Despite numerous studies on cadmium (Cd) uptake and accumulation in crops, relatively little is available considering the temporal dynamic of Cd uptake and responses to stress focused on the root system. Here we highlighted the responses to Cd-induced stress in roots of two tomato genotypes contrasting in Cd-tolerance: the tolerant Pusa Ruby and the sensitive Calabash Rouge. Tomato genotypes growing in the presence of 35 μM CdCl exhibited a similar trend of Cd accumulation in tissues, mainly in the root system and overall plants exhibited reduction in the dry matter weight. Both genotypes showed similar trends for malondialdehyde and hydrogen peroxide accumulation with increases when exposed to Cd, being this response more pronounced in the sensitive genotype. When the antioxidant machinery is concerned, in the presence of Cd the reduced glutathione content was decreased in roots while ascorbate peroxidase (APX), glutathione reductase (GR) and glutathione S-transferase (GST) activities were increased in the presence of Cd in the tolerant genotype. Altogether these results suggest APX, GR and GST as the main players of the antioxidant machinery against Cd-induced oxidative stress.

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The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype–environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.