Deciphering the genetic control of flowering and ripening periods in apple is essential for breeding cultivars adapted to their growing environments. We implemented a large Genome-Wide Association Study (GWAS) at the European level using an association panel of 1,168 different apple genotypes distributed over six locations and phenotyped for these phenological traits. The panel was genotyped at a high-density of SNPs using the Axiom®Apple 480 K SNP array. We ran GWAS with a multi-locus mixed model (MLMM), which handles the putatively confounding effect of significant SNPs elsewhere on the genome. Genomic regions were further investigated to reveal candidate genes responsible for the phenotypic variation. At the whole population level, GWAS retained two SNPs as cofactors on chromosome 9 for flowering period, and six for ripening period (four on chromosome 3, one on chromosome 10 and one on chromosome 16) which, together accounted for 8.9% and 17.2% of the phenotypic variance, respectively. For both traits, SNPs in weak linkage disequilibrium were detected nearby, thus suggesting the existence of allelic heterogeneity. The geographic origins and relationships of apple cultivars accounted for large parts of the phenotypic variation. Variation in genotypic frequency of the SNPs associated with the two traits was connected to the geographic origin of the genotypes (grouped as North+East, West and South Europe), and indicated differential selection in different growing environments. Genes encoding transcription factors containing either NAC or MADS domains were identified as major candidates within the small confidence intervals computed for the associated genomic regions. A strong microsynteny between apple and peach was revealed in all the four confidence interval regions. This study shows how association genetics can unravel the genetic control of important horticultural traits in apple, as well as reduce the confidence intervals of the associated regions identified by linkage mapping approaches. Our findings can be used for the improvement of apple through marker-assisted breeding strategies that take advantage of the accumulating additive effects of the identified SNPs

Powdery mildew is a worldwide major fungal disease for grapevine, which adversely affects both crop yield and produce quality. Disease identification is based on visible signs of a pathogen once the plant has already been infected; therefore, techniques that allow objective diagnosis of the disease are currently needed. In this study, the potential of hyperspectral imaging (HSI) technology to assess the presence of powdery mildew in grapevine bunches was evaluated. Thirty Carignan Noir grape bunches, 15 healthy and 15 infected, were analyzed using a lab-scale HSI system (900–1700 nm spectral range). Image processing was performed to extract spectral and spatial image features and then, classification models by means of Partial Least Squares Discriminant Analysis (PLS-DA) were carried out for healthy and infected pixels distinction within grape bunches. The best discrimination was achieved for the PLS-DA model with smoothing (SM), Standard Normal Variate (SNV) and mean centering (MC) pre-processing combination, reaching an accuracy of 85.33% in the cross-validation model and a satisfactory classification and spatial location of either healthy or infected pixels in the external validation. The obtained results suggested that HSI technology combined with chemometrics could be used for the detection of powdery mildew in black grapevine bunches.

A quick and accurate sampling method for determining yield in peach orchards could lead to better crop management decisions, more accurate insurance claim adjustment, and reduced expenses for the insurance industry. Given that sample size depends exclusively on the variability of the trees on the orchard, it is necessary to have a quick and objective way of assessing this variability. The aim of this study was to use remote sensing to detect the spatial variability within peach orchards and classify trees into homogeneous zones that constitute sampling strata to decrease sample size. Five mature peach orchards with different degrees of spatial variability were used. A regular grid of trees was established on each orchard, their trunk cross-sectional area (TCSA) was measured, and yield was measured as number of fruits/tree on the central tree of each one of them. Red Vegetation Index (RVI) was calculated from aerial images with 0.25 m·pixel⁻¹ resolution, and used, either alone or in combination with TCSA, to delineate sampling strata using cluster fuzzy k-means. Completely randomized (CRS) and stratified samplings were compared through 10,000 iterations, and the Minimum Sample Size required to obtain estimates of actual production for three quality levels of sampling was calculated in each case. The images allowed accurate determination of the number of trees, allowing a proper application of completely randomized sampling designs. Tree size and the canopy density estimated by means of multispectral indices are complementary parameters suitable for orchard stratification, decreasing the sample size required to determine fruit count up to 20–35% compared to completely randomized samples.

Core collections (CCs) constitute a key tool for the characterization and management of genetic resources (GR). When the institutions involved in GR preservation decide to define a CC, they frequently prefer to select accessions based not only on strictly objective criteria, but also to add others following expert knowledge considerations (popularity, prestige, role in breeding history, or presence of phenotypic features of interest). The aim of this study was to evaluate the implications of approaches that combine formal analytical procedures and expert knowledge on the efficiency of CC definition through a case study to establish a pear CC from the Swiss National Pear Inventory. The CC had to represent a maximum of the genetic diversity, not to exceed 150 accessions, and required to include a priority set (SPPS) with 86 genotypes selected based on expert knowledge. In total, nine strategies were evaluated, resulting of combining compositions of the dataset sampled, sampling sizes and methods. The CCs sampled by mixed approaches provided similar scores, irrespective of the approach considered, and obtained similar efficiency in optimizing the genetic diversity retained. Therefore, mixed approaches can be an appropriate choice for applications involving genetic conservation in tree germplasm collections.