Dairy ruminants provide a major part of the livestock and agriculture sectors. Due to the increase in world population and the subsequent increase in dairy product demands, the dairy sector has been intensified. Dairy farming intensification and the subsequent increase in animal nutritional demands and the increase in the average global temperature as well have subjected animals to various stress conditions that impact their health and welfare. Various management practices and nutritional strategies have been proposed and studied to alleviate these impacts, especially under heat stress, as well as during critical periods, like the transition period. Some of the nutritional interventions to cope with stress factors and ensure optimal health and production are the inclusion of functional fatty acids and amino acids and feed additives (minerals, prebiotics, probiotics, essential oils and herbs, phytobiotics, enzymes, etc.) that have been proven to regulate animals’ metabolism and improve their antioxidant status and immune function. Thus, these nutritional strategies could be the key to ensuring optimum growth, milk production, and reproduction efficiency. This review summarizes and highlights key nutritional approaches to support the remarkable metabolic adaptations ruminants are facing during the transition period and to reduce heat stress effects and evaluate their beneficial effects on animal physiology, performance, health, as well as welfare.

In order to deal with the effects of globalization, urbanization, increase in world population, global warming, and climate change; and according to the Sustainable Development Goals (SDG) 2 targets, which aim to end hunger, achieve food security and improved nutrition and promote sustainable agriculture, it is urgently needed to transform our agriculture and livestock farming systems by taking into account the environmental considerations. The Breeding and management practices of indigenous bovine breeds: Solutions towards a sustainable future (BOVISOL) project is a scientific cooperation between three Mediterranean countries (Greece, Tunisia and Algeria) supported and funded by the European Commission under the European Research Area Networks (ERA-NET) scheme of the 7th Framework Programme. This project has been formed around the hypothesis that the local bovine breeds must be preserved since they possess a valuable genetic pool, and they are a part of the landscape and the biodiversity of rural areas. Moreover, their products (milk, cheese, meat, etc.) could contribute significantly to the local economies as they could easily be associated with recent food trends like “local” and “slow food”, which are considered today, as, not only a mean of nutrition, but also a way of living and a part of people’s identity. BOVISOL project aims to: (i) identify the local breeds and populations in a national level, (ii) describe the existing farm and breeding practices, (iii) analyze the quality of the main local animal products, (iv) propose solutions that will promote the sustainability of the traditional farming systems, especially nowadays that climate change proposes new challenges on animal production, and (v) disseminate the solutions on all the levels of the sector (farmers, scientists, local communities, governmental agencies).

There is a growing interest in selective breeding in European sea bass (Dicentrarchus labrax), especially regarding family selection based on growth performance. In particular, quantitative trait loci (QTL) identification in sea bass enhances the application of marker-assisted breeding for the genetic improvement of the production traits. The aims of the study were to identify potential QTL affecting stress and immunological indicators, body weight, and mortality after vibriosis injection in sea bass as well as to estimate heritability and genetic/phenotypic correlations for the aforementioned traits. To this end, stress test was performed on 960 offspring and a sub-group of them (420) was selected to explore the mortality after vibrio injection. Selective genotyping was performed in 620 offspring for 35 microsatellite markers and distributed into 6 linkage groups. The length of the genetic linkage map was 283.6 cM and the mean distance between the markers was 8.1 cM. QTL affecting body weight in three different growth periods detected on linkage groups LG1, LG4, LG6, and LG14. A QTL associated with weight in early growth stages (290–306 days post-hatching) was also identified on LG3. QTL analysis confirmed the existence of QTL affecting cortisol levels, on LG3 and LG14. Moreover, new QTL affecting only cortisol and glucose levels were detected on LG1 and LG23. No QTL affecting hormonal or biochemical marks was found on LG4 and LG6. Heritability of cortisol, lysozyme levels, and mortality were high (0.36, 0.55, and 0.38, respectively).

The indigenous cattle populations are threatened by extinction in many countries of the Mediterranean area. The objective of this study is the analysis of local cattle breeds’ production systems in Algeria, Greece, and Tunisia and the identification of their future challenges and opportunities. A total of 385 surveys were conducted in these study areas: central and northern Greece (43); northern and northwestern Tunisia (167), and northeastern Algeria (175). Data collected concerned socio-economic parameters as well as the production system’s functionality, constraints, and opportunities. Results revealed an average farmers’ age of 52.6 years old. The illiteracy rate is high, especially in Algeria (39%) and Tunisia (44%), where the farm size is relatively small with an average of 14 and four animals per farm, respectively. In Greece, much higher numbers were recorded (89 animals/farm). The average cultivated feedstuffs’ area is larger in Greece (12.07 ha) and smaller in Algeria and Tunisia (6.11 and 2.88 ha, respectively). Feeding resources are based on rangelands. Farming systems are traditional extensive and complemented when needed. Milk and meat marketing vary throughout countries and are not well valorized. The main constraints are high feeding costs, low milk and meat prices, and absence of labeling. Local and local-crossbred bovine populations could be valorized based on their good adaptation criteria when applying convenient genetic and development strategies.

The present study aimed to evaluate the accuracy of a numerical model, quantifying real-time ultrasonographic (RTU) images of pregnant sows, to predict litter size. The time of the test with the least error was also considered. A number of 4165 pregnancies in Farm 1 and 438 in Farm 2 were diagnosed twice, with the quality of the RTU images translated into rated-scale values (RSV1 and RSV2). When a deep neural network (DNN) was trained, the evaluation of the method showed that the prediction of litter size can be performed with little error. Root square mean error (RMSE) for training, validation with data from Farm 1, and testing on the data from Farm 2 were 0.91, 0.97, and 1.05, respectively. Corresponding mean absolute errors (MAE) were 2.27, 2.41, and 2.58. Time appeared to be a critical factor for the accuracy of the model. The smallest MAE was achieved when the RTU was performed at days 20–22. It is concluded that a numerical, RTU imaging model is a prominent predictor of litter size, when a DNN is used. Therefore, early routinely evaluated RTU images of pregnant sows can predict litter size, with machine learning, in an automated manner and provide a useful tool for the efficient management of pregnant sows.