Abstract
The aim of this study was to determine a suitable plot size for field experiments with the sunflower. An experiment was carried out in a randomised complete block design with 14 sunflower cultivars and 10 replications. The plots consisted of four rows, six metres in length, spaced 0.7 m apart with 0.3 m between plants. The working area of the plot (7.56 m 2 ), consisting of the two central rows, was divided into 12 basic units, each consisting of three plants per row (0.63 m 2 ), from where the yield of the sunflower seeds was obtained. Suitable plot size was estimated using the intraclass correlation coefficient method. The detectable difference between treatments was also estimated (d). The optimum plot size for the evaluation of grain yield in the sunflower was 2.52 m 2 (working area), considering a boundary of one row on each side. Greater gains in experimental precision (16%) with increases in plot size, occurred up to eight basic units (5.04 m 2 ) using seven replications. Increasing the number of replications and the plot size was more efficient
Highlights
Among oilseeds, the sunflower (Helianthus annuus L.) is the crop with the fastest growth rate in the world (SMIDERLE, 2010)
Plot size tends to increase with the progress of the breeding program, i.e. the more advanced the population, the larger the plot size needed for the experiment, because as the generations advance there is a reduction in variation between the selected materials, requiring a larger number of plants for such variations to be detected and for selections to be made
This means that there was more variability between plots and less variability between basic units in the plot, resulting in a positive value not close to zero (0.2786) for the intraclass correlation coefficient ( ) for the basic units in the plot. This shows that there was some correlation between the basic units in the plot, which helped in the use of reasonably small plots, in this case of 4 working basic units (2.52 m2)
Summary
The sunflower (Helianthus annuus L.) is the crop with the fastest growth rate in the world (SMIDERLE, 2010). For a breeding program to be successful, it is necessary for experiments to be able to detect ever smaller variations, since the trend is for differences between new cultivars to decrease. For this reason, the challenge for breeders is to increase the precision of experiments, which would result in genetic advances and, as a consequence, materials which are more productive and of better quality (SILVA, 2009). From the point where increasing the size of the plot does not result in increased precision, additional increases in precision can be obtained through the use of a greater number of replications (CARGNELUTTI FILHO et al, 2012)
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