The application of different ovarian tissue vitrification techniques has been highlighted as an alternative to promote the formation of female germplasm banks. Given the scarcity of studies in canine species, this study aimed to use in vitro culture as an evaluation tool for different ovarian tissue vitrification techniques in female dogs. Five pairs of ovaries were collected from adult females. They were fragmented and subjected to vitrification using an ovarian tissue cryosystem (OTC) or adapted needle immersed vitrification (NIV). Fresh and vitrified fragments were cultured in vitro and evaluated for nine days for preantral follicle viability using a Trypan blue die and morphology through classic histology. The ultrastructural integrity of the ovarian tissue was checked by scanning electronic microscopy. The data were expressed as the mean and standard error and were compared using the Tukey test (P < 0.05). The fresh control group presented 77.2 ± 2.81% viability, similar to that of the control, cultured for 9 days (72.6 ± 1.8%), and also similar to both OTC (85.7 ± 3.6%) and NIV (75.7 ± 2.8%) treatments immediately after thawing. After 09 days of culture, however, viability in the OTC group dropped to 64.6 ± 16.2% (P< 0.05), while NIV maintained 72.8 ± 7.2% viability. For primordial preantral follicles morphology, there was no significant difference between the fresh control (85.5 ± 7.3%) and heated OTC (91.7 ± 5.4%), but after 09 days of culture, the OTC group (34.1 ± 9.0%) showed a significant decrease (P &lt; 0.05), while NIV preserved their morphological integrity (63,6 ± 6,7 %). Regarding other follicle categories, no differences were observed among vitrification techniques. Through ultrastructural evaluation, disorganization patterns were observed for stromal cells of ovarian tissue at the use of OTC. In conclusion, the NIV technique allowed for a more efficient preservation of canine ovarian tissue than OTC.

Health events represent a key constraint to farm sustainability in the dairy industry. Previous research tended to focus on short-term milk loss during disease periods. However, in the long term, milk yield is usually not fully recovered even after the disease is cured. In this context, our study aimed to quantify the long-term impacts of health events on milk yields through causal inference strategies. We collected high-throughput session milk yield records for a total of 37,246 Holstein cattle in China from 2020 to 2024, and 40,699 health event records. Three causal inference strategies were used to perform unbiased inference of causal effects on long-term milk losses, including propensity score matching (PSM), overlap weighting (OW), and convergent cross-mapping (CCM). PSM and OW estimated causal effects by balancing pre-existing confounders between diseased and healthy cows by matching or weighting to achieve conditions like randomization. CCM was used to detect causal relationships between two short time series of milk yield and inter-session variability during diseased and healthy periods. The confounders included herd–year–season, parity, stillbirths, calving ease, the number of inseminations, and the genetic levels of milk yield and resilience. Overall, the association between milk yield and inter-session variability was enhanced by all diseases. During the period of single disease, daily milk yield decreased by an average of 16.06% and the causal effect of different diseases ranged from 11.17% (reproductive disorders) to 24.76% (digestive disorders). After curing, the impacts of the five diseases (udder health, reproductive disorders, metabolic disorders, digestive disorders, and hoof health) on production performance were 7.88%, 4.99%, 4.06%, 8.42%, and 8.00%, respectively. The long-term effect of the second disease onset would be weakened to 3.28%. In summary, this study confirms the long-term impact of health events on production performance and shows the potential of causal inference to serve as a powerful tool for precision livestock farming.

Nowadays, microplastics are ubiquitous in the environment and are increasingly observed in species of economic interest such as crustaceans, mussels or fish. As a semi-enclosed sea, with a unique environment and biota inhabiting it, the Black Sea is no exception to this global issue, in recent years being considered one of the most polluted European seas.

For the first time, within this study, we investigated the accumulation of microplastics in a fish species of economic interest present in the Romanian coastal area (NW Black Sea). Microplastics presence was assessed in the gastro-intestinal tract of 90 individuals, collected from several locations on the Romanian coast (Tuzla, Agigea and Corbu), using the 10% potassium hydroxide (KOH) chemical digestion method, following MSFD TG-ML recommendations (Galgani et al., 2023).

We found a frequency of occurrence (FO%) of ingested microplastics ranging between 73 and 90%, with a mean of 1.6-3.4 items/individual, with fibers being the predominant type of ingested microplastics.

In this context, our results fill an important gap, as there was no other information on microplastics ingestion in economically valuable fish from the Romanian coast of the Black Sea.

Given the high level of microplastics in the European anchovies, an economically valuable species of fish, there is a concern and a necessity to understand the impact of microplastics on food webs and human health.

The use of mechanical brushes is a strategy for improving the welfare of dairy cows; however, measuring brush usage is a complex and time-consuming task. This study aimed to develop an artificial neural network (ANN) to identify dairy cows’ usage of mechanical brushes using image capture. The database (503 images) was built with publicly available images (e.g., Google Images and YouTube) and manually divided into two folders: 1 - "Using Brush" (251 images) and 2 - "Not Using" (252 images). The ANN performed image resizing, image preprocessing, and convolutional training over 10 epochs with 16 steps for data processing and machine learning, resulting in a total data loss of 0.0074 with an accuracy of 0.998. ANN algorithms and image processing software were implemented using the Python programming language and the open-source libraries TensorFlow, Keras, and Python Screen Capture. A data augmentation strategy (e.g., rotation and filters) was used to build the test datasets. ANN validation was performed based on its errors and successes in three distinct tests, resulting in a precision of 0.75, recall 0.62, and an average processing time of 40 milliseconds per image. In a farm environment, where cows need to be continuously monitored, a moderate precision of 0.75 can allow for the detection of relevant behaviors, such as the frequency of brush use. This information can be used to evaluate and improve animal welfare by ensuring cows are using the brushes as intended, which can lead to reduced stress and increased comfort. Monitoring brush usage may also offer insights into cow health and productivity. Our findings indicate that the developed convolutional learning neural network has the capacity to contribute to the stufy of animal behavior and assist in identifying the usage of mechanical brushes by dairy cows, as it demonstrated high processing speed and accuracy.

This study evaluates the impact of composting on the circularity of a poultry system in Mexico, specifically at the farm Avícolas Campesinos Bondojito (ACB), located in Huichapan, Hidalgo. A systems thinking approach was used to analyze the technological, social, and economic components of the poultry system and to characterize its by-products, such as poultry manure, based on their physicochemical properties and viability as biofertilizers. The composting process was experimentally evaluated by monitoring temperature, pH, and electrical conductivity (EC) at the pilot scale. The agro-inputs produced were analyzed to assess their quality as biofertilizers. Additionally, the Nested Circularity Framework was applied to evaluate the impact of composting on biomass production, energy efficiency, and nutrient recycling under two waste management scenarios. The results identified poultry manure as the primary by-product of the system, with 74.6 Mg per cycle and a composition of 36.7%_C, 3.27%_N, 1.51%_P, and 2.9%_K; a pH of 7.62; an EC of 1062 µS·cm^-1; and a C:N ratio of 11.34:1, which limited its use as direct fertilizer due to instability. Composting improved its viability as an agro-input by increasing the C:N ratio (to 31.84) through mixing it with carbon-rich materials, which enhanced the thermal evolution, sanitization, and nutrient availability, particularly that of nitrogen (from 102.3 to 258.2 ppm of N). Ultimately, integrating poultry manure composting and an agricultural subsystem within the poultry production system could significantly enhance the farm’s circularity. This would include better biomass utilization (the additional production of 7.72 Mg of oat grain and 9.6 Mg of straw), reduced reliance on external inputs (straw for bedding), and increased energy efficiency (56% vs 44%). These strategies suggest composting optimizes waste management and contributes to a more sustainable and efficient production system.

Nowadays, the growth in the human population is putting pressure on the livestock industry, especially in developing countries, requiring a corresponding surge in livestock production to meet the demand for human consumption. Climate change is considered a challenge to the livestock industry as it affects livestock health and welfare. Resilience means an animal’s ability to cope with different challenges such as the environment and to be minimally affected or return rapidly to their normal health and production state. Thus, it is necessary to develop management practices for climate resilience, including the implementation of heat mitigation strategies in addition to helping farmers comprehend climate change and its impact on their agricultural systems.

Egypt is highly vulnerable to climate change; thus, adapting to the adverse impacts of climate change is an imperative necessity. The FAO roundtable workshop with the Egyptian dairy industry under the Scaling up Climate Ambition on Land use and Agriculture (SCALA) program that was held in Cairo in April 2024 stated that to ensure the long-term sustainability of livestock farming, a comprehensive Climate Smart Livestock (CSL) approach is necessary. Therefore, to consolidate all aspects of climate change in one document, Egypt prepared its National Climate Change Strategy (NCCS) 2050 to be a basic reference that ensures the integration of the climate change dimension into the general planning of all sectors. In this review, Egypt's efforts toward climate resilience in livestock production according to their national climate change strategy were discussed. This was achieved by gathering relevant information from recent publications collected from databases such as Scopus, Web of Science and PubMed. Publications were reviewed, and a summarization of mitigation strategies implemented under changing climatic conditions was made. The main strategies were outlined including using fans and water sprays to lower head loads, in addition to nutritional interventions to decrease methane emissions.

This study investigated the influence of thermal environment on the drinking behavior of beef cattle using electronic water troughs. Forty-six weaned male Caracu calves (mean±SD: 247±230 days old, 226±35 kg body weight) were evaluated over a 68-day feed efficiency test during the dry season in São Paulo State, Brazil. Housed in a collective pen, the calves had access to 12 electronic feeders and four water troughs. The system recorded the number of visits to water troughs, duration of drinking events (in seconds), and water intake (kg). The drinking behaviors were summarized by day to create one observation per day per animal. The Temperature Humidity Index (THI), adjusted for wind speed and solar radiation, was calculated using data from a local weather station. Daily mean THI and 3-day moving averages of daily mean and maximum THI values were calculated. Linear regression models were used to determine which THI measure best-predicted drinking behavior. Random effects of day and individual animals were incorporated to refine the model fit. All analyses were performed using R, with model selection based on the lowest Akaike information criterion (AIC) and highest R² value. The results indicate that the daily mean THI was the best predictor of drinking behaviors, though it had no significant effect on the number of visits (p=0.55) or event duration (p=0.90). The number of visits was better explained by animals (p<0.05, R²=0.31), while the duration of events was better explained by day (p<0.001, R²=0.43). However, daily mean THI influenced water intake (p<0.001); for each unit increase in THI, calves were 7% more likely to increase their water intake. Overall, while water intake was sensitive to changes in daily mean THI, the number of visits and event duration were better explained by day and by animal factors.

The aim of this study was to compare models for describing the overall and individual lactation curves in a Florida dairy goat herd to determine which mathematical model would be the better fit.

The automatic milking system (AMS) allows the determination of the individual production of each milking throughout the whole lactation. The original dataset included 104,231 milking records of 206 does obtained by AMS (an average of 505.9 milking per animal, with a mean milk yield of 1.12±0.69 kg per milking). In this study, the analysis of lactation curves was applied to the daily milk production records from kidding to 240 days of lactation (an average of 239 daily records per animal, with a mean daily milk yield of 2.28±1.14 kg).

A total of six functions (Wood, Wilmink, Ali and Schaeffer, Cobby and Le Du, Cappio-Borlino, and cubic splines) were used to model overall and individual lactation curves as a function of days in milk. Environmental factors including the number of lactations, month of kidding, and type of kidding, which significantly affect the lactation curve, were also incorporated into the models.

The goodness of fit of the models was evaluated based on the BIC (Bayesian information criterion). A cubic Spline function provided the best fit and offered an accurate description of the overall curve (followed by the Wilmink and the Wood models). In the case of the individual lactation curve, the Spline model showed the best fitting performance for 62.74% of the total animals (followed by the Wood model at 25% and the Wilmink model at 10.78%). Results from this study will provide insight into a better understanding of the lactation curve of Florida dairy goats and will serve as a tool for better herd management and selection.

This study aimed to map the status of tilapia farming in Egypt’s Nile Delta region, particularly the knowledge, attitudes, and practices (KAPs) of Egyptian tilapia farmers towards the current state of animal welfare in the sector and the major challenges they face in their daily operations. To this end, a survey was conducted of tilapia farmers across different regions, asking about their farming practices, feed management, health and safety measures, and the economic aspects of tilapia farming. We found that just 11 % of the surveyed tilapia farmers had received some sort of training on the importance of animal welfare, while 76 % said they could benefit from such training. Farmers perceived maintaining good water quality as the most important metric for achieving good tilapia welfare. However, they were significantly less willing and able to intervene on this factor compared to other factors, such as changing feeding practices, minimising handling, and carrying out veterinary checks. In addition, we found evidence of better production outcomes for farmers that had received welfare training, with significantly fewer of them reporting high mortality rates and significantly fewer reporting frequent poor growth.

Most farmers expected the Egyptian tilapia production sector to continue to grow and intensify. We present the data as an overview of Egyptian tilapia farmers’ knowledge, attitudes, and practices relating to fish welfare and as a basis for future efforts to improve the welfare of farmed tilapia in Egypt given the lack of awareness around this topic and the fact that it is gaining increasing importance elsewhere. We can see a need for establishing minimum animal welfare standards in Egyptian tilapia farming, either through regulators or certification schemes. There is also a clear need for establishing training programmes that cover animal health and welfare aspects in aquaculture.

Advisors play a key role in advancing the dairy industry by helping farmers adopt practices that improve animal welfare and align with societal values. The effectiveness of knowledge transfer depends on the information that advisors have. Exploring their perceptions of new practices, such as the cow–calf system, helps expand the understanding of how these practices might be received in the industry. This preliminary study aimed to explore Brazilian livestock advisors' perceptions of systems that allow cow–calf contact after milking. Since data collection is ongoing, we present here the responses obtained so far for the Southeast region. Participants (n=57) answered an online questionnaire that included sociodemographic questions and a 3-point Likert scale. Participants were randomly assigned to one of two cow–calf contact (CCC) systems: Full-contact (Full-CCC, n=26), where calves could suckle, and cows could nurse or Partial-contact (Part-CCC, n=31), limited to smelling and licking. Data were analyzed descriptively, and the results are presented as percentages. Most participants (56%) were between 18 and 35 years old, with 51% being men. Most participants (62%) disagreed with the Full-CCC system, as 73% felt it was not beneficial for cows or calves and would not recommend it to dairy farmers. Perceptions on Part-CCC were conflicting: 42% agreed, 26% were neutral, and 32% disagreed. Nonetheless, most felt that Part-CCC is not beneficial for cows (58%) or calves (48%), but 52% would recommend it. Considering animal quality of life, 33% viewed Full-CCC positively, 40% were neutral, and 27% saw it as poor. In contrast, 73% felt that animals experience a good quality of life under Part-CCC. Our findings suggest advisors’ perceptions of CCC systems vary, with more positive views toward systems that do not allow calves to suckle. This insight highlights the importance of exploring the knowledge and viewpoints of professionals in the dairy chain to expand the debate on cow–calf systems in the industry.