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Artificial Neural Network for Discrimination and Classification of Tropical Soybean Genotypes of Different Relative Maturity Groups

ABSTRACT: Soybean has a recognized narrow genetic base that often makes it difficult to visualize available genetic and phenotypic variability and identify superior genotypes during the selection process. However, the phenotypic expression of soybean plants is highly affected by photoperiod and the cultivation of a given variety is performed in the latitude range that presents ideal conditions for its development based on its relative maturity group (RMG) for the optimization of the phenotypic expression of its genotype. Based on the above, this study aimed to evaluate the efficiency of artificial neural networks (ANNs) as a tool for the correct discrimination and classification of tropical soybean genotypes according to their relative maturity group during the population selection process with the aim of optimizing the phenotypic performance of these selected genotypes. For this purpose, three biparental populations were synthesized, one with a wide genetic variability for the RMG character obtained from the hybridization between genitors of maturity groups RMG 5 (Sub-tropical 23° LS) × RMG 9.4 (Tropical 0° LS) and two populations with a narrow variability obtained between genitors RMG 7.3 (Tropical 20° LS) × RMG 9.4 and RMG 5.3 × RMG 6.7, respectively. Criteria for comparing the developed ANN architecture with Fisher’s linear and Anderson’s quadratic parametric discriminant methodologies were applied to the data for the discrimination and classification of the genotypes. ANN showed an apparent error rate of less than 8.16% as well as a low influence of environmental factors, correctly classifying the genotypes in the populations even in cases of reduced genetic variability such as in the RMG 5 × RMG 6 population. In contrast, the discriminant functions were inefficient in correctly classifying the genotypes in the populations with genealogical similarity (RMG 5 × RMG 6) and wide genetic variability, with an error rate of more than 50%. Based on the results of this study, ANN can be used for the discrimination of genotypes in the initial generations of selection in breeding programs for the development of high performance cultivars for wide and reduced photoperiod amplitudes, even with fewer selection environments, more efficiently, and with fewer time and resources applied. As a result of similarity between the parents, ANN can correctly classify genotypes from populations with a narrow genetic base, in addition to pure lines and genotypes with a high degree of inbreeding.

SUBMITTER: Amaral L

PROVIDER: S-EPMC9328155 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Soybean is native to the temperate zones of East Asia. Poor yields of soybean in West African countries may be partially attributed to inadequate adaptation of soybean to tropical environments. Adaptation will require knowledge of the effects of allelic combinations of major maturity genes (E1, E2, and E3) and stem architecture. The long juvenile trait (J) influences soybean flowering time in short, ~ 12 h days, which characterize tropical latitudes. Soybean plant architecture includes determinate or indeterminate stem phenotypes controlled by the Dt1 gene. Understanding the influence of these genetic components on plant development and adaptation is key to optimize phenology and improve soybean yield potential in tropical environments. RESULTS:Soybean lines from five recombinant inbred populations were developed that varied in their combinations of targeted genes. The soybean lines were field tested in multiple environments and characterized for days to flowering (DTF), days to maturity (DTM), and plant height in locations throughout northern Ghana, and allelic combinations were determined for each line for associating genotype with phenotype. The results revealed significant differences based on genotype for DTF and DTM and allowed the comparison of different variant alleles of those genes. The mutant alleles of J and E1 had significant impact on DTF and DTM, and alleles of those genes interacted with each other for DTF but not DTM. The Dt1 gene significantly influenced plant height but not DTF or DTM. CONCLUSIONS:This research identified major and minor effect alleles of soybean genes that can be combined to control DTF, DTM, and plant height in short day tropical environments in Ghana. These phenotypes contribute to adaptation to a low latitude environment that can be optimized in a soybean breeding program with targeted selection of desired allele combinations. The knowledge of the genetic control of these traits will enhance molecular breeding to produce optimally adapted soybean varieties targeted to tropical environments.

Project description:Soybean is a major crop in Brazil and is usually grown in oxidic soils that need high rates of phosphate (P) fertilizers. Soybean is also very suitable for biofortification with Se, since its grains have high protein contents and are widely consumed worldwide (directly or indirectly). Few studies have addressed Se application under field conditions for soybean biofortification, especially in tropical soils. Here, we evaluated agronomic and physiological responses resulting from different strategies for biofortifying soybean grains with Se by applying this element via soil, using both conventional and enhanced-efficiency P fertilizers as Se carriers. The experiment was carried out at the Uva Farm, in Capão Bonito (São Paulo), Brazil. The experimental design was a randomized block split-plot design, with four fertilizer sources-conventional monoammonium phosphate (C-MAP), conventional monoammonium phosphate + Se (C-MAP + Se), enhanced-efficiency monoammonium phosphate (E-MAP), and enhanced-efficiency monoammonium phosphate + Se (E-MAP + Se), and four soybean genotypes (M5917, 58I60 LANÇA, TMG7061, and NA5909). The selenium rate applied via C-MAP + Se and E-MAP + Se was 80 g ha-1. The application of the tested fertilizers was carried out at the sowing of the 2018/2019 cropping season, with their residual effect being also assessed in the 2019/2020 cropping season. Selenium application increased grain yield for the TMG7061 genotype. For all evaluated genotypes, Se content in grains increased in the 2018/2019 harvest with the application of Se via C-MAP + Se and E-MAP + Se. In general, the application of Se via C-MAP favored an increase in amino acid contents in grains and decreased lipid peroxidation. In summary, the application of Se-enriched P fertilizers via soil increased soybean grain yield, leading to better grain quality. No residual effects for biofortifying soybean grains were detected in a subsequent soybean cropping season.

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Artificial Neural Network for Discrimination and Classification of Tropical Soybean Genotypes of Different Relative Maturity Groups

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