Uniformity trial size in estimates of plot size in restrict areas

Schwertner, D. V.; Lúcio, Alessandro Dal’Col; Filho, Alberto Cargnelutti

doi:10.5935/1806-6690.20150043

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…An important point that should be emphasized in this work is that from a given simulation uniformity size, the AIC95% has a stabilizing of tendency, that is, as the size of the uniformity trial increases, the AIC95% varies very little with the size increase of the uniformity trial (Figures 1-4). The results found in this study are consistent with Cargnelutti Filho et al (2011), Schwertner et al (2015a), Schwertner et al (2015b), and Sari and Lucio (2018) who concluded that small uniformity trial provide plot size estimates with low accuracy.…”

Section: Discussionsupporting

confidence: 91%

“…The estimation of plot size is based on uniformity trials without treatments (Schwertner et al, 2015a;Schwertner et al, 2015b) and depends directly on the magnitude of the experimental variability (Lúcio & Benz, 2017). The variability of the data in multiple-harvests horticultural crops, such as Solanum melongena L., is related to several factors such as (i) heterogeneity of soil fertility; (ii) intensive management; (iii) uneven application irrigation and fertilizers; (iv) occurrence of pests, diseases, and weeds; (v) uneven maturity of fruits; (vi) presence or absence of suitable fruits to be harvested in a given harvest (Lúcio & Sari, 2017;Krysczun et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…However, in these studies the authors did not have the concern to investigate the accuracy of the plot size estimates obtained in the uniformity trials. the other hand, studies that evaluated turnip forage (Cargnelutti Filho et al, 2011), tomato, green beans and Italian zucchini (Schwertner et al, 2015a) and lettuce and pepper (Schwertner et al, 2015b) pointed out that uniformity trials with a few number of basic experimental units (BEU) lead to inaccurate estimates of plot size, and it is important to elucidate the influence of uniformity trial size on plot size estimation.…”

Section: Introductionmentioning

confidence: 99%

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The Size of the Uniformity Trial Affects the Accuracy of Plot Size Estimation in Eggplant

Krysczun¹,

Lúcio²,

Sari³

et al. 2018

JAS

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The plot size estimation is based on uniformity trials, however little is known about and how the size of uniformity trial affects the estimate of the plot size. That way, the aim of this study was to determine the influence of uniformity trial size on the estimation of plot size in the eggplant crop. Two uniformity trials were performed with the eggplant culture in a plastic tunnel. The fresh mass of fruit and number of fruits were assessed in six harvests, with a seven-day interval between harvests. For each trial (Tunnel 1 and 2), 25 uniformity trials of different sizes were simulated (3, 4, 5, … 28 BEU) per harvest and harvest row (individual and grouped) since they presented heteroscedasticity. For each planned uniformity trial, bootstrap procedure was used to estimate 3,000 plot sizes by the maximum coefficient of variation curvature method. The mean and 95% confidence interval width was calculated by the difference between the 97.5th and 2.5th percentiles. The AIC95% and plot size averages were higher in individual harvests than grouped harvests. As the size of the simulated uniformity trial increased, it was verified a reduction of the AIC95% of the plot size. However, the mean plot size did not change with increasing uniformity trial size. In this way, it is possible to state that the size of the uniformity trial affects accuracy the plot size estimation because trials with few numbers of basic experimental units present high experimental variability and inaccurate estimates.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Size of the Uniformity Trial Affects the Accuracy of Plot Size Estimation in Eggplant

Krysczun¹,

Lúcio²,

Sari³

et al. 2018

JAS

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show abstract

“…It turns out that the adoption of this relationship in order to minimize the experimental error, in some cases, depending on the number of treatments, plot design and size, can lead to experiments of impractical sizes. Schwertner et al (2015) argued that for the intensive use of experimental areas, it is important to plan smaller and more efficient experiments regarding accuracy and costs.…”

Section: Introductionmentioning

confidence: 99%

Experimental planning for the evaluation of phenotipic descriptors in banana

et al. 2018

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This work aimed to subsidize experimental planning for the evaluation of phenotypic characters in banana. The experiment consisted of a uniformity trial with Tropical cultivar spaced of 3 m x 2 m, in a useful area with nine rows of 40 plants. Vegetative and yield aspects were evaluated in two production cycles. Plants and basic units (UB) were combined to form various plot sizes, from which the variation coefficient and the soil heterogeneity index were estimated. To define the experimental design, the Hatheway method was used. Variability, resulting from the soil anisotropy and other factors such as desuckering cultural practice increases between mother plant and daughter plant cycles, with effects on experimental design. The variation coefficient and number of replicates are the factors that most influenced the experimental design. For the same precision level required, the combination that maximizes efficient in the use of experimental area can be adopted, that is, smaller plot size and higher number of replicates or larger plot size and fewer replicates. Estimates of plot size and number of replicates are subsides for the design of experiments for the evaluation of phenotypic descriptors in banana.

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“…(Santos et al, 2016); fresh matter of lettuce (Lactuca sativa), and fresh matter of pepper fruits (Capsicum annuum) (Schwertner et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Basic experimental unit and plot sizes with the method of maximum curvature of the coefficient of variation in sunn hemp

Facco¹,

Filho²,

Alves³

et al. 2017

Afr. J. Agric. Res.

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The influence of the basic experimental unit size on the plot size estimation determined by the method of maximum curvature of the coefficient of variation model is unknown in sunn hemp. This study aimed to verify the influence of the basic experimental unit (BEU) size in the estimate of the optimum plot size obtained by the method of maximum curvature of the coefficient of variation model for the evaluation of fresh matter of sunn hemp (Crotalaria juncea L.). Fresh matter of sunn hemp at the flowering was evaluated in uniformity trials in two sowing dates. In each sowing date, 4,608 BEU of 0.5 × 0.5 m (0.25 m 2 ) were evaluated and 64 BEU plans were formed with sizes from 0.25 to 64 m 2 . In each evaluation period for each BEU plan, the first order spatial autocorrelation coefficient, variance, standard deviation, mean, coefficient of variation of the trial and the plot size were determined with the fresh matter data. For each BEU plan, the optimum plot size was determined by the method of maximum curvature of the coefficient of variation model. The estimate of optimum plot size depends on the basic experimental unit size. Determining the plot size to assess the fresh matter in basic experimental units as small as possible is recommended in order to prevent overestimation of the plot size and to contemplate all existing variability.Key words: Crotalaria juncea L., experimental design, basic experimental unit. INTRODUCTIONThe sunn hemp (Crotalaria juncea L.) is a cover crop option for soil protection due to its hardiness, high dry matter production and nitrogen fixation (Silva and Menezes, 2007), improving and maintaining soil quality, raising to considerable levels of soil organic matter and nutrients (Leite et al., 2010). The crop rapid development enables the use of sunn hemp in cropping systems with rotation and crop succession. It is the legume with greatest dry matter production in comparison with gray velvet bean (Mucuna nivea), jack bean (Canavalia ensiformis), velvet bean (Mucuna aterrina), lab-lab (Dolichos lablab), showy crotalaria (Crotalaria spectabilis), and dwarf pigeon pea (Cajanus cajan) (Teodoro et al., 2011); in a study carried out by Andrade Neto et al. (2010), the fresh matter of aerial part values of sunn hemp were 13.9 t ha -1 . One aspect to be considered is the inferences made in agricultural research representing experimental reality which is the use of an optimum plot size to minimize the experimental error. The optimum plot size can be calculated based on data obtained from uniformity trials in which treatments are not applied (Ramalho et al., 2012;Storck et al., 2016). In order to evaluate traits of the studied crop, the experimental area is divided into basic experimental units (BEU) with the smallest possible size. Therefore, based on this information, the plot size is determined.The influence of the BEU size in estimating the optimum plot size is still an area with few studies but Oliveira et al. (2005) verified in potato (Solanum tuberosum L.) the BEU size effect on the optimum p...

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Uniformity trial size in estimates of plot size in restrict areas

Cited by 4 publications

References 12 publications

The Size of the Uniformity Trial Affects the Accuracy of Plot Size Estimation in Eggplant

The Size of the Uniformity Trial Affects the Accuracy of Plot Size Estimation in Eggplant

Experimental planning for the evaluation of phenotipic descriptors in banana

Basic experimental unit and plot sizes with the method of maximum curvature of the coefficient of variation in sunn hemp

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