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.

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.

The control strategies for the olive crop key pest, Bactrocera oleae, involve synthetic chemical insecticides and few eco-sustainable alternatives, such as ovideterrents and lures. In the last few decades, the interest concerning the formulation of botanical based biopesticides increased, but little research investigated the suitability of these approaches for B. oleae control. This research aimed to investigate the residual contact toxicity and the oviposition deterrence of three essential oil (EO)-based nano-emulsions (Pimpinella anisum, Foeniculum vulgare, Mentha × piperita) against B. oleae adult flies. All the nano-emulsions possessed optimal physical characteristics, with droplets dimensions ranging from 115 to 152 nm and low PDI values (<0.2), even after 1 year of storage. Although no notable residual contact toxicity was noted, all the tested formulations reduced the number of oviposition puncture in no-choice tests (percent repellence: mint < fennel < anise). In choice trials, olives treated with fennel and anise EO-formulations at the highest concentration (7.5%, 75 g of EO/L) were less attractive respect to control fruits and a significant reduction of olive punctures was recorded. Nano-biopesticides are promising eco-friendly tools to integrate B. oleae pest management programs and to reduce the use of harmful conventional active ingredients.

Grasspea (Lathyrus sativus L.) and its relatives are considered resilient legumes due to their high ability to cope with different stresses. In this study, the genetic diversity of three Lathyrus species (L. sativus, L cicera and L. ochrus) was assessed by agronomic traits and molecular markers (Simple Sequence Repeat-SSR) in order to detect accessions useful for future breeding strategies. Phenotypic traits showed a high significant variation in which 1000 seed weight (1000 SW) and protein content appeared the most discriminant, as observed by principal component analysis (PCA). SSR analysis was able to detect forty-eight different alleles with an average of 9.6 allele per locus, and a Polymorphic Information Content (PIC) and a gene diversity of 0.745 and 0.784, respectively. Cluster analysis based on agronomic traits as well as molecular data grouped accessions by species but not by geographical origin. This result was confirmed by Principal Coordinates Analysis (PCoA) and Structure Analysis as well. Moreover, genetic structure analysis revealed a high genetic differentiation between L. ochrus and the other species. Analysis of MOlecular Variance (AMOVA) displayed a greater genetic diversity within species (77%) than among them (23%). Finally, a significant positive correlation was observed between agronomic and genetic distances (Mantel’s test). In conclusion, the variability detected within accessions in each species and the differences among species may be useful to plan next breeding programs, focusing on biomass production as well as protein content.