A generalized test variable approach for grain yield comparisons of rice

Abstract

Traditionally, an assessment for grain yield of rice is to split it into the yield components, including the number of panicles per plant, the number of spikelets per panicle, the 1000-grain weight and the filled-spikelet percentage, such that the yield performance can be individually evaluated through each component, and the products of yield components are employed for grain yield comparisons. However, when using the standard statistical methods, such as the two-sample t-test and analysis of variance, the assumptions of normality and variance homogeneity cannot be fully justified for comparing the grain yields, leading to that the empirical sizes cannot be adequately controlled. In this study, based on the concepts of generalized test variables and generalized p-values, a novel statistical testing procedure is developed for grain yield comparisons of rice. The proposed method is assessed by a series of numerical simulations. According to the simulation results, the proposed method performs reasonably well in Type I error control and empirical power. In addition, a real-life field experiment is analyzed by the proposed method, some productive rice varieties are screened out and suggested for a follow-up investigation.

Keywords:

• generalized p-value
• generalized test variable
• reproductive success
• yield comparison
• yield component

Acknowledgements

The author is grateful to the three referees for their constructive comments and suggestions that resulted in a much improved article. The author thanks Malcolm D. Castillo, a grain officer of Ministry of Natural Resources and Agriculture of Belize, and Yaw-Chuan Lin, a specialist of Taiwan Technical Mission in Belize, for their fruitful discussions on the analysis of rainfed data. The rainfed experiment was implemented, when the author worked with Taiwan Technical Mission in Belize as an assistant specialist.