Package {safuzzy}

Annicchiarico (1992) method

Description

Adaptability and Stability Analysis based on the interpretation of the Annicchiarico 1992 methodology, developed by Carneiro et al. 2019.

Usage

annicchiarico(data, env, gen, rep, var)

Arguments

Value

A data frame containing the following estimates:

Gen

Genotype.

Wg

General recommendation index for environments.

Wd

Recommendation index for unfavorable environments.

Wf

Recommendation index for favorable environments.

GE

Membership (%) to the general stability genotypes group.

PA

Membership (%) to the poorly adapted genotypes group.

FAV

Membership (%) to the favorable adapted genotypes group.

UNF

Membership (%) to the unfavorable adapted genotypes group.

Author(s)

Douglas de Oliveira Maciel douglasmaciel@discente.ufg.br

References

Carneiro, A. R. T., Sanglard, D. A., Azevedo, A. M., Souza, T. L. P. O. D., Pereira, H. S., & Melo, L. C. (2019). Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies. Scientia Agricola, 76(2), 123-129.

See Also

hybrid

lin_binns

Examples

data(ge_data)
annicchiarico(data = ge_data, env = environment, gen = genotype, rep = block, var = gy)

Cruz, Torres & Vencovsky (1989) method

Description

Adaptability and Stability Analysis based on the interpretation of the Cruz, Torres & Vencovsky (1989), developed by Carneiro et al. 2019.

Usage

cruz_torres_vencovsky(data, env, gen, rep, var)

Arguments

Value

A data frame containing the following estimates:

Gen

Genotype.

B_0

Mean of the variable for each genotype.

B_1

Regression coefficient ($B_1$) for each genotype.

B1_B2

Regression coefficient ($B_1 + B_2$) for each genotype.

R2

Coefficient of determination ($R^2$) for each genotype.

MdAF

Membership (%) to the average adaptability to favorable environments genotypes group.

Nda

Membership (%) to the poorly adapted genotypes group.

MdAG

Membership (%) to the general adaptability to favorable environments genotypes group.

MaxGF

Membership (%) to the maximum adaptability to favorable environments genotypes group.

MaxDes

Membership (%) to the maximum adaptability to unfavorable environments environments group.

BE

Membership (%) to the low stability genotypes group.

BP

Membership (%) to the low yield genotypes group.

Author(s)

Douglas de Oliveira Maciel douglasmaciel@discente.ufg.br

References

Carneiro, A. R. T., Sanglard, D. A., Azevedo, A. M., Souza, T. L. P. O. D., Pereira, H. S., & Melo, L. C. (2019). Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies. Scientia Agricola, 76(2), 123-129.

See Also

hybrid

lin_binns

eberhart_russell

Examples

data(ge_data)

cruz_torres_vencovsky(data = ge_data, env = environment, gen = genotype, rep = block, var = gy)

Eberhart & Russel (1966) method

Description

Adaptability and Stability Analysis based on the interpretation of the Eberhart & Russel 1966 methodology, developed by Carneiro et al. 2018.

Usage

eberhart_russell(data, env, gen, rep, var)

Arguments

Value

A data frame containing the following estimates:

Gen

Genotype.

B_0

Mean of the variable for each genotype.

B_1

Regression coefficient ($B_1$) for each genotype.

R2

Coefficient of determination ($R^2$) for each genotype.

GE

Membership (%) to the general stability genotypes group.

PA

Membership (%) to the poorly adapted genotypes group.

FAV

Membership (%) to the favorable adapted genotypes group.

UNF

Membership (%) to the unfavorable adapted genotypes group.

Author(s)

Douglas de Oliveira Maciel douglasmaciel@discente.ufg.br

References

Carneiro, V. Q., Prado, A. L. D., Cruz, C. D., Carneiro, P. C. S., Nascimento, M., & Carneiro, J. E. D. S. (2018). Fuzzy control systems for decision-making in cultivars recommendation. Acta Scientiarum. Agronomy, 40, e39314.

See Also

hybrid

lin_binns

Examples

data(ge_data)

eberhart_russell(data = ge_data, env = environment, gen = genotype, rep = block, var = gy)

Rice Lines Adaptability and Stability Data Using Fuzzy Logic

Description

A real dataset containing the grain yield and plant heigth performance of upland rice lines evaluated across multiple environments. This dataset is used to demonstrate the application of the fuzzy logic methodology for adaptability and stability analysis implemented in the safuzzy package.

Usage

ge_data

Format

A data frame (or tibble) with columns representing the experimental factors:

genotype

Factor representing the evaluated upland rice lines/genotypes.

environment

Factor representing the test environments (combinations of locations and crop years).

block

Factor representing the local control.

gy

Numeric variable containing the grain yield performance (e.g., kg/ha).

ph

Numeric variable containing the plant heigth performance (e.g., cm).

Details

The data consists of phenotypic evaluations of elite lines and commercial cultivars of upland rice. The analysis provides membership degrees that assist breeders in selecting stable and high-yielding genotypes for target environments.

Source

Data obtained from the breeding trials conducted and published by Maciel et al. (2025).

References

Maciel, D. D. O., Guimarães, P. H. R., & Melo, P. G. S. (2025). Harnessing fuzzy logic for adaptive and stable selection of upland rice lines. Crop Breeding and Applied Biotechnology, 25(2), e527425213. doi:10.1590/1984-70332025v25n2a28

Examples

data(ge_data)
head(ge_data)

Hybrid method

Description

Stability and Adaptability Analysis based on the interpretation of the Eberhart & Russel 1966 methodology, associated with the modified Lins & Bins (1988) methodology, developed by Carneiro et al. (2020).

Usage

hybrid(data, env, gen, rep, var)

Arguments

Value

Um data frame contendo as seguintes estimativas:

Gen

Genotype.

PIF

Performance index in favorable environments.

PID

Performance index in unfavorable environments.

B_1

Regression coefficient ($B_1$) for each genotype.

R2

Standardized coefficient of determination ($R^2$) for each genotype.

GE

Membership (%) to the general stability genotypes group.

PA

Membership (%) to the poorly adapted genotypes group.

FAV

Membership (%) to the favorable adapted genotypes group.

UNF

Membership (%) to the unfavorable adapted genotypes group.

Author(s)

Douglas de Oliveira Maciel douglasmaciel@discente.ufg.br

References

Carneiro, A. R. T., Sanglard, D. A., Azevedo, A. M., Souza, T. L. P. O. D., Pereira, H. S., Melo, L. C., & Carneiro, P. C. S. (2020). Fuzzy logic applied to different adaptability and stability methods in common bean. Pesquisa agropecuária brasileira, 55, e01609.

See Also

eberhart_russell

lin_binns

annicchiarico

Examples

data(ge_data)

hybrid(data = ge_data, env = environment, gen = genotype, rep = block, var = gy)

Modified method of Lin & Binns (1988)

Description

Stability and Adaptability Analysis based on the interpretation of the modified Lins and Bins (1988) methodology.

Usage

lin_binns(data, env, gen, rep, var)

Arguments

Value

A data frame containing the following estimates:

Gen

Genotype.

PIF

Performance index in favorable environments.

PID

Performance index in unfavorable environments.

GE

Membership (%) to the general stability genotypes group.

PA

Membership (%) to the poorly adapted genotypes group.

FAV

Membership (%) to the favorable adapted genotypes group.

UNF

Membership (%) to the unfavorable adapted genotypes group.

Author(s)

Douglas de Oliveira Maciel douglasmaciel@discente.ufg.br

References

Carneiro, P. C. S. (1998). Novas metodologias de análise da adaptabilidade e estabilidade de comportamento (Doctoral dissertation, Universidade Federal de Viçosa.).

See Also

eberhart_russell

hybrid

Examples

data(ge_data)

lin_binns(data = ge_data, env = environment, gen = genotype, rep = block, var = gy)