluations demonstrated that camembert samples containing buttermilk were distinguished by high sensory quality and satisfactory taste profiles. In addition, a dense and tightly fused protein matrix was observed in the microstructure of the buttermilk fortified cheese. The results also emphasized that the acidic nature of buttermilk significantly affected the production yield, total solids content, and textural characteristics, evidenced by a hardness of 3.36 N and fracturability of 1.75 N. Conclusion: The results validate the use of buttermilk as an effective alternative in the production of a new type of soft cheese, manifesting improved sensory, structural, and physico-chemical characteristics. This investigation supports the innovative utilization of buttermilk in cheese production, potentially offering a valuable avenue for dairy industry by-products. DOI: 10.18502/jfqhc.11.2.15647

Mengya Wang, Lei Zhang, Xingwei Jiang et al.

ABSTRACT: The nutritional components and quality of milk are influenced by the rumen microbiota and its metabolites at different lactation stages. Hence, rumen fluid and milk samples from 6 dairy cows fed the same diet were collected during peak lactation, early mid-lactation, and later mid-lactation. Untargeted metabolomics and 16S rRNA sequencing were applied for analyzing milk and rumen metabolites, as well as rumen microbial composition, respectively. The levels of lipid-related metabolites, l-glutamate, glucose-1-phosphate, and acetylphosphate in milk exhibited lactation-dependent attenuation. Maltol, N-acetyl-d-glucosamine, and choline, which are associated with milk flavor or coagulation properties, as well as l-valine, lansioside A, clitocine, and ginsenoside La, increased significantly in early mid-lactation and later mid-lactation, especially in later mid-lactation. The obvious increase in rumen microbial diversities (ACE and Shannon indices) were observed in early mid-lactation compared with peak lactation. Twenty-one differential bacterial genera of the rumen were identified, with Succinivibrionaceae_UCG-001, Candidatus Saccharimonas, Fibrobacter, and SP3-e08 being significantly enriched in peak lactation. Rikenellaceae_RC9_gut_group, Eubacterium_ruminantium_group, Lachnospira, Butyrivibrio, Eubacterium_hallii_group, and Schwartzia were most significantly enriched in early mid-lactation. In comparison, only 2 bacteria (unclassified_f__Prevotellaceae and Prevotellaceae_UCG-001) were enriched in later mid-lactation. For rumen metabolites, LysoPE(16:0), l-glutamate, and l-tyrosine had higher levels in peak lactation, whereas PE(17:0/0:0), PE(16:0/0:0), PS(18:1(9Z)/0:0), l-phenylalanine, dulcitol, 2-(methoxymethyl)furan, and 3-phenylpropyl acetate showed higher levels in early mid-lactation and later mid-lactation. Multiomics-integrated analysis revealed that a greater abundance of Fibrobacter contributed to phospholipid content in milk by increasing ruminal acetate, l-glutamate, and LysoPE(16:0). Prevotellaceae_UCG-001 and unclassified_f_Prevotellaceae provide substrates for milk metabolites of the same category by increasing ruminal l-phenylalanine and dulcitol contents. These results demonstrated that milk metabolomic fingerprints and critical functional metabolites during lactation, and the key bacteria in rumen related to them. These findings provide new insights into the development of functional dairy products.