Agriculture is facing several challenges related to its sustainability. In this regard, the need to reduce its environmental impact related to the use of synthetic inputs and its potential role in mitigating global warming and climate change call for a review of crop management. In this context, and in the framework of achieving sustainable development goals, the use of organic fertilizers and bio-waste represents a valuable contribution to the agricultural transition towards a bioeconomy model by reducing the negative impacts of waste disposal. Farmyard manure, composts, digestate from agrifood processes, and biochar are, among organic fertilizers, commonly used to manage soils and support crop growth. These fertilizers can provide essential nutrients, improve structure, and enhance microbial activity, thus increasing soil fertility and agriculture sustainability. While organic fertilizers offer the benefits of soil fertility and plant nutrition, their impact on greenhouse gas (GHG) emissions is complex and varies depending on factors such as fertilizer type, soil conditions, and management practices. Although organic fertilizers may initially increase GHG emissions, they often lead to carbon sequestration in soils highlighting a negative C balance. Additionally, organic fertilizers promote a reduction in fossil fuel consumption used for synthetic fertilizer production, further contributing to GHG emissions’ reduction. Therefore, while organic fertilizers pose challenges in managing GHG emissions, their various benefits warrant careful consideration and strategic implementation in agricultural systems.

The crop yield and quality of seven annual forages (four grasses and three legumes) in sole crop and in mixtures (ratio 50:50) for oat (Avena sativa L.), Italian ryegrass (Lolium multiflorum Lam.), triticale (x Triticosecale Wittmack), barley (Hordeum vulgare L.), pea (Pisum sativum L.), berseem (Trifolium alexandrinum L.) and common vetch (Vicia sativa L.) were evaluated in a two-year field experiment adopting two harvesting times, green fodder and silage. The main bio-agronomic traits, dry matter forage yield (DMY) and quantity of crude protein (CP) were determined in both sole crop and intercrop. The land equivalent ratio (LER) was used for evaluating biological efficiency and competitive ability of the intercrops. Our results showed that the total calculated LER for fodder and protein yields was always greater than one and corresponded to crop yield advantages of 16.0% and 11.5%, respectively. Our data also highlighted the low competitive ability of the ryegrass in intercrop, which achieved the lowest yield among all the mixtures. Conversely, the same grass showed the best green fodder quality, due to the high incidence of the legume, equal (on average) to 46%. Triticale and barley, harvested for silage (hard dough stage), provided the best quantitative and qualitative results both in sole crop and intercropped with common vetch and pea, determined mainly by the cereal grain.

During the XX Century, the widespread use of modern wheat cultivars drastically reduced the cultivation of ancient landraces, which nowadays are confined to niche cultivation areas. Several durum wheat landraces adapted to the extreme environments of the Mediterranean region, are still being cultivated in Sicily, Italy. Detailed knowledge of the genetic diversity of this germplasm could lay the basis for their efficient management in breeding programs, for a wide-range range of traits. The aim of the present study was to characterize a collection of durum wheat landraces from Sicily, using single nucleotide polymorphisms (SNP) markers, together with agro-morphological, phenological and quality-related traits. Two modern cv. Simeto, Claudio, and the hexaploid landrace, Cuccitta, were used as outgroups. Cluster analysis and Principal Coordinates Analysis (PCoA) allowed us to identify four main clusters across the analyzed germplasm, among which a cluster included only historical and modern varieties. Likewise, structure analysis was able to distinguish the ancient varieties from the others, grouping the entries in seven cryptic genetic clusters. Furthermore, a Principal Component Analysis (PCA) was able to separate the modern testers from the ancient germplasm. This approach was useful to classify and evaluate Sicilian ancient wheat germplasm, supporting their safeguard and providing a genetic fingerprint that is necessary for avoiding commercial frauds to sustaining the economic profits of farmers resorting to landraces cultivation.

Remote sensing (RS) platforms such as unmanned aerial vehicles (UAVs) represent an essential source of information in precision agriculture (PA) as they are able to provide images on a daily basis and at a very high resolution. In this framework, this study aims to identify the optimal level of nitrogen (N)-based nutrients for improved productivity in an onion field of “Cipolla Rossa di Tropea” (Tropea red onion). Following an experiment that involved the arrangement of nine plots in the onion field in a randomized complete block design (RCBD), with three replications, three different levels of N fertilization were compared: N150 (150 kg N ha⁻¹), N180 (180 kg N ha⁻¹), and e N210 (210 kg N ha⁻¹). The crop cycle was monitored using multispectral (MS) UAV imagery, producing vigor maps and taking into account the yield of data. The soil-adjusted vegetation index (SAVI) was used to monitor the vigor of the crop. In addition, the coverage’s class onion was spatially identified using geographical object-based image classification (GEOBIA), observing differences in SAVI values obtained in plots subjected to differentiated N fertilizer treatment. The information retrieved from the analysis of soil properties (electrical conductivity, ammonium and nitrate nitrogen), yield performance and mean SAVI index data from each field plot showed significant relationships between the different indicators investigated. A higher onion yield was evident in plot N180, in which SAVI values were higher based on the production data.

Durum wheat (Triticum durum Desf.) is one of the most widely cultivated cereal species in the Mediterranean basin, supporting pasta, bread and other typical food productions. Considering its importance for the nutrition of a large population and production of high economic value, its supply is of strategic significance. Therefore, an early and accurate crop yield estimation may be fundamental to planning the purchase, storage, and sale of this commodity on a large scale. Multispectral (MS) remote sensing (RS) of crops using unpiloted aerial vehicles (UAVs) is a powerful tool to assess crop status and productivity with a high spatial–temporal resolution and accuracy level. The object of this study was to monitor the behaviour of thirty different durum wheat varieties commonly cultivated in Italy, taking into account their spectral response to different vegetation indices (VIs) and assessing the reliability of this information to estimate their yields by Pearson’s correlation and different machine learning (ML) approaches. VIs allowed us to separate the tested wheat varieties into different groups, especially when surveyed in April. Pearson’s correlations between VIs and grain yield were good (R² > 0.7) for a third of the varieties tested; the VIs that best correlated with grain yield were CVI, GNDVI, MTVI, MTVI2, NDRE, and SR RE. Implementing ML approaches with VIs data highlighted higher performance than Pearson’s correlations, with the best results observed by random forest (RF) and support vector machine (SVM) models.

Agronomic Evaluation and Genetic Characterization of Different Accessions in Lentil (Lens culinaris Medik.)