Tamanho de amostra para estimar coeficientes de correlação linear de Pearson em espécies de crotalária

The objective of this work was to determine the necessary sample size to estimate Pearson’s linear correlation coefficients of four species of crotalaria at precision levels. The experiment was carried out with Crotalaria juncea, Crotalaria spectabilis, Crotalaria breviflora, and Crotalaria ochroleuca, during the 2014/2015 crop year. Eight crotalaria traits were evaluated in 1,000 randomly collected pods per species. For each species, the correlation coefficients were estimated for the 28 pairs of traits, and the sample size necessary to estimate the correlation coefficients was determined at four precision levels [0.10, 0.20, 0.30, and 0.40 amplitudes of the 95% (CI95%) confidence interval] by resampling with replacement. The sample size varies between crotalaria species and, especially, between pairs of traits, as a function of the magnitude of the correlation coefficient. At a certain precision level, the smallest sample size is required to estimate the correlation coefficients between highly correlated traits and vice-versa. To estimate the correlation coefficients with CI95% of 0.20, 10 to 440 pods are required, depending on the species, pairs of traits, and magnitude of the correlation coefficient.O objetivo deste trabalho foi determinar o tamanho de amostra necessário para estimar os coeficientes de correlação linear de Pearson em quatro espécies de crotalária, em níveis de precisão. O experimento foi realizado com Crotalaria juncea, Crotalaria spectabilis, Crotalaria breviflora e Crotalaria ochroleuca, no ano agrícola 2014/2015. Oito características da crotalária foram avaliadas em 1.000 vagens coletadas aleatoriamente por espécie. Para cada espécie, estimaram-se os coeficientes de correlação para os 28 pares de características e determinou-se o tamanho de amostra necessário para a estimação dos coeficientes de correlação, em quatro níveis de precisão [amplitudes do intervalo de confiança de 95% (CI95%) de 0,10, 0,20, 0,30 e 0,40] por reamostragem com reposição. O tamanho de amostra varia entre as espécies de crotalária e, principalmente, entre os pares de características, em função da magnitude do coeficiente de correlação. Em determinado nível de precisão, o menor tamanho de amostra é necessário para a estimação de coeficientes de correlação de alta magnitude e vice-versa. Para estimar coeficientes de correlação com CI95% de 0,20, são necessárias de 10 a 440 vagens, a depender da espécie, dos pares de características e da magnitude do coeficiente de correlação

Differences between strawberry cultivars based on principal component analysis

Strawberry culture is of extreme economic importance, especially for small producers, as it has the capacity to add value to small family farms, in addition to absorbing family labor. Principal component analysis (PCA) is a multivariate technique for modeling covariance structure, where a basic idea is to find latent variables that represent linear combinations of a group of variables under study, which in turn are related between itself. In this way, the objective of the work was estimated, through the analysis of main components (PCA), as relationships between development variables, products and fruit quality in different strawberry cultivars. The design used was a randomized block with 11 treatments, consisting of strawberry cultivars of Italian and American origins, with four replications. During the culture cycle, the following variables were evaluated: phyllochron, number of commercial (FC) and non-commercial (FNC) fruits, mass of commercial (MFC) and non-commercial (MFNC) fruits, total titratable acidity (AT), total soluble quantities (SST) and total soluble ratio, titratable acidity (SST / AT). The relationships between the variables were evaluated by the PCA analysis and the results were plotted on the Biplot graph. From the analysis, it was possible to identify the relationships between the variables that show how to cultivate the same photoperiod and the same characteristic origin. Growing short photoperiods are more productive, for example, as the neutral photoperiod has less phyllochron and less acidity. The increase in soluble solids can cause a reduction in acidity, which is one of the characteristics that add flavor to the fruit

Tamanho de parcela e número de repetições para experimentos de azevém semeados em filas

The objective of this work was to determine the optimal plot size and the number of replicates for the evaluation of the fresh weight of ryegrass sowed in rows. Seventy uniformity trials were performed with 'Barjumbo' ryegrass, in 16 basic experimental units (BEUs) of 0.51 m2 each. The fresh weight of ryegrass in the BEUs of 18, 18, 6, 6, and 22 uniformity trials was determined, respectively, at 130, 131, 133, 134, and 137 days after sowing. The optimal plot size was determined through the method of the maximum curvature of the coefficient of variation. The number of replicates was determined in scenarios formed by combinations of treatments and differences between means to be detected as significant by Tukey’s test, at 5% probability. The optimal plot size ranged from 1.73 to 3.18 m2, and the variation coefficient in the optimal plot size from 7.58 to 13.96%. The number of replicates varied from 3.95 (~4) to 32.27 (~33), depending on the experimental design, the number of treatments, and the adopted minimum difference. The optimal plot size is 2.29 m2, and, in experiments with up to 50 treatments, eight replicates are required to identify as significant the differences between treatment means of 20.24%.O objetivo deste trabalho foi determinar o tamanho ótimo de parcela e o número de repetições para avaliação da massa de matéria fresca de azevém semeado em filas. Setenta ensaios de uniformidade foram realizados com azevém 'Barjumbo', em 16 unidades experimentais básicas (UEBs) de 0,51 m2 cada uma. A massa de matéria fresca do azevém nas UEBs de 18, 18, 6, 6 e 22 ensaios de uniformidade foi determinada, respectivamente, aos 130, 131, 133, 134 e 137 dias após a semeadura. O tamanho ótimo de parcela foi determinado pelo método da máxima curvatura do coeficiente de variação. O número de repetições foi determinado em cenários formados por combinações de número de tratamentos e de diferenças entre médias a serem detectadas como significativas pelo teste de Tukey, a 5% de probabilidade. O tamanho ótimo de parcela oscilou de 1,73 a 3,18 m2, e o coeficiente de variação no tamanho ótimo de parcela de 7,58 a 13,96%. O número de repetições oscilou de 3,95 (~4) a 32,27 (~33), conforme o delineamento, o número de tratamentos e a diferença mínima adotada. O tamanho ótimo de parcela é de 2,29 m2 e, em experimentos com até 50 tratamentos, são necessárias oito repetições para identificar como significativas as diferenças entre médias de tratamentos de 20,24%

Época de colheita da cana-de-açúcar para processamento e qualidade tecnológica de açúcar mascavo

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'.O objetivo deste trabalho foi avaliar a época ideal de colheita de dez genótipos de cana-de-açúcar (Saccharum officinarum) para o processamento e a qualidade de açúcar mascavo. O experimento foi realizado em delineamento de blocos ao acaso, em arranjo fatorial 3x10, em parcelas subdivididas, com três épocas de colheita e dez genótipos de cana-de-açúcar, no estado de São Paulo, Brasil. Os parâmetros qualitativos de açúcar mascavo foram avaliados pelo teste de Scott-Knott, a 5% de probabilidade. A época de colheita em setembro, conhecida como meio de safra, é a mais adequada para a produção de açúcar mascavo, em razão dos maiores valores de ºBrix da cana, ºBrix do caldo, valor pol do mascavo e açúcares totais redutores nesse período. A colheita dos genótipos de cana-de-açúcar em junho-julho é a mais favorável para a produção de açúcar mascavo quanto às características de coloração a*, b*, L* e croma; no entanto, é também o período de produção de açúcar mascavo com menores teores de açúcar. A terceira época de colheita (novembro) é a menos recomenda para a produção de açúcar mascavo, em razão dos maiores valores de fibra e pureza. O genótipo mais adequado para a produção e a qualidade de açúcar mascavo é o 'IACSP04-704'

Canonical correlations in agricultural research: Method of interpretation used leads to greater reliability of results

Canonical correlations analyzes are being used in the agrarian sciences and constitute an important tool in the interpretation of results. This analysis is performed by complicated mathematical equations and it is only possible to use it thanks to the development of computational software, which allow different interpretations of results, and it is up to the researcher to choose according to his knowledge. Canonical correlations can be interpreted using canonical weights, canonical loadings, or canonical cross-loadings. In Brazil, most of the works that use these analyzes interpret the canonical weights. Therefore, this study aims to show, through an analysis of canonical correlations, the best way to interpret the results, so that they are presented in the most reliable way possible. Data from an experiment with two cultivars of biquinho pepper seeded in 5 light spectrums were performed. The variables were root length and volume, plant height, number of leaves, fresh shoot and root mass, shoot dry mass. Two groups of variables were organized, the multicollinearity was determined through the condition number and the inflation factor of the variance. Canonical correlations analysis was carried out, and weights, loadings, and canonical cross-loadings were estimated for the interpretation of the results. After the interpretations, it was defined that the canonical cross-loadings should be preferred for the interpretation of the canonical correlations. Weights or canonical coefficients provide dubious results of relationships between groups of characters and should be avoided