The objectives of this study were to determine the optimum plot size (Xo) and the number of replications to evaluate grain yield and verify the variability of Xo among oat cultivars. Thirtytwo uniformity trials of 3 × 3 m were performed, being 8 from each cultivar (URS Charrua, URS Taura, URS Estampa, and URS Corona). Each uniformity trial was divided in 36 basic experimental units (BEU) of 0.5 × 0.5 m. Grain yield was determined in each BEU. The Xo was determined by the method of maximum curvature of the coefficient of variation model. Mean comparisons among cultivars were performed by the Scott-Knott test via bootstrap. The number of replications was calculated by an iterative process until convergence for experiments in completely randomized design (CRD) and randomized block CRop pRoduCTion And MAnAgeMenT-Article

Studies on growth models for productive character of sunn hemp are important to know the behavior of the culture. Therefore, the objective of this research was to adjust non-linear models, Gompertz and Logistic, in the description of productive traits of sunn hemp in two sowing periods. Two uniformity trials were performed. The evaluations began on October the 29 th 2014 and December the 16 th 2014, totaling 94 and 76 evaluation days for periods 1 and 2, respectively. After the emergence of the seeds of sunn hemp, for first period from 7 days after sowing, and from 2 to 13 days after sowing, on each day, they were collected randomly four plants. The traits: fresh matter leaf, stem, root, shoot, and total, and dry matter leaf, stem, root, shoot, and total. For both models the confidence interval was calculated of parameters a, b and c. The adjustment quality of the Gompertz and Logistic models was verified by the determination coefficient, the Akaike information criteria, residual standard deviation, mean absolute deviation, mean absolute percentage error and mean prediction error. The Gompertz model when compared between the sowing periods through the confidence interval of the parameters, for the productive traits, differs. The same result was found for the Logistic model. The growth models of Gompertz and Logistic presented good adjustment quality.

ResumoO objetivo deste trabalho foi modelar e identificar os melhores modelos para a estimação da área foliar determinada por fotos digitais, de três híbridos de canola, em função do comprimento, ou da largura e/ou do produto comprimento vezes largura do limbo foliar. Foram conduzidos três ensaios de uniformidade com canola (Brassica napus L.), e em cada ensaio avaliou-se um dos seguintes híbridos: Hyola 61, Hyola 76 e Hyola 433. Em cada híbrido, foram coletadas 125 folhas aos 77, 84, 91 e 97 dias após a semeadura, totalizando 1.500 folhas. Nessas 1.500 folhas, foram mensurados o comprimento (C) e a largura (L), e calculado o produto do comprimento vezes a largura (C×L) do limbo foliar. Determinou-se a área foliar de cada folha, por meio do método de fotos digitais (Y). Posteriormente, para cada híbrido, foram separadas, aleatoriamente, 80% das folhas (100 folhas por coleta × 4 coletas por híbrido = 400 folhas por híbrido) para a geração de modelos do tipo quadrático, potência e linear, de Y em função do C, da L, e/ou do C×L. Os 20% das folhas restantes (100 folhas por híbrido) foram usadas, separadamente, para a validação dos modelos. Em canola, os modelos do tipo potência, para os híbridos Hyola 61 (Ŷ = 1,3000x , R 2 = 0,9613), são adequados para a estimação da área foliar determinada por fotos digitais (Y) em função da largura do limbo foliar (x).Palavras-chave: Brassica napus L., fotos digitais, modelagem, método não destrutivo. Leaf area estimation of canola by leaf dimensions AbstractThe objective of this work was to model and identify the best models to estimate the leaf area determined by digital photos, of three canola hybrid, with the length or width and / or the product length width of the leaf. Three uniformity trials were carried with the culture of canola (Brassica napus L.). In each trial was valued one of the following hybrids: Hyola 61, Hyola 76, Hyola 433. In each hybrid were collected 125 leaves at 77, 84, 91, 97 days after sowing, totaling 1,500 leaves. In these 1,500 leaves were measured length (C) and width (L) and calculated the length width (C×L) of the leaf. Was determined the leaf area of each leaf, by the method of digital photos (Y). After, for each hybrid were separated, randomly, 80% of the leaves (100 leaves by collects × 4 collects by hybrid = 400 leaves per hybrid) to build models of quadratic, potency and linear type for Y function of the C, L and/or C×L. The remaining 20% of the leaves (100 leaves by hybrid), separately, were used to validate the models. In canola, the potency model for hybrid Hyola 61 (Ŷ = 1.3000x

A B S T R A C TThe objective of this study was to determine the optimum plot size (Xo) and number of replicates to evaluate millet shoot fresh matter in times of sowing and cuts. Uniformity trials of 6 × 4 m (24 m 2 ) were carried out in three sowing times, in the agricultural year of 2013-2014. Each uniformity trial was divided into 24 basic experimental units (BEU) of 1 × 1 m (1 m 2 ) and the shoot fresh matter of plants in each BEU was weighed. The Xo was determined by the method of maximum curvature of the coefficient of variation model. The number of replicates for experiments in completely randomized and randomized block design, in scenarios of combinations of i treatments (i = 3, 4, ..., 50) and d minimal differences between treatment means, to be detected as significant at 0.05 probability level by Tukey test, expressed in percentage of the experiment mean (d = 10, 12, ..., 30%), was determined by iterative process until convergence. The optimum plot size to evaluate millet shoot fresh matter is 4.97 m 2 , for the three times of sowing and cuts. For the evaluation of up to 50 treatments, in completely randomized and randomized block design, five replicates are sufficient to identify as significant, at 0.05 probability level by Tukey test, differences between treatment means of 28.66% of the mean of the experiment.Tamanho de parcela e número de repetições em milheto em épocas de semeadura e cortes (1 m 2 ) e pesada a massa verde da parte aérea das plantas de cada UEB. O Xo foi determinado por meio do método da curvatura máxima do modelo do coeficiente de variação. O número de repetições para experimentos nos delineamentos inteiramente casualizados e blocos ao acaso, em cenários formados pelas combinações de i tratamentos (i = 3, 4, ..., 50) e diferenças mínimas entre médias de tratamentos a serem detectadas como significativas a 0,05 de probabilidade, pelo teste de Tukey, expressas em percentagem da média do experimento (d = 10, 12, ..., 30%) foi realizado por processo iterativo até a convergência. O tamanho ótimo de parcela para avaliar a massa verde da parte aérea de milheto é de 4,97 m 2 , para as três épocas de semeadura e cortes. Para avaliar até 50 tratamentos nos delineamentos inteiramente casualizados e blocos ao acaso, cinco repetições são suficientes para identificar, como significativas, pelo teste de Tukey, a 0,05 de probabilidade, diferenças entre médias de tratamentos de 28,66% da média do experimento. Key words:Pennisetum glaucum (L.) R. Brown maximum curvature of the coefficientof variation model experiment planning Palavras-chave:Pennisetum glaucum (L.) R. Brown curvatura máxima do modelo do coeficiente de variação planejamento experimental

ResumoOs objetivos deste trabalho foram determinar o tamanho ótimo de parcela (Xo) e o número de repetições para avaliar a massa verde de parte aérea de milheto em épocas de avaliação. Foram conduzidos 42 ensaios de uniformidade de 6 m×4 m (24 m 2 ), sendo cada ensaio dividido em 24 unidades experimentais básicas (UEB) de 1 m×1 m (1 m 2 ). Aos 52, 69 e 82 dias após a semeadura, foram avaliados, respectivamente 18, 6 e 18 ensaios, nos quais se pesou a massa verde das plantas de cada UEB. O Xo foi determinado pelo método da curvatura máxima do modelo do coeficiente de variação, e as comparações de médias entre as épocas de avaliação foram realizadas pelo teste t de Student. O número de repetições, para experimentos nos delineamentos inteiramente casualizados e blocos ao acaso, em cenários formados pelas combinações de i tratamentos (i=3, 4, ..., 50) e d diferenças mínimas entre médias de tratamentos a serem detectadas como significativas a 5% de probabilidade, pelo teste de Tukey, expressas em percentagem da média do experimento (d=10%, 12%, ..., 30%), foi realizado por processo iterativo até a convergência. O tamanho ótimo de parcela para avaliar a massa verde de parte aérea de milheto é de 4,46 m 2 , para as três épocas de avaliação. Para avaliar até 50 tratamentos, nos delineamentos inteiramente casualizados e blocos ao acaso, quatro repetições são suficientes para que diferenças entre médias de tratamentos de 28,75% da média do experimento sejam significativas, pelo teste de Tukey, a 5% de probabilidade.Palavras-chave: Pennisetum glaucum L., ensaio de uniformidade, planejamento experimental, precisão experimental. Plot size and number of repetitions in evaluation times in millet crop AbstractThe objectives of this work were to determine the optimum plot size (Xo) and number of repetitions, to evaluate the fresh weight of aerial part of millet, in evaluation times. Forty-two uniformity trials with 6 m×4 m (24 m 2 ) were conducted. Each trial was divided in 24 experimental units basic (UEB) with 1 m×1 m (1 m 2 ). At 52, 69 and 82 days after sowing were evaluated, respectively, 18, 6 and 18 trials, where the fresh weight of plants was weighed in each UEB. The Xo was determined by the method of maximum curvature of the model coefficient of variation and the means compared, among evaluation times, by Student's t test. The number of repetitions for experiments on completely randomized and randomized block designs, in scenarios of combinations of i treatments (i=3, 4, ..., 50) and d minimal differences between treatments means, to be detected as significant, 5% probability by Tukey test, expressed in percentage of the average of the experiment (d=10%, 12%, ..., 30%), was determined by iterative process until convergence. The optimum plot size to evaluate the fresh weight of aerial part of millet is 4.46 m 2 , to the three evaluation times. To evaluate up to 50 treatments, in completely randomized and randomized block designs, four replications are sufficient for differences between treatment means of 28.75% of the av...