The objectives of this work were estimate the leaf area of squash ‘Brasileirinha’ by linear dimensions of the leaves and check models available in the literature. An experiment was conducted in the 2015/16 sowing season. Were collected 500 leaves and in each one, were measured the length (L), width (W) and length×width product (LW) and determined the real leaf area (LA). Then, 400 leaves were separated to generate models of the leaf area (LA) as a function of linear dimension (L, W or LW) of squash. The remaining 100 leaves were used for the validation of models. A second experiment was conducted in the 2016/17 sowing season. Were collected 250 leaves, used only for the validation of the models of the first experiment. There is collinearity between L and W and, therefore, models using the LW product are not recommended. The model LA=0.5482W2 + 0.0680W (R²=0.9867) is adequate for leaf area estimation of squash ‘Brasileirinha’.

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.

The objective of this study was to estimate the leaf area of triticale in function of linear dimensions from flags and other (non-flag) leaves. An experiment was conducted with the IPR111 cultivar in the 2016 agricultural year. At 93 days after sowing, 400 leaves were collected in order to generate the mathematical models of leaf area estimation in function of linear leaf dimensions. A total of 200 leaves were collected at 106 days after sowing in order to validate the models. In each of the 600 leaves, the length (L) and the width (W) were measured, and the product of length times width (L×W) and the ratio between length and width (L/W) were estimated. Afterwards, the leaves were digitized and the real leaf area determined by means of digital images. Linear, quadratic and power models were generated and validated for the estimation of the real leaf area (Y). The morphology of flag and other (non-flag) leaves is distinct and, thus, leaf area estimation models should be generated for each leaf type. In triticale, the most precise models of leaf area estimation are those that use L×W as the explanatory variable.

The objective of this research was to determine the optimal plot size and the number of replications to evaluate the fresh matter of ryegrass sown to haul. Twenty uniformity trials were conducted, each trial with 16 basic experimental units (BEU) of 0.5 m2. At 117, 118 and 119 days after sowing, the fresh matter of ryegrass in the BEUs of 5, 10 and 5 uniformity trials, respectively, were determined. The optimal plot size was determined by the maximum curvature method of the variation coefficient model. Next, the replications number was determined in scenarios formed by combinations of i treatments (i = 3, 4, ... 50) and d minimum differences between means of treatments to be detected as significant at 5% of probability by the Tukey test, expressed in experimental mean percentage (d = 10, 11, ... 20%). The optimal plot size to determine the fresh matter of ryegrass seeded at the haul is 2.19 m2, with a variation coefficient of 9.79%. To identify as significant at 5% probability, by the Tukey test, differences between treatment means of 20%, are required five, six, seven and eight replications, respectively, in ryegrass experiments with up to 5, 10, 20 and 50 treatments.

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.