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RESEARCH ARTICLE

Development of improved genotypes for extra early maturity, higher yield and Mungbean Yellow Mosaic India Virus (MYMIV) resistance in soybean (Glycine max)

Shivakumar Maranna https://orcid.org/0000-0002-3461-4225 A # * , Giriraj Kumawat A # , Vennampally Nataraj A , Balwinder S. Gill B , Raghavendra Nargund A , Avani Sharma A , Laxman Singh Rajput A , Milind B. Ratnaparkhe A and Sanjay Gupta A
+ Author Affiliations
- Author Affiliations

A Indian Council of Agricultural Research (ICAR)-Indian Institute of Soybean Research, Indore - 452 001, India.

B Punjab Agricultural University, Ludhiana, Punjab, India.

* Correspondence to: M.Shivakumar@icar.gov.in
# These authors contributed equally to this paper

Handling Editor: Rajeev Varshney

Crop & Pasture Science 74(12) 1165-1179 https://doi.org/10.1071/CP22339
Submitted: 13 October 2022  Accepted: 3 May 2023  Published: 13 June 2023

© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing

Abstract

Context

Breeding for early maturity and higher yield is the principal objective in genetic improvement of Indian soybean. Yellow Mosaic Disease caused by Mungbean Yellow Mosaic India Virus (MYMIV) causes 80% yield loss in soybean.

Aims

This study aimed to develop early maturing, MYMIV resistant and high yielding soybean genotypes for enhancing soybean production and expanding the land area under cropping.

Methods

MYMIV resistance was introgressed from G. soja in to a widely adaptable cultivar JS 335 through a series of four generations of backcrosses and by evaluating derived progeny against MYMIV at a disease hot spot.

Key results

An extra-early maturing (71 days) genetic stock called NRC 252 was developed, which can be a potential gene donor in breeding for early maturing soybean varieties. Introgression lines YMV 1, YMV 2, YMV 11 and YMV 16 with MYMIV resistance and higher yield performance over recurrent parent and other check varieties were identified and characterised. Biplot analysis, assessing the main effect of genotype and the interaction of genotype with environment, revealed an ideal genotype with respect to 100-seed weight and grain yield that was also promising under sugarcane-soybean intercropping system in spring season.

Conclusions

Alleles from wild type soybean could improve yield attributing traits and MYMIV resistance in cultivated soybean. Improved genotypes such as YMV 1, YMV 2, YMV 11 and YMV 16 were found superior to the recurrent parent JS 335 as well as other check varieties.

Implications

The genotypes developed in the present study will help in reducing the damage caused by MYMIV disease and expansion of the area of soybean cultivation through intercropping with sugarcane.

Keywords: early maturity and narrow genetic base, G×E interaction, GGE biplots, grain yield, G. soja, intercropping, MGIDI, MYMIV, wider adaptability.

References

Balakrishnan D, Surapaneni M, Yadavalli VR, Addanki KR, Mesapogu S, Beerelli K, Neelamraju S (2020) Detecting CSSLs and yield QTLs with additive, epistatic and QTL × environment interaction effects from Oryza sativa × O. nivara IRGC81832 cross. Scientific Reports 10, 7766.
| Crossref | Google Scholar |

Bhattacharyya PK, Ram HH, Kole PC (1999) Inheritance of resistance to yellow mosaic virus in interspecific crosses of soybean. Euphytica 108, 157-159.
| Crossref | Google Scholar |

Bhuiyan MAR, Narimah MK, Abdul Rahim H, Abdullah MZ, Wickneswari R (2011) Transgressive variants for red pericarp grain with high yield potential derived from Oryza rufipogon × Oryza sativa: field evaluation, screening for blast disease, QTL validation and background marker analysis for agronomic traits. Field Crops Research 121, 232-239.
| Crossref | Google Scholar |

Bisen A, Khare D, Nair P, Tripathi N (2015) SSR analysis of 38 genotypes of soybean (Glycine max (L.) Merr.) genetic diversity in India. Physiology and Molecular Biology of Plants 21, 109-115.
| Crossref | Google Scholar |

Chintalapati P, Balakrishnan D, Venu Gopal Nammi TV, Javvaji S, Muthusamy SK, LellaVenkata SR, Neelamraju S, Katti G (2019) Phenotyping and genotype × environment interaction of resistance to leaf folder, Cnaphalocrocis medinalis Guenee (Lepidoptera: Pyralidae) in rice. Frontiers in Plant Science 10, 49.
| Crossref | Google Scholar |

Concibido VC, La Vallee B, Mclaird P, Pineda N, Meyer J, Hummel L, Yang J, Wu K, Delannay X (2003) Introgression of a quantitative trait locus for yield from Glycine soja into commercial soybean cultivars. Theoretical and Applied Genetics 106, 575-582.
| Crossref | Google Scholar |

DAC (2020) Annual report. Department of Agriculture, Cooperation and Farmer’s Welfare, Ministry of Agriculture and Farmer’s Welfare, Government of India, Krishi Bhavan, New Delhi. Available at agricoop.nic.in

Delannay X, Rodgers DM, Palmer RG (1983) Relative genetic contributions among ancestral lines to North American soybean cultivars. Crop Science 23, 944-949.
| Crossref | Google Scholar |

DGCIS (2020) Collection, compilation and dissemination of India’s Trade Statistics and Commercial Information. Directorate General of Commercial Intelligence and Statistics, Kolkata, India. Available at http://www.dgciskol.gov.in/

Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Science 6, 36-40.
| Crossref | Google Scholar |

Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding program. Australian Journal of Agricultural Research 14, 742-754.
| Google Scholar |

Frutos E, Galindo MP, Leiva V (2014) An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stochastic Environmental Research and Risk Assessment 28, 1629-1641.
| Crossref | Google Scholar |

Gizlice Z, Carter TE, Jr, Burton JW (1994) Genetic base for North American public soybean cultivars released between 1947 and 1988. Crop Science 34, 1143-1151.
| Crossref | Google Scholar |

Graham PH, Vance CP (2003) Legumes: importance and constraints to greater use. Plant Physiology 131, 872-877.
| Crossref | Google Scholar |

Guo X, Jiang J, Liu Y, Yu L, Chang R, Guan R, Qiu L (2021) Identification of a novel salt tolerance-related locus in wild soybean (Glycine soja Sieb. & Zucc.). Frontiers in Plant Science 12, 791175.
| Crossref | Google Scholar |

IBPGR (1984) Descriptors of soybean, IBPGR 1984/84/183. International Board for Plant Genetic Resources (IBPGR) Secretariat, Rome, Italy.

ICAR (2015–16) Annual Report, Indian Council of Agricultural Research (ICAR)-Indian Institute of Soybean Research, Indore.

ICAR (2020) Annual Report, Indian Council of Agricultural Research (ICAR)-Indian Institute of Soybean Research, Indore.

Iftekharuddaula KM, Ahmed HU, Ghosal S, Amin A, Moni ZR, et al. (2016) Development of early maturing submergence-tolerant rice varieties for Bangladesh. Field Crop Research 190, 44-53.
| Crossref | Google Scholar |

Jadhav S, Balakrishnan D, Shankar VG, Beerelli K, Chandu G, Neelamraju S (2019) Genotype by environment (G×E) interaction study on yield traits in different maturity groups of rice. Journal of Crop Science and Biotechnology 22(5), 425-449.
| Crossref | Google Scholar |

Kofsky J, Zhang H, Song BH (2018) The untapped genetic reservoir: the past, current, and future applications of the wild soybean (Glycine soja). Frontiers in Plant Science 9, 949.
| Crossref | Google Scholar |

Kumar B, Talukdar A, Verma K, Bala I, Harish GD, Gowda S, et al. (2015a) Mapping of yellow mosaic virus (YMV) resistance in soybean (Glycine max L. Merr.) through association mapping approach. Genetica 143, 1-10.
| Crossref | Google Scholar |

Kumar V, Rani A, Rawal R, Mourya V (2015b) Marker assisted accelerated introgression of null allele of kunitz trypsin inhibitor in soybean. Breeding Science 65, 447-452.
| Crossref | Google Scholar |

Kumawat G, Singh G, Gireesh C, Shivakumar M, Arya M, Agarwal DK, Husain SM (2015) Molecular characterization and genetic diversity analysis of soybean (Glycine max (L.) Merr.) germplasm accessions in India. Physiology and Molecular Biology of Plants 21(1), 101-107.
| Crossref | Google Scholar |

Kumawat G, Yadav A, Satpute GK, Gireesh C, Patel R, Shivakumar M, et al. (2019a) Genetic relationship, population structure analysis and allelic characterization of flowering and maturity genes E1, E2, E3 and E4 among 90 Indian soybean landraces. Physiology and Molecular Biology of Plants 25, 387-398.
| Crossref | Google Scholar |

Kumawat G, Yadav A, Shivakumar M, Gill BS, Patel RM, Gupta S, Satpute GK, Chand S, Husain SM (2019b) Validation of QTLs for seed weight in a backcross population derived from an interspecific cross in soybean [Glycine max (L.) Merr.]. Journal of Oilseed Research 36(4), 210-216.
| Google Scholar |

Li M, Liu Y, Wang C, Yang X, Li D, Zhang X, Xu C, Zhang Y, Li W, Zhao L (2020) Identification of traits contributing to high and stable yields in different soybean varieties across three chinese latitudes. Frontiers in Plant Science 10, 1642.
| Crossref | Google Scholar |

Maranna S, Verma K, Talukdar A, Lal SK, Kumar A, Mukherjee K (2016) Introgression of null allele of Kunitz trypsin inhibitor through marker-assisted backcross breeding in soybean (Glycine max L. Merr. BMC Genetics 17, 106.
| Crossref | Google Scholar |

Maranna S, Nataraj V, Kumawat G, et al. (2021) Breeding for higher yield, early maturity, wider adaptability and waterlogging tolerance in soybean (Glycine max L.): a case study. Scientific Reports 11, 22853.
| Crossref | Google Scholar |

Mendiburu FD (2021) agricolae: Statistical Procedures for Agricultural Research. R package version 1.3-5. Available at https://CRAN.R-project.org/package=agricolae

Nataraj V, Bhartiya A, Singh CP, Devi HN, Deshmukh MP, Verghese P, Singh K, Mehtre SP, Kumari V, Maranna S, et al. (2021a) WAASB-based stability analysis and simultaneous selection for grain yield and early maturity in soybean. Agronomy Journal 113, 3089-3099.
| Crossref | Google Scholar |

Nataraj V, Pandey N, Ramteke R, Vargees P, Reddy R, Onkarappa T, Mehetre SP, Gupta S, Satpute GK, Mohan Y, Shivakumar M, Chandra S, Rajesh V (2021b) GGE biplot analysis of vegetable type soybean genotypes under multi-environmental conditions in India. Journal of Environmental Biology 42, 247-253.
| Crossref | Google Scholar |

Olivoto T, Lúcio ADC (2020) metan: An R package for multi-environment trial analysis. Methods in Ecology and Evolution 11, 783-789.
| Crossref | Google Scholar |

Olivoto T, Nardino M (2020) MGIDI: a novel multi-trait index for genotype selection in plant breeding. Bioinformatics 37(10), 1383-1389.
| Crossref | Google Scholar |

Parihar AK, Basandrai AK, Saxena DR, Kushwaha KPS, Chandra S, Sharma K, Singha KD, Singh D, Lal HC, Sanjeev G (2017) Biplot evaluation of test environments and identification of lentil genotypes with durable resistance to fusarium wilt in India. Crop & Pasture Science 68, 1024-1030.
| Crossref | Google Scholar |

Parihar AK, Basandrai AK, Kushwaha KPS, Chandra S, Singh KD, Bal RS, Saxena D, Singh D, Gupta S (2018) Targeting test environments and rust-resistant genotypes in lentils (Lens culinaris) by using heritability-adjusted biplot analysis. Crop & Pasture Science 69, 1113-1125.
| Crossref | Google Scholar |

Phuke RM, Anuradha K, Radhika K, Jabeen F, et al. (2017) Genetic variability, genotype × environment interaction, correlation, and GGE biplot analysis for grain iron and zinc concentration and other agronomic traits in RIL population of sorghum (Sorghum bicolor L. Moench). Frontiers in Plant Science 8, 712.
| Crossref | Google Scholar |

Poli Y, Balakrishnan D, Desiraju S, Panigrahy M, Voleti SR, Mangrauthia SK, Neelamraju S (2018) Genotype × Environment interactions of Nagina22 rice mutants for yield traits under low phosphorus, water limited and normal irrigated conditions. Scientific Reports 8, 15530.
| Crossref | Google Scholar |

R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at https://www.R-project.org/

Rani A, Kumar V, Gill BS, Rathi P, Shukla S, Singh RK, Husain SM (2017) Linkage mapping of Mungbean yellow mosaic India virus (MYMIV) resistance gene in soybean. Breeding Science 67, 95-100.
| Crossref | Google Scholar |

Rani A, Kumar V, Gill BS, Shukla S, Rathi P, Singh RK (2018) Mapping of duplicate dominant genes for Mungbean yellow mosaic India virus resistance in Glycine soja. Crop Science 58, 1566-1574.
| Crossref | Google Scholar |

Ravikiran KT, Krishnamurthy SL, Warraich AS, Sharma PC (2018) Diversity and haplotypes of rice genotypes for seedling stage salinity tolerance analyzed through morpho-physiological and SSR markers. Field Crops Research 220, 10-18.
| Crossref | Google Scholar |

Sahu PP, Sharma N, Puranik S, Muthamilarasan M, Prasad M (2014) Involvement of host regulatory pathways during geminivirus infection: a novel platform for generating durable resistance. Functional & Integrative Genomics 14, 47-58.
| Crossref | Google Scholar |

Schoener CS, Fehr WR (1979) Utilization of plant introductions in soybean breeding populations. Crop Science 19, 185-188.
| Crossref | Google Scholar |

Singamsetti A, Shahi JP, Zaidi PH, Seetharam K, Vinayan MT, Kumar M, Singla S, Shikha K, Madankar K (2021) Genotype × environment interaction and selection of maize (Zea mays L.) hybrids across moisture regimes. Field Crops Research 270, 108224.
| Crossref | Google Scholar |

Singh BB, Mallick AS (1978) Inheritance of resistance to yellow mosaic in soybean. Indian Jounal of Genetics and Plant Breeding 38, 258-261.
| Google Scholar |

Singh BB, Gupta SC, Singh BD (1974) Sources of field resistance to rust and yellow mosaic diseases of soybean. Indian Jounal of Genetics and Plant Breeding 34, 400-404.
| Google Scholar |

SOPA (2021) Statistics at Glance in India and World. The Soybean Processor Association of India (SOPA) Available at https://www.sopa.org/

Stomph TJ, Dordas C, Baranger A, de Rijk J, Dong B, Evers J, Gu C, Li L, Simon J, Jensen ES, Wang Q, Wang Y, Wang Z, Xu H, Zhang C, Zhang L, Zhang W-P, Bedoussac L, van der Werf W (2020) Designing intercrops for high yield, yield stability and efficient use of resources: are there principles? Advances in Agronomy 160(1), 1-50.
| Crossref | Google Scholar |

Talukdar A, Harish GD, Shivakumar M, Kumar B, Verma K, Lal SK, et al. (2013) Genetics of yellow mosaic virus (ymv) resistance in cultivated soybean [Glycine max (L.) Merr.]. Legume Research 36, 263-267.
| Google Scholar |

Thorne JC, Fehr WR (1970) Exotic germplasm for yield improvement in 2-way and 3-way soybean crosses. Crop Science 10, 677-678.
| Crossref | Google Scholar |

Tolorunse KD, Gana AS, Bala A, Sangodele EA (2018) Yield stability studies of soybean (Glycine max (L.) Merrill) under rhizobia inoculation in the savanna region of Nigeria. Plant Breeding 137, 262-270.
| Crossref | Google Scholar |

van Berloo R (2008) GGT 2.0: versatile software for visualization and analysis of genetic data. Journal of Heredity 99, 232–236. 10.1093/jhered/esm109

Wickham H (2016) ‘ggplot2: elegant graphics for data analysis.’ (Springer-Verlag: New York, NY, USA) Available at https://ggplot2.tidyverse.org

Yadav CB, Bhareti P, Muthamilarasan M, Mukherjee M, Khan Y, Rathi P, et al. (2015) Genome-wide SNP identification and characterization in two soybean cultivars with contrasting Mungbean yellow mosaic India virus disease resistance traits. PLoS ONE 10, e0123897.
| Crossref | Google Scholar |