The EU rural development policy has addressed challenges related to climate change in agriculture by introducing public voluntary schemes, which financially support the adoption of climate-smart agricultural practices. Several factors, most of which are non-financial ones, drive adoption and continuation of these schemes by farmers. Despite the importance of these factors, only a few studies explore their role in the European context. This paper contributes to filling this gap from a twofold perspective. First, it investigates the role of the farming factors, technology accessibility, environmental features, policy design and social expertise at the territorial level on early adoption. Second, it sheds light on farmers' attitudes and motivations and on social pressure on their decision to continue or discontinue the practices, by surveying a sample of early adopters. Three schemes for the Veneto region rural development programme are considered: no-tillage, fertiliser reduction, and water and fertiliser reduction. The results highlight that non-financial factors should be considered in order to design more effective schemes to prompt farmers to adopt and continue such practices over the long run. The paper also stresses the need to complement financial support with proactive information-based instruments.

Frequent checks on livestock’s body growth can help reducing problems related to cow infertility or other welfare implications, and recognizing health’s anomalies. In the last ten years, optical methods have been proposed to extract information on various parameters while avoiding direct contact with animals’ body, generally causes stress. This research aims to evaluate a new monitoring system, which is suitable to frequently check calves and cow’s growth through a three-dimensional analysis of their bodies’ portions. The innovative system is based on multiple acquisitions from a low cost Structured Light Depth-Camera (Microsoft Kinect™ v1). The metrological performance of the instrument is proved through an uncertainty analysis and a proper calibration procedure. The paper reports application of the depth camera for extraction of different body parameters. Expanded uncertainty ranging between 3 and 15 mm is reported in the case of ten repeated measurements. Coefficients of determination R² > 0.84 and deviations lower than 6% from manual measurements where in general detected in the case of head size, hips distance, withers to tail length, chest girth, hips, and withers height. Conversely, lower performances where recognized in the case of animal depth (R² = 0.74) and back slope (R² = 0.12).

Over the last two decades, the dairy industry has adopted the use of Automatic Milking Systems (AMS). AMS have the potential to increase the effectiveness of the milking process and sustain animal welfare. This study assessed the state of the art of research activities on AMS through a systematic review of scientific and industrial research. The papers and patents of the last 20 years (2000–2019) were analysed to assess the research tendencies. The words appearing in title, abstract and keywords of a total of 802 documents were processed with the text mining tool. Four clusters were identified (Components, Technology, Process and Animal). For each cluster, the words frequency analysis enabled us to identify the research tendencies and gaps. The results showed that focuses of the scientific and industrial research areas complementary, with scientific papers mainly dealing with topics related to animal and process, and patents giving priority to technology and components. Both scientific and industrial research converged on some crucial objectives, such as animal welfare, process sustainability and technological development. Despite the increasing interest in animal welfare, this review highlighted that further progress is needed to meet the consumers’ demand. Moreover, milk yield is still regarded as more valuable compared to milk quality. Therefore, additional effort is necessary on the latter. At the process level, some gaps have been found related to cleaning operations, necessary to improve milk quality and animal health. The use of farm data and their incorporation on herd decision support systems (DSS) appeared optimal. The results presented in this review may be used as an overall assessment useful to address future research.

Information on the body shape of pigs is a key indicator to monitor their performance and health and to control or predict their market weight. Manual measurements are among the most common ways to obtain an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. The present paper proposes the implementation of a Structure from Motion (SfM) photogrammetry approach as a new tool for on-barn animal reconstruction applications. This is possible also to new software tools allowing automatic estimation of camera parameters during the reconstruction process even without a preliminary calibration phase. An analysis on pig body 3D SfM characterization is here proposed, carried out under different conditions in terms of number of camera poses and animal movements. The work takes advantage of the total reconstructed surface as reference index to quantify the quality of the achieved 3D reconstruction, showing how as much as 80% of the total animal area can be characterized.