Deciphering genotype-by-environment interaction of grass pea genotypes under rain-fed conditions and emphasizing the role of monthly rainfall.
Multi-trait stability index
Partial least square regression
Seed and forage yield
WAASB stability analysis
Journal
BMC plant biology
ISSN: 1471-2229
Titre abrégé: BMC Plant Biol
Pays: England
ID NLM: 100967807
Informations de publication
Date de publication:
15 Jun 2024
15 Jun 2024
Historique:
received:
06
12
2023
accepted:
05
06
2024
medline:
15
6
2024
pubmed:
15
6
2024
entrez:
14
6
2024
Statut:
epublish
Résumé
Rainfed regions have inconsistent spatial and temporal rainfall. So, these regions could face water deficiency during critical stages of crop growth. In this regard, multi-environment trials could play a key role in introducing stable genotypes with good performance across several rainfed regions. Grass pea, as a potential forage crop, is a resilient plant that could grow in unsuitable circumstances. In this study, agro-morphological attributes of 16 grass pea genotypes were examined in four semi-warm rain-fed regions during the years 2018-2021. The MLM analysis of variance showed a significant genotype-by-environment interaction (GEI) for dry yield, seed yield, days to maturity, days to flowering, and plant height of grass pea. The PLS (partial least squares) regression revealed that rainfall in the grass pea establishment stage (October and November) is meaningful. For grass pea cultivation, monthly rainfall during plant growth is important, especially in May, with an aim for seed yield. Regarding dry yield, G5, G10, G11, G12, G13, and G15 were selected as good performers and stable genotypes using DY × WAASB biplots, while SY × WAASB biplot manifested G2, G3, G12, and G13 as superior genotypes with stable seed yield. Considering equal weights for yield as well as the WAASB stability index (50/50), G13 was selected as the best one. Among test environments, E2 and E11 played a prominent role in distinguishing the above genotypes from other ones. In this study, MTSI (multi-trait stability index) analysis was applied to select a stable genotype, considering all measured agro-morphological traits simultaneously. Henceforth, the G5 and G15 grass pea genotypes were discerningly chosen due to their commendable performance in the WAASBY plot. In this context, G13 did not emerge as the winner based on MTSI; however, it exhibited an MTSI value in close proximity to the outer boundary of the circle. Consequently, upon comprehensive consideration of all traits, it is deduced that G5, G13, and G15 can be appraised as promising superior genotypes with stability across diverse environmental conditions.
Identifiants
pubmed: 38877456
doi: 10.1186/s12870-024-05256-5
pii: 10.1186/s12870-024-05256-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
559Informations de copyright
© 2024. The Author(s).
Références
Campbell CG, Clayton G. International Plant Genetic Resources Institute. Grass pea, Lathyrus sativus L. 1997.
Voltas J, van Eeuwijk FA, Igartua E, García Del Moral LF, Molina-cano JL, Romagosa I. Genotype by environment interaction and adaptation in barley breeding: basic concepts and methods of analysis. In: Barley Sci Recent Adv from\nMolecular Biol to Agron Yield Qual. 2002. p. 205–41.
Rubiales D, Araújo SS, Vaz Patto MC, Rispail N, Valdés-López O. Editorial: Advances in legume research. Front Plant Sci. 2018;9:359934.
Feng S, Fu Q. Expansion of global drylands under a warming climate. Atmos Chem Phys. 2013;13:10081–94.
doi: 10.5194/acp-13-10081-2013
Kondić-Špika A, Mladenov N, Grahovac N, Zorić M, Mikić S, Trkulja D, et al. Biometric analyses of yield, oil and protein contents of wheat (Triticum aestivum L.) genotypes in different environments. Agronomy. 2019;9:270.
doi: 10.3390/agronomy9060270
Fikre A, Negwo T, Kuo YH, Lambein F, Ahmed S. Climatic, edaphic and altitudinal factors affecting yield and toxicity of Lathyrus sativus grown at five locations in Ethiopia. Food Chem Toxicol. 2011;49:623–30.
doi: 10.1016/j.fct.2010.06.055
pubmed: 20655975
Yan W, Frégeau-Reid J. Genotype by yield∗trait (GYT) biplot: a novel approach for genotype selection based on multiple traits. Sci Rep. 2018;8:8242.
doi: 10.1038/s41598-018-26688-8
pubmed: 29844453
pmcid: 5974279
Gauch HG. Model selection and validation for yield trials with interaction. Biometrics. 1988;44:705.
doi: 10.2307/2531585
Chatterjee C, Debnath M, Karmakar N, Sadhukhan R. Stability of grass pea (Lathyrus sativus L.) genotypes in different agroclimatic zone in eastern part of India with special reference to West Bengal. Genet Resour Crop Evol. 2019;66:1515–31.
doi: 10.1007/s10722-019-00809-2
Gauch HG. A simple protocol for AMMI analysis of yield trials. Crop Sci. 2013;53:1860–9.
doi: 10.2135/cropsci2013.04.0241
Piepho HP. Best linear unbiased prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis. Theor Appl Genet. 1994;89:647–54.
doi: 10.1007/BF00222462
pubmed: 24177943
Olivoto T, Lúcio ADC, da Silva JAG, Marchioro VS, de Souza VQ, Jost E. Mean performance and stability in multi-environment trials i: combining features of AMMI and BLUP techniques. Agron J. 2019;111:2949–60.
doi: 10.2134/agronj2019.03.0220
Olivoto T, Lúcio AD. metan: An R package for multi-environment trial analysis. Methods Ecol Evol. 2020;11:783–9.
doi: 10.1111/2041-210X.13384
Benakanahalli NK, Sridhara S, Ramesh N, Olivoto T, Sreekantappa G, Tamam N, et al. A framework for identification of stable genotypes basedon mtsi and mgdii indexes: an example in guar (cymopsis tetragonoloba l.). Agronomy. 2021;11:1221.
doi: 10.3390/agronomy11061221
Singamsetti A, Zaidi PH, Seetharam K, Vinayan MT, Olivoto T, Mahato A, et al. Genetic gains in tropical maize hybrids across moisture regimes with multi-trait-based index selection. Front Plant Sci. 2023;14:1147424.
doi: 10.3389/fpls.2023.1147424
pubmed: 36938016
pmcid: 10020505
Azrai M, Aqil M, Efendi R, Andayani NN, Makkulawu AT, Iriany RN, et al. A comparative study on single and multiple trait selections of equatorial grown maize hybrids. Front Sustain Food Syst. 2023;7:7.
doi: 10.3389/fsufs.2023.1185102
Sellami MH, Pulvento C, Lavini A. Selection of suitable genotypes of lentil (Lens culinaris medik.) under rainfed conditions in south Italy using multi-trait stability index (MTSI). Agronomy. 2021;11:1807.
doi: 10.3390/agronomy11091807
Taleghani D, Rajabi A, Saremirad A, Fasahat P. Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI. Sci Rep. 2023;13:13.
doi: 10.1038/s41598-023-37217-7
Oghan HA, Bakhshi B, Rameeh V, Tabrizi HZ, Faraji A, Ghodrati G, et al. Comparative study of univariate and multivariate selection strategies based on an integrated approach applied to oilseed rape breeding. Crop Sci. 2024;63:1–9.
Ahmadi J, Vaezi B, Shaabani A, Khademi K, Fabriki Ourang S, Pour-Aboughadareh A. Non-parametric measures for yield stability in grass pea (Lathyrus sativus L.) advanced lines in semi warm regions. J Agric Sci Technol. 2015;17:1825–38.
Vaezi B, Hatami Maleki H, Yousefzadeh S, Pirooz R, Jozeyan A, Mohtashami R, et al. Graphical analysis of forage yield stability under high and low potential circumstances in 16 grass pea (Lathyrus sativus L.) genotype. Acta Agric Slov. 2023;119:1.
doi: 10.14720/aas.2023.119.1.2227
Sellami MH, Pulvento C, Amarowicz R, Lavini A. Field phenotyping and quality traits of grass pea genotypes in South Italy. J Sci Food Agric. 2022;102:4988–99.
doi: 10.1002/jsfa.11008
pubmed: 33301170
Pacheco A, Vargas M, Alvarado G, Rodriguez F, Lopez M, Crossa J, et al. GEA-R (Genotype x Environment Analysis with R for windows) version 4.0. Cimmyt; 2016. https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/10203 .
Ates S, Feindel D, El Moneim A, Ryan J. Annual forage legumes in dryland agricultural systems of the West Asia and North Africa Regions: research achievements and future perspective. Grass Forage Sci. 2014;69:17–31.
doi: 10.1111/gfs.12074
Almeida NF, Leitão ST, Krezdorn N, Rotter B, Winter P, Rubiales D, et al. Allelic diversity in the transcriptomes of contrasting rust-infected genotypes of, a lasting resource for smart breeding. BMC Plant Biol. 2014;14:376.
doi: 10.1186/s12870-014-0376-2
pubmed: 25522779
pmcid: 4331309
Polignano GB, Bisignano V, Tomaselli V, Uggenti P, Alba V, Della GC. Genotype environment interaction in grass pea (Lathyrus sativus L.) Lines. Int J Agron. 2009;2009:1–7.
doi: 10.1155/2009/898396
Ahakpaz F, Abdi H, Neyestani E, Hesami A, Mohammadi B, Mahmoudi KN, et al. Genotype-by-environment interaction analysis for grain yield of barley genotypes under dryland conditions and the role of monthly rainfall. Agric Water Manag. 2021;245:106665.
doi: 10.1016/j.agwat.2020.106665
Alipour H, Abdi H, Rahimi Y, Bihamta MR. Genotype-by-year interaction for grain yield of Iranian wheat cultivars and its interpretation using Vrn and Ppd functional markers and environmental covariables. Cereal Res Commun. 2021;49:681–90.
doi: 10.1007/s42976-021-00130-8
Rockström J, Falkenmark M. Semiarid crop production from a hydrological perspective: gap between potential and actual yields. CRC Crit Rev Plant Sci. 2000;19:319–46.
doi: 10.1080/07352680091139259
Reynolds MP, Trethowan R, Crossa J, Vargas M, Sayre KD. Physiological factors associated with genotype by environment interaction in wheat. F Crop Res. 2002;75:139–60.
doi: 10.1016/S0378-4290(02)00023-0
Mohammadi R, Sadeghzadeh B, Ahmadi MM, Amri A. Biological interpretation of genotype × environment interaction in rainfed durum wheat. Cereal Res Commun. 2020;48:547–54.
doi: 10.1007/s42976-020-00056-7
Nataraj V, Bhartiya A, Singh CP, Devi HN, Deshmukh MP, Verghese P, et al. WAASB-based stability analysis and simultaneous selection for grain yield and early maturity in soybean. Agron J. 2021;113:3089–99.
doi: 10.1002/agj2.20750
Dudhe MY, Jadhav M V, Sujatha M, Meena HP, Rajguru AB, Gahukar SJ, et al. WAASB-based stability analysis and validation of sources resistant to Plasmopara halstedii race-100 from the sunflower working germplasm for the semiarid regions of India. Genet Resour Crop Evol. 2023;71:1435–52.
Olivoto T, Lúcio ADC, da Silva JAG, Sari BG, Diel MI. Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agron J. 2019;111:2961–9.
doi: 10.2134/agronj2019.03.0221