Region-specific local landraces represent a germplasm diversity adapted and acclimatized to local conditions, and are ideal to breed for targeted market niches while maintaining the variability of heirloom traits. A collection of 180 pepper accessions, collected from 62 diverse locations across six Balkan countries, were characterized and evaluated for phenotypic and biochemical variation during a multi-year environment. An assortment of 32 agro-morphological, fruit quality, and virus resistance traits were evaluated, and the top 10% accessions were identified. A wide range of trait variation concerning plant architecture, inflorescence and fruit traits, yield and fruit quality was observed, and appreciable variation was noticed. According to hierarchical clustering, six distinct clusters were established based on pre-defined varietal groups. Divergence among accessions for phenotypic and fruit compositional variability was analyzed, and eight principal components were identified that contributed ~71% of the variation, with fruit shape, width, wall thickness, weight, and fruit quality traits being the most discriminant. Evaluation of the response to tobacco mosaic virus (TMV) and pepper mild mottle mosaic virus (PMMoV) showed that 24 and 1 accession were resistant, respectively while no tomato spotted wilt virus (TSWV) resistance was found. Considerable diversity for agro-bio-morphological traits indicates the Balkan pepper collection as good gene sources for pre-breeding and cultivar development that are locally adapted.

Morphological variation in vegetative and fruit traits is a key determinant in unraveling phenotypic diversity. This study was designed to assess phenotypic diversity in tomatoes and examine intra- and intervarietal groups’ variability using 28 conventional descriptors (CDs) and 47 Tomato Analyzer (TA) descriptors related to plant and fruit morphometry. Comprehensive phenotyping of 150 accessions representing 21 countries discerned noticeable variability for CD vegetative traits and TA quantified fruit features, such as shape, size, and color. Hierarchical cluster analysis divided the accessions into 10 distinct classes based on fruit shape and size. Multivariate analysis was used to assess divergence in variable traits among populations. Eight principal components with an eigenvalue >1 were identified by factor analysis, which contributed 87.5% variation to the total cumulative variance with the first two components contributing 32.0% and 18.1% variance, respectively. The relationship between vegetative and fruit descriptors was explained by respective CD and TA correlation networks. There was a strong positive correlation between fruit shape and size whereas negative correlations were between fruit shape index, internal eccentricity, and proximal end shape. The combined approach of CD and TA phenotyping allowed us to unravel the phenotypic diversity of vegetative and reproductive trait variation evaluated at pre- and post-harvest stages.

Selection of high-performance lines with respect to traits of interest is a key step in plant breeding. Genomic prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers, e.g. single-nucleotide polymorphisms (SNPs), and machine learning approaches, which can therefore shorten breeding cycles, referring to genomic selection (GS). Here, we applied GS approaches in two populations of Solanaceous crops, i.e. tomato and pepper, to predict morphometric and colorimetric traits. The traits were measured by using scoring-based conventional descriptors (CDs) as well as by Tomato Analyzer (TA) tool using the longitudinally and latitudinally cut fruit images. The GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits, respectively. The results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous fruits. The highest predictability of 0.89 was achieved for fruit width in pepper, with an average predictability of 0.69 over all traits. The multi-trait GS models are of slightly better predictability than single-trait models for some colorimetric traits in pepper. While model validation performs poorly on wild tomato accessions, the usage as many as one accession per wild species in the training set can increase the transferability of models to unseen populations for some traits (e.g. fruit shape for which predictability in unseen scenario increased from zero to 0.6). Overall, GS approaches can assist the selection of high-performance Solanaceous fruits in crop breeding.

The size and the weight of wheat grains vary across the length of each spike (Triticum aestivum L.). High temperature and water scarcity often reduce the single grain weight, and this reduction also varies across the spike length. Plants tend to cope with high temperature and drought stress through inherent mechanisms such ascanopy cooling through transpiration, which can contribute to yield stability. The effect of canopy cooling on the average grain weight at different positions in spike is still unknown. In this study, we planned to assess the role of canopy temperature, yield-related traits, and spike shape in final grain weight. For two years (2017–2018 and 2018–2019), fifteen diverse genotypes released for cultivation in different environmental conditions were grown in the field. They were examined for canopy temperature, spikelets spike⁻¹, grain number spike⁻¹, grain yield spike⁻¹, and grain weight of the spike’s basal, median, and distal regions. The Pearson correlation coefficient (r) was obtained for all pair-wise combinations of traits under different treatments and spike shapes. The results indicated that cooler canopy is correlated to grain weight in normal spike shape at all three positions within the spike irrespective of stress. The advantage of the cooler canopy in improving grain-filling at basal, median, and distal regions was more conspicuous in the high temperature stress conditions compared to non-stressed and drought conditions.