This study aimed to determine the emissions of methane (CH₄) and nitrous oxide (N₂O) from seven Tunisian livestock species and their evolution over eleven consecutive years (2008–2018). The species of animals used were cattle (dairy and others), sheep, goats, camelids, horses, donkeys and mules, and poultry. The estimations of CH₄ and N₂O emissions were based on the Intergovernmental Panel on Climate Change (IPCC) guidelines for national inventories, using Tier 1 and Tier 2 approaches, with its default emission factors (EFs). The Tier 2 approach was applied only for the calculation of EF to estimate CH₄ emissions related to livestock manure management. CH₄ emission represented more than 92% of the total greenhouse gas (GHG) from livestock emissions. Moreover, 53% of the total CH₄ emissions from livestock were derived from cattle, followed by sheep, goats, other mammals (camelids, horses, mules, and donkeys), and poultry. During the period covered by the study (2008–2018), a slight and continuous decrease of both livestock population and total GHG emissions was observed, mainly in terms of CH₄. In mammals, CH₄ emissions were greater than N₂O emissions, whereas in poultry, N₂O emissions were up to 2.6 times greater than CH₄ emissions. The aggressive drive of the government to increase cattle and sheep production might affect CH₄ emissions in the future. Therefore, periodic estimations of GHG emissions from livestock are required to follow the time trends for more rational decision-making regarding livestock and GHG emissions policies.

Background: Cortisol is known as the main hormone released during stress responses in cattle and has been used to assess various stressors, including heat stress. This study investigated hair cortisol concentration (HCC) in different hair coat colors in dairy cows under natural heat stress conditions (temperature humidity index = 75). Methods: Hair samples were collected from the forehead region of ten multiparous cows (Brown Swiss, Montbéliarde, and Holstein) per group color at both the beginning and end of a three-week peak summer period in 2024 in the region of Jendouba, North Tunisia. Cows were grouped according to hair coat color (black, brown, red, white, and yellow) for subsequent analysis. Hair samples were prepared using a methanol-based separation protocol and analyzed via enzyme-linked immunosorbent assay (ELISA). Results: Meteorological data confirmed that cows were sustained under heat stress, with an average temperature humidity index value of 75; results indicated that black hair had considerably more HCC than white hair (p < 0.05). The results showed that there is a significant difference between HCC under three clusters (p < 0.05) according to hair color. Conclusions: The study emphasizes that hair color, along with factors such as breed and environmental conditions, should be carefully considered when using HCC to assess stress in cattle beyond simply black or white hair color.

This study aimed to assess the welfare of Tunisian sheep in extensive sheep production systems using animal-based measures of ewe welfare. This study encompasses the first national survey of sheep welfare in which animal-based outcomes were tested. Animal-based welfare measures were derived from previous welfare protocols. Fifty-two Tunisian farms were studied and a number from 20 to 100 animals by flock were examinated. The whole flock was also observed to detect clinical diseases, lameness, and coughing. The human-animal relationship was selected as welfare indicators. It was evaluated through the avoidance distance test. The average avoidance distance was 10.47 ± 1.23 and 8.12 ± 0.97 m for a novel person and farmer, respectively. The global mean of body condition score (BCS) was 2.4 with 47% of ewes having a BCS of two, which may be associated with an increased risk of nutritional stress, disease, and low productivity. Ten farms had more than 7% of lambs with a low body condition score, which may be an indication of a welfare problem. The results obtained in the present study suggest that the used animal-based measures were the most reliable indicators that can be included in welfare protocols for extensive sheep production systems.

This study examined the effects of seasonal climatic variations and parity on the milk yield and composition of Holstein (HST), Brown Swiss (BSW), and Montbéliard (MBM) dairy cows in North Tunisia from 2020 to 2024. Test day records (TD) from 199762 observations for different classes of parities (1, 2, 3 and 4+), with calculation of the Temperature-Humidity Index (THI) through Ambient Temperature (AT, ℃) and Relative Humidity (RH, %), were merged with meteorological data for analysis of the effect of seasonal heat stress and parity on the milk yield and composition. Meteorological data indicated a significant seasonal variation, with THI surpassing heat-stress thresholds during summer (mean = 75.29) and reaching its maximum in July and August. According to the results, Holstein cows had the highest peak milk yields (about 24.5 kg/d), but they were also the most sensitive to heat stress, as shown by their significant production decrease in summer. On the other hand, despite environmental challenges, BSW and MBM showed better thermotolerance and maintained consistent lactation. Analysis of milk composition revealed a strong seasonal concentration effect in all breeds, with the percentages of milk fat (MF) and milk proteins (MP) increasing in the summer and autumn months, with BSW producing the highest solid concentrations (up to 3.87% fat). Statistical analysis demonstrated significant interactions among breed, season, and parity (p < 0.05) for all traits except parity with milk fat. The results indicate that, whereas Holsteins maximize performance in temperate environments, incorporating Brown Swiss and Montbéliard genotypes provides a more resilient strategy for maintaining dairy productivity and milk quality in the increasingly hot Mediterranean climate of North Tunisia.