The aim of this study was to determine the required sample size for estimation of the Pearson coefficient of correlation between cherry tomato variables. Two uniformity tests were set up in a protected environment in the spring/summer of 2014. The observed variables in each plant were mean fruit length, mean fruit width, mean fruit weight, number of bunches, number of fruits per bunch, number of fruits, and total weight of fruits, with calculation of the Pearson correlation matrix between them. Sixty eight sample sizes were planned for one greenhouse and 48 for another, with the initial sample size of 10 plants, and the others were obtained by adding five plants. For each planned sample size, 3000 estimates of the Pearson correlation coefficient were obtained through bootstrap re-samplings with replacement. The sample size for each correlation coefficient was determined when the 95% confidence interval amplitude value was less than or equal to 0.4. Obtaining estimates of the Pearson correlation coefficient with high precision is difficult for parameters with a weak linear relation. Accordingly, a larger sample size is necessary to estimate them. Linear relations involving variables dealing with size and number of fruits per plant have less precision. To estimate the coefficient of correlation between productivity variables of cherry tomato, with a confidence interval of 95% equal to 0.4, it is necessary to sample 275 plants in a 250m² greenhouse, and 200 plants in a 200m² greenhouse.

The objective of this work was to evaluate the interference of sample size on multicollinearity diagnosis in path analysis. From the analyses of productive traits of cherry tomato, two Pearson correlation matrices were obtained, one with severe multicollinearity and the other with weak multicollinearity. Sixty-six sample sizes were designed, and from the amplitude of the bootstrap confidence interval, it was observed that sample size interfered on multicollinearity diagnosis. When sample size was small, the imprecision of the diagnostic criteria estimates interfered with multicollinearity diagnosis in the matrix with weak multicollinearity.

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Correct experimental planning is important to obtain more reliable data with high experimental precision. In this way, the results obtained and the technical recommendations generated are more reliable and representative. Thus, the objective of this work is to estimate the plot and sample sizes and the number of repetitions for the variables (a) number of pods per plant and (b) mass of pods per plant for pea (Pisum sativum L.) cultivation. Uniformity tests were carried in the years 2016, 2017, and 2018 in the experimental area of the Crop Science Department at Federal University of Santa Maria. The cultivar used was Pea Grain 40, which has an indeterminate growth habit, with a cycle of 75-90 d and a cylindrical pod. The plot size for evaluating the number of pods per plant and the mass of pods per plant for pea cultivation is eight and nine plants, respectively. The sample size for evaluating the number of pods per plant and the mass of pods per plant is eight plants in the direction of the line with a half-width of the 20% confidence interval of the mean. For the variables number of pods per plant and pod mass per pea plant, 10 and 12 repetitions are required, respectively, to evaluate up to 20 treatments in a randomized block design and in the incomplete blocks design with up to 100 treatments for significant differences of 35% between treatment averages. INTRODUCTIONThe pea (Pisum sativum L.) is an annual herbaceous legume, with cultivars classified as being of determined and indeterminate growth. Cultivars that show indeterminate growth are used for edible pods production, whereas cultivars with determined growth are used for grain production (Filgueira, 2008). Worldwide, in 2018, the production of dry pea occupied 7.88 million ha of cultivated area, with a production of 13.53 million tons; the largest producers are Europe (38.8%), North and South America (33.6%), Asia (20.5%

The great economic importance attributed to strawberry cultivation raises the interest in cultivars of high productivity and superior fruit quality. The quality of fruit is the most impacting factor for the strawberry marketing, but selecting genotypes that combine high production and high fruit quality has been a difficult task. The objective of this study was to determine the linear relationships between phenological, quality and production variables of strawberry aiming at identifying potential variables for indirect selection in future selection processes of strawberry genotypes. A trial was conducted in a randomized block design with two cultivars and two transplant origins, grown in four types of substrate. Fifteen variables, including phenological, productive and fruit quality-related variables were assessed. The selected variables explained 45.2 and 39.1 % for PC1 and PC2 respectively, totaling 84.3 % of the total variance of the variables in the PCA, and indicated important relationships between the variables, and a path analysis revealed success for indirect selection of total mass of fruits based on the total number of fruits (0.81413). Changes in crop management that reduces the period between planting date and full flowering may be an alternative to increase the production of strawberry and provide fruits with higher quality.

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This study aimed to identify the productive cycle response of Italian zucchini genotypes grown under field conditions in two growing seasons using the nonlinear logistic model and its critical points. Two randomized block experiments were conducted, with three genotypes (Caserta, PX13067051, and Tronco) and two growing seasons (spring-summer and summer-fall), with eight replicates and each experimental unit consisting of 7 plants. The logistic nonlinear model was adjusted for the fruit mass variable, as a function of the accumulated thermal sum, and the critical points were estimated by the partial derivatives of the adjusted function. Adjustment by bootstrap resampling was performed to address the violation of assumptions. The results of intrinsic and parametric nonlinearity confirm the quality of the model fit. This experiment demonstrated that the zucchini genotypes evaluated were more productive in the spring-summer growing season, using the parameters and critical points obtained from the logistic nonlinear model. Genotypes PX13067051 and Caserta showed superior productivity to the Tronco genotype, and also fruited earlier and at a higher rate of production. The logistic growth model and its critical points characterized the production cycle of the zucchini genotypes in different growing seasons and allowed inferences to be made to differentiate the genotypes and the growing seasons.

The objective of this work was to evaluate the optimal harvest time of ten genotypes of sugarcane (Saccharum officinarum) for the processing and quality of brown sugar. The experiment was carried out in a randomized complete block design in a 3x10 factorial arrangement in split plots, with three harvest times and ten sugarcane genotypes, in the state of São Paulo, Brazil. The qualitative parameters of brown sugar were evaluated by Scott-Knott’s test, at 5% probability. The harvest season in September, known as the middle of the harvest, is the most suitable for the production of brown sugar due to the higher of ºBrix values of cane, ºBrix of the broth, pol of brown sugar, and total reducing sugars in this period. The harvesting of the sugarcane genotypes in June-July is the most favorable for the production of brown sugar for the color characteristics a*, b*, L*, and chroma; however, it is also the period of production of brown sugar with a lower sugar content. The third harvest season (November) is the least recommended for brown sugar production due to the higher fiber and purity values. The most suitable genotype for brown sugar production and quality is 'IACSP04-704'.