Importance of agronomic traits in the individual selection process in sugarcane determined using the logistic regression

Brasileiro, Bruno Portela; Peternelli, Luiz Alexandre; Silveira, Luís Cláudio Inácio da; Barbosa, Márcio Henrique Pereira

doi:10.4025/actasciagron.v38i3.28424

Cited by 3 publications

(5 citation statements)

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“…Thus, this strategy could considerably reduce the weighing process in the field and optimize the selection procedure. Also, because every plot in the field is needed to be visited to have the predicting variables measured, other agronomic traits of the families may be recorded (Brasileiro, Peternelli, Silveira, & Barbosa, 2016b) and used as additional criteria to select these families.…”

Section: Discussionmentioning

confidence: 99%

Can statistical learning models make early selection among sugarcane families easier and still efficient?

2020

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The selection of genotypes at the early stages is one of the main challenges facing sugarcane (Saccharum officinarum L.) breeding programs. The present work aimed to compare classification techniques, namely, logistic regression (LR), k‐nearest neighbor (KNN), random forests (RF), and support vector machine (SVM) against the selection among families of sugarcane via artificial neural networks (ANN) and via a procrefers to the families incorrectly selected byedure based on the weighing of the plots. The data used in this work were obtained from 110 families. In the families, the number of stalks (NS), stalk diameter (SD), and stalk height (SH) were collected, in addition to the actual yield, expressed in tons of cane per hectare (TCH). We considered the NS, SD, and SH as explanatory variables for the training of the classifiers. The response used was the indicator Y = 0 if the family is not selected via TCH or Y = 1 otherwise. To increase the efficiency in training, we produced synthetic data based on the simulation of NS, SD, SH, and TCH values. Two models were also considered: a full model with all the predictors and a reduced model without the SH. We used the apparent error rate (AER) and the true positive rate (TPR) for the evaluation of the classifiers. All classifiers present low values for the AER and high values for the TPR in both models. The best performance was observed in the SVM. The reduced model should be preferred, since its performance is very close to that of the full model and its operation is more straightforward.

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Section: Discussionmentioning

confidence: 99%

Can statistical learning models make early selection among sugarcane families easier and still efficient?

2020

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“…Smut, caused by the fungus Sporisorium scitamineum, is an extremely important disease in sugarcane cultivation, since it can cause significant losses to productivity, being an important factor in the decision making at the time of selection. In the research done by BRASILEIRO et al, (2016), smut incidence was a trait that did not have much importance in the selection of the clones as the study was completed on another population in which the incidence of the disease was low due to the environmental conditions. Environmental factor influences the occurrence of diseases in the southern region of Brazil.…”

Section: Discussionmentioning

confidence: 99%

“…In visual selection, the rejected of a genotype involves a combination of different traits such as floral induction, presence of diseases, and sugar and fiber contents, which influence the decision (BRASILEIRO et al, 2016). Knowledge of characters that are most important in the selection of sugar cane 1 Departamento de Fitotecnia e Fitossanidade, Universidade Federal do Paraná (UFPR), 80035-050, Curitiba, PR, Brasil.…”

Section: Introductionmentioning

confidence: 99%

“…The use of the logistic model as a statistical tool to support selection based on production components has been shown to be efficient in sugarcane (ZHOU et al, 2014;BRASILEIRO et al, 2016); however, to date, no studies have used this technique for the selection of energy cane clones. Therefore, the objective of this research was to identify the most important agronomic traits during the energy cane clonal selection process and to verify the efficiency of the logistic model in predicting the genotypes to be selected.…”

Section: Introductionmentioning

confidence: 99%

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Logistic model to selection of energy cane clones

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Logistic regression analysis is a technique that may aid genetic breeding programs in the selection of clones, especially in the early stages where experimental accuracy is low. This research aimed to identify the most important agronomic traits for energy cane clonal selection, and to verify the efficiency of the logistic model in predicting the genotypes to be selected. Evaluations were carried out on 220 clones in the first ratoon. The data were subjected to binary logistic regression analysis. Stalk number per meter was the most important trait in the selection of energy cane clones. In addition, plants with lower grade for smut incidence had a greater chance of being selected. The predictive capacities of the qualitative and quantitative models were 94% and 88%, respectively. The use of a qualitative model proved to be effective at predicting the number of energy cane genotypes to be selected and could be used as a selection strategy.

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“…The advantage of this method is that it does not require the evaluation of individual plants in the experiment and instead indicates the number of individuals to be selected per family, automatically eliminating families with negative genotypic effects. Studies involving the use of the BLUPIS methodology emphasize the efficiency of the selection method in sugarcane (Brasileiro, Peternelli, Silveira, & Barbosa, 2016;Carvalho et al, 2014;Resende & Barbosa, 2006;Silva et al, 2015). Table 4 shows the classifications of the effects of the SCA (ordered for TPH) of the 15 best full-sib families for the following traits: brix, fiber (%), PCC, TCH (t ha −1 ), TPH (t ha −1 ), and TFH (t ha −1 ).…”

Section: Components Of Variance and Genetic Parametersmentioning

confidence: 99%

Establishment of gene pools for systematic heterosis exploitation in sugarcane breeding

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In sugarcane (Saccharum spp) breeding, despite the potential use of reciprocal recurrent selection (RRS) to explore both nonadditive and additive effects for economic value traits, to date, no publications have targeted obtaining heterotic groups to support this selection strategy. The aim of this study was to (a) to identify sugarcane gene pools to effectively explore nonadditive and additive genetic effects via reciprocal recurrent selection, (b) estimate genetic parameters, and (c) select parents and cross combinations. Seventy-nine sugarcane families were obtained from crossing 38 parents in a crossing scheme known as disconnected factorial design. The families were evaluated in randomized complete block design with five replicates. The statistical genetic analysis was performed with linear mixed models through restricted maximum likelihood/best linear unbiased predictor. The results revealed that nonadditive effects were predominant in explaining genetic variation for cane yield (t ha −1), pol yield (t ha −1), and fiber yield (t ha −1). For brix, fiber and corrected Pol (PCC) traits, additive effects were predominant. Estimates of specific combining ability and pedigree information allowed the establishment of gene pools for systematic heterosis exploitation through RRS breeding efforts. The prediction of the additive genetic values of parents for selection based on the progeny's behavior obtained via disconnected factorial design is an alternative that should be used in sugarcane breeding programs.

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Importance of agronomic traits in the individual selection process in sugarcane determined using the logistic regression

Cited by 3 publications

References 23 publications

Can statistical learning models make early selection among sugarcane families easier and still efficient?

Can statistical learning models make early selection among sugarcane families easier and still efficient?

Logistic model to selection of energy cane clones

Establishment of gene pools for systematic heterosis exploitation in sugarcane breeding

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