The goal of this study was to estimate the leaf area of Crotalaria juncea according to the linear dimensions of leaves from different ages. Two experiments were conducted with C. juncea cultivar IAC-KR1, in the 2014/2015 sowing seasons. At 59, 82, 102, 129 days after sowing (DAS) of the first and 61, 80, 92, 104 DAS of the second experiment, 500 leaves were collected, totaling 4,000 leaves. In each leaf, the linear dimensions were measured (length, width, length/width ratio and length × width product) and the specific leaf area was determined through Digimizer and Sigma Scan Pro software, after scanning images. Then, 3,200 leaves were randomly separated to generate mathematical models of leaf area (Y) in function of linear dimension (x), and 800 leaves for the models validation. In C. juncea, the leaf areas determined by Digimizer and Sigma Scan Pro software are identical. The estimation models of leaf area as a function of length × width product showed superior adjustments to those obtained based on the evaluation of only one linear dimension. The linear model Ŷ=0.7390x (R 2 =0.9849) of the real leaf area (Y) as a function of length × width product (x) is adequate to estimate the C. juncea leaf area.

No abstract

The objective of this study was to determine the sample size necessary to estimate the mean and coefficient of variation in four species of crotalarias (C. juncea, C. spectabilis, C. breviflora and C. ochroleuca). An experiment was carried out for each species during the season 2014/15. At harvest, 1,000 pods of each species were randomly collected. In each pod were measured: mass of pod with and without seeds, length, width and height of pods, number and mass of seeds per pod, and mass of hundred seeds. Measures of central tendency, variability and distribution were calculated, and the normality was verified. The sample size necessary to estimate the mean and coefficient of variation with amplitudes of the confidence interval of 95% (ACI95%) of 2%, 4%, ..., 20% was determined by resampling with replacement. The sample size varies among species and characters, being necessary a larger sample size to estimate the mean in relation of the necessary for the coefficient of variation.

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.

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.

No abstract

The objective of this study was to characterize the production of biquinho pepper through the interpretation of parameter estimates from the logistic model and its critical points obtained by the partial derivatives of the function, and to indicate the best cultivar and growing season for subtropical climate sites. For this, a 2x3 factorial experiment was conducted with two cultivars of biquinho pepper (BRS Moema and Airetama biquinho) in three growing seasons (E1: October 2015, E2: November 2015, E3: January 2016). The logistic non-linear model for fruit mass was specified as a function of the accumulated thermal sum, and the critical points were calculated through the partial derivatives of the model, in order to characterize the productive performance of the crop by the biological interpretation of the estimates of the three set parameters. In E3, temperatures close to 0 ºC during the experiment were lethal to the plants, and a linear regression model was used in this case. The production of the cultivars in E1 and E2 were well characterized by the estimated logistic models, and the most productive cultivar was Airetama biquinho in all evaluated seasons. This cultivar also presented higher concentration of production. The two cultivars did not differ significantly with regards to productive precocity. For E3, it was not possible to interpret the parameters in the same way as for E1 and E2, since the use of the linear model did not allow the same interpretations performed for the nonlinear model, reaffirming its applicability horticultural crops of multiple harvests.

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%