Accuracy in the estimates of zucchini production related to the plot size and number of harvests

Lúcio, Alessandro Dal’Col; Benz, Vilson

doi:10.1590/0103-8478cr20160078

Cited by 10 publications

(9 citation statements)

References 10 publications

(15 reference statements)

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“…This situation occurs due to the uneven maturation of the fruits due to physiological characteristics and the growth habit. This means that the researcher needs to harvest the staggered production and, in some of these harvests, the plant may not have fruit or present fruit not suitable for harvest (Cargnelutti Filho et al, 2004;Lúcio & Benz, 2017). The immediate consequence of this situation is the RESUMO Uma abordagem para avaliações de experimentos para culturas de múltiplas colheitas baseado em regressão não linear Modelos de crescimento de base biológica podem ser uma alternativa na identificação da resposta produtiva de hortaliças de múltiplas colheitas.…”

Section: Invited Articleunclassified

An approach for experiment evaluations for multiple harvests crops based on non-linear regression

2021

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Biologically based growth models can be an alternative in identifying the productive response of multiple harvest vegetables. By interpreting the estimates of the parameters of the models, it is possible to estimate the total production, the rate of fruit production, and the moment when the crop reaches its maximum production potential. Besides, by estimating confidence intervals, these responses can be compared between genotypes or between different treatments. Therefore, the purpose of this manuscript is to present a literature review, and a detailed step-by-step, to interpreting the evolution of the production cycle of vegetables with multiple harvests crops based on non-linear regression. All the requirements that must be met in this type of analysis were presented in detail based on non-linear regression, providing the necessary steps for this type of analysis in details. Demonstration is given using data from strawberry cultivation along with the associated R scripts and interpretation of analysis output in material supplemental. This approach can allow for more relevant inferences than standard means analyses through better examination and modeling of the underlying biological processes.

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Section: Invited Articleunclassified

An approach for experiment evaluations for multiple harvests crops based on non-linear regression

2021

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“…The most effective and also most used strategies to improve the quality of experiments with vegetables with multiple harvests are, namely, identification of crop-specific plot (Table 1) and sample sizes; determining the variability behavior between rows and between harvests, and; the study of data transformation and the use of Papadakis method to minimize the effects of excess zeros causing overdispersion in the database (Lúcio et al, 2011(Lúcio et al, , 2016Santos et al, 2012Santos et al, a,b, 2014Benz et al, 2015;Lúcio & Benz, 2017).…”

Section: Reducing the Variability In Vegetable Cropsmentioning

confidence: 99%

“…Studies in several vegetables have shown that harvest clustering reduces the variability between plants, decreasing data dispersion, and mitigating the negative effect of excess zeros in the database (Carpes et al, 2008(Carpes et al, , 2010Lúcio et al, 2011Lúcio et al, , 2016Santos et al, 2012Santos et al, a,b, 2014Benz et al, 2015;Lúcio & Benz, 2017). However, even with harvest clustering, variability may go on increasing, as the cumulative value of harvest "n" remains the same as that observed in harvest "n-1", when there are no products to be harvested in a plant, while in another, in which there are harvestable products, the value increases.…”

Section: Reducing the Variability In Vegetable Cropsmentioning

confidence: 99%

Planning and implementing experiments and analyzing experimental data in vegetable crops: problems and solutions

Lúcio

Sari

2017

Hortic. Bras.

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The statistical interpretation of experimental results is inherent to the research process. Therefore, every researcher is expected to have basic understanding on the subject. In vegetable crops, the planning, implementing and data gathering is more complex due to specific aspects related to this group of plants, such as intensive management and high labor requirement to carry out the experiments, uneven fruit maturation and heterogeneity of the experimental area. Since all these factors are sources of variability within the experiment, circumventing them in the experiment planning and implementing phases is fundamental to reduce the experimental error. Furthermore, the knowledge of statistical tests and the assumptions for their use is equally critical to make the research statistically valid. The present work presents the problems of unwanted variability within an experiment with vegetables and the possibilities to reduce and manage it. We discuss alternatives to reduce the variability due to uncontrolled effects within an experiment; the most common experimental designs; recommendation of appropriate statistical tests for each type of treatment; and techniques for the diagnosis of residues. We expect to contribute with researchers dealing with vegetable crops, offering subsidies to aid researchers in the planning and implementation of experiments and in the analysis and interpretation of experimental results.

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“…The experimental design aims to ensure the experimental accuracy and credibility of the results generated by the research (Lúcio & Benz, 2017). Correct choice of plot size is an important step of experimental planning aiming at increasing the accuracy and reliability of the results generated in the experiments and, at the same time, reducing the use of material, financial and human resources (Lúcio et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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Accuracy in the estimates of zucchini production related to the plot size and number of harvests

Abstract: The productive variability in horticultural crops affects the planning and quality of the experiments, inflating the error and

Cited by 10 publications

References 10 publications

An approach for experiment evaluations for multiple harvests crops based on non-linear regression

An approach for experiment evaluations for multiple harvests crops based on non-linear regression

Planning and implementing experiments and analyzing experimental data in vegetable crops: problems and solutions

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

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