Industrial tomatoes are cultivated in about 4000 ha of the Pinios river basin (central Greece), providing significant income to the farmers. In this study, the water footprint (WF) of industrial tomatoes between planting and harvest was estimated in 24 different farms for three consecutive years. The selected farms were representative of the main agro-climatic zones and soil textural classes within the river basin. Green, blue and grey WF calculations were based on datasets of the experimental plots for each farm, including irrigation water volume, meteorological, soil, and crop yield data. The results showed that the WF of tomatoes ranged from 37 to 131 m 3 water/ton tomatoes with an average of 61 m 3 /ton. The WF variation depended mainly on crop yield, local agro-climatic and soil conditions. The green, blue, and grey WF components averaged 13, 27 and 21 m 3 /ton, respectively. The results reveal the importance of WF in understanding how tomato production relates to the sustainable use of freshwater and pollution at local level.

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.

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Surface visible-near infrared (NIR) reflectance of bare soil by remote sensing devices has been used to infer topsoil properties such as organic matter, soil texture, water content, salinity, and crop residue cover. Spectral mapping of soil properties can be ultimately used as a tool for the implementation of site-specific management practices at the field scale or for soil-landscape modeling at a regional scale. The accuracy of prediction of soil properties with satellite imagery is affected by conditions and properties of the soil surface, by radiometric and atmospheric effects, and by spatial and spectral resolutions. In this study, a high-resolution World View 2 image was used to map soil reflectance in three 10-ha fields of differing soil types and textures that were located in different sections of the east Thessaly Plain. Radiance data from four visible-NIR channels were extracted from the same coordinates that soil samples were taken at two soil depths within each field. Point radiance values were correlated to soil organic matter, total carbon (C) and nitrogen (N) contents, their isotopic composition, carbonate content, nitrate content, pH, electrical conductivity, and soil texture that were analyzed in the laboratory. Strong correlation coefficients emerged between green/NIR image reflectance and total soil N, organic matter, and carbonate content across the three fields in both soil depths. The greatest negative correlation coefficient (R 2 = 0.77) was obtained between satellite NIR reflectance and soil N content. More data are needed to verify these relationships, but the results indicated the potential of high-resolution satellite imagery to quantify within-field and regional-scale variability of soil N and C in the Thessaly Plain.

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