In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.

In the last few decades, the increase in the world’s population has created a need to produce more food, generating, consequently, greater pressure on agricultural production. In addition, problems related to climate change, water scarcity or decreasing amounts of arable land have serious implications for farming sustainability. Weeds can affect food production in agricultural systems, decreasing the product quality and productivity due to the competition for natural resources. On the other hand, weeds can also be considered to be valuable indicators of biodiversity because of their role in providing ecosystem services. In this sense, there is a need to carry out an effective and sustainable weed management process, integrating the various control methods (i.e., cultural, mechanical and chemical) in a harmonious way, without harming the entire agrarian ecosystem. Thus, intensive mechanization and herbicide use should be avoided. Herbicide resistance in some weed biotypes is a major concern today and must be tackled. On the other hand, the recent development of weed control technologies can promote higher levels of food production, lower the amount of inputs needed and reduce environmental damage, invariably bringing us closer to more sustainable agricultural systems. In this paper, we review the most common conventional and non-conventional weed control strategies from a sustainability perspective, highlighting the application of the precision and automated weed control technologies associated with precision weed management (PWM).

Goats have always been considered very useful animals. Goats success is related to its excellent adaptability to the difficult mountain conditions, extreme weather and low value feed acceptance, versatile habits and high production considering their size. These are some reasons because goats are among the first animals to be domesticated. In terms of evolution, goats could be separated by their dispersion area in three large groups: the European, the Asian, and the African. Global goat populations, mainly in Africa and in Asia, have increased for centuries but very strongly in the past decades, well above the world population growth. They are also used for forest grazing, an integrated and alternative production system, very useful to control weed growth reducing fire risk. Despite some exceptions, no large‐scale effort to professionalize this industry has been made so far. There are consumers for goat dairy products and there is enough global production, but misses a professional network between both. Regarding goat meat, the world leadership also stays in Africa and Asia, namely in China, and there is a new phenomenon, the spreading of goat meat tradition through Europe due to migrants from Africa and other places with strong goat meat consumption.

Ultrasonic measurements were taken on 46 sheep using a real-time ultrasound machine equipped with 2 probes (5 and 7.5 MHz). Measurements of subcutaneous fat thickness (SC) and muscle LM depth (MD) and area (MA) were taken at 2 locations: over the 13th thoracic vertebra (SC13, MD13, and MA13, respectively) and at the interval between the third and fourth lumbar vertebrae (SC34, MD34, and MA34, respectively). Fat thickness was also measured over the third sternebra of the sternum. The relationship between carcass and in vivo ultrasound measurements was high for all the measurements (r² between 0.54 and 0.96, P < 0.01). Concerning MD and SC, the 7.5 MHz probe estimates were consistently more precise than the 5-MHz estimates (r² increased between 0.09 and 0.13), but the reverse occurred with the MA estimates, although to a lesser extent. Estimates of carcass composition for muscle, subcutaneous fat, intermuscular fat, internal fat, and total fat based on BW explained a large amount of variation in muscle (87%), subcutaneous fat (85%), intermuscular fat (79%), internal fat (74%), and total fat (87%). In most cases (55 of 70) the introduction of one ultrasound measurement in addition to BW in the multiple regression equations further improved the explanation of variation for weight of carcass tissues, internal fat, and total fat. For carcass muscle estimation, the ultrasound measurements of muscle provided an increase of r² between 0.05 and 0.10 (P < 0.01). The SC13 and SC34 gave the best improvements in estimating subcutaneous fat, intermuscular fat, internal fat, and total fat (r² increased between 0.05 and 0.17; P < 0.01). Prediction of the proportions of the carcass components (internal and total fat from BW) was clearly lower than the prediction of the absolute amounts of these traits. Inclusion of one or more ultrasound measurements in addition to BW increased the predictive ability of the equations. Both probes were useful to estimate carcass muscle depth and area and fat depth, but the 7.5-MHz probe showed a greater ability to estimate depth. For all traits, the stepwise procedure demonstrated that the best fit was obtained with BW and one or more ultrasound measurement with the 7.5-MHz probe.

Body condition scoring (BCS) is the most widely used method to assess changes in body fat reserves, which reflects its high potential to be included in on-farm welfare assessment protocols. Currently used scoring systems in dairy goats require animal restraint for body palpation. In this study, the Animal Welfare Indicators project (AWIN) proposes to overcome this constraint by developing a scoring system based only on visual assessment. The AWIN visual body condition scoring system highlights representative animals from 3 categories: very thin, normal, and very fat, and was built from data sets with photographs of animals scored by a commonly used 6-point scoring system that requires palpation in 2 anatomical regions. Development of the AWIN scoring system required 3 steps: (1) identification and validation of a body region of interest; (2) sketching the region from photographs; and (3) creation of training material. The scoring system's reliability was statistically confirmed. An initial study identified features in the rump region from which we could compute a set of body measurements (i.e., measures based on anatomical references of the rump region) that showed a strong correlation with the assigned BCS. To validate the result, we collected a final data set from 171 goats. To account for variability in animal size and camera position, we mapped a subset of features to a standard template and aligned all the rump images before computing the body measurements. Scientific illustrations were created from the aligned images of animals identified as representative of each category to increase clarity and reproducibility. For training material, we created sketches representing the threshold between consecutive categories. Finally, we conducted 2 field reliability studies. In the first test, no training was given to 4 observers, whereas in the second, training using the threshold images was delivered to the same observers. In the first experiment, interobserver results was substantial, showing that the visual scoring system is clear and unambiguous. Moreover, results improved after training, reaching almost perfect agreement for the very fat category. The visual body condition scoring system is not only a practical tool for BCS in dairy goats but also shows potential to be fully automated, which would enhance its use in welfare assessment schemes and farm management.

Goat (Capra hircus) is one of the main sources of milk and meat products for human consumption. Goat milk differs from cow and human milk in both composition and nutritional properties. Goat milk and other goat-derived products contain several bioactive compounds that might be useful in patients suffering from a variety of chronic diseases. Several peptides, fats, and oligosaccharides present in goat’s milk can be potentially useful in cardiovascular disease, metabolic disorders, neurological degeneration, or in promoting intestinal health. They have also shown chemopreventive properties in cancer. In addition, the oligosaccharides present in goat’s milk have immunomodulatory properties, prevent adhesion of pathogenic bacteria, and have prebiotic, probifidogenic effects. Due to its potential health benefits, goat milk is particularly recommended for infants, older adults, and convalescing people. This chapter gives an overview of the biological activities of goat products and the effects of peptides, fats, and oligosaccharides present in goat milk on pathogenic bacteria, as well as their ability to regulate immunological, gastrointestinal, hormonal, and neurological responses in humans.