Targeting Cultivars onto Rice Growing Environments Using AMMI and SREG GGE Biplot Analyses

Samonte, Stanley Omar P. B.; Wilson, L. T.; McClung, Anna M.; Medley, James C.

doi:10.2135/cropsci2004.0627

Cited by 225 publications

(170 citation statements)

References 22 publications

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“…Genotype × environment interaction (GE) have shown there supremeness for delineating stability of genotypes and have partitioned variation that better aid for selection of consistent stable genotypes in many different studies (Dehghani et al, 2006;Yan et al, 2007;Sabaghnia & Sabaghpour, 2008). There are several methods for stability analysis among which GGE biplot analysis was used in the present study to deduce stability of the crop with aim to investigate the stability of seed yield in amaranths and graphically summarize and considerate the assets of G and GE interaction (Yan & Kang, 2003;Samonte et al, 2005;Dehghani et al, 2009, Balestre et al, 2009Oliveira et al, 2010;Tonk et al, 2011). Present study was undertaken for the first time to gather information about GE interaction of different amaranth genotypes for high seed yield using three year data using GGE biplot analysis.…”

Section: Introductionmentioning

confidence: 99%

GGE biplot analysis for yield performance of grain Amaranth genotypes across different environments in western India

Pagi¹,

Prajapati²,

Pachchigar³

et al. 2017

JEBAS

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Stable performance of grain amaranths genotypes at specific environment is critical for obtaining high and consistent yield. This study was conducted to assess grain yield stability of thirteen genotypes in varied environments in three successive growing seasons and effects of genotype (G) and genotype×environment (GE) interaction were illustrate graphically based on "what-won-where" to advocate genotypes for a specific environment, using GGE biplot. Result of study revealed that GGE biplot was effective to recognize stable and highest yield (1648.92 kg/ha) genotype as G5 followed by G6 (1457.24 kg/ha) and G3 (1268.62 kg/ha) genotypes while the genotype G7 had the lowest grain yield and was the least stable across varying environments. This study could be aid tool for endorsement of amaranths genotypes for specific environment, taking into elucidation the peculiarities of genotypes and growing stipulation. KEYWORDS GGE biplotGrain amaranths Grain yieldWhat-won-where

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Section: Introductionmentioning

confidence: 99%

GGE biplot analysis for yield performance of grain Amaranth genotypes across different environments in western India

Pagi¹,

Prajapati²,

Pachchigar³

et al. 2017

JEBAS

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“…1). In comparison, a GGE biplot analysis of six genotypes in 12 environments in Texas explained 77% of the variation in grain yield due to GGE (Samonte et al 2005). The difference in the variation due to GGE can be attributed to the diverse environments across five states and the large number of genotypes evaluated.…”

Section: Anova and Gge Biplot Analyses Of Main Crop Grain Yieldmentioning

confidence: 93%

“…This revealed the instability of yield performance and demonstrated the difficulty in identifying the highest yielding genotype for a specific location or group of locations. Samonte et al (2005) reported significant GE interaction when six cultivars were grown in 12 environments (3 yr)4 locations) and used additive main effects and multiplicative interactions (AMMI) and GGE biplot analyses to identify the highest yielding cultivar for specific environments in Texas.…”

Section: Methodsmentioning

confidence: 99%

“…Since essentially all current commercial cultivars have Abbreviations: AMMI, additive main effects and multiplicative interactions; DCP, direct and countercyclical payments; GE, genotype)environment; GGE, genotype plus genotype )environment; PC, principal component; URRN, uniform regional rice nursery good milling quality in the areas where they are predominantly grown, farmers usually select cultivars that provide the highest potential field yield for production on their farm. For example, although a genotype ) environment (GE) interaction study identified Wells as the highest yielding cultivar for Beaumont, TX (Samonte et al 2005), its lower and more variable whole milled rice percentage (54.5 to 59.5%) relative to Cocodrie (59.7 to 60.9%) (McClung et al 2006) is one of the reasons why Cocodrie is planted to a relatively higher acreage in this area.…”

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confidence: 99%

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Use of gross income as a measure of productivity in rice breeding

Samonte

Wilson²,

Tabien³

et al. 2008

Can. J. Plant Sci.

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Rice breeders consider grain yield and milled rice percentages in developing cultivars, but usually do not consider gross income. This study’s objectives were to identify rice genotypes that produced high and stable expected gross incomes using genotype plus genotype × environment (GGE) biplot analysis. Uniform Regional Rice Nursery data on 47 long-grain genotypes grown at five locations (AR, LA, MO, MS, and TX) during 2001 to 2003 were analyzed. Gross income of each genotype was estimated based on grain yield, milled rice percentages, market prices, and direct and counter-cyclical payments. Based on GGE biplot analysis, the genotypes with the highest yield and highest gross income for the main crop were different in 12 out of 13 environments. RU0103184, Francis, and RU0003178 were the genotypes with the highest gross income in six, four, and three environments, respectively. Rice breeders should consider gross income as a selection criterion in the release of new cultivars. Key words: Rice, GGE, GE, breeding, gross income

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“…Yan et al (2000) classified the winter wheat environment of Ontario Canada into two; western and southern Ontario, a major winter mega-environment and eastern Ontario, a minor mega-environment. Samonte et al (2005) identified mega-environments, superior rice cultivars and ideal rice cultivars in different mega-environments using site regression GGE biplot. More recently, Yan and Holland (2010) demonstrated the appropriateness of the use of heritability-adjusted (Scale = 2) GGE (HA-GGE) biplot in simultaneous evaluation of genotypes and test locations.…”

Section: Multi-environment Analysismentioning

confidence: 99%

Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists

Akinwale¹,

Fakorede²,

Badu‐Apraku³

et al. 2014

Cereal Research Communications

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Genotype main effect plus genotype-by-environment interaction (GGE) biplot produces a graphical display of results that facilitates a better understanding of complex genotype-by-environment interaction in multi-environment trials of breeding and agronomic experiments. However, the full potential and weaknesses of this powerful tool are not fully understood by breeders, agronomists, entomologists and pathologists. The objective of this paper was to review the usefulness of this statistical tool and enumerate some of its weaknesses. Its main application has so far been in the analysis of multi-environment data. It has been used to analyze the performance of crop cultivars under multiple stress environments, from which ideal cultivars, mega-environments, and core testing sites were identified. More recently, GGE biplot has been employed in genetic analysis of diallel data to estimate the combining abilities and identify heterotic groups among inbred parents. Genotype-by-trait biplot has also been utilized in trait profile analysis, and in identification of traits that are reliable for indirect selection of a target primary trait. Two major shortcomings of this tool are (i) failure to identify more than two distinct, contrasting groups in diallel studies and (ii) lack of statistical tests for most of its graphical displays. Other aspects of GGE biplot that need further study and development are (i) estimation of genetic variances, covariances, and heritability, including the analysis of data generated from North Carolina Designs I, II, and III as well as other genetic designs, considering their importance in plant breeding programs; (ii) analysis of Quantitative Trait Loci (QTL) data for proper understanding of the genetic constitution of each individual plant or line; and (iii) analysis of Genotype-by-pathogen or insect strain interaction data. Nevertheless, GGE biplot has helped greatly in the accurate analysis and interpretation of data from breeding and agronomic field evaluation experiments.

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Targeting Cultivars onto Rice Growing Environments Using AMMI and SREG GGE Biplot Analyses

Cited by 225 publications

References 22 publications

GGE biplot analysis for yield performance of grain Amaranth genotypes across different environments in western India

GGE biplot analysis for yield performance of grain Amaranth genotypes across different environments in western India

Use of gross income as a measure of productivity in rice breeding

Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists

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