Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach

Evangelista, Jeniffer Santana Pinto Coelho; Peixoto, Marco Antônio; Coelho, Igor Ferreira; Alves, Rodrigo Silva; Silva, Fabyano Fonseca e; Resende, Marcos Deon Vilela de; Silva, Felipe Lopes da

doi:10.1590/1984-70332021v21n1a11

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…To query a given canopy, a fingerprint of the canopy was generated and then compared with the existing database of fingerprints to identify the possible match. This capability could enable further in-depth development and exploration of the germplasm resources for ideotypes ( Kokubun, 1988 ; Evangelista et al., 2021 ; Lukas et al, 2022 ; Roth et al., 2022 ). While this work evaluated a single time point, a more in-depth and temporal fingerprint could be developed to evaluate the canopy growth and development across time.…”

Section: Resultsmentioning

confidence: 99%

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

et al. 2023

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Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surface area, height, etc.) for downstream analysis. We extend feature extraction from 3D point cloud data to various additional features, which we denote as ‘canopy fingerprints’. This is motivated by the successful application of the fingerprint concept for molecular fingerprints in chemistry applications and acoustic fingerprints in sound engineering applications. We developed an end-to-end pipeline to generate canopy fingerprints of a three-dimensional point cloud of soybean [Glycine max (L.) Merr.] canopies grown in hill plots captured by a terrestrial laser scanner (TLS). The pipeline includes noise removal, registration, and plot extraction, followed by the canopy fingerprint generation. The canopy fingerprints are generated by splitting the data into multiple sub-canopy scale components and extracting sub-canopy scale geometric features. The generated canopy fingerprints are interpretable and can assist in identifying patterns in a database of canopies, querying similar canopies, or identifying canopies with a certain shape. The framework can be extended to other modalities (for instance, hyperspectral point clouds) and tuned to find the most informative fingerprint representation for downstream tasks. These canopy fingerprints can aid in the utilization of canopy traits at previously unutilized scales, and therefore have applications in plant breeding and resilient crop production.

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Section: Resultsmentioning

confidence: 99%

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

et al. 2023

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“…CF Azevedo et al Bayesian approach is used for several studies in genetic breeding (Azevedo et al 2015, Evangelista et al 2021. Specifically, Bayesian methods applied genomic prediction differ regarding the distribution assumed for marker effects, and the assumptions encompass the genetic architecture of the desirable trait (Gianola 2013).…”

Section: Introductionmentioning

confidence: 99%

Bayesian methods for genomic association of chromosomic regions considering the additive-dominance model

Azevedo

Lima

Nascimento

et al. 2022

Crop Breed. Appl. Biotechnol.

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Bayesian approaches applied in association studies select regions of single-nucleotide polymorphisms, indicating genes with important effects. The Bayesian methods differ in terms of the distribution assumed for the marker effects. Here, we used the window posterior probability of association to detect potential regions. The present study evaluated the efficiency of these methods in identifying regions located close to genes. Data were simulated in six scenarios. Considering the lack of dominance, BayesA was more efficient in the scenario with three QTLs. For scenarios with 10 or 100 QTLs, BayesCπ and BayesDπ were more efficient according to the false positive rate and detection power. Considering the presence of dominance, all methods were similar in the scenario with three QTLs, except in terms of accuracy. BayesDπ was superior in the scenario with 10 QTLs, while BRR was more efficient in the scenario with 100 QTLs.

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Genetic analysis of genotype-specific parameters in the DSSTA-CROPGRO-soybean phenology simulation model via a multi-GWAS method

Yan,

Jiang,

et al. 2023

Field Crops Research

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Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach

Cited by 3 publications

References 23 publications

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

Bayesian methods for genomic association of chromosomic regions considering the additive-dominance model

Genetic analysis of genotype-specific parameters in the DSSTA-CROPGRO-soybean phenology simulation model via a multi-GWAS method

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