Phenotypic stability of eggplant for yield and quality through AMMI, GGE and cluster analyses

Koundinya, A. V. V.; Pandit, M. K.; Ramesh, D; Mishra, Pradeep

doi:10.1016/j.scienta.2018.12.019

Cited by 21 publications

(21 citation statements)

References 16 publications

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“…In addition, the MTSI was found to have many practical applications by breeders and agronomists who desire the simultaneous selection for MPE when data of several traits are available. For example, it could have been useful for already published studies that evaluated the stability and mean performance of genotypes considering several traits [e.g., Koundinya et al (2019), six traits evaluated in Solanum melongena genotypes; Nduwumuremyi et al (2017), nine traits evaluated in Manihot esculenta genotypes; and Bocianowski et al (2019), five traits evaluated in Brassica spp. lines].…”

Section: Discussionmentioning

confidence: 99%

“…Reliability in recommending genotypes, however, could be increased if the mean performance and stability (MPE) of several traits were considered. Recent studies have assessed several variables in MET (e.g., Adjebeng‐Danquah et al, 2017; Bocianowski et al, 2019; Koundinya et al, 2019; Nduwumuremyi et al, 2017; Shahriari et al, 2018; Veenstra et al, 2019), and the simultaneous selection for stability and mean performance has met with some success when each variable was analyzed individually.…”

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confidence: 99%

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Mean Performance and Stability in Multi‐Environment Trials II: Selection Based on Multiple Traits

et al. 2019

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Abbreviations: AMMI, additive main effects and multiplicative interaction; ASV, additive main effects and multiplicative interaction stability value; AUDPC, area under the disease progress curve; BLUP, best linear unbiased prediction; CW, caryopses weight; GEI, genotype × environment interaction; GSI, genotype stability index; GW, grain weight; GWP, grain weight per panicle; GY, grain yield; HI, hulling index; HW, hectoliter weight; IGY, industrial grain yield; IPCA, interaction principal component axis; LMM, linear mixed-effect model; MET, multi-environment trial; MPE, mean performance and stability; MTSI, multi-trait stability index; NEP, number of spikelets per panicle; NG2, number of grains >2 mm; NGP, number of grains per panicle; PL, panicle length; PM, panicle mass; TGW, thousand-grain weight; WAASB, weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype × environment interaction effects generated by an linear mixed-effect model; WAASBY, weighted average of WAASB and response variable. bIoMetrY, ModeLInG, And stAtIstIcsPublished in Agron.

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Section: Discussionmentioning

confidence: 99%

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Mean Performance and Stability in Multi‐Environment Trials II: Selection Based on Multiple Traits

et al. 2019

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“…El fenotipo es el efecto acumulativo del genotipo (G), del ambiente (A) y su interacción (G x A). Cuando el ambiente es altamente variable entre localidades y estaciones, mientras que los efectos de los genotipos son constantes, el ambiente es el factor que afecta en mayor medida el fenotipo (Koundinya et al, 2017(Koundinya et al, , 2019. Así, la evaluación de genotipos en ensayos en múltiples ambientes (MET, por sus siglas en inglés) es una técnica usada para determinar la estabilidad de un gran número de ellos en diferentes ambientes (localidades y años) (Casanoves et al, 2005).…”

Section: Introductionunclassified

“…Este índice se define como el cuadrado medio de la distancia entre la respuesta de un genotipo y el genotipo de máxima respuesta, en un ambiente determinado (Vargas et al, 2016); de tal manera, que los genotipos y las poblaciones con menores valores corresponden a los de mayor estabilidad fenotípica. Con ello se logra una adecuada identificación de genotipos estables para una producción agrícola sostenible, bajo diferentes condiciones ambientales (Mehta et al, 2011), a partir de la selección de aquellos que menos interacción evidencien con los ambientes en el que son cultivados (Koundinya et al, 2019;Eberhart y Russell, 1966).…”

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Estabilidad fenotípica de genotipos promisorios de berenjena (Solanum melongena L.) para la región Caribe de Colombia

et al. 2021

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El fenotipo es el efecto acumulativo del genotipo (G), del ambiente (A) y su interacción (G x A). El ambiente es el factor que afecta en mayor medida el fenotipo debido a que varía entre localidades, temporadas y estaciones del año, mientras que los efectos de los genotipos son constantes. El objetivo de este trabajo fue identificar genotipos de berenjena (Solanum melongena L.) altamente productivos y fenotípicamente estables, con frutos aceptables para los mercados de exportación y nacional, calificados, respectivamente, de primera y segunda categoría, a partir del análisis de estabilidad mediante el modelo AMMI y el índice de superioridad (Pi) de Lin y Binns. Para el efecto se utilizaron variables asociadas con rendimientos de 15 genotipos de berenjena evaluados en tres ambientes de la región Caribe de Colombia. Los resultados mostraron que: (1) El rendimiento de frutos de primera y segunda categoría fueron significativos (P < 0.001) para la interacción genotipo x ambiente, (2) los materiales Corpoica (C) C015, C027, C035, C036 y C049 lo fueron para el rendimiento de primera categoría y C014, C025, C032, C040 y C049 para el rendimiento de segunda categoría, ya que se ubicaron cerca al cero del CP1, por tanto contribuyen en menor medida a la interacción genotipo x ambiente y son los materiales con el comportamiento más estable; y (3) los genotipos más estables según el Pi para el rendimiento de primera categoría fueron el C006, C026, C027, C035 y C040; y para el de segunda categoría fueron C014, C035, C040, C042 y C049; los cuales, en su mayoría, coinciden con los genotipos más estables y de mayor rendimiento de acuerdo al análisis AMMI.

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“…In recent years, along with the widely application of the mathematical methods and models in fields of water-saving irrigation technology and water resources evaluation-such as grey correlative grade, fuzzy evaluation and clustering analysis, etc. [17][18][19][20] , the grey correlation model becomes a unique mathematical analysis method initiated by a famous scholar professor Deng [21] . In recent years, more and more reports on multi-program optimization based on grey correlation evaluation model have been published, and it has also been widely applied in agricultural field.…”

Section: Introductionmentioning

confidence: 99%

Water and nitrogen input optimization for jujube trees under surge root irrigation based on weighted grey correlation model

Dai

Fei

Yun

et al. 2019

International Journal of Agricultural and Biological Engineering

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In order to obtain the optimal input of water and nitrogen (W&N) suitable for the jujube trees in Northern Shaanxi, 12 treatments had been set in the demonstration base of micro-irrigation jujube tree of Mizhi County in 2014-2015 through field experiment of jujube trees under surge root irrigation (SRI), in which three soil moisture levels (high moisture level W1: 60%-85%θ, middle moisture level W2: 50%-75%θ, low moisture level W3: 40%-65%θ) and four nitrogen input levels (high nitrogen F75: 0.75 kg/plant, middle nitrogen F50: 0.50 kg/plant, low nitrogen F25: 0.25 kg/plant, no nitrogen F0: 0.00 kg/plant) were set. Through analyzing the effects of different W&N inputs on the yield, net income (NI) and W&N utilization efficiency (WUE and NUE) of jujube trees and establishing a weighted grey correlation model, the optimization of W&N input for jujube trees could be achieved under SRI. Results showed that different W&N inputs had significant influences on yield, NI, WUE and NUE. Along with the increase of soil moisture content, the yield and NUE increased, while WUE decreased gradually, and the NI had the tendency to increase first and then decrease. Along with the increase of nitrogen input, the yield, NI and WUE had the tendency to increase first and then decrease while the NUE decreased significantly, and the yield, NI, WUE and NUE could not reach the maximum at the same time. Through the study on the grey correlation model of jujube tree's comprehensive benefit based on the yield, NI, WUE and NUE, results showed the comprehensive benefit of jujube trees was significantly different when W&N input was different, and the gray correlation degree of W2F25 treatment was the highest in two years, which means that the soil moisture content of 50%-75%θ and nitrogen input of 0.25 kg/plant was the optimal W&N input for jujube trees under SRI in Northern Shaanxi area.

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Phenotypic stability of eggplant for yield and quality through AMMI, GGE and cluster analyses

Cited by 21 publications

References 16 publications

Mean Performance and Stability in Multi‐Environment Trials II: Selection Based on Multiple Traits

Mean Performance and Stability in Multi‐Environment Trials II: Selection Based on Multiple Traits

Estabilidad fenotípica de genotipos promisorios de berenjena (Solanum melongena L.) para la región Caribe de Colombia

Water and nitrogen input optimization for jujube trees under surge root irrigation based on weighted grey correlation model

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