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This study analyses the effect of heat stress (HS) on milk production, composition, and somatic cell count in Holstein cows in Galicia. For this purpose, the study uses the temperature and humidity index (THI) obtained from the official network of weather stations and data from the official milk control between 2016 and 2021. For milk production, critical THI thresholds were established at THI = 72, while the somatic cell count increased when THI exceeded 78. For the fat and protein components of milk, the critical thresholds are lower, at 64 and 63, respectively. The analysis shows that for each unit increase in THI above the critical threshold, up to 0.249 kg of milk, 0.008 kg of protein, and 0.006 kg of fat can be lost per cow per day. HS has a greater impact on milk composition than on volume, particularly on protein content. In addition, it was observed that the impact of HS on milk production can last between 7 and 12 days. Similarly, consecutive cumulative days of HS significantly impact both milk production and quality, especially in higher productivity stages multiparous cows. Although Galicia has a less extreme climate than other regions, the presence of moderate HS during the warmer months causes significant economic losses in the dairy sector.

This study analyses the effect of heat stress (HS) on milk production, composition, and somatic cell count in Holstein cows in Galicia. For this purpose, the study uses the temperature and humidity index (THI) obtained from the official network of weather stations and data from the official milk control between 2016 and 2021. For milk production, critical THI thresholds were established at THI = 72, while the somatic cell count increased when THI exceeded 78. For the fat and protein components of milk, the critical thresholds are lower, at 64 and 63, respectively. The analysis shows that for each unit increase in THI above the critical threshold, up to 0.249 kg of milk, 0.008 kg of protein, and 0.006 kg of fat can be lost per cow per day. HS has a greater impact on milk composition than on volume, particularly on protein content. In addition, it was observed that the impact of HS on milk production can last between 7 and 12 days. Similarly, consecutive cumulative days of HS significantly impact both milk production and quality, especially in higher productivity stages multiparous cows. Although Galicia has a less extreme climate than other regions, the presence of moderate HS during the warmer months causes significant economic losses in the dairy sector.

Chinese traditional herbs are used widely as feed supplements to improve the immune response and antioxidant capacity of livestock. Twenty early-weaned 4-month-old yak calves (72.3 ± 3.65 kg) were divided randomly into four groups (n = 5 per group); three groups were provided with supplementary 80 mL/kg DMI of the root water extracts of either Angelica sinensis, Codonopsis pilosula or Glycyrrhiza uralensis, and one group (control) was not provided with a supplement. Compared to control calves, calves consuming the three herbal extracts increased serum concentrations of albumin (ALB) and glutathione peroxidase (GSH-Px), but decreased serum concentrations of free fatty acids (FFAs) and malondialdehyde (MDA) (p < 0.05). Calves consuming A. sinensis decreased (p < 0.05) serum concentration of total cholesterol (TC), and increased (p < 0.05) serum concentration of total proteins (TP). Serum FFA concentrations increased (p = 0.004) linearly with time in the control group, but not in the groups consuming herbs. Serum metabolomic data demonstrated that A. sinensis and C. pilosula regulate mainly amino acid metabolism, while G. uralensis regulates mainly carbon and amino acid metabolism. It was concluded that the three herbal root extracts, as dietary supplements, improved energy and nitrogen metabolism, and enhanced the antioxidant capacity of yak calves.

Chinese traditional herbs are used widely as feed supplements to improve the immune response and antioxidant capacity of livestock. Twenty early-weaned 4-month-old yak calves (72.3 ± 3.65 kg) were divided randomly into four groups (n = 5 per group); three groups were provided with supplementary 80 mL/kg DMI of the root water extracts of either Angelica sinensis, Codonopsis pilosula or Glycyrrhiza uralensis, and one group (control) was not provided with a supplement. Compared to control calves, calves consuming the three herbal extracts increased serum concentrations of albumin (ALB) and glutathione peroxidase (GSH-Px), but decreased serum concentrations of free fatty acids (FFAs) and malondialdehyde (MDA) (p < 0.05). Calves consuming A. sinensis decreased (p < 0.05) serum concentration of total cholesterol (TC), and increased (p < 0.05) serum concentration of total proteins (TP). Serum FFA concentrations increased (p = 0.004) linearly with time in the control group, but not in the groups consuming herbs. Serum metabolomic data demonstrated that A. sinensis and C. pilosula regulate mainly amino acid metabolism, while G. uralensis regulates mainly carbon and amino acid metabolism. It was concluded that the three herbal root extracts, as dietary supplements, improved energy and nitrogen metabolism, and enhanced the antioxidant capacity of yak calves.

Too high or too low temperature in the sheep house will directly threaten the healthy growth of sheep. Prediction and early warning of temperature changes is an important measure to ensure the healthy growth of sheep. Aiming at the randomness and empirical problem of parameter selection of the traditional single Extreme Gradient Boosting (XGBoost) model, this paper proposes an optimization method based on Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO). Then, using the proposed PCA-PSO-XGBoost to predict the temperature in the sheep house. First, PCA is used to screen the key influencing factors of the sheep house temperature. The dimension of the input vector of the model is reduced; PSO-XGBoost is used to build a temperature prediction model, and the PSO optimization algorithm selects the main hyperparameters of XGBoost. We carried out a global search and determined the optimal hyperparameters of the XGBoost model through iterative calculation. Using the data of the Xinjiang Manas intensive sheep breeding base to conduct a simulation experiment, the results show that it is different from the existing ones. Compared with the temperature prediction model, the evaluation indicators of the PCA-PSO-XGBoost model proposed in this paper are root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), mean absolute error (MAE) , which are 0.0433, 0.0019, 0.9995, 0.0065, respectively. RMSE, MSE, and MAE are improved by 68, 90, and 94% compared with the traditional XGBoost model. The experimental results show that the model established in this paper has higher accuracy and better stability, can effectively provide guiding suggestions for monitoring and regulating temperature changes in intensive housing and can be extended to the prediction research of other environmental parameters of other animal houses such as pig houses and cow houses in the future.

Too high or too low temperature in the sheep house will directly threaten the healthy growth of sheep. Prediction and early warning of temperature changes is an important measure to ensure the healthy growth of sheep. Aiming at the randomness and empirical problem of parameter selection of the traditional single Extreme Gradient Boosting (XGBoost) model, this paper proposes an optimization method based on Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO). Then, using the proposed PCA-PSO-XGBoost to predict the temperature in the sheep house. First, PCA is used to screen the key influencing factors of the sheep house temperature. The dimension of the input vector of the model is reduced; PSO-XGBoost is used to build a temperature prediction model, and the PSO optimization algorithm selects the main hyperparameters of XGBoost. We carried out a global search and determined the optimal hyperparameters of the XGBoost model through iterative calculation. Using the data of the Xinjiang Manas intensive sheep breeding base to conduct a simulation experiment, the results show that it is different from the existing ones. Compared with the temperature prediction model, the evaluation indicators of the PCA-PSO-XGBoost model proposed in this paper are root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), mean absolute error (MAE) , which are 0.0433, 0.0019, 0.9995, 0.0065, respectively. RMSE, MSE, and MAE are improved by 68, 90, and 94% compared with the traditional XGBoost model. The experimental results show that the model established in this paper has higher accuracy and better stability, can effectively provide guiding suggestions for monitoring and regulating temperature changes in intensive housing and can be extended to the prediction research of other environmental parameters of other animal houses such as pig houses and cow houses in the future.

Global climate change inflicts unambiguous risks on agricultural systems and food security. Small ruminants are known for their ability to adapt to changing environmental conditions. This paper aims to characterize sheep production systems in a vulnerable agricultural zone and the breeders’ perceptions of climate change to apprehend challenges that they are confronting, and formulate resilience actions. The data analysis is based on 94 semi-structured surveys of sheep farmers carried out in the Tunisian semi-arid region. The PCA analysis results revealed three main sheep production systems. The agro-pastoral rain-fed system (AGPRF) is dominant (55%), with large farms and common pastures integrating cereals and fodder. The agro-pastoral irrigated system (AGPI: 20%) is characterized by small-area and forage irrigation (1.8 ha) and a smaller number of ewes but a greater use of animal feed supplementation. The agro-sylvo-pastoral system (AGSP: 25%) is a system where grazing is based on common lands and using tree sub-products, while the agricultural area is exclusively used to cultivate cereal crops. Sheep breeders’ climate perceptions are summarized as unpredictable climate events, a decrease in precipitation, and an increase in temperature. Resilience actions principally consist of reducing flocks’ numbers, using alternative local feed, fodder, and water resources, and building more shelters and planting more trees in the grazing areas. Nevertheless, cost-effectiveness should be considered in such vulnerable zones to insure the sheep production systems’ sustainability.