Genetic and genotype × environment interaction effects for the content of seven essential amino acids inIndica rice

2004 ◽  
Vol 83 (2) ◽  
pp. 171-178 ◽  
Author(s):  
J. G. Wu ◽  
C. H. Shi ◽  
X. M. Zhang ◽  
T. Katsura
2019 ◽  
Vol 62 (1) ◽  
Author(s):  
Eun-Ha Kim ◽  
So-Young Lee ◽  
Da-Young Baek ◽  
Soo-Yun Park ◽  
Sang-Gu Lee ◽  
...  

Abstract Red peppers are a remarkable source of nutrients in the human diet. However, comprehensive studies have not reported on the effects of genotype, cultivation region, and year on pepper fruit characteristics. To address this, 12 commercial pepper varieties were grown at two locations in South Korea, during 2016 and 2017, representing four environments, and concentrations of proximate, minerals, amino acids, fatty acids, capsaicinoids, and free sugars in pepper pericarps were determined. Variation in most nutrients was observed among the 12 varieties grown within each location in each year, indicating a significant genotype effect. Statistical analysis of combined data showed significant differences among varieties, locations, and years for the measured components. The % variability analysis demonstrated that environment (location and year) and genotype-environment interaction contributed more to the nutritional contents than genotype alone. Particularly, variation in many amino acids, capsaicinoids, free sugars, and myristic acid was attributed to location. Year effect was significant for palmitoleic acid, ash, tryptophan, copper, linolenic acid, crude fiber, and tyrosine. Insoluble dietary fiber, soluble dietary fiber, sodium, sulfate, linoleic acid, and alanine were primarily varied by genotype–environment interaction. Palmitic acid was the trait the most highly affected by genotype. Cultivation and the genotype–environment interaction have a major role in determining the composition of 12 pepper varieties across four environments. The data from this study could explain the natural variation in the compositional data of peppers by genotypes and environments.


2001 ◽  
Vol 137 (3) ◽  
pp. 329-336 ◽  
Author(s):  
M. A. IBAÑEZ ◽  
M. A. DI RENZO ◽  
S. S. SAMAME ◽  
N. C. BONAMICO ◽  
M. M. POVERENE

Genotype–environment interaction and yield stability were evaluated for 19 genotypes of lovegrass (Eragrostis curvula). The study was conducted in the central semi-arid region of Argentina. Three locations and two growing seasons in combination generated six environments. Genotypic responses and stability of yield under variable environments were investigated. The genotype–environment interaction was analysed by three methods: regression analysis, AMMI and principal coordinates analysis (PCO). Analysis of variance showed that effects of genotype, environment and genotype–environment interaction were highly significant (P < 0·01). The genotypes accounted for 20% of the treatment sum of squares, with environment responsible for 65% and interaction for 14·5%. The biplot indicated that there was partial agreement between the AMMI and regression model. However the scatter point diagrams obtained from PCO analysis revealed only limited agreement with the results obtained by the regression analysis and the AMMI model. The results show that the AMMI model as a whole explained twice as much of the interaction sum of squares as did regression analysis and was more adequate than PCO analysis in quantifying environment and genotype effects for forage yield. AMMI analysis of the genotype–environment interaction effects showed that there were responses characteristic of a particular location. This type of association implies some predictability of genotype–environment interaction effects on forage yield production when differential responses across genotypes are associated with locations. Environmental factors may contribute to the interpretations of genotype–environment interaction. However in the semi-arid region, where fluctuations in growing conditions are unpredictable, additional research is required to obtain an integration of interaction analysis with external environmental (or genotypic) variables.


Crop Science ◽  
2008 ◽  
Vol 48 (1) ◽  
pp. 317-330 ◽  
Author(s):  
Kraig L. Roozeboom ◽  
William T. Schapaugh ◽  
Mitchell R. Tuinstra ◽  
Richard L. Vanderlip ◽  
George A. Milliken

2014 ◽  
Vol 2 (5) ◽  
pp. 329-337 ◽  
Author(s):  
Robooni Tumuhimbise ◽  
Rob Melis ◽  
Paul Shanahan ◽  
Robert Kawuki

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