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.
The objectives of this study were to determine the optimum plot size (X o ) and the number of replications to evaluate the grains yield of rye (Secale cereale L.) and investigate the variability of X o between two cultivars and three sowing dates. Eighteen uniformity trials were conducted with rye. The X o was determined by the method of maximum curvature of the coefficient of variation model. The number of repetitions 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 0.05 of probability, by Tukey test, expressed in percentage of the average of the experiment (d = 10, 12, ... 30%). There is variability in optimum plot size to evaluate the grains yield among the cultivars BRS Progresso and Temprano and among sowing dates in the rye crop. The optimum plot size to evaluate the grains yield of rye is 6.08 m 2 . Seven replicates are sufficient to evaluate the grains yield of rye in experiments with up to 50 treatments, and identify, as significant at 5% probability by Tukey test, differences among averages of treatments of 29.65% of the mean of the experiment in designs completely randomized and randomized block.
This study aimed to verify the influence of the basic experimental unit (BEU) size in the estimation of the optimum plot size to evaluate the fresh matter of sunn hemp (Crotalaria juncea L.) using the modified maximum curvature method. The fresh matter of sunn hemp was evaluated in uniformity trials in two sowing season in flowering. In each sowing season, 4,608 BEUs of 0.5×0.5m (0.25m2) were evaluated and 36 BEU plans were formed with sizes from 0.25 to 16m2. In each evaluation period for each BEU plan, using fresh matter data, optimum plot size was estimated through the modified maximum curvature method. Estimation of the optimum plot size depends on the BEU size. Assessing fresh matter in BEUs that are as small as possible is recommended in order to use it to estimate the optimum plot size.
The determination of the optimum plot size in agricultural crops is important for obtaining accurate inferences in the treatments in question. This study aimed at determining the optimum plot size (Xo) and the number of replications to evaluate the fresh matter (FM) and the dry matter (DM) of oat and at verifying the variability of Xo among cultivars and sowing dates. Ninety-six uniformity trials of 3×3 m were performed and each assay was divided into 36 basic experimental units (BEU) of 0.5×0.5 m. The 96 uniformity trials were distributed in four cultivars and three sowing dates. At the flowering stage, FM and DM were determined in each BEU. Then, the Xo was determined in each uniformity assay, using the maximum curvature method of the coefficient of variation model. In oat, there is variability of Xo among cultivars and sowing dates to measure FM and DM. For the four cultivars on the three sowing dates, the Xo of 1.66 m2 and of 1.73 m2 are suitable to evaluate FM and DM, respectively. Four replications to evaluate the maximum of 50 treatments in completely randomized design and randomized blocks design are sufficient so that the differences among treatment means of 44.75% of the experiment mean may be significant, using the Tukey test at 5% probability to measure FM and DM in oat.
RESUMO: Os objetivos deste trabalho foram determinar o tamanho ótimo de parcela (Xo) e o número de repetições para avaliar as massas de matéria fresca e seca de centeio (Secale cereale L.), e investigar a variação do Xo entre duas cultivares e cinco épocas de semeadura. Foram conduzidos 30 ensaios de uniformidade. Cada ensaio com dimensões de 6 × 4 m foi dividido em 24 unidades experimentais básicas (UEB) de 1 × 1 m. Foram avaliadas as massas de matéria fresca e seca de centeio nas 720 UEB. O Xo foi determinado pelo método da curvatura máxima do modelo do coeficiente de variação. O número de repetições foi determinado em cenários formados por combinações de tratamentos, delineamentos experimentais e níveis de precisão. Os tamanhos ótimos de parcela para avaliar as massas de matéria fresca e seca de centeio são, respectivamente, 3,43 e 3,82 m 2. Seis repetições são suficientes para avaliar as massas de matéria fresca e seca, em delineamentos inteiramente casualizado e blocos ao acaso com até 50 tratamentos, e possibilitam obter diferença mínima significativa menor ou igual a 20% da média do experimento. Palavras-chave: cultura de cobertura de solo; dimensionamento experimental; ensaio de uniformidade
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.