RESUMO
Given the increasing problems of water and soil contamination with cadmium (Cd), it is necessary to investigate the genetic and physiological mechanisms of tolerance to this metal in different crops, which can be used for the development of effective crop management strategies. This study aimed to assess the potential of grafting as a strategy to increase Cd tolerance and reduce absorption in tomato by evaluating the contribution of the root system and aerial parts for tolerance mechanisms. To this end, reciprocal grafting and diallel analyses were used to examine the combining ability of contrasting tomato genotypes under exposure to 0 and 35 µM CdCl2. Roots and above-ground parts were found to have specific mechanisms of Cd tolerance, absorption, and accumulation. Grafting of the USP15 genotype (scion) on USP16 (rootstock) provided the greatest synergism, increasing the tolerance index and reducing the translocation index and Cd accumulation in leaves. USP163 exhibited potential for breeding programs that target genotypes with high Cd tolerance. In tomato, both Cd tolerance and accumulation in aerial parts are genotype- and tissue-specific, controlled by a complex system of complementary mechanisms that need to be better understood to support the development of strategies to reduce Cd contamination in aerial parts.
Assuntos
Poluentes do Solo , Solanum lycopersicum , Cádmio , Raízes de Plantas , Melhoramento Vegetal , ÁguaRESUMO
In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.