Soybean adaptability and stability analyzes to the organic system through AMMI, GGE Biplot and mixed models methodologies

Pradebon, Leonardo Cesar; Carvalho, Ivan Ricardo; Loro, Murilo Vieira; Port, Eduarda Donadel; Bonfada, Bruno; Sfalcin, Inaê Carolina; Santos, Iago Jardim; Bruinsma, Gabriel Mathias Weimer; Silva, Jose Antonio Gonzalez da; Alban, Aljian Antônio; Challiol, Marcio Alberto

doi:10.1590/0103-8478cr20220262

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CROP PRODUCTION • Cienc. Rural 53 (9) • 2023 • https://doi.org/10.1590/0103-8478cr20220262 copy

Soybean adaptability and stability analyzes to the organic system through AMMI, GGE Biplot and mixed models methodologies

Análises de adaptabilidade e estabilidade da soja ao sistema orgânico por meio de metodologias AMMI, GGE Biplot e modelos mistos

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

This research determined the adaptability and stability of soybean yield for organic systems in different environments. Grain yield data (GY, kg ha^-1) from experiments with six soybean genotypes evaluated in six environments in Brazil and one environment in Paraguay were used. The experimental design used was randomized blocks, organized in an incomplete factorial scheme, with six environments in Brazil (Toledo- PR, Palotina- PR, Mangueirinha- PR, Três Passos- RS, Passo Fundo- RS, Major Vieira- SC) and one in Paraguay (Bela Vista do Norte- PY) with six soybean genotypes (BRS 284, BRS 391, BRS 511, BRS 523, BRS 525, BRS 535) arranged in four replications per environment. BRS 511 genotype was characterized by high average grain yield and stability by the AMMI method. BRS 284 genotype was identified as the ideal genotype in the GGE biplot method. WAASBY and BLUP index selected BRS511 and BRS284 genotypes.

Key words:
Glycine max; Genotype x environment interaction; WAASBY

Authorship

Leonardo Cesar Pradebon

Departamento de Estudos Agrários, Universidade Regional do Noroeste do Rio Grande do Sul, 98700-000, Ijuí, RS, Brasil. Universidade Regional do Noroeste do Rio Grande do SulBrazilIjuí, RS, BrazilDepartamento de Estudos Agrários, Universidade Regional do Noroeste do Rio Grande do Sul, 98700-000, Ijuí, RS, Brasil.

Ivan Ricardo Carvalho ^*E-mail: carvalhoirc@gmail.com. *Correponding author

https://orcid.org/0000-0001-7947-4900

Murilo Vieira Loro

Departamento de Fitotecnia, Centro de Ciência Rurais (CCR), Universidade Federal de Santa Maria (UFSM), Santa Maria, RS, Brasil.Universidade Federal de Santa Maria (UFSM),BrazilSanta Maria, RS, BrazilDepartamento de Fitotecnia, Centro de Ciência Rurais (CCR), Universidade Federal de Santa Maria (UFSM), Santa Maria, RS, Brasil.

Eduarda Donadel Port

Bruno Bonfada

Inaê Carolina Sfalcin

Iago Jardim Santos

Gabriel Mathias Weimer Bruinsma

Jose Antonio Gonzalez da Silva

Aljian Antônio Alban

Gebana Brasil, Campo Largo, PR, Brasil.Gebana BrasilBrasilCampo Largo, PR, BrasilGebana Brasil, Campo Largo, PR, Brasil.

Marcio Alberto Challiol

Gebana Brasil, Campo Largo, PR, Brasil.Gebana BrasilBrasilCampo Largo, PR, BrasilGebana Brasil, Campo Largo, PR, Brasil.

E-mail: carvalhoirc@gmail.com. ^*Correponding author

Editors: Alessandro Dal’Col Lúcio(0000-0003-0761-4200) Leandro Souza da Silva(0000-0002-1636-6643)

DECLARATION OF CONFLICT OF INTEREST

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of the data; in the writing of the manuscript, and in the decision to publish the results.

AUTHORS’ CONTRIBUTIONS

All authors contributed equally for the conception and writing of the manuscript. All authors critically revised the manuscript and approved of the final version.

SCIMAGO INSTITUTIONS RANKINGS

Gebana Brasil, Campo Largo, PR, Brasil.Gebana BrasilBrasilCampo Largo, PR, BrasilGebana Brasil, Campo Largo, PR, Brasil.

Figures | Tables

Figures (7)
Tables (2)

Thumbnail

Figure 1
Medium air temperatures (ºC) and average precipitation (mm) for the different environments. The circles represent the maximums and minimums values of the variable in each of the months.

Thumbnail

Figure 2
Principal component analysis for minimum, average and maximum air temperature (ºC) and rainfall (mm) for the environments.

Thumbnail

Figure 3
Descriptive analysis of grain yield showing the interaction between genotypes x environments (A) and representation of estimates obtained by the method of analysis of restricted maximum likelihood/best unbiased linear predictor (REML/BLUP) (B), for the grain yield trait of six soybean genotypes. Genotypes: G1: BRS 284, G2: BRS 391, G3: BRS 511, G4: 523, G5: BRS 525, G6: BRS 535 (B).

Thumbnail

Figure 4
Estimates of the components of yield averages by specific BLUP measured in six genotypes and six environments. Genotypes: G1: BRS 284, G2: BRS 391, G3: BRS 511, G4: BRS 523, G5: BRS 525, G6: BRS 535. Environments: E1-MA (Mangueirinha- PR), E2-MV (Major Vieira), E3-PA (Palotina- PR), E4-PF (Passo Fundo- RS), E5-PY (Bela Vista do Norte- PY), E6-TO (Toledo- PR), TP (Três Passos- RS).

Thumbnail

Figure 5
Plotting the scores of the main components of the AMMI2 method (A) and estimating the WAASBY value (B) for soybean grain yield, relative to six genotypes (G) cultivated in six environments (E). Environments: Env E1: Mangueirinha- PR, E2: Major Vieira- SC, E3: Palotina- PR, E4: Passo Fundo- RS, E5: Bela Vista do Norte- PY, E6: Toledo- PR; E7: Três Passos- RS. Genotypes: G1: BRS 284, G2: BRS 391, G3: BRS 511, G4: BRS 523, G5: BRS 525 and G6: 535.

Thumbnail

Figure 6
GGE biplot (Genotypes and Genptypes by Environmentes Interaction) indicating the ranking of the seven environments (E) and six soybean genotypes (Gen), with their respective stabilities, discrimination and representativeness of production environments regarding the variable grain yield. Environments: Env E1: Mangueirinha- PR, E2: Major Vieira- SC, E3: Palotina- PR, E4: Passo Fundo- RS, E5: Bela Vista do Norte- PY, E6: Toledo- PR; E7: Três passos- RS. Genotypes: G1: BRS 284, G2: BRS 391, G3: BRS 511, G4: BRS 523, G5: BRS 525, G6: BRS 535.

Thumbnail

Figure 7
Environmental predictor model for Grain Yield (GY). Tmed: Medium air temperature (ºC); Tmin: Minimum air temperature (ºC); Tmax: Maximum air temperature (ºC); Rad: Radiation (MJ m² day^-1).

Thumbnail

Table 1
Meteorological variables, soil characteristics, relief and location of environments: Bela Vista do Norte- PY (PY); Palotina -PR (PA); Toledo- PR (TO); Mangueirinha- PR (MA); Major Vieira- SC (MV); Três Passos- RS (TP); Passo Fundo- RS (PF). Conjunct analysis of variance for grain yield evaluated in number of genotypes and environments.

Thumbnail

Table 2
Estimates of variance components and genetic parameters for soybean grain yield in different environments in Brazil and Paraguay.

Figure 1 Medium air temperatures (ºC) and average precipitation (mm) for the different environments. The circles represent the maximums and minimums values of the variable in each of the months.

Figure 2 Principal component analysis for minimum, average and maximum air temperature (ºC) and rainfall (mm) for the environments.

Figure 3 Descriptive analysis of grain yield showing the interaction between genotypes x environments (A) and representation of estimates obtained by the method of analysis of restricted maximum likelihood/best unbiased linear predictor (REML/BLUP) (B), for the grain yield trait of six soybean genotypes. Genotypes: G1: BRS 284, G2: BRS 391, G3: BRS 511, G4: 523, G5: BRS 525, G6: BRS 535 (B).

Figure 4 Estimates of the components of yield averages by specific BLUP measured in six genotypes and six environments. Genotypes: G1: BRS 284, G2: BRS 391, G3: BRS 511, G4: BRS 523, G5: BRS 525, G6: BRS 535. Environments: E1-MA (Mangueirinha- PR), E2-MV (Major Vieira), E3-PA (Palotina- PR), E4-PF (Passo Fundo- RS), E5-PY (Bela Vista do Norte- PY), E6-TO (Toledo- PR), TP (Três Passos- RS).

Figure 5 Plotting the scores of the main components of the AMMI2 method (A) and estimating the WAASBY value (B) for soybean grain yield, relative to six genotypes (G) cultivated in six environments (E). Environments: Env E1: Mangueirinha- PR, E2: Major Vieira- SC, E3: Palotina- PR, E4: Passo Fundo- RS, E5: Bela Vista do Norte- PY, E6: Toledo- PR; E7: Três Passos- RS. Genotypes: G1: BRS 284, G2: BRS 391, G3: BRS 511, G4: BRS 523, G5: BRS 525 and G6: 535.

Figure 6 GGE biplot (Genotypes and Genptypes by Environmentes Interaction) indicating the ranking of the seven environments (E) and six soybean genotypes (Gen), with their respective stabilities, discrimination and representativeness of production environments regarding the variable grain yield. Environments: Env E1: Mangueirinha- PR, E2: Major Vieira- SC, E3: Palotina- PR, E4: Passo Fundo- RS, E5: Bela Vista do Norte- PY, E6: Toledo- PR; E7: Três passos- RS. Genotypes: G1: BRS 284, G2: BRS 391, G3: BRS 511, G4: BRS 523, G5: BRS 525, G6: BRS 535.

Figure 7 Environmental predictor model for Grain Yield (GY). Tmed: Medium air temperature (ºC); Tmin: Minimum air temperature (ºC); Tmax: Maximum air temperature (ºC); Rad: Radiation (MJ m² day^-1).

Table 1 Meteorological variables, soil characteristics, relief and location of environments: Bela Vista do Norte- PY (PY); Palotina -PR (PA); Toledo- PR (TO); Mangueirinha- PR (MA); Major Vieira- SC (MV); Três Passos- RS (TP); Passo Fundo- RS (PF). Conjunct analysis of variance for grain yield evaluated in number of genotypes and environments.

------------------------------------------------------------------Environmental characterization-------------------------------------------------------------
ENV	Lat¹		Long²	Alt³	Soil Type	Tmax (ºC)⁶	Tmin (ºC)⁸
PY	22º 7’ 48” S		56º 31’ 12” O	176 m	RL¹²	32.32	18.45
PA	24°17'38.81'' S		53°48'03.77'' O	338 m	DRL¹²	33.39	19.68
TO	24º 42’ 50” S		53º 44’ 34” O	550 m	DRL¹²	32.27	18.61
MA	25º 56’ 27” S		52º 10’ 33” O	921 m	NLH¹⁴	28.51	15.75
MV	26º 22’ 04” S		50º 19’ 40” O	786 m	LBDR¹⁵	27.88	15.57
TP	27°29'52.30'' S		53°54'00.34'' O	438 m	DRL¹²	31.95	17.64
PF	28º 15’ 46” S		35º 24’ 25” O	687 m	DRL¹²	28.85	16.6
--------------------------------------------------------------------Analisys of Variance-----------------------------------------------------------------------				687 m	DRL¹²	28.85	16.6
Variation	DF⁹	SS¹⁰	MS¹¹	F value	Pr (>F)	Proportion	Accumulated
---------------------------------------------------------------------------Factor----------------------------------------------------------------------------------				F value	Pr (>F)	Proportion	Accumulated
ENV	6	9.93E+07	1.70E+07	59.1	0.00E+00	NA	NA
REP (ENV)	63	1.76E+07	280031	1.34	5.73E-02	NA	NA
GEN	5	2.22E+07	4440065	21.2	2.80E-18	NA	NA
GEN:ENV	30	5.39E+00	1795940	8.58	2.82E-26	NA	NA
PC1	10	2.76E+07	2763239	13.2	0.00E+00	51.3	51.3
PC2	8	1.84E+07	2303634	11	0.00E+00	34.2	85.5
PC3	6	5.90E+06	983804	4.7	1.00E-04	11	96.4
PC4	4	1.52E+06	380660	1.82	1.25E-01	2.8	99.3
PC5	2	3.91E+05	195630	0.93	3.96E-01	0.7	100
RESIDUAL	315	6.60E+07	209401
Total	449	3.13E+08	696774

¹Lat - Latitude; Long - ²Longitude; ³Alt - Altitude; ⁴Pre - Precipitation; ⁵Rad - Radiation; ⁶Tmax - Maximum Air Temperature; ⁷Tmed - Medium Air Temperature; ⁸Tmin - Minimum Air Temperature. ⁹Degrees of Freedom (DF); ¹⁰Sum of Squares (SS); ¹¹Mean Square (MS); ¹²Red Latosol (RL); ¹²Dystrophic Red Latosol (DRL); ¹⁴Neosolo Litólico Húmico (NLH); ¹⁵Latosol Bruno/Dark (LBDR).

Table 2 Estimates of variance components and genetic parameters for soybean grain yield in different environments in Brazil and Paraguay.

VAR	MODEL	LOG LIK	AIC	LRT	PR (> CHISQ)
GY	GEN	-2744	5633	2.16	1.42E-01
GY	GEN:ENV	-2798	5731	110	9.14E-26
Parameters				GY
σ²P				405829
σ²G				37782
H²				0.0931
GEIr²				0.391
H²mg				0.596
Accuracy				0.772
RGE %				0.431
CVg %				5.93
CVr %				14
CVratio%				0.425

Var: Variable; Model: Model; Log Lik: Restricted Maximum Likelihood Logarithm; AIC: Akaike Informative Criterion; LRT: Restricted Maximum Likelihood Ratio; PR: Probability by Chi-Square test: σ²P: Phenotypic variance; σ²G: Genotypic variance; H²: broad-sense heritability GEI: coefficient for determining the effects of the genotype-environment interaction; H²mg: Mean heritability of the genotype; RGE: genotypic correlation between genotypes x environments performance; CVg: Genotypic variation coefficient; Cvr: Coefficient of residual variation; CV ratio: coefficient of variation of the proportion between genotypic and residual coefficient of variation.

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E-mail: cienciarural@mail.ufsm.br

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Soybean adaptability and stability analyzes to the organic system through AMMI, GGE Biplot and mixed models methodologies

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[1] E-mail: carvalhoirc@gmail.com. ^*Correponding author