Genotype by environment interaction and indirect selection for yield in sunflower: II. Three-mode principal component analysis of oil and biomass yield across environments in Argentina |
| |
Affiliation: | 1. Advanta Semillas S.A.I.C., Ruta Nac. 33 Km 636, CC 294, 2600 Venado Tuerto, Argentina;2. CSIRO Plant Industry, Long Pocket Laboratories, 120 Meiers Road, Indooroopilly, Qld 4068, Australia;1. G. A. Razuvaev Institute of Organometallic Chemistry of RAS, 49 Tropinina str., GSP-445, 603950 Nizhny Novgorod, Russia;2. International Tomography Center, SB RAS, Institutskaya str., 3a, Novosibirsk 630090, Russia;3. Institute of Physical and Organic Chemistry at Southern Federal University, Stachka Avenue 194/2, 344090 Rostov-on-Don, Russia;4. Astrakhan State Technical University, Tatisheva str. 16, Astrakhan 414025, Russia;1. ChemNanoBio Research Laboratory, Department of Chemistry, TATA College, Chaibasa, 833202, India;2. Centro de Nanociencia y Nanotecnologia, Universidad de las Fuerzas Armadas ESPE, Av. Gral. Rumiñahui s/n, Sangolqui, P.O. BOX, 171 5 231B, Ecuador;1. Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Universidad de la República, Montevideo 780, Av. E. Garzón, Uruguay;2. Departamento de Morfología y Desarrollo, Facultad de Veterinaria, Universidad de la República, Montevideo 1620, A. Lasplaces, Uruguay;1. Department of Biochemistry, Universidade Federal de Santa Maria (UFSM), Santa Maria, RS, Brazil;2. Laboratory of Veterinary Pathology, Instituto Federal Catarinense (IFC), Concórdia, SC, Brazil;3. Laboratory of Clinical Biochemistry, Department of Clinical and Toxicological Analysis, Center of Health Sciences, UFSM, Santa Maria, RS, Brazil;4. Laboratory of General Biochemistry and Microorganisms, Center of Biological and Health Sciences, Federal University of Mato Grosso do Sul (UFMS), Campo Grande, MS, Brazil;5. Department of Animal Science, Universidade do Estado de Santa Catarina, Chapecó, SC, Brazil;1. Graduate Program in Veterinary Medicine, Department of Small Animal, Veterinary Hospital, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil;2. Department of Animal Science, Universidade do Estado de Santa Catarina, Chapecó, SC, Brazil;3. Graduate Program in Veterinary Medicine, Department of Microbiology and Parasitology, Prédio 20, Universidade Federal de Santa Maria, Santa Maria , RS, Brazil;4. Graduate Program in Biological Sciences: Toxicological Biochemistry, Division of Biochemistry and Oxidative Stress, Laboratory of Cell Therapy, Centro de Ciências Rurais, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil;5. Graduate Program in Biological Sciences: Toxicological Biochemistry, Laboratory of Toxicology Enzymology, Department of Chemistry, Centro de Ciências Naturais e Exatas; Universidade Federal de Santa Maria, Santa Maria, RS, Brazil;6. Instituto de Pesquisas Veterinárias Desidério Finamor, Fundação Estadual de Pesquisa Agropecuária (FEPAGRO), Eldorado do Sul, RS , Brazil;7. Centre for Biological Sciences, Department of Epidemiology and Microbiology, Health Sciences, Universidade Federal do Piauí, Campus Senador Helvídio Nunes de Barros,Picos, PI , 64600-000, Brazil;1. Unité de Chimie Environnementale et Interactions sur le Vivant (UCEIV), Université du Littoral Côte d''Opale (ULCO), 50 Rue Ferdinand Buisson, BP 699, 62228 Calais Cedex, France;2. Laboratoire de Biologie Végétale et Environnement, Université Badji Mokhtar, BP 12, 23000 Annaba, Algeria |
| |
Abstract: | The genotype by environment (G×E) interactions observed for sunflower oil yield in different regions of Argentina can be analyzed in terms of differences among genotypes in individual environments for its components grain number, grain weight, and oil content (yield analysis). Similarly, G×E interactions observed for oil-corrected grain yield can be analyzed in terms of its determinants total biomass and harvest index (physiological analysis). Three-mode (genotypes×environments×attributes) principal component analysis was applied to 10×21×4 and 10×11×3 matrices, for each of the first and the second analyses, respectively, to collectively interpret the changes in these attributes in a sunflower genotype–environment system, and to assess the relative importance of each trait as underlying determinant of the observed G×E interaction for oil yield. The 3×2×3 and 4×4×2 (genotypes×environments×attributes) principal component models explained about 65% of the variation computed for first and second approaches, respectively.For the yield analysis, the first environment component (54% of the variation) explained the common pattern of oil yield over environments and showed that oil content was highly positively correlated to oil yield, while grain number and grain weight showed lack of association with oil yield and were negatively correlated. The second environment component (11% of the variation) contrasted northern and central environments and showed that grain number is the main underlying determinant of the observed G×E interactions between these two mega-environments for oil yield.In the physiological analysis, the first environment component (29% of the variation) explained the common pattern of oil-corrected grain yield over environments and showed that harvest index was more strongly positively correlated to oil-corrected grain yield, but not to total oil-corrected biomass. The second environmental component (19% of the variation) contrasted northern and central environments and showed that oil-corrected biomass is the physiological attribute that is largely responsible for the G×E interactions for oil-corrected grain yield. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|