Search

Millet is largely explored for a diversity of applications. The use of millet high-quality seeds is essential to increase grain yield. Accordingly, the purpose of this study was to evaluate the yield variability of millet plants from higher and lower-quality seeds in the expression of dry matter yield parameters. The study was conducted in the experimental and didactic area of IFRS - campus Ibirubá, Rio Grande do Sul, Brazil, with a completely randomized design. The treatments were plants from seeds of higher (HQS) and lower (LQS) quality and two season crops (2015/16 and 2017/18) in eight replications. The emergence speed was applied to identify plants from seeds of different qualities. The phyllochron, leaf number, height, stem diameter, number of nodes, and dry mass yield were the agronomic components evaluated for this study. Meteorological information was used to calculate phyllochron, water deficit, and average temperature. The collected data were submitted for analysis of variance and the Tukey test at a 5% probability of error. The HQS expressed lower phyllochron and most significant agronomic components. Furthermore, the 2015/16 crop had the lowest water deficit and obtained the best results. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ignoring the violation of the error normality assumption is a common mistake that can cause inconsistencies in the results of the analysis of variance. To avoid this, normality tests such as the Shapiro-Wilk test can be used to verify whether this assumption is met or violated. However, the test accuracy relies on a representative number of observations sampled in trials, once the calculated W statistic used for the test is considered sensitive or capable of being altered according to the sample size. Therefore, the aims of this study were to analyze the Shapiro-Wilk test response to sample size for soybean grain yield and to define the sample size for optimizing the W statistic estimate, making it more efficient and reliable. For this, the replacement sampling method was applied in trials that measured soybean grain yield per plant. The trials were performed in two locations in the state of Rio Grande do Sul, Brazil, and in each, three sowing dates were assessed, using a total of 30 commercial soybean cultivars. Grain yield per plant was assessed by weighing grains at maturity and correcting the value to 13 % moisture. Posteriorly, thirty-one sample scenarios were simulated per experimental unit, performing an analysis of variance and applying the Shapiro-Wilk test at 5 % error probability to the experimental unit errors in each pre-defined sampling scenario. Also, four methodologies for determining the maximum curvature point to define sample size were compared, which are the general, spline, perpendicular distance, and linear plateau methods. The 95 % confidence interval width of the W statistic had an exponential decreasing response for trials that either meet or violate the error normality assumption. Small sample sizes per experimental unit led to biased estimates of the test, where it was either under or overestimated. The perpendicular distance and linear plateau methods were the most adequate to define the sufficient sample size for the W statistic, and at least 17 plants per experimental unit should be used for estimating error normality for soybean grain yield reliably. [Display omitted] • Shapiro-Wilk test efficiency depends on soybean sample size. • A low number of sampled plants generates a bias in the error normality analysis. • Methodologies for defining the optimal sample size presented distinct results. • The perpendicular distances and linear plateau methods are recommended. [ABSTRACT FROM AUTHOR]