Plot size variation to quantify yield of potato clones

Abstract

The objective of this paper was to study plot size variation among potato clones to increase experimental precision of yield performance trials. The experiment was carried out at the experimental area of the Horticulture Dept., UFSM, Santa Maria, Rio Grande do Sul State, Brazil. Tubers of five potato clones were planted in two rows of 60 hills in August 2004. For all ten experimental rows, combined plots were formed adding a different number of adjacent hills of the same row. Soil heterogeneity index and optimum plot size were estimated for each row. Real differences between treatment means for each clone and all clones were estimated using the Hatheway technique. The experimental precision of potato yield trials varied with the evaluated clone because of different optimum plot sizes. Eight-hill plots are the optimum size to quantify yield of potato clones, but experimental precision depends upon adjustments of the number of treatments and replications to the availability of experimental area. Genetic diversity indeed decreases experimental precision and justifies the use of different plot sizes. Practical applicattions and strategies to increase experimental precision of yields performance trials of potato clones are discussed.

Allocation of Experimental Resources Used in Potato Breeding to Minimize the Variance of Genotype Mean Chip Color and Tuber Composition

“…1) demonstrates the curvilinear decrease in variance as the number of chips or clonal propagules evaluated (sampling) was reduced and the number of trialing environments (replications) increased. These results are consistent with previous studies focused on potato tuber yield where increasing sampling within a plot was much less eff ective that increasing the number of plots (Bisognin et al, 2006;Caligari et al, 1985). Figure 1 also highlights the need to balance experimental replicates between years and locations.…”

Optimal Plot Dimensions for Performance Testing of Hybrid Potato in the Field

“…The variation in yield traits was high with an LSD% of still 21% at a plot size of 20 plants, although Talbot (1984) described high variation in potato yield trials compared with other crops. For tuber weight, the point of maximum curvature was a plot size of 12 plants, which is slightly higher than the optimal plot size found by Bisognin et al (2006) using the same method of up to 10 plants, depending on the genotype.…”

“…This was done by creating a linear regression model between the logarithm of LSD% and plot size. The point of maximum curvature on the back transformed relationship between LSD% and plot size (Bisognin et al 2006;Donato et al 2018) was used as a convenient point to compare traits with respect to the dependence of precision on plot size. To assess optimal plot size, we provide equations to calculate LSD% for different plot sizes because plot size should be determined by the need of precision for a given trial or set of trials.…”

“…It is obvious that in addition to plot size, experimental design and the number of replicates will affect the plot error (Sripathi et al 2017). Research by Caligari et al (1985) and Bisognin et al (2006) shows that increasing the number of replicates will be more effective for a higher precision of the trial than increasing plot size. Although the LSD% for different numbers of replicates can be estimated by multiplying the LSD% determined with an equation from Table 3 with the square root of (3/n r ), in which n r is the number of replicates, it would be advisable to include different numbers of replicates in the analyses in future research.…”

“…Plot size is an important decision in plant breeding trials, as plots should be large enough to allow identification of superior genotypes, but small enough to not waste resources that could be used differently, such as for evaluating more genotypes. Plot size has been a topic of interest in potato as well as in other crops for a long time (Caligari et al 1985;Vallejo and Mendoza 1992;Bisognin et al 2006;Schmildt et al 2016;Khan et al 2017;Lavezo et al 2017;Lohmor et al 2017;Donato et al 2018). Error variances and standard errors for phenotypic traits will be a function of the number of plants within a plot and the correlations between plants within a plot as a consequence of soil heterogeneity and inter plant competition.…”