Key locations for soybean genotype assessment in South Brazil region

Samuel Cristian Dalló, Andrei Daniel Zdziarski, Leomar Guilherme Woyann, Anderson Simionato Milioli, Rodrigo Zanella, Vinícius de Bitencourt Bez Batti, Giovani Benin


Mitigating the high costs of soybean breeding programs requires constant improvement of all the involved processes. Identifying representative and discriminating test locations, as well as excluding redundant and/or non-representative locations, makes it possible to select genotypes with more accuracy while reducing the costs of the multi-environment trials (MET). Therefore, this study had three objectives: to evaluate the representativeness and discriminating power of test locations; to identify similar test locations for each Edaphoclimatic Region (ECR) and locations that did not contribute to genotype evaluation; and to recommend the best locations for evaluating MET in order to reduce breeding program costs in the soybean macro regions 1 (M1) and 2 (M2). Grain yield (GY) data from ‘Value-for-Cultivation-and-Use’ (VCU) trials obtained during the 2012-2016 crop seasons were used, totaling 132 environments (location x year) and 43 genotypes. The experiments were arranged in a randomized complete block design with three replications. Representative and discriminant locations were identified by GGL (genotype main effects plus genotype × location interaction) + GGE (genotype main effects plus genotype × environment interaction) analysis, using GGEbiplot software. Representative and discriminant locations were identified for each ECR and can be used as core locations for breeding programs. Similarly, locations that were not representative and discriminant, or that present redundancy in the results, should be excluded from or replaced in MET. The most recommended locations for conducting VCU trials in M1 are: Cachoeira do Sul (ECR 101); Ronda Alta, Passo Fundo, Santa Bárbara do Sul, and Ciríaco (ECR 102); and Castro (ECR 103). For M2, the most suitable locations are Rolândia, Marechal Cândido Rondon, Campo Mourão, Santa Terezinha de Itaipu, Palotina, Floresta, and Londrina (ECR 201); Naviraí (ECR 202); and Ponta Porã and Maracajú (ECR 204).


Glycine max (L.) Merrill; GGL + GGE Biplots; Representativeness and Discriminating power.

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Dia, M., Wehner. T. C., Hassell, R., Price, D. S., Boyhan, G. E., Olson, S.,… Juarez, B. (2016). Value of locations for representing mega-environments and for discriminating yield of watermelon in the US. Crop Science, 56(4), 1726-1735. doi:10.2135/cropsci2015.11.0698

Kaster, M., & Farias, J. R. B. (2012). Regionalização dos testes de valor de cultivo e uso e da indicação de cultivares de soja: terceira aproximação. Londrina: EMBRAPA Soja. Recuperado de

Khatun, H., Islam, R., Anisuzzaman, M., Ahmed, H. U., & Haque, M. (2015). GGE biplot analysis of genotype × environment interaction in rice (Oryza sativa L.) genotypes in Bangladesh. Scientia Agriculturae, 12(1), 34-39. doi: 10.15192/PSCP.SA.2015.12.1.3439

Krishnamurthy, S. L., Sharma, P. C., Sharma, D. K., Ravikiran, K. T., Singh, Y. P., Mishra, V. K.,… Singh, R. K. (2017). Identification of mega-environments and rice genotypes for general and specific adaptation to saline and alkaline stresses in India. Scientific Reports, 7(7968), 1-14. doi:10.1038/s41598-017-08532-7

Luo, J., Pan, Y., Que, Y., Zhang, H., Grisham, M. P., & Xu, L. (2015). Biplot evaluation of test environments and identification of mega-environment for sugarcane cultivars in China. Scientific Reports, 5(15505), 1-11. doi: 10.1038/srep15505

Yan, W. (2001). GGEbiplot – A Windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agronomy Journal, 93(5), 1111-1118. doi: 10.2134/agronj2001.9351111x

Yan, W. (2014). Crop variety trials: data management and analysis. Chichester, West Sussex, UK: Wiley Blackwell.

Yan, W. (2015). Mega-environment analysis and test location evaluation based on unbalanced multiyear data. Crop Science, 55(1), 113-122. doi:10.2135/cropsci2014.03.0203

Yan, W. (2016). Analysis and handling of G × E in a practical breeding program. Crop Science, 56(5), 2106-2118. doi:10.2135/cropsci2015.06.0336

Yan, W., Frégeau-Reid, J., Martin, R., Pageau, D., & Mitchell-Fetch, J. (2015). How many test locations and replications are needed in crop variety trials for a target region? Euphytica, 202(3), 361-372. doi: 10.1007/s10681-014-1253-7

Yan, W., & Holland, J. B. (2010). A heritability-adjusted GGE biplot for test environment evaluation. Euphytica, 171(3), 355-369. doi: 10.1007/s10681-009-0030-5

Yan, W., Pageau, D., Frégeau-Reid, J., & Durand, J. (2011). Assessing the representativeness and repeatability of test locations for genotype evaluation. Crop Science, 51(4), 1603-1610. doi:10.2135/cropsci2011.01.0016


Semina: Ciênc. Agrár.
Londrina - PR
E-ISSN 1679-0359
DOI: 10.5433/1679-0359
Este obra está licenciado com uma Licença Creative Commons Atribuição-NãoComercial 4.0 Internacional