Plot Size Related to Numbers of Treatments and Replications, and Experimental Precision in Conilon Coffee From Clonal Seedlings of LB1


  •  Gleyce Pereira Santos    
  •  Karina Tiemi Hassuda dos Santos    
  •  Renan Garcia Malikouski    
  •  Vinicius de Souza Oliveira    
  •  Jéssica Sayuri Hassuda Santos    
  •  Marcio Paulo Czepak    
  •  Omar Schmildt    
  •  Sara Dousseau Arantes    
  •  Robson Bonomo    
  •  Edilson Romais Schmildt    

Abstract

In the execution of experiments involving agricultural crops, it is essential that the researcher is able to initially establish the outline, the amount of treatments, replication and the plot sizes, to predict the physical space and waste of material. The planning is not always easy due to the lack of research pointing out the adequate plot size, especially in experiments throughout seedlings stage. The objective of this work was to determine the adequate plot size for experiments with conilon coffee clone LB1 seedlings. Fort this Hatheway’s suggested methodology was used, in which the coefficient values of variation and the heterogeneity index were obtained through bootstrap simulation with replications. The findings emphasized that in the experiments involving the conilon coffee tree LB1 manufactured in bags, with the delineation in randomized blocks, when the evaluation of destructive characteristics require a larger experimental plot size than when characteristics are non-destructive, considering the same margin error. In the installation of experiments with conilon coffee tree LB1 clone, in randomized blocks with 7 to 40 treatments and three replications, plots holding nine seedlings are enough to identify significant differences between average of treatments of non-destructive kinds to 5% of prospects and variation between the average of treatments 30% of overall experimental average. However, for destructive characteristics in randomized blocks with 7 to 40 treatments in three replications, plots holding 14 seedlings are enough to identify significant differences between treatment averages to 5% of prospects and variation between average treatments 30% of overall experimental average.



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