Identificação de locais de teste para melhoramento de soja no Sul do Brasil

O objetivo deste trabalho foi identificar locais-chave de teste em duas macrorregiões sojícolas no Brasil, para avaliação e seleção de linhagens de soja (Glycine max). Foram testadas 22 cultivares em 23 locais, nessas duas macrorregiões (MR1 e MR2), durante quatro anos, de 2012 a 2015. Os ensaios foram conduzidos em delineamento de blocos ao acaso, com três repetições. Todas as análises foram realizadas com uso do programa GGEbiplot. As análises efeito principal de genótipo mais interação genótipo × local + efeito principal de genótipo mais interação genótipo × ambiente e efeito genotípico vs. efeito do genótipo x ambiente foram utilizadas para identificar locais de teste, isto é, locais com alta representatividade e consistência de resultados. Chapada, no Rio Grande do Sul, e Maracaju, no Mato Grosso do Sul, foram os principais locais em MR1 e MR2, respectivamente. Estes locais foram os mais representativos e consistentes ao longo dos anos, e o efeito genotípico explicou uma alta proporção da variância fenotípica.

Termos para indexação:
Glycine max; GGL + GGE; macrorregião sojícola; locais de teste

Autoria

Gilvani Matei

Universidade Tecnológica Federal do Paraná, Campus Pato Branco, Via do Conhecimento, Km 1, CEP 85503-390 Pato Branco, PR, Brazil. E-mail: gilvani.matei@syngenta.com, dz_andrei@hotmail.com, leowoyann@gmail.com, zanella_rodrigo@hotmail.com, viniciusbezbatti95@gmail.com, benin@utfpr.edu.brUniversidade Tecnológica Federal do ParanáBrazilPato Branco, PR, Brazil Universidade Tecnológica Federal do Paraná, Campus Pato Branco, Via do Conhecimento, Km 1, CEP 85503-390 Pato Branco, PR, Brazil. E-mail: gilvani.matei@syngenta.com, dz_andrei@hotmail.com, leowoyann@gmail.com, zanella_rodrigo@hotmail.com, viniciusbezbatti95@gmail.com, benin@utfpr.edu.br

http://orcid.org/0000-0002-0749-4686

Andrei Daniel Zdziarski

http://orcid.org/0000-0002-6051-1836

Leomar Guilherme Woyann

http://orcid.org/0000-0002-7707-7984

Rodrigo Zanella

http://orcid.org/0000-0002-7278-8613

Vinícius de Bitencourt Bez Batti

http://orcid.org/0000-0002-6210-2518

Giovani Benin ^**Corresponding author: benin@utfpr.edu.br

http://orcid.org/0000-0002-7354-5568

*Corresponding author: benin@utfpr.edu.br

SCIMAGO INSTITUTIONS RANKINGS

Figuras | Tabelas

Figuras (5)
Tabelas (4)

Thumbnail

Figure 1.
Brazilian map with trial locations (black dots) in the two evaluated macroregions (MR1 in dark gray and MR2 in light gray) of soybean (Glycine max) adaptation in Southern Brazil. States: MS, Mato Grosso do Sul; MG, Minas Gerais; SP, São Paulo; PR, Paraná; SC, Santa Catarina; and RS, Rio Grande do Sul.

Thumbnail

Figure 2.
Biplot for genotype main effects plus genotype × location interaction (GGL) + genotype main effects plus genotype × environment interaction (GGE), using data from the value for cultivation and use trials in the 2012/2013-2015/2016 crop seasons in macroregion 1 of soybean (Glycine max) adaption in Brazil. Genotypes are represented by +. Environments consist of locations (municipalities) in the Southern region of the country: SFA, São Francisco de Assis; GUA, Guarapuava; VAC, Vacaria; ABL, Abelardo Luz; RLZ, Realeza; PSO, Palma Sola; CHA, Chapada; CPN, Campos Novos; STO, Santo Augusto; ERC, Erechim; and STC, Santa Cruz do Sul.

Thumbnail

Figure 3
Biplot displaying the ability of each environment to select according to genotypic effect (G) vs. genotype x environment effects (GE) (A) and environment vector view of the GGE biplot in a linear map (B), using data from the value for cultivation and use trials in the 2012/2013-2015/2016 crop seasons in macroregion 1 of soybean (Glycine max) adaptation in Brazil. Environments consist of location (Brazilian municipality) combined with different experimental years: STC, Santa Cruz do Sul; GUA, Guarapuava; SFA, São Francisco de Assis; RLZ, Realeza; VAC, Vacaria; CHA, Chapada; ABL, Abelardo Luz; PSO, Palma Sola; STO, Santo Augusto; CPN, Campos Novos; and ERC, Erechim.

Thumbnail

Figure 4.
Biplot for genotype main effects plus genotype × location interaction (GGL) + genotype main effects plus genotype × environment interaction (GGE), using data from the value for cultivation and use trials in the 2012/2013-2015/2016 crop seasons for macroregion 2 of soybean (Glycine max) adaption in Brazil. Genotypes are represented by +. Environments consist of locations (municipalities) in the Southern region of the country: PLT, Palotina; UBI, Ubiratã; SJI, São Jorge do Ivaí; CCV, Cascavel; PPO, Ponta Porã; LON, Londrina; CMO, Cândido Mota; SID, Sidrolândia; MCJ, Maracaju; DOU, Dourados; PRB, Perobal; BRA, Brasilândia do Sul.

Thumbnail

Figure 5.
Biplot displaying the ability of the environment to select for genotypic effect (G) vs. genotype x environment effects (GE) (A) and environment vector view of the GGE biplot in a linear map (B), using data from the value for cultivation and use trials in the 2012/2013-2015/2016 crop seasons in microregion 2 of soybean (Glycine max) adaptation in Brazil. Environments consist of location (Brazilian municipality) combined with different experimental years: CCV, Cascavel; PRB, Perobal; SID, Sidrolândia; PLT, Palotina; UBI, Ubiratã; SJI, São Jorge do Ivaí; PPO, Ponta Porã; BRA, Brasilândia do Sul; CMO, Cândido Mota; LON, Londrina; DOU, Dourados; and MCJ, Maracaju.

Thumbnail

Table 1.
Locations (municipalities) of the value for cultivation and use trials in the two evaluated macroregions (MR1 and MR2) of soybean (Glycine max) adaptation in Brazil.

Thumbnail

Table 2.
Soybean (Glycine max) genotypes and lineages tested in the value for cultivation and use trials in the 2012/2013-2015/2016 crop seasons in the two evaluated macroregions (MR1 and MR2) of soybean adaptation in Brazil.

Thumbnail

Table 3.
Numerical values of the genotypic effect (G) vs. genotype x environment effect (GE) analysis for locations with the ability to select superior genotypes (AEC_X), stable by G and unstable by GE (AEC_Y), as well as vector length and representativeness of the environment correlated with the average environment axis (AEA), for macroregion 1 (MR1) of soybean (Glycine max) adaptation in Brazil.

Thumbnail

Table 4.
Numerical values of the genotypic effect (G) vs. genotype x environment effect (GE) analysis for locations with the ability to select superior (AEC_X) genotypes, stable by G and unstable by GE (AEC_Y), as well as vector length and representativeness of the environment correlated with the average environment axis (AEA), for macroregion 2 (MR2) of soybean (Glycine max) adaptation in Brazil.

Figure 1. Brazilian map with trial locations (black dots) in the two evaluated macroregions (MR1 in dark gray and MR2 in light gray) of soybean (Glycine max) adaptation in Southern Brazil. States: MS, Mato Grosso do Sul; MG, Minas Gerais; SP, São Paulo; PR, Paraná; SC, Santa Catarina; and RS, Rio Grande do Sul.

Figure 2. Biplot for genotype main effects plus genotype × location interaction (GGL) + genotype main effects plus genotype × environment interaction (GGE), using data from the value for cultivation and use trials in the 2012/2013-2015/2016 crop seasons in macroregion 1 of soybean (Glycine max) adaption in Brazil. Genotypes are represented by +. Environments consist of locations (municipalities) in the Southern region of the country: SFA, São Francisco de Assis; GUA, Guarapuava; VAC, Vacaria; ABL, Abelardo Luz; RLZ, Realeza; PSO, Palma Sola; CHA, Chapada; CPN, Campos Novos; STO, Santo Augusto; ERC, Erechim; and STC, Santa Cruz do Sul.

Figure 3 Biplot displaying the ability of each environment to select according to genotypic effect (G) vs. genotype x environment effects (GE) (A) and environment vector view of the GGE biplot in a linear map (B), using data from the value for cultivation and use trials in the 2012/2013-2015/2016 crop seasons in macroregion 1 of soybean (Glycine max) adaptation in Brazil. Environments consist of location (Brazilian municipality) combined with different experimental years: STC, Santa Cruz do Sul; GUA, Guarapuava; SFA, São Francisco de Assis; RLZ, Realeza; VAC, Vacaria; CHA, Chapada; ABL, Abelardo Luz; PSO, Palma Sola; STO, Santo Augusto; CPN, Campos Novos; and ERC, Erechim.

Figure 4. Biplot for genotype main effects plus genotype × location interaction (GGL) + genotype main effects plus genotype × environment interaction (GGE), using data from the value for cultivation and use trials in the 2012/2013-2015/2016 crop seasons for macroregion 2 of soybean (Glycine max) adaption in Brazil. Genotypes are represented by +. Environments consist of locations (municipalities) in the Southern region of the country: PLT, Palotina; UBI, Ubiratã; SJI, São Jorge do Ivaí; CCV, Cascavel; PPO, Ponta Porã; LON, Londrina; CMO, Cândido Mota; SID, Sidrolândia; MCJ, Maracaju; DOU, Dourados; PRB, Perobal; BRA, Brasilândia do Sul.

Figure 5. Biplot displaying the ability of the environment to select for genotypic effect (G) vs. genotype x environment effects (GE) (A) and environment vector view of the GGE biplot in a linear map (B), using data from the value for cultivation and use trials in the 2012/2013-2015/2016 crop seasons in microregion 2 of soybean (Glycine max) adaptation in Brazil. Environments consist of location (Brazilian municipality) combined with different experimental years: CCV, Cascavel; PRB, Perobal; SID, Sidrolândia; PLT, Palotina; UBI, Ubiratã; SJI, São Jorge do Ivaí; PPO, Ponta Porã; BRA, Brasilândia do Sul; CMO, Cândido Mota; LON, Londrina; DOU, Dourados; and MCJ, Maracaju.

Table 1. Locations (municipalities) of the value for cultivation and use trials in the two evaluated macroregions (MR1 and MR2) of soybean (Glycine max) adaptation in Brazil.

Location	Code	Region	Crop season
Location	Code	Region	2012	2013	2014	2015
Abelardo Luz	ABL	MR1	X	X	X	X
Brasilândia do Sul	BRA	MR2			X	X
Cascavel	CCV	MR2	X	X	X	X
Chapada	CHA	MR1	X	X	X	X
Cândido Mota	CMO	MR2	X	X	X
Campos Novos	CPN	MR1	X		X	X
Dourados	DOU	MR2	X	X	X	X
Erechim	ERC	MR1	X	X
Guarapuava	GUA	MR1		X	X	X
Londrina	LON	MR2	X	X	X	X
Maracaju	MCJ	MR2	X	X		X
Palotina	PLT	MR2	X	X	X	X
Ponta Porã	PPO	MR2	X		X	X
Perobal	PRB	MR2	X		X	X
Palma Sola	PSO	MR1	X	X	X
Realeza	RLZ	MR1	X	X	X	X
São Francisco de Assis	SFA	MR1			X	X
Sidrolândia	SID	MR2	X		X	X
São Jorge do Ivaí	SJI	MR2			X	X
Santa Cruz do Sul	STC	MR1	X	X	X	X
Santo Augusto	STO	MR1	X	X		X
Ubiratã	UBI	MR2		X	X	X
Vacaria	VAC	MR1	X	X		X

Table 2. Soybean (Glycine max) genotypes and lineages tested in the value for cultivation and use trials in the 2012/2013-2015/2016 crop seasons in the two evaluated macroregions (MR1 and MR2) of soybean adaptation in Brazil.

Genotype	Specification⁽¹⁾	Crop season
Genotype	Specification⁽¹⁾	2012/ 2013	2013/ 2014	2014/ 2015	2015/ 2016
A 4724RG	RG	X	X
BMX ENERGIA RR	RG	X	X
DMario 58i	RG	X	X	X	X
NA 5909 RG	RG	X	X	X	X
BMX TURBO RR	RG	X	X	X	X
NS 6262	RG	X	X	X	X
BMX Potência RR	RG	X	X	X	X
NK 7059 RR	RG	X	X	X	X
NS L01	L	X	X
NS L02	L	X	X
NS L06	L	X	X
NS L07	L	X	X	X	X
NS L08	L	X	X
NS L11	L	X	X
NS L12	L	X	X	X	X
NS L13	L	X	X
NS L14	L	X	X	X	X
NS L23	L			X	X
NS L24	L			X	X
NS L25	L			X	X
NS L26	L			X	X
NS L27	L			X	X

⁽¹⁾RG, released genotype; and L, line.

Table 3. Numerical values of the genotypic effect (G) vs. genotype x environment effect (GE) analysis for locations with the ability to select superior genotypes (AEC_X), stable by G and unstable by GE (AEC_Y), as well as vector length and representativeness of the environment correlated with the average environment axis (AEA), for macroregion 1 (MR1) of soybean (Glycine max) adaptation in Brazil.

Environment⁽¹⁾	AEC_X	AEC_Y	Vector length	Correlation with AEA
CPN_2014	1.449	0.373	1.496	0.968
GUA_2014	1.327	0.644	1.475	0.9
PSO_2014	1.289	-0.37	1.341	0.961
ABL_2014	1.252	-0.459	1.333	0.939
VAC_2012	1.24	0.105	1.244	0.996
STC_2013	1.16	0.703	1.356	0.855
VAC_2015	1.155	0.112	1.16	0.995
CHA_2015	1.145	0.602	1.294	0.885
ERC_2013	1.143	0.816	1.404	0.814
RLZ_2014	1.049	-0.352	1.106	0.948
CPN_2015	1.047	-0.106	1.052	0.995
CPN_2012	1.033	1.029	1.458	0.708
STO_2013	0.995	0.673	1.201	0.828
GUA_2015	0.981	-0.74	1.229	0.798
ERC_2012	0.98	0.501	1.101	0.89
CHA_2013	0.919	-0.264	0.956	0.961
ABL_2015	0.917	-0.387	0.995	0.921
STO_2012	0.83	1.147	1.416	0.586
CHA_2012	0.783	0.788	1.111	0.705
PSO_2012	0.766	0.94	1.212	0.632
STO_2015	0.756	-0.201	0.782	0.966
STC_2014	0.734	-0.415	0.843	0.87
ABL_2013	0.684	-1.037	1.243	0.551
STC_2012	0.641	1.305	1.454	0.441
RLZ_2012	0.57	0.05	0.573	0.996
PSO_2013	0.545	-0.935	1.082	0.503
ABL_2012	0.507	0.877	1.013	0.501
RLZ_2015	0.437	-0.642	0.776	0.563
CHA_2014	0.435	-1.258	1.331	0.327
SFA_2014	0.422	-1.112	1.189	0.355
VAC_2013	0.317	-1.186	1.227	0.258
RLZ_2013	0.308	0.109	0.327	0.943
SFA_2015	0.134	-0.194	0.236	0.568
GUA_2013	-0.184	-0.969	0.986	-0.187
STC_2015	-1.04	-0.149	1.051	-0.99

⁽¹⁾Environment consists of location (Brazilian municipality) combined with different experimental years: CPN, Campos Novos; GUA, Guarapuava; PSO, Palma Sola; ABL, Abelardo Luz; VAC, Vacaria; STC, Santa Cruz do Sul; CHA, Chapada; ERC, Erechim; RLZ, Realeza; STO; Santo Augusto; and SFA, São Francisco de Assis. Data are r_gh values of AEC_X, which is an index used to evaluate test environments and that is defined by the ratio between r_g (close to the cosine of the angle between environment and AEA) and h (environment vector length).

Table 4. Numerical values of the genotypic effect (G) vs. genotype x environment effect (GE) analysis for locations with the ability to select superior (AEC_X) genotypes, stable by G and unstable by GE (AEC_Y), as well as vector length and representativeness of the environment correlated with the average environment axis (AEA), for macroregion 2 (MR2) of soybean (Glycine max) adaptation in Brazil.

Environ-ment⁽¹⁾	AEC_X	AEC_Y	Vector length	Correlation with AEA
MCJ_2013	1.779	0.263	1.799	0.989
MCJ_2012	1.643	0.517	1.722	0.954
LON_2012	1.611	-0.234	1.628	0.99
PRB_2012	1.373	0.006	1.373	1
DOU_2012	1.36	0.359	1.407	0.967
CMO_2013	1.316	-0.151	1.325	0.993
LON_2013	1.307	-0.335	1.349	0.969
MCJ_2015	1.133	-0.326	1.179	0.961
PPO_2015	1.133	-0.326	1.179	0.961
CCV_2012	1.091	-0.924	1.429	0.763
DOU_2013	0.993	1.09	1.474	0.674
SID_2015	0.973	-0.076	0.976	0.997
SID_2012	0.937	0.79	1.225	0.765
PPO_2012	0.918	-0.542	1.066	0.861
CMO_2012	0.835	-1.197	1.46	0.572
UBI_2015	0.793	0.752	1.093	0.726
PLT_2015	0.762	0.469	0.895	0.852
DOU_2015	0.678	0.349	0.763	0.889
PLT_2012	0.655	-1.466	1.606	0.408
PLT_2013	0.655	-1.466	1.606	0.408
CCV_2014	0.642	1.07	1.248	0.514
SJI_2015	0.56	0.36	0.665	0.841
CCV_2015	0.446	0.29	0.532	0.838
DOU_2014	0.42	-0.014	0.421	0.999
BRA_2015	0.417	0.996	1.079	0.386
LON_2015	0.272	0.198	0.336	0.809
LON_2014	0.237	-0.936	0.966	0.246
PRB_2015	0.224	-0.313	0.384	0.582
UBI_2013	0.186	-0.487	0.522	0.356
CMO_2014	-0.098	1.358	1.361	-0.072
BRA_2014	-0.108	0.609	0.619	-0.174
SJI_2014	-0.234	-0.624	0.666	-0.351
PPO_2014	-0.253	0.163	0.301	-0.84
UBI_2014	-0.274	-0.816	0.861	-0.319
SID_2014	-0.335	-0.495	0.598	-0.56
PLT_2014	-0.379	1.065	1.13	-0.335
PRB_2014	-0.542	1.061	1.191	-0.455
CCV_2013	-0.707	-1.036	1.254	-0.564

⁽¹⁾Environment consists of location (Brazilian municipality) combined with different experimental years: MCJ, Maracaju; LON, Londrina; PRB, Perobal; DOU, Dourados; CMO, Cândido Mota; PPO, Ponta Porã; CCV, Cascavel; SID, Sidrolândia; UBI, Ubiratã; PLT, Palotina; SJI, São Jorge do Ivaí; and BRA, Brasilândia do Sul. Data are r_gh values of AEC_X, which is an index used to evaluate test environments and that is defined by the ratio between r_g (close to the cosine of the angle between environment and AEA) and h (environment vector length)

Como citar

copiar

Embrapa Secretaria de Pesquisa e Desenvolvimento; Pesquisa Agropecuária Brasileira Caixa Postal 040315, 70770-901 Brasília DF Brazil, Tel. +55 61 3448-1813, Fax +55 61 3340-5483 - Brasília - DF - Brazil
E-mail: pab@embrapa.br

Acompanhe os números deste periódico no seu leitor de RSS

Versão para download de PDF

PDF

Inglês

Versões e tradução automática

Versão original do texto

English

Tradução automática

Google Translator
Microsoft Translator

Como citar

RIS

BIBTEX

Outros formatos de citação e exportação:

SciELO - Scientific Electronic Library Online
Rua Dr. Diogo de Faria, 1087 – 9º andar – Vila Clementino 04037-003 São Paulo/SP - Brasil
E-mail: scielo@scielo.org

Leia a Declaração de Acesso Aberto

Métricas

Identificação de locais de teste para melhoramento de soja no Sul do Brasil

PlumX

Mensagem

[1] *Corresponding author: benin@utfpr.edu.br