Proximal soil sensors are receiving strong attention from several disciplinary fields, and this has led to a rise in their availability in the market in the last two decades. The aim of this work was to validate agronomically a zone management delineation procedure from electromagnetic induction (EMI) maps applied to two different rainfed durum wheat fields. The k-means algorithm was applied based on the gap statistic index for the identification of the optimal number of management zones and their positions. Traditional statistical analysis was performed to detect significant differences in soil characteristics and crop response of each management zones. The procedure showed the presence of two management zones at both two sites under analysis, and it was agronomically validated by the significant difference in soil texture (+24.17%), bulk density (+6.46%), organic matter (+39.29%), organic carbon (+39.4%), total carbonates (+25.34%), total nitrogen (+30.14%), protein (+1.50%) and yield data (+1.07 t ha−1). Moreover, six unmanned aerial vehicle (UAV) flight missions were performed to investigate the relationship between five vegetation indexes and the EMI maps. The results suggest performing the multispectral images acquisition during the flowering phenological stages to attribute the crop spatial variability to different soil proprieties.

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The forecasting of crop yield is one of the most critical research areas in crop science, which allows for the development of decision support systems, optimization of nitrogen fertilization, and food safety. Many tested modeling approaches can be differentiated according to the models and data used. The models used are traditional crop models that require data that are often difficult to measure. New modeling approaches based on artificial intelligence algorithms have proven to be of high performance, flexible, and can be tested based on available data. In this study, four independent field experiments conducted on Triticum turgidum subsp. durum Desf. in central–southern Italy were used to train a set of machine learning (ML) algorithms to predict the yield using 16 variables: fertilization, nitrogen management, pedoclimatic, and remote sensing data. Four ML algorithms were calibrated and validated over two independent sites, and a linear regression model was used as a control. The calibrated models can predict the grain yield in the two regions by using ancillary data, topsoil physical and chemical properties, multispectral drone imagery, climatic data, and nitrogen fertilizer applied at the site. Among the four ML algorithms, stochastic gradient boosting (root‐mean‐square error  = 0.58 t ha−1) outperformed others during calibration and transferability. Nitrogen application rate, seasonal precipitation, and temperature are the most important features for predicting wheat yield.

Recently, the use of biostimulant substances of different origins has been affirmed. They act differently on the physiological processes of the plant, helping to improve its productive response and resistance to biotic and abiotic stress. Therefore, the response of the wild rocket to two substances known to have biostimulating activity (Azoxystrobin, and a fluid extract of brown algae and yeast), was evaluated. Two experimental trials (Exp 1 and Exp 2) were carried out in the greenhouse. The collected product, in addition to being evaluated from a qualitative point of view, was used for evaluation of shelf life. Exp 1 involved the comparison of two N levels with two Azoxystrobin levels (treated–Azo+, and untreated control). Exp 2 involved the comparison of two N levels, and two biostimulating substances based on Azoxystrobin (Azo+) and on fluid extracts of yeast and brown algae (YBA+), in addition to untreated control. A split-plot experimental design with three replications was used. Azo+ increased marketable yield of wild rocket by 16.8% and enhanced some qualitative features at harvest as the increase in chlorophyll (+17.8%) and carotenoids (+13.5%), and decrease in nitrates (−10.6%), regardless of the nitrogen level. Furthermore, Azo+ increased the shelf life (+2.5 days) of wild rocket stored at 3.5 °C. In particular, Azo+ slowed the loss of chlorophyll (yellowing) and the worsening of odor and visual appearance. As Azoxystrobin is a fungicide effective for the control of some diseases of wild rocket, its use should be promoted as it would offer not only the benefit of disease control but also improved production and shelf life. YBA+ caused an increase in the chlorophyll content (+12.5%) at harvest of wild rocket, but reduced its antioxidant activity (−40%). YBA+ did not cause substantial variations in shelf life with the exception of a slowdown in the degradation of carotenoids. Further research is desirable to evaluate other variables such as the dose and time of application.

The current social context requires an increase in food production, improvement of its quality characteristics and greater environmental sustainability in the management of agricultural systems. Technological innovation plays a great role in making agriculture more efficient and sustainable. One of the main aims of precision farming (PF) is optimizing yield and its quality, while minimizing environmental impacts and improving the efficient use of resources. Variable rate techniques (VRT) are amongst the main management options for PF, and they require spatial information. This work incorporates maps of soil properties from low induction electromagnetic measurements into nitrogen (N) balance calculations for a field application of VRT nitrogen fertilization of (Triticum durum Desf., var. Tirex). The trial was conducted in 2018–19 at Genzano di Lucania (PZ, Italy) geologically located on the clayey hillsides of the Bradanica pit and the Sant’Arcangelo basin. Three soil homogeneous areas were detected through low induction electromagnetic measurements and used as uniform management zones. The amount of nitrogen fertilizer to be applied by VRT was calculated on the base of estimated crop nitrogen uptake and soil characteristics of each homogeneous area. Crop response to VRT was compared to uniform nitrogen application (UA) on the whole field. The application of VRT resulted in a reduction of 25% nitrogen fertilizer with the same level of yield respect to UA. Grain protein content, as well as gluten content and N content, were significantly higher in VRT than in UA. As a consequence of lower nitrogen input and higher levels of N removal, VRT reached a higher nitrogen use efficiency than UA, and this indicates a lower environmental impact and a higher economic profitability.