Applications of Quantitative Genetics and Statistical Analyses in Sugarcane Breeding

Abstract

Sugarcane breeding programs aim to deliver new high-yielding varieties, resistant to diseases and pests, which contribute to profitability and sustainability of cane industries. These programs generally mobilize significant experimental, technological and human resources on long-term basis. Their efficiency in terms of genetic gains per unit of cost and time and their ability to release new varieties rely on the development of many breeding applications based on quantitative genetics theory and on statistical analyses of numerous experimental data from selection schemes including DNA marker data developed for some genomic breeding applications. New methodological approaches and new technologies that might better guide and support breeding research in cultivars development programs are continually sought. This paper presents an overview of the main applications developed in statistical methodology in support of the efficiency of sugarcane breeding programs. For each type of application, its conceptual and methodological framework is presented. Implementation issues are reviewed as well as the main scientific and practical achievements so far obtained.

Novel Tools for Adjusting Spatial Variability in the Early Sugarcane Breeding Stage

“…The microenvironment, or the residual, can be controlled by experimental design and statistical methodologies. For example, linear mixed models increase the analysis precision due to the flexibility in modeling the field trial source of variations (Cursi et al, 2020(Cursi et al, , 2021Hoarau et al, 2021). Additionally, residuals can be controlled by collecting data from the field (electroconductivity, physical and chemical variables) to include in the statistical model.…”

“…However, controlling for environmental factors (nongenetic variation) can improve the selection accuracy in field experiments, reducing the experimental error with increasing genetic gain (Crossa et al, 2021;Hoarau et al, 2021;Resende et al, 2021).…”

“…Plant breeding pipelines incorporate new techniques, such as those derived from genomics, that can support the identification of superior individuals due to genetic factors ( Balsalobre et al, 2017 ; Crossa et al, 2017 , 2021 ; Barreto et al, 2019 ; Yadav et al, 2020 ). However, controlling for environmental factors (non-genetic variation) can improve the selection accuracy in field experiments, reducing the experimental error with increasing genetic gain ( Crossa et al, 2021 ; Hoarau et al, 2021 ; Resende et al, 2021 ).…”

“…Although the plant breeding techniques that are currently employed are effective, in traditional breeding methods, field experiments are essential for selecting and recommending improved cultivars. High experimental precision is desirable in these experiments and can be obtained with statistical techniques to reduce possible natural variations within experimental fields ( Gilmour et al, 1997 ; Hoarau et al, 2021 ). When a non-genetic source of variation is modeled, and consequently, isolated from either genetic or residual variations, the accuracy of the model increases, and the comparison between two genotypes is more effective.…”

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“…Although the plant breeding techniques that are currently employed are effective, in traditional breeding methods, field experiments are essential for selecting and recommending improved cultivars. High experimental precision is desirable in these experiments and can be obtained with statistical techniques to reduce possible natural variations within experimental fields (Gilmour et al, 1997;Hoarau et al, 2021). When a nongenetic source of variation is modeled, and consequently, isolated from either genetic or residual variations, the accuracy of the model increases, and the comparison between two genotypes is more effective.…”