Crop Science Research in Arid Regions

Crop Science Research in Arid Regions

Evaluation of the efficiency of WAASB, WAASBY indices and and linear mixed effects model (LMM) for identifying high- yielding lentil genotypes adapted to rainfed regions

Document Type : Original Article

Authors
1 Crop and Horticultural Science Research Department, Lorestan Agricultural and Natural Resources Research and Education Center, AREEO, Khorramabad, Iran
2 Crop and Horticultural Science Research Department, Ilam Agricultural and Natural Resources Research and Education Center, AREEO, Ilam, Iran
3 Dryland Agricultural Research Institute, Sararood Branch, Agricultural Research, Education and Extension Organization (AREEO), Kermanshah, Iran
Abstract
Introduction: lentil is one of the legumes due to its protein percentage and high nutritional value, and it can be cultivated in the fall in rainy conditions. Due to the different reactions of crop genotypes in different environments, the evaluation of the genotype × environment interaction in the process of introducing new cultivars is fundamentally important. The development of high yielding cultivars with wide adaptability is the ultimate aim of plant breeders. However, attaining this goal is made more complicated by genotype-environment interactions. The genotype by environment interaction is a major problem in the study of quantitative traits because it complicates the interpretation of genetic experiments and makes predictions difficult, also it reduces grain yield stability in different environments. Multi-environment trials are often analyzed to assess the yield stability of genotypes. Combining features of the best linear unbiased predictions (BLUP) and additive main effects and multiplicative interaction (AMMI) throught “ Weighted average of absolute scores of best linear unbiased predictions ” (WAASB) index in multi- environment experiments may lead to more precise evaluation of genotypes and assessment of genotype × environment interaction. This statistical models has been widely used to explain complicate G×E interaction, to enhance selection efficiency and to ensure genetic gain from selection. The objective of this study was to investigate the response of the lines in studied locations and to identify lines adapted to the test environments. The objective of this study was to investigate the response of the lines in studied locations and to identify lines adapted to the test environments.
Materials and Methods: In the present study, the seed yield stability of 18 advanced lentil genotypes was evaluated in a multi-environment trials in three locations including; Khoramabad, Ilam and Kermanshah, Iran in 2010-2013 cropping seasons. The experimental design was s randomized complete block design with three replications. Statistical analyzes were performed using multi-environment trials analysis. In order to evaluate genotype × environment interaction, AMMI and BLUP methods were combined by introducing WAASB and WAASBY indicators and the yield stability was evaluated by drawing various graphs.
Results and Discussion: Considering the significant G×E interaction based on the results of the relative likelihood test (LRT), it was possible to perform BLUP analysis on the data. The results of the mosaic diagram showed that the contribution of genotype and genotype × environment interaction were 15.45% and 84.55% of the total variation, respectively. The highest predicted seed yield by BLUP method belonged to genotype no. 2 followed by genotypes no. 4, 19, 5 and 1 which had higher than average predicted seed yield. To enable simultaneous selection based on both seed yield and yield stability, by combining seed yield (Y) and WAASB, a new index “WAASBY” was created. Considering 50% contribution of each of the two components of seed yield and yield stability, fourteen genotypes showed above average WAASBY. Genoypes no. 19, 2, 4,6, 1, 3 and 13 had considerably higher WAASBY when compared with other genotypes and was identified. control Gachsaran cultivar (genotype 20) had lower than average WAASBY
Conclusion: In conclusion, considering WAASBY index, genotypes 2, 4, 6, 1, 3 and 11 were identified as genotypes with high seed yield and yield stability, and can be considered for being released as new lentil cultivars.In general, usin mixed model as well as all the components in calculation the WAASBY index, it can be concluded that this index is superior to other indices.
Keywords

Abbas, G., Asghar, M.J., Shahid, M., Hussain, J., Akram, M. and Ahmad, F., 2019. Yield performance of some lentil genotypes over different environments. Agrosystems, Geosciences & Environment, 2(1), pp.1-3. doi: 10.2134/age2018.10.0051
Abo-Hegazy, S.R.E., Selim, T. and Ashrie, A.A.M., 2013. Genotype× environment interaction and stability analysis for yield and its components in lentil. Journal of Plant Breeding and Crop
Science, 5(5), pp.85-90. doi: 10.5897/jpbcs12.066
Ajay, V. and Singh, G.P., 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), pp.118-124.  doi: 10.17352/2455-815x.000098
Akbari, S., Akbarpour, O. and Pezeshkpour, P., 2021. Evaluation of grain yield stability of lentil genotypes using non-parametric methods. Plant Genetic Researches, 8(1), pp.95-114. doi: 10.52547/pgr.8.1.7
Akıncı, C., Biçer, B.T., Kızılgeçi, F., Albayrak, Ö. and Yıldırım, M., 2018. Stability parameters in lentil genotypes. El-Cezeri, 5(2), pp.287-291.
Azam, M.G., Iqbal, M.S., Hossain, M.A. and Hossain, M.F., 2020. Stability investigation and genotype× environment association in chickpea genotypes utilizing AMMI and GGE biplot model. Genetics and Molecular Research, 19(3), pp.1-15.
Barrios, A., Aparicio, T., Rodríguez, M.J., de la Vega, M.P. and Caminero, C., 2016. Winter sowing of adapted lines as a potential yield increase strategy in lentil (Lens culinaris Medik.). Spanish Journal of Agricultural Research, 14(2), pp.e0702-e0702. doi: 10.5424/sjar/2016142-8092
Barbosa, M.H., Ferreira, A., Peixoto, LA., Resende, M.D., Nascimento, M. and Silva, F.F., 2014. Selection of sugar cane families by using BLUP and multi-diverse analyses for planting in the Brazilian savannah. Genetics and Molecular Research, (13), pp.1619-1626. doi: 10.4238/2014.march.12.14
Baretta, D., Nardino, M., Carvalho, I.R., Oliveira, A.D., Souza, V.D. and Maia, L.D., 2016. Performance of maize genotypes of Rio Grande do Sul using mixed models. Científica, 44(3), pp. 403-411. doi: 10.15361/1984-5529.2016v44n3p403-411
Bermejo, C., Cazzola, F., Maglia, F. and Cointry, E., 2020. Selection of parents and estimation of genetic parameters using BLUP and molecular methods for lentil (Lens culinaris Medik.) breeding program in Argentina. Experimental Agriculture, 56(1), pp.12-25. doi: 10.1017/s0014479719000061
Branković-Radojčić, D.V., Babić, V., Filipović, M., Srdić, J., Girek, Z., Zivanović, T. and Radojčić, A., 2018. Evaluation of maize grain yield and yield stability by AMMI analysis. Genetika, 50(3), pp.1067-1080.  doi: 10.2298/gensr1803067b
Chen, C., Etemadi, F., Franck, W., Franck, S., Abdelhamid, M.T., Ahmadi, J., Mohammed, Y.A., Lamb, P., Miller, J., Carr, P.M. and McPhee, K., 2022. Evaluation of environment and cultivar impact on lentil protein, starch, mineral nutrients, and yield. Crop Science, 62(2), pp.893-905. doi: 10.1002/csc2.20675
Dehghani, H., Sabaghpour, S.H. and Sabaghnia, N., 2008. Genotype x environment interaction for grain yield of some lentil genotypes and relationship among univariate stability statistics. Spanish Journal of Agricultural Research, (3), pp.385-394.
Elias, A.A., Robbins, K.R., Doerge, R.W. and Tuinstra, M.R., 2016. Half a century of studying genotype× environment interactions in plant breeding experiments. Crop Science, 56(5), pp.2090-2105. doi: 10.2135/cropsci2015.01.0061
Finlay, K.W. and Wilkinson, G.N., 1963. The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research, 14(6), pp.742-754.
Gan, Y., Hamel, C., Kutcher, H.R. and Poppy, L., 2017. Lentil enhances agroecosystem productivity with increased residual soil water and nitrogen. Renewable Agriculture and Food Systems, 32(4), pp.319-330. doi: 10.1017/s1742170516000223
Gauch Jr, H.G. and Zobel, R.W., 1997. Identifying mega‐environments and targeting genotypes. Crop Science, 37(2), pp.311-326.
Jeberson, M.S., Shashidhar, K.S., Wani, S.H., Singh, A.K. and Dar, S.A., 2019. Identification of stable lentil (Lens culinaris Medik) genotypes through GGE biplotand AMMI analysis for North Hill Zone of India. Legume Research-An International Journal, 42(4), pp.467-472. doi: 10.18805/lr-3901
Laffont, J.L., Hanafi, M. and Wright, K., 2007. Numerical and graphical measures to facilitate the interpretation of GGE biplots. Crop Science, 47(3), pp.990-996. doi: 10.2135/cropsci2006.08.0549
Karaköy, T., Erdem, H., Baloch, F.S., Toklu, F., Eker, S., Kilian, B. and Özkan, H., 2012. Diversity of macro‐and micronutrients in the seeds of lentil landraces. The Scientific World Journal, 2012(1), p.710412. doi: 10.1100/2012/710412
Karimizadeh, R., Safikhani Nasimi, M., Mohammadi, M., Seyyedi, F., Mahmoodi, A.A. and Rostami, B., 2008. Determining rank and stability of lentil genotypes in rainfed condition by nonparametric statistics. JWSS-Isfahan University of Technology, 12(43), pp.93-102. [In Persian].
Karimizadeh, R. and Mohammadi, M., 2010. AMMI adjustment for rainfed lentil yield trials in Iran. Bulgarian Journal of Agricultural Science, 16(1), pp.66-73.
Karimizadeh, R., Mohammadi, M. and Sabaghmia, N., 2013. Site regression biplot analysis for matching new improved lentil genotypes into target environments. Journal of Plant Physiology and Breeding, 3(2), pp.51-65.
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), pp.160-170. [In Persian]. doi: 10.52547/jcb.12.36.160
Karimizadeh, R., Pezeshkpour, P. and Mirzaii, A., 2021. Evaluation of grain yield stability of rainfed lentil genotypes by parametric and non-parametric methods. Applied Field Crops Research, 34(3), pp.155-140. [In Persian]. doi: 10.22092/aj.2022.351573.1500
Meng, Y., Ren, P., Ma, X., Li, B., Bao, Q., Zhang, H., Wang, J., Bai, J. and Wang, H., 2016. GGE Biplot-based evaluation of yield performance of barley genotypes across different environments in China. Journal of Agricultural Science and Technology, (18), pp.533-543. doi: 10.1149/ma2023-024556mtgabs
Muehlbauer, F.J., Cho, S., Sarker, A., McPhee, K.E., Coyne, C.J., Rajesh, P.N. and Ford, R., 2006. Application of biotechnology in breeding lentil for resistance to biotic and abiotic stress. Euphytica, 147, pp.149-165. doi: 10.1007/s10681-006-7108-0
Namdari, A., Pezeshkpour, P., Mehraban, A., Naseri, A., Vaezi, B. and Nazarli, H., 2022. Evaluation the grain yield stability of promising rainfed lentil genotypes using parametric and non-parametric statistics. Iranian Journal of Field Crop Science, 53(3), pp.153-167. [In Persian]. doi: 10.22059/ijfcs.2021.330502.654854
Nardino, M., Baretta, D., Carvalho, I.R., Olivoto, T., Follmann, D.N., Szareski, V.J., Ferrari, M., de Pelegrin, A.J., Konflanz, V.A. and de Souza, V.Q., 2016. Restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) for analyzing the agronomic performance of corn. African Journal of Agricultural Research, 11(48), pp.4864-4872. doi: 10.5897/ajar2016.11691
Olivoto, T.2019. Metan: multi environment trials analysis. R package version 1.1.0. https://github.com/TiagoOlivoto/metan. doi: 10.1101/2020.01.14.906750
Olivoto, T., Lúcio, A.D., da Silva, J.A., Marchioro, V.S., de Souza, V.Q. and Jost, E., 2019. Mean performance and stability in multi‐environment trials I: combining features of AMMI and BLUP techniques. Agronomy Journal, 111(6), pp.2949-2960. doi: 10.2134/agronj2019.03.0220
Olivoto, T., Lúcio, A.D., da Silva, J.A., Sari, B.G. and Diel, M.I., 2019. Mean performance and stability in multi‐environment trials II: Selection based on multiple traits. Agronomy Journal, 111(6), pp.2961-2969. doi: 10.2134/agronj2019.03.0221
Olivoto, T. and Lúcio, A.D.C., 2020. metan: An R package for multi‐environment trial analysis. Methods in Ecology and Evolution, 11(6), pp.783-789. doi: 10.1111/2041-210x.13384
Piepho, H.P., Möhring, J., Melchinger, A.E. and Büchse, A., 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica, 161(1), pp.209-228. doi: 10.1007/s10681-007-9449-8
Pawar, I.S. and Singh, S., 2010.Theory and Application of Biometrical Genetics. CBS Publisher and Distributors Pvt. Ltd. Softcover, 1st edition. New Delhi, IND.
Pezeshkpour, P., Karimizadeh, R., Mirzaei, A. and Barzali, M., 2021. Analysis of Yield Stability of lentil Genotypes using AMMI Method. Journal of Crop Breeding, 13(37), pp.132-145. [In Persian].  doi: 10.52547/jcb.13.37.132
Sa’diyah, H. and Hadi, A.F., 2016. AMMI Model for yield estimation in multi-environment trials: A comparison to BLUP. Agriculture and Agricultural Science Procedia, 9, pp.163-169. doi: 10.1016/j.aaspro.2016.02.113
Sharifi, P., 2020. Application of Multivariate Analysis Methods in Agriculural Sciences. Rasht branch, Islamic Azad University Press. pp. 288. [In Persian].
Sarker, A., Erskine, W. and Singh, M., 2003. Regression models for lentil seed and straw yields in Near East. Agricultural and Forest Meteorology, 116(1-2), pp.61-72. doi: 10.1016/s0168-1923(02)00247-2
Sánchez-Gómez, D., Cervera, M.T., Escolano-Tercero, M.A., Vélez, M.D., de María, N., Diaz, L., Sánchez-Vioque, R., Aranda, I. and Guevara, M.Á., 2019. Drought escape can provide high grain yields under early drought in lentils. Theoretical and Experimental Plant Physiology, 31, pp.273-286. doi: 10.1007/s40626-018-0136-z
Sellami, M.H., Pulvento, C. and Lavini, A., 2021. Selection of suitable genotypes of lentil (Lens culinaris Medik.) under rainfed conditions in south Italy using multi-trait stability index (MTSI). Agronomy, 11(9), p.1807. doi: 10.3390/agronomy11091807
Shobeiri, S., Sadeghzadeh Ahari, D., Pezeshkpour, P. and Azimi, M., 2021. Stability Analysis of Grain yield of Lens Culinaris L lentil Genotypes in Dryland Conditions by GGE biplot Method. Journal of Crop Breeding, 13(40), pp.1-10. [In Persian]. doi: 10.52547/jcb.13.40.1
Smith, A.B., Cullis, B.R. and Thompson, R., 2005. The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. The Journal of Agricultural Science, 143(6), pp.449-462. doi: 10.1017/s0021859605005587
Smirnov, N., 1948. Table for estimating the goodness of fit of empirical distributions. The Annals of Mathematical Statistics, 19(2), pp.279-281.
Subedi, M., Khazaei, H., Arganosa, G., Etukudo, E. and Vandenberg, A., 2021. Genetic stability and genotype× environment interaction analysis for seed protein content and protein yield of lentil. Crop Science, 61(1), pp.342-356. doi: 10.1002/csc2.20282
Tadesse, T., Sefera, G., Asmare, B. and Tekalign, A., 2021. AMMI Analysis for Grain Yield Stability in Lentil Genotypes Tested in the Highlands of Bale, Southeastern Ethiopia. Journal of Plant Sciences, 9(1), pp.9-12. doi: 10.11648/j.jps.20210901.12
Tadesse, T., Tekalign, A. and Asmare, B., 2021. Identification of stable lentil genotypes using AMMI analysis for the highlands of bale, Southeastern Ethiopia. Chemical and Biomolecular Engineering, 19(6), pp.74-79.  doi: 10.11648/j.cbe.20210604.12
Thennarasu, K., 1995. On Certain Non-parametric Procedures for Studying Genotype-Environment Inertactions and Yield Stability (Doctoral dissertation, IARI, Division of Agricultural Statistics, New Delhi).
Truberg, B. and Huehn, M., 2000. Contributions to the analysis of genotype× environment interactions: Comparison of different parametric and non‐parametric tests for interactions with emphasis on crossover interactions. Journal of Agronomy and Crop Science, 185(4), pp.267-274. doi: 10.1046/j.1439-037x.2000.00437.x
Wright, K. and J.L. Laffont. 2018. Package ‘GGE’. https://github.com/kwstat/gge/issues.
Yadav, N.K., Ghimire, S.K., Sah, B.P., Sarker, A., Shrestha, S.M. and Sah, S.K., 2016. Genotype x environment interaction and stability analysis in lentil (Lens culinaris Medik.). International Journal of Environment, Agriculture and Biotechnology, 1(3), pp.238539.doi: 10.22161/ijeab/1.3.7
Yan, W. and Tinker, N.A., 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science, 86(3), pp.623-645. doi: 10.4141/p05-169
Zaccardelli, M., Sonnante, G., Lupo, F., Branca, F. and de Falco, E., 2010. Leguminose Minori (Cece, Lenticchia, Cicerchia, Fava); Consiglio per Ricerca Sperimentazione Agricoltura: Rome, Italy.pp.73.          
Volume 6, Issue 2 - Serial Number 13
Summer 2024
Pages 431-452

  • Receive Date 12 December 2022
  • Revise Date 25 March 2023
  • Accept Date 11 April 2023