Determination of stable safflower genotypes in variable environments by parametric and non-parametric methods

Afzal, Obaid; Hassan, Faiza; Ahmed, Mukhtar; Shabbir, Ghulam; Ahmed, Shakeel

doi:10.1016/j.jafr.2021.100233

Cited by 6 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was based on stability factors such as lower values of Wi 2 , σ 2i , P i , and s 2 di, a high BLUP factor, and bi equal to 1. The use of different parametric stability assessments has also been reported in wheat (Bornhofen et al, 2017), rice (Lee et al, 2023), safflower (Afzal et al, 2021), and oat (Kebede et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

Majidian,

Masoudi,

Hezarjaribi

et al. 2024

Food Science & Nutrition

View full text Add to dashboard Cite

The multi‐environmental trials aid breeders in selecting the best genotypes for specific or general adaptability to different environments before commercial release. This study aimed to assess the stability of 13 new soybean pure lines, along with two controls, in terms of seed yield and important agronomic traits. The assessment was based on a completely randomized block design with three replications across four areas during 2020–2022. Various adaptability methods, including parametric, AMMI, GGE biplot, PCA, and SIIG were employed. The mixed analysis showed that the effects of environment, genotype, and genotype–environment (GE) interaction were significant for most studied traits. The AMMI showed the highest portion of environment (65.89%) in soybean seed yield. Based on all stability parameters, lines 2 and 5 were selected for their average seed yields of 3349 and 3142 kg ha−1, respectively. Additionally, lines 6 and 5 showed the most stability, yielding higher than the average, which were 2992 and 3142 kg ha−1, respectively, according to GGE biplot results. Furthermore, lines 2, 5, and 8 were identified as the ideal genotypes concerning seed yield and other agronomic traits, with high SIIG parameters and yields exceeding the average. Finally, the soybean line 5 was deemed the most suitable due to its higher yield, stability, and early maturity (128‐day growth period). Therefore, line 5 is considered appropriate for its high stability and earliness in various regions of Iran.

show abstract

Section: Discussionmentioning

confidence: 99%

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

Majidian,

Masoudi,

Hezarjaribi

et al. 2024

Food Science & Nutrition

View full text Add to dashboard Cite

show abstract

“…Alizadeh et al [ 50 ] investigated the GEI effect in a set of winter rapeseed lines, demonstrating that parameters such as S (2) , S (3) , S (6) , NP (2) , NP (3) , and NP (6) along with KR, due to their strong correlation with seed yield, which enabled them to identify high-yielding and stable lines. Afzl et al [ 51 ] reported that among non-parametric stability statistics, S (1) , S (3) , S (6 , ) and NP (4) with a dynamic concept are superior to evaluate high-yielding and stable safflower genotypes. In another study in canola, Mortazavian and Azizinia [ 52 ] reported that for nonparametric methods, TOP, Si (1) , and RK parameters were useful in detecting the stability of the genotypes.…”

Section: Deciphering Ge Interaction Using Different Statistical Methodsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Modelling and Field-Based Evaluation of Vernalisation Requirement of Canola for Higher Yield Potential

Yaqub

Ahmed

Hamza

et al. 2022

Global Agricultural Production: Resilience to Climate Change

View full text Add to dashboard Cite

Determination of stable safflower genotypes in variable environments by parametric and non-parametric methods

Cited by 6 publications

References 35 publications

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

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

Modelling and Field-Based Evaluation of Vernalisation Requirement of Canola for Higher Yield Potential

Contact Info

Product

Resources

About