BIPLOT ANALYSIS FOR IDENTIFICATION OF SUPERIOR GENOTYPES IN A RECOMBINANT INBRED POPULATION OF WHEAT UNDER RAINFED CONDITIONS

Authors

  • Ashutosh Srivastava Department of Plant Breeding & Genetics, Punjab Agricultural University, Ludhiana-141004, India
  • Puja Srivastava Department of Plant Breeding & Genetics, Punjab Agricultural University, Ludhiana-141004, India
  • R S Sarlach Department of Plant Breeding & Genetics, Punjab Agricultural University, Ludhiana-141004, India
  • Mayank Anand Gururani Department of Biology, College of Science, United Arab Emirates University, Al Ain, P.O. Box 15551, UAE

DOI:

https://doi.org/10.18006/2021.9(5).598.609

Keywords:

Rainfed, Environment, GGE Biplot, Recombinant inbred lines (RILs), Traditional landraces, Wheat

Abstract

Physiological traits of wheat genotypes and their trait relation to drought conditions are important to identify the genotype in target environments. Thus, genotype selection should be based on multiple physiological traits in variable environments within the target region. This study was conducted at Punjab Agricultural University during rabi crop seasons 2012-13 and 2013-14 to study the recombinant inbred lines (RILs) of wheat genotypes derived from traditional landraces and modern cultivars (C518/2*PBW343) based on various morpho-physiological traits. A total of 175 RILs were selected for this study based on various tolerance indices. The genotype by trait (GT) biplot analysis was applied to data from seven high-yielding RILs grown under irrigated (E1) and rainfed environments (E2). The GGE biplot explained 100% of the total variation for chlorophyll content, grain filling period, peduncle length, water-soluble carbohydrates, grain number, grain yield, and 95.1% for canopy temperature, 94.9% for thousand-grain weight. GT-biplots indicated that the relationships among the studied traits were not consistent across environments, but they facilitated visual genotype comparisons and selection in each environment. RIL 84 and RIL108 were close to the average environment (ideal genotype) for all traits studied except chlorophyll content. A well-performing genotype with great environmental stability is called an "ideal genotype. Among all entries, these genotypes performed well. Therefore, among the traits studied, grain filling period, peduncle length, canopy temperature, water soluble carbohydrates, and 1000 grain weight contributed to grain yield under a stress environment. Furthermore, it may be used as a donor material in breeding programs and QTLs mapping.

References

Alyammahi O, Gururani, MA (2020) Chlorophyll-a Fluorescence Analysis Reveals Differential Response of Photosynthetic Machinery in Melatonin-Treated Oat Plants Exposed to Osmotic Stress. Agronomy10(10): 1520.

Bala S, Asthir B, Bains NS (2014) High temperature response leads to altered membrane permeability in conjunction with carbon utilization in wheat. Seed Science and Biotechnology 4: 10–14.

Bányai J, Kiss T, Gizaw SA, Mayer M, Spitkó T, Tóth V, Kuti C, Mészáros K, Láng L, Karsai I, Vida G (2020) Identification of superior spring durum wheat genotypes under irrigated and rain fed conditions. Cereal Research Communications 48:355–364.

Bishwas KC, Mukti RP, Dipendra R (2021) AMMI and GGE biplot analysis of yield of different elite wheat line under terminal heat stress and irrigated environments. Heliyon-Cell Press 7: e07206.

Božović D, Popović V, Rajicić V, Kostić M, Filipović V, Kolarić L, Ugranovic V, Spalević V (2020) Stability of the expression of the maize productivity parameters by AMMI models and GGE-biplot analysis. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 48(3): 1387-1397.

Chowdhury MK, Hasan MA, Bahadur MM, Islam MR, Hakim MA, Iqbal MA, Javed T, Raza A, Shabbir R, Sorour S, Elsanafawy NE (2021) Evaluation of Drought Tolerance of Some Wheat (Triticum aestivum L.) Genotypes through Phenology, Growth, and Physiological Indices. Agronomy 11: 1792.

Egesi CN, Ilona P, Ogbe FO, Akoroda M, Dixon A (2007) Genetic variation and genotype X environment interaction for yield and other agronomic traits in cassava in Nigeria. Agronomy Journal 99: 1137–1142.

Fernández-Aparicio M, Flores F, Rubiales D (2009) Field response of Lathyrus cicera germplasm to crenate broomrape (Orobanche crenata). Field Crop Research 113: 321–327.

Gupta PK, Balyan HS, Sharma S, Kumar R (2020) Genetics of yield, abiotic stress tolerance and biofortification in wheat (Triticum aestivum L.). Theoretical and Applied Genetics 133(5): 1569-1602.

Gururani MA, Venkatesh J, Tran LSP (2015) Regulation of photosynthesis during abiotic stress-induced photoinhibition. Molecular Plant 8:1304–1320.

Jha UC, Bohra A, Nayyar H (2020) Advances in “omics” approaches to tackle drought stress in grain legumes. Plant Breeding 139(1): 1-27.

Kappachery S, Sasi S, Alyammahi O, Alyassi A, Venkatesh J, Gururani MA (2021). Overexpression of cytoplasmic Solanum tuberosum Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene improves PSII efficiency and alleviates salinity stress in Arabidopsis. Journal of Plant Interactions 16(1): 398-410.

Katuuramu DN, Luyima GB, Nkalubo ST, Wiesinger JA, Kelly JD, Cichy KA (2020) On-farm multi-location evaluation of genotype by environment interactions for seed yield and cooking time in common bean. Scientific reports 10(1): 1-12.

Kaya Y, Akçura M, Taner S (2006) GGE-Biplot analysis of multi-environment yield trials in bread wheat. Turkish Journal of Agriculture and Forestry 30:325–337.

Kumar A (2015) Association of Physio-biochemical traits with drought tolerance in a recombinant inbred population of Wheat. Ph. D. Thesis submitted to Punjab Agricultural University, Luhdiana, India. Pp. 31-33

Kumar S, Sehgal SK, Kumar U, Prasad PVV, Joshi AK, Gill BS (2012) Genomic characterization of drought tolerance-related traits in spring wheat. Euphytica 186:265–276.

Mwiinga B, Sibiya J, Kondwakwenda A, Musvosvi C, Chigeza G (2020) Genotype x environment interaction analysis of soybean (Glycine max L.) Merrill) grain yield across production environments in Southern Africa. Field Crops Research 256: 107922.

Peterson DM, Wesenberg DM, Burrup DE, Erickson CA (2005) Relationships among agronomic traits and grain composition in oat genotypes grown in different environments. Crop Science 45: 1249–1255.

Ranjith P, Rao MS (2021) Breeding for Drought Resistance. In: Plant Breeding-Current and Future Views. Intech Open.

Srivastava A, Srivastava P, Khobra R, Sharma A, Sarlach R, Dogra A, Bains N (2016) Association of morpho-physiological traits in recombinant inbred population of wheat under rainfed environments. Indian Journal of Ecology 43 (Special Issue): 72-77.

Srivastava A, Srivastava P, Sharma A, Sarlach RS, Bains NS (2017) Effect of stem reserve mobilization on grain filling under drought stress conditions in recombinant inbred population of wheat. Journal of Applied and Natural Science 9(1): 1-5.

Tian R, Yang Y, Chen M (2020) Genome-wide survey of the amino acid transporter gene family in wheat (Triticum aestivum

L.): Identification, expression analysis and response to abiotic stress. International Journal of Biological Macromolecules 162: 1372-1387.

Wang X, Mao Z, Zhang J, Hemat M, Huang M, Cai J, Jiang D (2019) Osmolyte accumulation plays important roles in the drought priming induced tolerance to postanthesis drought stress in winter wheat (Triticum aestivum L.). Environmental and Experimental Botany 166: 103804.

Yaduvanshi A, Srivastava PK, Pandey AC (2015) Integrating TRMM and MODIS satellite with socio-economic vulnerability for monitoring drought risk over a tropical region of India.Physics and Chemistry of the Earth 83–84:14–27.

Yan W (2001) GGEbiplot - A windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agronomy Journal93: 1111–1118.

Yan W (2002) Singular-value partitioning in biplot analysis of multienvironment trial data. Agronomy Journal 94: 990–996.

Yan W, Frégeau-Reid J (2008) Breeding line selection based on multiple traits. Crop Science 48: 417–423.

Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science 40:597–605.

Yan W, Kang MS (2002) GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists, CRC Press, Boca Raton, FL.109-140.

Yan W, Rajcan I (2002) Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Science 42:11–20.

Yan W, Tinker NA (2005) An integrated biplot analysis system for displaying, interpreting, and exploring genotype x environment interaction. Crop Science 45: 1004–1016.

Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623–645.

Downloads

Published

2021-10-30

How to Cite

Srivastava, A. ., Srivastava, P. ., Sarlach, R. S. ., & Gururani, M. A. . (2021). BIPLOT ANALYSIS FOR IDENTIFICATION OF SUPERIOR GENOTYPES IN A RECOMBINANT INBRED POPULATION OF WHEAT UNDER RAINFED CONDITIONS. Journal of Experimental Biology and Agricultural Sciences, 9(5), 598–609. https://doi.org/10.18006/2021.9(5).598.609

Issue

Section

RESEARCH ARTICLES