Statistical methods to study adaptability and stability in breeding lines of food-type soybeans

Freiria, Gustavo Henrique; Gonçalves, Leandro Simões Azeredo; Furlan, Felipe Favoretto; Fonseca, N.; Lima, Walcir Ferreira; Prete, Cássio Egídio Cavenaghi

doi:10.1590/1678-4499.2017076

Cited by 24 publications

(25 citation statements)

References 37 publications

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“…These results indicating the presence of variability among these components justify the use of stability statistics for the identification of stable genotypes with superior seed yield of soybean under the various environments. Maia et al [17], Yokomizo et al [18] and Freiria et al [19] analyzed the adaptability and stability of soybean genotypes and found that the mean squares of G, E and GEI were significant (p ≤ 0.01). Therefore, the environments evaluated were distinct and the soybean genotypes presented a differentiated performance in response to environmental variations.…”

Section: Ammi Anovamentioning

confidence: 99%

Comparison of Non-parametric Stability Statistics for Selecting Stable and Adapted Soybean Genotypes under Different Environments

El-Hashash

Tarek

Rehab

et al. 2019

AJRCS

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The objectives of this study were to investigate the comparison among non-parametric stability statistics and to evaluate seed yield stability of the sixteen soybean genotypes across four locations during the 2016, 2017 and 2018 growing seasons in Egypt. All trials were laid down in a randomized complete block design (RCBD) with three replications. The AMMI analysis showed ahighly significant effect of genotype (G), environment (E) and G x E interaction (GEI). The major contributions to treatment sum of squares were GEI, followed by G and E. The AMMI analysis also partitioned the total GEI component into eleven PCAs and Residual. The first eight PCAs were highly significant and accounted for about 99.56% of the total GEI. Based on the static and dynamic concepts, the results of spearman’s rank correlation and PCA showed that stability measures could be classified into three groups. The non-parametric stability statistics i.e., YSi, KR, TOP, RSM and δgy related to the dynamic concept and strongly correlated with mean seed soybean yield of stability. While, the other non-parametric stability statistics (Si(1) ,Si(2) ,Si(3) and Si(6),NPi(1) ,NPi(2) ,NPi(3) and NPi(4) , δr, MID, LOW) represented the concept of static stability, which were influenced simultaneously by both yield and stability. The non-parametric stability statistics in each the groups I, II, and III were positively and significantly correlated with each other, thus; any of these parameters could be considered as appropriate alternatives for each other. According to cluster analysis, soybean genotypes G6, G4, G8, G11, G9, G1, G7 and G2 were more stable varieties on the basis of mean seed yield and non-parametric stability statistics. In conclusion, both yield and stability should be considered simultaneously to exploit the useful effect of GEI and to make the selection of genotypes more precise and refined. Thus, the YSi, KR, TOP, RSM and δgy were more useful statistics in soybean breeding programmes and could be useful alternatives to parametric stability statistics. According to most non-parametric stability statistics, the genotypes G6 and G11 were more stable coupled with high seed yield; therefore, these genotypes might be used for genetic improvement of soybean and they must be released in studied regions and other regions in Egypt.

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Section: Ammi Anovamentioning

confidence: 99%

Comparison of Non-parametric Stability Statistics for Selecting Stable and Adapted Soybean Genotypes under Different Environments

El-Hashash

Tarek

Rehab

et al. 2019

AJRCS

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“…Users who research the associations between stability indexes (e.g., Woyann et al, 2018; Bornhofen et al, 2017; Freiria et al, 2018; Shahbazi, 2019) often find difficulties in computing the set of statistics and binding them into a “ ready-to-read ” file. provides an efficient solution for doing that.…”

Section: The Packagementioning

confidence: 99%

“…Although useful, these packages do not offer options to perform a complete analysis of MET data, i.e., to provide tools for all steps of the analysis (check, manipulation, analysis, and visualization of data). For example, requires as input data a two-way table containing genotype by environment means with genotypes in rows and environments in columns, but doesn’t provide any function to create quickly such table from data that often is in a “ long ” format in R. In addition, several studies often compare different stability methods (e.g., Woyann et al, 2018; Scapim et al, 2010; Bornhofen et al, 2017; Freiria et al, 2018; Shahbazi, 2019; Teodoro et al, 2019). This requires a range of different packages to be used, making it the coding tedious and difficult to follow.…”

Section: Introductionmentioning

confidence: 99%

metan: an R package for multi-environment trial analysis

Olivoto

Lúcio

2020

Preprint

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10 1. Multi-environment trials (MET) are crucial steps in plant breeding programs that aim 11 increasing crop productivity to ensure global food security. The analysis of MET data 12 requires the combination of several approaches including data manipulation, visualization, 13 and modeling. As new methods are proposed, analyzing MET data correctly and 14 completely remains a challenge, often intractable with existing tools. 15 2. Here we describe the metan R package, a collection of functions that implement a 16 workflow-based approach to (a) check, manipulate and summarise typical MET data; (b) 17 analyze individual environments using both fixed and mixed-effect models; (c) compute 18 parametric and non-parametric stability statistics; (c) implement biometrical models 19 widely used in MET analysis; and (d) plot typical MET data quickly.203. In this paper, we present a summary of the functions implemented in metan and how 21 they integrate into a workflow to explore and analyze MET data. We guide the user 22 along a gentle learning curve and show how adding only a few commands or options at 23 a time, powerfull analyzes can be implemented. 24 4. metan offers a flexible, intuitive, and richly documented working environment with tools 25 that will facilitate the implementation of a complete analysis of MET data sets. 26 Key-words: AMMI, biometry, genotype-environment interaction, GGE biplot, multi-27 environment trials, R software, stability, statistics 28 29In 50 years the world average of cereal yields has increased by 64%, from 1.68 to 30 2.76 t ha −1 . In the same period, the total production of cereals has raised from 1.305 × 10 9 to 31 3.6 × 10 9 t, an increase of 175%, while the cultivated area increased by only 7.9% in the same 32 period (FAOSTAT, 2019). These unparallel increases have been possible due to the improved 33 cultivation techniques in combination with superior cultivars. For maize, for example, 50% 34 of the increase in yield was due to breeding (Duvick, 2005). Plant breeding programs have 35 been developing new cultivars for adaptation to new locations, management practices, or 36 growing conditions, in a clear and crucial example of exploitation of genotype-vs-environment 37 interaction (GEI). 38The breeders' desire to modeling the GEI appropriately has led to the development of 39 the so-called stability analyses, which includes ANOVA-based methods (Yates ) and some methods that combines 43 different statistical techniques, such as the Additive Main Effect and Multiplicative Interaction, 44 AMMI, (Gauch, 2013), and Genotype plus Genotype-vs-Environment interaction, GGE, (Yan 45 & Kang, 2003). Then, it is no surprise that scientific production related to multi-environment 46 trial analysis has been growing fast in the last decades. A bibliometric survey in the SCOPUS 47 database revealed that in the last half-century (1969-2019) 6590 documents were published 48 in 902 sources (Journals, books, etc.) by 19.351 authors. In this period, the number of 49 publications has been increased on av...

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“…However, it is recommended that the regression models and other methods be used simultaneously to complement each other in estimating adaptability and phenotypic stability effects (Ferreira et al, 2006;Toler & Burrows, 1998). The use of these techniques is widespread in studies on plant species and many other commercial crops (Borges et al, 2010;Breseghello et al, 2011;Carbonell et al, 2007;Freiria et al, 2018). In microbiological studies, almost no reports have this focus (Gunnabo et al, 2019).…”

Section: Core Ideasmentioning

confidence: 99%

Selection of elite Bradyrhizobium strains by biometric techniques for inoculation in cowpea

Oliveira

Soares

Ferreira

et al. 2020

Soil Science Soc of Amer J

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Plant breeding programs seek to obtain genotypes with high yield, yield stability, and wide adaptability to environments. Selection of strains (genotypes) for adoption as crop inoculants has a similar aim. However, in both cases, the genotype × environment interaction hinders selection of materials adapted to the most diverse edaphic and climatic conditions. Nevertheless, with adequate biometric techniques, the variation in the response of each strain in the environments can be analyzed, allowing selection of those with the type of response and standard desired. In the present pioneering field study, the Toler and additive main effects and multiplicative interaction analysis models and the Annicchiarico method were used in a complementary manner with strains of nitrogen‐fixing bacteria that are symbionts with cowpea to determine their adaptabilities and phenotypic stabilities. The adaptability and phenotypic stabilities of strains INPA 03‐11B and UFLA 03‐164 were confirmed by these techniques. These strains have the most stable behavior regardless of the environment. The reliability of adopting these strains exceeded 85%. The good response of strain UFLA03‐164, still in the selection phase, is surprising, with results equivalent to application of mineral N, allowing it to be included among the strains approved as inoculants for cowpea.

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Statistical methods to study adaptability and stability in breeding lines of food-type soybeans

Cited by 24 publications

References 37 publications

Comparison of Non-parametric Stability Statistics for Selecting Stable and Adapted Soybean Genotypes under Different Environments

Comparison of Non-parametric Stability Statistics for Selecting Stable and Adapted Soybean Genotypes under Different Environments

metan: an R package for multi-environment trial analysis

Selection of elite Bradyrhizobium strains by biometric techniques for inoculation in cowpea

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