Comparison of five methods for calculating the optimal size of the experimental plot with sugarcane
DOI:
https://doi.org/10.29312/remexca.v15i4.3101Keywords:
estimators, experimental error, soil heterogeneityAbstract
Given the need to make efficient use of the resources allocated to experimental research in sugarcane crops, this research was proposed with the aim of determining the optimal size of the experimental plot in sugarcane crops in the Brunca region of Costa Rica. During the 2018-2019 harvest, an experimental uniformity trial was established, and five methods were compared: maximum curvature method, maximum curvature of the coefficient of variation, linear regression with constant, quadratic regression with constant and maximum distance method. The results indicate that the most efficient estimators were obtained with models that consider all sizes and forms of the uniformity trial ( ). Segmented regression and linear regression with constant models produced the best estimators of the optimal size of the experimental plot: 72.16 and 93.22 m2, respectively. With the other three methods, considerable and inconsistent differences in the sizes of the experimental plot were obtained. With the methods of maximum curvature and maximum curvature of the coefficient of variation, the results were so small: 14.01 and 12.5 m2, respectively, that they are inadequate to carry out research in sugarcane; on the contrary, with the method of maximum distance, the size obtained was 157.48 m2, statistically and economically inefficient. Therefore, the linear regression with constant and quadratic regression with constant models are appropriate for determining the experimental plot size in sugarcane. It was concluded that the recommended size to be used in the area is 72 m2. This research was completed in December 2021.
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