&lt;b&gt;A SAS macro for computing statistical tests for two-way table and stability indices of nonparametric method from genotype-by-environment interaction

Akbarpour, Omid Ali; Dehghani, Hamid; Sorkhi-Lalelo, Bezad; Kang, Manjit S.

doi:10.4025/actasciagron.v38i1.26381

Cited by 10 publications

(5 citation statements)

References 33 publications

(57 reference statements)

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“…Analysis of variance (ANOVA) for Genotype × Environment interaction (GEI) was determined using three nonparametric statistical methods viz: [28][29][30], as described in the equation below. The parametric combined ANOVAFtest was carried out using Statistical Analysis System version 9.4 [31] following the procedure of SAS macro program developed by [32] for computing statistical tests for a twoway table and stability indices of a nonparametric method from GEI. The analysis of parametric and nonparametric statistical parameters was performed using GEA-R 4.1 [33] statistical tools and STABILITYSOFT software [34].…”

Section: Discussionmentioning

confidence: 99%

Parametric and Non-parametric Procedures for Identifying Stable and Adapted Tropical Maize Genotypes in NLB Disease Infested Environments

Ohunakin¹,

C.²,

O.³

et al. 2021

AJBIO

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Multi-locational trials are critical for establishing stable and adaptable genotypes across different geographic areas prior to considering commercial release. The stability and adaptation of 20 tropical maize hybrids in environments infected with Northern leaf blight disease were assessed using 12 parametric and 14 nonparametric parameters across five environments. The purpose of this research is to estimate the genotype-environment interaction (GEI) for grain yield in selected maize genotypes and to identify associated stability factors to aid in the rationalization of stability analysis in Multi-Environment Trial (MET) data used in breeding programs. Except for De Kroon and Van der Laan (1981), both the combined ANOVA and nonparametric tests of GEI showed significant differences across hybrids, as well as significant crossover and non-crossover interactions. This suggests differential genotypes responses to the test environments. Spearman correlation analysis revealed significant differences between many nonparametric and parametric parameters, indicating that the two may be utilized interchangeably. Additionally, the correlation matrix and principal component analysis results from parametric and nonparametric parameters demonstrated their potential to assess the responses of maize genotypes to changing environments. G13 and G20 appeared most phenotypically stable with associated high mean yield based on the high values expressed by most parametric and nonparametric parameters.

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Section: Discussionmentioning

confidence: 99%

Parametric and Non-parametric Procedures for Identifying Stable and Adapted Tropical Maize Genotypes in NLB Disease Infested Environments

Ohunakin¹,

C.²,

O.³

et al. 2021

AJBIO

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“…Harvesting was done from four center rows in 0.7 m × 5.5 m plots by experimental combine and grain yield (kg ha -1 ) was obtained by converting the grain yields obtained from plots to hectares. A SAS macro program (Akbarpour et al, 2016) was used for calculation of four nonparametric statistical procedures for nonparametric testing of genotype, environment and GE interaction according to Bredenkamp (1974), Hildebrand (1980), de Kroon and van Der Laan (1981), and Kubinger (1986. These nonparametric statistical tests have been described in detail by Huehn and Leon (1995) and Truberg and Hühn (2000).…”

Section: Methodsmentioning

confidence: 99%

Nonparametric Analysis of Genotype Grain Yield Performance of Barley Trials Based on Ranks

Vaezi,

Sabgahnia,

Mehraban

et al. 2022

RAR

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Plant breeding has been concerned with genotype by environment (GE) interaction and high yielding genotypes with stable performance are desirable while this target is difficult to achieve due to high environmental variations and unpredictable GE interaction. Stability of grain yield performance of 18 barley genotypes was evaluated at 5 locations for 3 years in the rainfed conditions and it was studied through 25 nonparametric stability methods. Four nonparametric tests indicated highly significant GE interaction due to differential performance of genotypes across fifteen environments. Regarding mean yield and six Hühn’s statistics, genotype G12 (1946 kg ha-1) was the most favorable genotype while based on the RN1, G4, G10 and G11 were the most stable genotypes. Genotypes G4, G8 and G10 were the most favorable genotypes according to rank-sum while genotypes G2, G13 and G18 were the most favorable genotypes based on nonparametric superiority. The relative interactivity index identified G4, G16 and G8 as the most stable genotypes while the genotypic classification identified G1, G2, G13 and G18 as the most stable genotypes. Clustering of the nonparametric stability methods indicated that there were two groups with different static and dynamic characteristics. In this study, five nonparametric stability methods as GC, FM, PA, RN2 and KR2 were associated with high grain yield and reflected the dynamic concept of stability, but the other twenty nonparametric stability methods were not positively correlated with yield and characterized a static concept of stability. Finally, genotypes genotype G13 (2114.13 kg ha-1) and G18 (2062.69 kg ha-1) were found to be the most favorable genotypes and are thus recommended for commercial release in semiarid areas of Iran.

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“…Piepho [ 105 ] published the first SAS’s code, which only was able to calculate the Shukla’s stability variance (

), coefficient of variance (CV), and deviation from regression (

). After then, Hussein et al [ 106 ], Akbarpour et al [ 107 ], and Dia et al [ 108 ] developed other codes that enable breeders to calculate other stability statistics. Among these, the SAS macro code developed by Dia et al [ 108 ]––which has been called SASG×E––is more complete compared to other codes.…”

Section: How We Can Compute Stability Statistics?mentioning

confidence: 99%

Stability Indices to Deciphering the Genotype-by-Environment Interaction (GEI) Effect: An Applicable Review for Use in Plant Breeding Programs

Pour-Aboughadareh

Khalili

Poczai

et al. 2022

Plants

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Experiments measuring the interaction between genotypes and environments measure the spatial (e.g., locations) and temporal (e.g., years) separation and/or combination of these factors. The genotype-by-environment interaction (GEI) is very important in plant breeding programs. Over the past six decades, the propensity to model the GEI led to the development of several models and mathematical methods for deciphering GEI in multi-environmental trials (METs) called “stability analyses”. However, its size is hidden by the contribution of improved management in the yield increase, and for this reason comparisons of new with old varieties in a single experiment could reveal its real size. Due to the existence of inherent differences among proposed methods and analytical models, it is necessary for researchers that calculate stability indices, and ultimately select the superior genotypes, to dissect their usefulness. Thus, we have collected statistics, as well as models and their equations, to explore these methods further. This review introduces a complete set of parametric and non-parametric methods and models with a selection pattern based on each of them. Furthermore, we have aligned each method or statistic with a matched software, macro codes, and/or scripts.

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<b>A SAS macro for computing statistical tests for two-way table and stability indices of nonparametric method from genotype-by-environment interaction

Cited by 10 publications

References 33 publications

Parametric and Non-parametric Procedures for Identifying Stable and Adapted Tropical Maize Genotypes in NLB Disease Infested Environments

Parametric and Non-parametric Procedures for Identifying Stable and Adapted Tropical Maize Genotypes in NLB Disease Infested Environments

Nonparametric Analysis of Genotype Grain Yield Performance of Barley Trials Based on Ranks

Stability Indices to Deciphering the Genotype-by-Environment Interaction (GEI) Effect: An Applicable Review for Use in Plant Breeding Programs

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