Maize is considered one of the most susceptible crops to mycotoxin-producing fungi throughout the world, mainly belonging to the Fusarium spp. and Aspergillus spp. Maize is mainly used as animal feeds in Italy, as well as for human consumption, being essential for all the protected designation of origin (DOP) products. Our study investigated the occurrence of regulated mycotoxins in 3769 maize grain samples collected from 88 storage centers by the National Monitoring Network over an 11-year period (2011–2021). Moreover, an in-depth survey over a 4-year period, characterized by extremely different meteorological conditions, was conducted to investigate the co-occurrence of regulated, masked, and emerging mycotoxins. The survey confirmed that Fusarium spp. was the most frequent fungi and fumonisins were the main mycotoxins that were constantly detected in the different years and areas. Moreover, the areas characterized by high fumonisin levels were also the most prone to contamination by emerging mycotoxins produced by the same Fusarium species of the Liseola section. On the other hand, as a result of climatic changes, maize grains have also been affected by the increased frequency of aflatoxin accumulation. Deoxynivalenol, zearalenone, and other emerging mycotoxins produced by the same Fusarium species as the Discolor section occurred more abundantly in some areas in Northern Italy and in years characterized by predisposing meteorological conditions.

In this study, the life cycle assessment (LCA) principle was performed to estimate the environmental impact of three dairy farms that operate using different farming systems, namely, conventional (CON), organic (ORG), and high-quality (HQ) modes. In Italy, the typical style of high-quality (HQ) farming is commonly included in the conventional system but is more strictly regulated by the Decree of the Italian Ministry of Health N° 185/1991. Although the farms are not fully representative of each conduct, they showed intrinsic peculiarities, e.g., the cow-culling rate of each system. This rate requires a quantification as it may be related to loss of income. Allocation criteria were applied to attribute the quantities of pollutants to the co-products: wheat, involved in the congruence and number of cows culled, the latter being undesirable and therefore necessary to quantify. Analysis of variance (ANOVA) highlighted that the no-dairy products significantly mitigated (p < 0.05) some of the impacts’ categories. The allocation of culled cows decreased the impacts of the CON and particularly those of the ORG farms when the mass mode was adopted. HQ showed values similar to the results without allocation. Overall, the objective of identifying a “marker” of undesirable products, estimated by the culling rate, was partially achieved.

Automation reduces the impact of farming on climate change and helps farmers adapt to its financial impact. Automatic feeding systems (AFSs) increase the ruminant’s feeding precision and ease operators’ workload. Such systems exist on a spectrum, requiring varied levels of operator support and installation complexity. A recent survey on farmers pointed out that those already running an AFS and those willing to buy one appreciate its ease of use, the resulting animal welfare, and the resulting overall benefits (increased production, farm profitability, and reduced feed waste). Whether technologically complex or not, studies have confirmed the benefits that farmers perceive to be underlying the remarkable reduction in the environmental impact of feeding operations (AFSs are electrically powered), the increase in animal welfare resulting from reduced conflicts around accessing the feed bunk, and the constant availability of fresh feed. However, their introduction should follow accurate and holistic structural and economic planning for existing and newly built facilities. The availability of public subsidies plays an essential role in pushing farmers to adopt the most modern digital technologies, whose benefits may even increase when farmers couple them with interconnected sensors to monitor animals’ physiological states.

Simulation systems have become essential tools for both researchers and virtual laboratory experiments. In the Agri-food-chain, SimAgri, a driving simulator for tractors and operating machines, has been developed for precision agriculture (PA) research and to train professional farm drivers. Using the virtual environment of the simulator, the influence and fine-tuning of PA operations logic may be evaluated by simulating existing systems, or designing new ones, in specially compared scenarios and setups. Current configurations include an agricultural tractor carrying or towing farm equipment such as sprayers, seeders and fertilizer, embedded sensors, human–machine interfaces that may be configured like a joystick, console and touchscreen, and four virtual environment monitors. The study describes the design choices that have made it possible to create a simulator aimed at precision agriculture, keeping auto guidance, geolocation, and operations with ISOBUS implements as pillars. This research aims to use a unique purpose-designed simulation platform, installed on a driver-in-the-loop simulator to provide data to objectify the benefits of PA criteria. Numerical and experimental data have been compared to ensure results reliability.

One of the main questions facing precision agriculture is the evaluation of different algorithms for the delineation of homogeneous management zones. In the present study, a new approach based on the use of time series of satellite imagery, collected during two consecutive growing seasons, was proposed. Texture analysis performed using the Gray-Level Co-Occurrence Matrix (GLCM) was used to integrate and correct the sum of the vegetation indices maps (NDVI and MCARI2) and define the homogenous productivity zones on ten durum wheat fields in southern Italy. The homogenous zones identified through the method that integrates the GLCM indices with the spectral indices studied showed a greater accuracy (0.18–0.22 Mg ha⁻¹ for ∑NDVIs + GLCM and 0.05–0.49 Mg ha⁻¹ for ∑MCARI2s + GLCM) with respect to the methods that considered only the sum of the indices. Best results were also obtained with respect to the homogeneous zones derived by using yield maps of the previous year or vegetation indices acquired in a single day. Therefore, the survey methods based on the data collected over the entire study period provided the best results in terms of estimated yield; the addition of clustering analysis performed with the GLCM method allowed to further improve the accuracy of the estimate and better define homogeneous productivity zones of durum wheat fields.