Terraced areas have existed in Italy since ancient times, and they continue to be characteristic elements of the cultural identity of the country. The progressive abandonment of rural areas and farmland that began in the 1960s has led to the disintegration and disappearance of many terraces, representing one of the problems connected with the deterioration of the historic Tuscan agricultural landscape. This research aims to provide a contribution to the territorial analysis of the agricultural terraced landscapes at a regional scale. The preliminary phase of the study involved setting up a working method in Geographic Information System (GIS) for the quantitative definition of the population. Afterwards, for the territorial analysis at a more detailed scale, a method was devised to identify the areas of greater significance in terms of a terracing intensity index. The final results concerned considerations on the distribution of the terraced landscapes and analyses related to land use and the main environmental parameters of the most representative terraced systems.

The objective of this study was to evaluate the potential of the practical application of unmanned aerial vehicles and RGB vegetation indices (VIs) in the monitoring of a coffee crop. The study was conducted in an experimental coffee field over a 12-month period. An RGB digital camera coupled to a UAV was used. Nine VIs were evaluated in this study. These VIs were subjected to a Pearson correlation analysis with the leaf area index (LAI), and subsequently, the VIs with higher R² values were selected. The LAI was estimated by plant height and crown diameter values obtained by imaging, which were correlated with these values measured in the field. Among the VIs evaluated, MPRI (0.31) and GLI (0.41) presented greater correlation with LAI; however, the correlation was weak. Thematic maps of VIs in the evaluated period showed variability present in the crop. The evolution of weeds in the planting rows was noticeable with both VIs, which can help managers to make the decision to start crop management, thus saving resources. The results show that the use of low-cost UAVs and RGB cameras has potential for monitoring the coffee production cycle, providing producers with information in a more accurate, quick and simple way.

Terraced systems currently represent an indubitable added value for Tuscany, as well as for other Italian regions and for several Mediterranean countries. This value goes beyond their original function of hosting new areas for cultivation. The hydrological functions performed by these systems, including control of erosion, stabilisation of the slopes, prolongation of runoff times and the possible reduction of the volumes of surface runoff, are well-known. In addition, they also play a strategic role in the conservation of biodiversity and in maintaining local identity. At a national level, the terraced agricultural systems fall within the scope of actions scheduled in the National Strategic Plan for Rural Development 2007–2013, and the standards of Good Agricultural and Environmental Conditions (GAECs) envisages that they be maintained through the granting of economic aid as laid down in the Rural Development Plans 2007–2013 and 2014–2020. Eighteen sample areas, previously selected on the basis of the terracing intensity index (defined as the ratio between the lines representing the walls and the surface of 1 ha), were subjected for on-site surveys to determine the geo-typological features through the identification and measurement of the main technical-construction parameters of the dry stone walls. This analysis also enabled assessments of the overall state of conservation of the dry stone walls in order to suggest operations for safeguarding and protection.

Automatic milking systems (AMS) have aroused worldwide interest recently. The first installation was by the company Lely in a project in the Netherlands (its homeland) in 1992. But nowadays, AMS represents a growing reality due to lobby for labor issues, rising costs, difficulty finding well-trained workers, and/ or difficulty keeping people on farms. This work aimed to present a review of the literature on AMS, beginning with a brief history of the evolution of the technology, showing advantages and limitations of its use, and ultimately giving some suggestions. The understanding of the technical functioning and operational running can help farmers and technicians in decision making on the adoption of the new technology. Besides workforce reduction and labor quality promotion, AMS has potential to improve feed conversion to milk, milk quality (with lower SCC), and cow productivity, as well as providing useful data and parameters for better farm management. Potential limitations include high investment costs, changes in milk composition (solids, free fatty acids), and increased risk of ketosis in cows.

Reference evapotranspiration (ET₀) is one important agrometeorological parameter for hydrological studies and climate risk zoning. ET₀ calculation by the FAO Penman–Monteith method requires several input data. However, the availability of climate data has been a problem in many places around the world, so the study of scenarios with different combinations of climate data has become essential. The aim of this study was to evaluate the performance of artificial neural network (ANN), random forest (RF), support vector machine (SVM), and multiple linear regression (MLR) approaches to estimate monthly mean ET₀ with different input data combinations and scenarios. Three scenarios were evaluated: at the state level, where all climatological stations were used (Scenario I–SI), and at the regional level, where the Minas Gerais state was divided according to the climatic classifications of Thornthwaite (Scenario II–SII) and Köppen (Scenario III–SIII). ANN and RF performed better in ET₀ estimation among the models evaluated in the SI, SII, and SIII scenarios with the following data combinations: (i) latitude, longitude, altitude, month, mean, maximum and minimum temperature, and relative humidity and (ii) latitude, longitude, altitude, month, mean temperature, and relative humidity. SVM and MLR models are recommended for all scenarios in situations with limited climatic data where only air temperature and relative humidity data are available. The results and information presented in this study are important for the agricultural chain and water resources in Minas Gerais state.

This study aimed to explore the influence of flight altitude, density, and distribution of ground control points (GCPs) on the digital terrain model (DTM) in surveys conducted by unmanned aerial vehicles (UAVs). A total of 144 photogrammetric projects consisting of 399 aerial photos were carried out in a 2 ha area. These photogrammetric projects involved six GCP distributions (edge, center, diagonal, parallel, stratified, and random), six GCP densities, and four flight altitudes (30, 60, 90, and 120 m). The response surface methodology was used to find interference factors and total root-mean-square error (RMSEt) as well. The 60 m flight altitude presented was the most efficient. Central GCP distribution was observed to have low precision. Using stratified and random edge distributions, 10 GCPs are recommended to achieve geometric precision below 0.07 m at any flight height. However, for studies requiring up to 0.07 m precision, the best distribution was parallel with 4 GCPs at any altitude. Diagonal positioning of the GCPs showed RMSEt values below 0.11 m with 4 GCPs at any altitude. A good distribution of GCPs was found to be important, but the density of GCPs per image was more relevant when obtaining a lower RMSEt.