Plateau regression reveals that eight plants per accession are representative for Jatropha germplasm bank

Bhering, Leonardo Lopes; Filho, Janeo Eustáquio de Almeida; Laviola, Bruno Galvêas; Gomes, Bruno Ermelindo Lopes

doi:10.1016/j.indcrop.2014.11.056

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…This plot size was selected due to the stabilization of the coefficient of variation, which means an improved use of resources, as illustrated in Figure 3. The linear response plateau regression model has been used for estimating the optimum plot size for several crops, such as forest species (Bhering et al, 2015), papaya (Celanti et al, 2016;Silva et al, 2019), castor bean (Sampaio Filho et al, 2019), among other crops. This method is considered among researchers as an efficient method as it is possible, using a single model, to have precision, an effective visualization, and practical determination.…”

Section: Resultsmentioning

confidence: 99%

Didactic tool for experimental demonstration with ‘Gigante’ forage cactus pear

Guimarães¹,

Donato²,

Aspiazú³

et al. 2020

J. Edu. Res. Rev.

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To make scientific knowledge easier to grasp, interpret and understand, the researcher must make an effort using didactic resources and innovative tools to meet the purposes of research. The art of representing the scientific knowledge as an alternative to the traditional model renders the pedagogical process more efficient. This work aimed to present a model representing the optimum experimental plot size for 'Gigante' forage cactus pear, defined using the linear response plateau method, by an in vivo model with the species Opuntia brasiliensis. We developed a model with 28 basic units arranged in a 4 × 7 grid of which ten basic units are plants for measurements arranged in a 2 × 5 grid or 40 cm 2 with the species Opuntia brasiliensis. The in vivo model scale, 1:10, represents a ten basic unit optimum plot size (40 cm 2 ), which in the field corresponds to 4 m 2 of the forage crop. Using the experimental model as a didactic and strategic resource was successful in improving the practical understanding of agronomic concepts; therefore, the in vivo model may be developed not only for thesis qualification and defense, but also for any teaching, research and extension environment as an innovative tool.

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Section: Resultsmentioning

confidence: 99%

Didactic tool for experimental demonstration with ‘Gigante’ forage cactus pear

Guimarães¹,

Donato²,

Aspiazú³

et al. 2020

J. Edu. Res. Rev.

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“…The Jatropha curcas progenies evaluated in this study are non-domesticated, with great potential of improvement [1,5,27]. However, many studies have pointed out the small genetic variability in this species [7,[28][29][30][31]. In this sense, carry out a recurrent selection program is a suitable strategy for the exploration of the additive genetic variability [30,32].…”

Section: Plos Onementioning

confidence: 99%

Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy

et al. 2021

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Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρg ≤ 0.33), moderate (0.34 ≤ ρg ≤ 0.66), and high magnitude (ρg ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.

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“…Renewable energy sources have been the focus of numerous research in recent years (Bhering et al 2015;Junqueira et al 2016). Considering the renewable energy sources, the agricultural sector presents some crops, such as Jatropha curcas L., with great potential for bioenergy production (Nithiyanantham et al 2012).…”

Section: Introductionmentioning

confidence: 99%