The objective of this study was to evaluate the influence of substrates on the growth and yield of Cascade and Samambaia cherry tomato cultivars under protected environment. A completely randomized experimental design with six treatments and four replications was adopted. The treatments consisted of six substrates resulting from the combination of soil (CS), bovinemanure (BM), rice husk (RH) and rice hull ash (RA), in the proportions: S1 -70% SC + 10% BM + 10% RH + 10% RA; S2 -60% SC + 20% BM + 10% RH + 10% RA; S3 -50% SC + 30% BM + 10% RH + 10% RA;S4 -60% SC + 30% BM + 10% RH; S5 -70% SC + 20% BM + 10% RH; and S6 -100% SC. Plant growth, yield and development parameters were evaluated. Cherry tomato growth and yield changed according to the substrate characteristics. The substrate composed by soil was not efficient, presenting the lowest values for all the vegetative and reproductive parameters studied. The substrates made from alternative ecologically based residues are interesting and alternative sources for tomato cultivation aiming at the reuse of the matter and the sustainability of the production system.

Genotype x environment interaction (GEI) causes constant interference in soybean[Glycine max (L.) Merr.] grain yield. This complexity tends to increase when comparing highland and lowland cultivation systems, and there has been little referenced work for these in the situation of subtropical conditions. Hence, the aims of this study were to verify the effects of genotypes, environments and GEI for soybean grain yield in highland and lowland areas with subtropical climate and to compare the adaptability and stability methodologies. The trials were carried out in two locations of the state of Rio Grande do Sul, Brazil, with 20 soybean cultivars on three sowing dates for each location. The experiments were conducted in a randomised block design with three replications. Given the observed magnitude of the GEI, its simple and complex parts were quantified for further analysis of adaptability and stability, using the Additive Main Effect and Multiplicative Interaction (AMMI), Genotype plus Genotype-Environment interaction (GGE) and Best Linear Unbiased Prediction (BLUP) modelling approaches, followed by the verification of similarity between the methodologies via Spearman's correlation coefficient. The complex part of the GEI represented 82.11% of the total variation associated with the GEI, whereas the simple part accounted for 17.89% of that variation. The second sowing date, in both locations, was the one that presented the best rankings according to the models used, and also, cases of specific genotype adaptability were identified in each environment. The highest yield averages were obtained in the highlands. The GGE and BLUP techniques presented genotypic ranking concordance.

Leaf area is an important growth variable in agricultural crops and the leaf is the main variable of interest in the tobacco industry. So, the aim of this scientific research was to estimate the Burley tobacco leaf area by linear dimensions of the leaves and to determine which mathematical model is more adequate for this purpose. Two experiments were carried out with Burley tobacco, cultivar DBH 2252, in 2016/2017 and 2018/2019 agricultural years, respectively, in the municipalities of Itaqui and Vanini - RS - Brazil. In 600 leaves were measured length (L), width (W), length×width product (LW), length/width ratio (L/W) and determined the real leaf area (LA). Four hundred and fifty leaves were separated to generate models of the leaf area as a function of linear dimension and the other 150 leaves were used for model’s validation. The power model LA = 0.5037LW1.04435 (R² = 0.9960) is the most adequate for Burley tobacco ‘DBH 2252’ leaf area estimation. Alternatively, the models LA=2.0369W1.8619 (R²=0.9796) and LA=0.1222L2.2771 (R²=0.9738) based on width and length, respectively, can be used when only one leaf dimension is measured.

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