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.

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.)

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.

Durum wheat (Triticum durum Desf.), among the herbaceous crops, is one of the most extensively grown in the Mediterranean area due to its fundamental role in supporting typical food productions like bread, pasta, and couscous. Among the environmental and technical aspects, nitrogen (N) fertilization is crucial to shaping plant development and that of kernels by also affecting their protein concentration. Today, new techniques for monitoring fields using uncrewed aerial vehicles (UAVs) can detect crop multispectral (MS) responses, while advanced machine learning (ML) models can enable accurate predictions. However, to date, there is still little research related to the prediction of the N nutritional status and its effects on the productivity of durum wheat grown in the Mediterranean environment through the application of these techniques. The present research aimed to monitor the MS responses of two different wheat varieties, one ancient (Timilia) and one modern (Ciclope), grown under three different N fertilization regimens (0, 60, and 120 kg N ha⁻¹), and to estimate their quantitative and qualitative production (i.e., grain yield and protein concentration) through the Pearson’s correlations and five different ML approaches. The results showed the difficulty of obtaining good predictive results with Pearson’s correlation for both varieties of data merged together and for the Timilia variety. In contrast, for Ciclope, several vegetation indices (VIs) (i.e., CVI, GNDRE, and SR_RE) performed well (r-value > 0.7) in estimating both productive parameters. The implementation of ML approaches, particularly random forest (RF) regression, neural network (NN), and support vector machine (SVM), overcame the limitations of correlation in estimating the grain yield (R² > 0.6, RMSE = 0.56 t ha⁻¹, MAE = 0.43 t ha⁻¹) and protein (R² > 0.7, RMSE = 1.2%, MAE 0.47%) in Timilia, whereas for Ciclope, the RF approach outperformed the other predictive methods (R² = 0.79, RMSE = 0.56 t ha⁻¹, MAE = 0.44 t ha⁻¹).