Evaluation of performance stability of chickpea genotypes using AMMI, BLUP, MTSI and MGIDI Indexes

Document Type : Research Paper

Authors

1 Crop and Horticultural Science Research Department, Lorestan Agricultural and Natural Resources Research and Education Center, AREEO, Khorramabad, Iran

2 Crop and Horticultural Sciences Research Department, Lorestan Agricultural and Natural Resources Research and Education Center, AREEO, Khorramabad, Iran

3 Dryland Agricultural Research Institute, Sararood Branch, Agricultural Research, Education and Extension (AREEO), Kermanshah, Iran

Abstract

Background & Objectives: Chickpea is considered one of the most important legumes due to their protein percentage and high nutritional value.  Multi-trait stability selection (MTSI) helps to better evaluate plant genotypes and achieve more accurate results. This research was done to identify stable and high-yielding chickpea genotypes.
 
Material and Methods: In this study, thirteen advanced chickpea genotypes were evaluated along with two check varieties (Mansour and Bivanij) based on randomized complete block design with three replications at Sarab Changai Agricultural and Natural Resources Research Station Khoramabad Lorestan for three crop years (2020-2022). Seeds were sown in four lines of four meters in length and 30 cm between the lines. grain yield, 100-seed weight, plant height, number of days to 50% flowering, number of days to maturity, grain filling rate and grain filling period, rainfall efficiency, grain yield formation rate, and single seed weight were measured for each genotype in different years. All statistical analyses were performed using the “Metan” and “GGE” multi-environmental analysis packages in R software.
 
Results: The mosaic diagram showed that the contribution of the sum of squares of genotype and the genotype × environment in the total sum of squares was 29.97 and 36.79%, respectively. The Likelihood Ratio Test (LRT) showed that the effect of genotype by environment interactions (GEI) was significant on grain yield, 100-seed weight, plant height, number of days to flowering, number of days to maturity, grain filling rate and grain filling period. The Scree test showed that the first two principal components had a significant contribution to the GEI matrix derived from BLUP, as the first and second principal components explained 69.5% and 30.58% of the GEI variation respectively.
 
Conclusion: In general, based on the results of all methods and simultaneous selection based on grain yield stability and all measured traits (MTSI), genotypes 2(X010TH163K2) and 12(X010TH72K2) were stable and superior genotypes, compared to the average of the total traits of the genotypes.

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Ajay V and Singh GP. 2021. AMMI with BLUP analysis for stability assessment of wheat genotypes under multi locations timely sown trials in Central Zone of India. International Journal of Agriculture and Food Science, 7: 118-124. https://doi.org/10.17352/2455-815X.000098
Authrapun J, Lertsuchatavanich U and Kang D. 2021. Selection for improving field resistance to capsicum chlorosis virus and yield-related traits using selection indices in peanut breeding. Acta Scientific Agriculture, 5: 22–31. https://doi.org/10.31080/asag.2021.05.1001
Balalić I, Zorić M, Branković G, Terzić S and Crnobarac J. 2012. Interpretation of hybrid× sowing date interaction for oil content and oil yield in sunflower. Field Crops Research, 137: 70-77. https://doi.org/10.1016/j.fcr.2012.08.005
Barbosa MH, Ferreira A, Peixoto LA, Resende MD, Nascimento M and Silva FF. 2014. Selection of sugar cane families by using BLUP and multi-diverse analyses for planting in the Brazilian savannah. Genetics and Molecular Research, 13: 1619-1626. https://doi.org/10.4238/2014.March.12.14
Baretta D, Nardino M, Carvalho IR, Oliveira AD, Souza VD and Maia LD. 2016. Performance of maize genotypes of Rio Grande do Sul using mixed models. Científica, 44(3): 403-411. https://doi.org/10.15361/1984-5529.2016v44n3p403-411
Benakanahalli NK, Sridhara S, Ramesh N, Olivoto T, Sreekantappa G, Tamam N, Abdelbacki AM, Elansary HO and Abdelmohsen SA. 2021. A Framework for Identification of stable genotypes based on MTSI and MGDII Indexes: An example in Guar (Cymopsis tetragonoloba L.). Agronomy, 11(6): 1221. https://doi.org/10.3390/agronomy11061221
Brankovic-Radojcic D, Babic V, Girek Z, Ţivanovic T, Radojcic A, Filipovic M and Srdic J. 2018. Evaluation of maize grain yield and yield stability by AMMI analysis. Genetika, 50: 1067-1080. https://doi.org/10.2298/GENSR1803067B
Chongo G, Gossen BD, Buchwaldt L, Adhikari T and Rimmer SR. 2004. Genetic diversity of Ascochyta rabiei in Canada. Plant Disease, 88(1):4-10. https://doi.org/10.1094/PDIS.2004.88.1.4
Finlay K and Wilkinson G. 1963. The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research, 14(6): 742-754. http://dx.doi.org/10.1071/AR9630742
Forouzi M, Ehteshami SM, Esfahani M and Rabiei M. 2016. Study the amount of dry matter remobilization and current photosynthesis in different seed sizes of four wheat (Triticum aestivum L.) cultivars in Rasht. Iranian Journal of Seed Science and Research, 3(1): 47-61. (In Persian). 20.1001.1.24763780.1395.3.1.4.8
Gauch H and Zobel R. 1988. Predictive and postdictive success of statistical analyses of yield trials. Theoretical and Applied Genetics, 76(1): 1-10. https://doi.org/10.1007/BF00288824
Hasan M and Deb A. 2017. Stability analysis of yield and yield components in chickpea (Cicer arietinum L.). Horticulture International Journal, 1(1): 4-14. https://doi.org/10.15406/hij.2017.01.00002
IBPGR, ICRISAT and ICARDA, 1993. Descriptors for Chickpea (Cicer arietinum L.) International Board for Plant Genetic Resources, Rome, Italy; International Crops Research Institute for the Semi-Arid Tropics, Patancheru, India and International Center for Agriculture Research in the Dry Areas, Aleppo, Syria.
Jahangiri A, Sadeghzadeh-Ahari D, Safikhani M, Pezeshkpour P, Saeid A, Sarparast R and Mohammadi M. 2015. Adel, a new rainfed chickpea cultivar for autumn planting under moderate cold and semi-warm regions of Iran. Research Achievements for Field and Horticulture Crops, 4(1): 1-13. (In Persian). https://doi.org/10.22092/RAFHC.2015.106539
Jahufer M and Casler M. 2015. Application of the Smith-Hazel Selection Index for improving biomass yield and quality of switchgrass. Crop Science, 55(3): 1212-1222. https://doi.org/10.2135/cropsci2014.08.0575
Joudi M and Ebadi A. 2015. Evaluation of agronomic traits of Iranian wheat (Triticum aestivum L.) cultivars and their associations under terminal heat stress. Research in Field Crop Journal, 3(1):42-54.
Karimizadeh R, Pezeshkpour P, Barzali M, Armion M and Sharifi P. 2021. Stability of some of chickpea (Cicer arietinum L.) genotypes by AMMI indices and biplots. Iranian Journal Pulses Research, 12(2): 214-228. (In Persian). https://doi.org/10.22067/IJPR.V12I2.2103-1006
Karimizadeh R, Pezeshkpour P, Barzali M, Mehraban A and Sharifi P. 2020. Evaluation the mean performance and stability of lentil genotypes by combining features of AMMI and BLUP techniques. Journal of Crop Breeding, 12(36): 160-170. (In Persian). https://doi.org/10.52547/jcb.12.36.160
Laffont JL, Hanafi M and Wright K. 2007. Numerical and graphical measures to facilitate the interpretation of GGE biplots. Crop Science, 47: 990-996. https://doi.org/10.2135/cropsci2006.08.0549
Mohammadi GH, Golezani KG, Javanshir A and Moghaddam M. 2006. The influence of water limitation on the yield of three chickpea cultivars. JWSS-Isfahan University of Technology, 10(2):109-120. (In Persian). 20.1001.1.24763594.1385.10.2.9.6
Nardino M, Baretta D, Carvalho IR, Olivoto T, Follmann DN, Szareski VJ, Ferrari M, de Pelegrin AJ, Konflanz VA and de Souza VQ. 2016. Restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) for analyzing the agronomic performance of corn. African Journal of Agricultural Research, 11(48): 4864-4872. https://doi.org/10.5897/AJAR2016.11691
Olivoto T and Nardino, M. 2020. MGIDI: A novel multi-trait index for genotype selection in plant breeding. Bioinformatics, 1-22. https://doi.org/10.1101/2020.07.23.217778
Olivoto T, Lúcio AD, da Silva JA, Marchioro VS, de Souza VQ and Jost E. 2019b. Mean performance and stability in multi‐environment trials I: combining features of AMMI and BLUP techniques. Agronomy Journal, 111(6): 2949-2960. https://doi.org/10.2134/agronj2019.03.0220
Olivoto T, Lúcio AD, da Silva JA, Sari BG and Diel MI. 2019a. Mean performance and stability in multi‐environment trials II: Selection based on multiple traits. Agronomy Journal, 111(6): 2961-2969. https://doi.org/10.2134/agronj2019.03.0221
Olivoto T and Lúcio AD. 2020. Metan: An R package for multi-environment trial analysis. Methods in Ecology and Evolution, 11(6): 783-789. https://doi.org/10.1111/2041-210X.13384
Oweis T and Hachum A. 2003. Improving water productivity in the dry areas of west Asia and north Africa CAB International, water productivity in agriculture: Limits and Opportunities for improvement (eds Kijne JW, Barker R and Molden D), Pp: 179-198. https://doi.org/10.1079/9780851996691.0179
Sarker A. 2011. Lentils in production and food systems in West Asia and Africa. Grain Legumes, 57: 46-48.
Sellami MH, Lavini A and Pulvento C. 2021. Phenotypic and quality traits of chickpea genotypes under rainfed conditions in south Italy. Agronomy, 11(5): 962. https://doi.org/10.3390/agronomy11050962
Sharifi P, Aminpanah H, Erfani R, Mohaddesi A and Abbasian A. 2017. Evaluation of Genotype × Environment Interaction in Rice Based on AMMI model in Iran. Rice Science, 24(3):173-180. https://doi.org/10.1016/j.rsci.2017.02.001
Sharifi P. 2020. Evolution, Domestication, Breeding Methods and the Latest Breeding Findings in Rice. Agricultural and Natural Resources Engineering Organization of IRAN, IR, 254 PP (In Persian).
Smith HF. 1936. A discriminant function for plant selection. Annals of Eugenics, 7(3): 240-250. https://doi.org/10.1111/j.1469-1809.1936.tb02143.x
Tekalign A, Sibiya J, Derera J and Fikre A. 2017. Analysis of genotype × environment interaction and stability for grain yield and chocolate spot (Botrytis fabae) disease resistance in faba bean (Vicia faba). Australian Journal of Crop Science, 11(10): 1228 -1235.
Ullman JB. 2006. Structural equation modeling: Reviewing the basics and moving forward. Journal of Personality Assessment, 87: 35-50. https://doi.org/10.1207/s15327752jpa8701_03
Varshney RK, Thudi M and Muehlbauer FJ. 2017. The Chickpea Genome. Springer International Publishing, 3(319): 66117-9. https://doi.org/10.1007/978-3-319-66117-9_1
Wright K and Laffont JL. 2018. Genotype plus genotype-by-environment biplots. R package. https:// Kwstat. Github.io/gge/index.html. 
Yue H, Wei J, Xie J, Chen S, Peng H, Cao H, Bu J and Jiang X. 2022. A Study on genotype-by-environment interaction analysis for agronomic traits of maize genotypes across Huang-Huai-Hai region in China. Phyton, 91(1):57-81. https://doi.org/10.32604/phyton.2022.017308
Zali H, Sabaghpour SH, Farshadfar E, Pezeshkpour P, Safikhani M, Sarparast R and Hashem A. 2009. Stability analysis of chickpea genotypes using ASV parameter compare to other stability methods. Iranian Journal of Field Crop Science, 40(2): 21-29. (In Persian). 20.1001.1.20084811.1388.40.2.3.7
Zali H, Farshadfar E, Sabaghpour SH and Karimizadeh R. 2012. Evaluation of genotype × environment interaction in chickpea using measures of stability from AMMI model. Annals of Biological Research, 3(7): 3126-3136.