Abstract
The objective of this study was to update the ranking of the coefficients of variation (CVs) from maize experiments and evaluate the accuracy of the data from the latest Brazilian publications. We rank-ordered the CVs for grain yield, plant and ear heights, number of ears per plant, and weight of commercial ears, except for the weight of 100 grains. The data were obtained from 143 scientific papers published from 2005 to 2010. The classification was based on the average (m) and standard deviation (SD) and the CVs were ranked as low, intermediate, high and very high. All of the random variables had the CVs normally distributed. For most of the traits, we observed a large difference between the ranks from Scapim and Pimentel Gomes. In summary, the coefficients of variation have to be classified for each variable, significant improvement occurred in the accuracy of the national maize experiments in the last 15 years, and they still require periodic updating.
Highlights
The accuracy in predicting the responses of agronomic traits is crucial for the success of plant breeding programmes
The coeficientes de variação (CV) is highly important to comment the analyses of agriculture experiments because it represents the level of variation from random variables that follow the Normal distribution
We surveyed CV data from grain yield, plant and ear height, ear diameter, number of spikes per plant, weight of commercial ears, weight of 100 seeds, lodging and broken plants, days to silking and effective digestibility because these are the most significant traits currently investigated in plant breeding programmes
Summary
The accuracy in predicting the responses of agronomic traits is crucial for the success of plant breeding programmes. The experimental error, quantified by the CV, represents the ratio of the standard deviation to the mean. The CV is highly important to comment the analyses of agriculture experiments because it represents the level of variation from random variables that follow the Normal distribution. Lower is the power of a test to discriminate the treatments (COSTA et al, 2002). This dimensionless statistics permits to the researchers compare the accuracy of. Agronomy experiments despite the different units of measurements (PIMENTEL-GOMES, 2000)
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