Microbes inhabiting the phyllosphere of crops are exposed to pesticides applied either directly onto plant foliage or indirectly through soil. Although, phyllosphere microbiology has been rapidly evolving, little is still known regarding the impact of pesticides on the epiphytic microbial community and especially on fungi. We determined the impact of two systemic pesticides (metalaxyl and imidacloprid), applied either on foliage or through soil, on the epiphytic fungal and bacterial communities via DGGE and cloning. Both pesticides induced mild effects on the fungal and the bacterial communities. The only exception was the foliage application of imidacloprid which showed a more prominent effect on the fungal community. Cloning showed that the fungal community was dominated by putative plant pathogenic ascomycetes (Erysiphaceae and Cladosporium), while a few basidiomycetes were also present. The former ribotypes were not affected by pesticides application, while selected yeasts (Cryptococcus) were stimulated by the application of imidacloprid suggesting a potential role in its degradation. A less diverse bacterial community was identified in pepper plants. Metalaxyl stimulated an Enterobacteriaceae clone which is an indication of the involvement of members of this family in fungicide degradation. Further studies will focus on the isolation of epiphytic microbes which appear to be stimulated by pesticides application.

On-farm genotype screening is at the core of every breeding scheme, but it comes with a high cost and often high degree of uncertainty. Phenomics is a new approach by plant breeders, who use optical sensors for accurate germplasm phenotyping, selection and enhancement of the genetic gain. The objectives of this study were to: (1) develop a high-throughput phenotyping workflow to estimate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge index (NDRE) at the plot-level through an active crop canopy sensor; (2) test the ability of spectral reflectance indices (SRIs) to distinguish between sesame genotypes throughout the crop growth period; and (3) identify specific stages in the sesame growth cycle that contribute to phenotyping accuracy and functionality and evaluate the efficiency of SRIs as a selection tool. A diversity panel of 24 sesame genotypes was grown at normal and late planting dates in 2020 and 2021. To determine the SRIs the Crop Circle ACS-430 active crop canopy sensor was used from the beginning of the sesame reproductive stage to the end of the ripening stage. NDVI and NDRE reached about the same high accuracy in genotype phenotyping, even under dense biomass conditions where “saturation” problems were expected. NDVI produced higher broad-sense heritability (max 0.928) and NDRE higher phenotypic and genotypic correlation with the yield (max 0.593 and 0.748, respectively). NDRE had the highest relative efficiency (61%) as an indirect selection index to yield direct selection. Both SRIs had optimal results when the monitoring took place at the end of the reproductive stage and the beginning of the ripening stage. Thus, an active canopy sensor as this study demonstrated can assist breeders to differentiate and classify sesame genotypes.

The main objective of this study was to assess the quality of water from five monitoring stations in Asopos River (central Greece), and evaluate the main factors that affect the river water quality. Fifty one biochemical parameters measured both in situ and in the laboratory three times a year for two consecutive years. Multivariate analysis of Hierarchical cluster analysis and principal component analysis were used to interpret water quality characteristics. Cluster analysis grouped the samples in two main clusters (classes) corresponding to different main activities which affect water quality characteristics. The first cluster included the two sampling stations located near to industrial areas and the second one the three stations located in areas affected by agricultural activities. Principal Component Analysis identified two factors which explain 84,5% of the variability of the original mean data set. The first factor explaining 50,5 % of the whole data variability related mainly to “chemical quality parameters”, while the second factor explaining 34% of total data variability related mainly to “biological quality parameters”. This study demonstrated that multivariate statistical techniques prove effective in river water quality classification based on large and complex water quality data sets.

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