Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

Spanish Journal of Agricultural Research

Osvaldo Pérez¹  Marina Castro²  Diego Vilaró³  Pablo Gonzalez-Barrios⁴ 

[1] DuPont Pioneer. Av. Fulvio S. Pagani 47, 2434 Córdoba;INIA, Estación Experimental La Estanzuela. Ruta 50 km 11, 70006 Colonia;UdelaR, Facultad de Agronomía, Dept. Biometría, Estadística y Cómputo. Av. Garzón 780, 12900 Montevideo, Uruguay University of Wisconsin at Madison, Agronomy Dept. 1575 Linden Dr., 53705 Madison, USA;UdelaR, Facultad de Agronomía, Dept. Biometría, Estadística y Cómputo. Av. Garzón 780, 12900 Montevideo, Uruguay.University of Wisconsin at Madison, Agronomy Dept. 1575 Linden Dr., 53705 Madison, USA.

关键词: genotype by environment interaction;    multi-environment trials;    sunflower;    network efficiency;    yield stability;   

DOI  :  10.5424/sjar/2017154-11016

学科分类：农业科学（综合）

来源: I N I A

【 摘 要 】

Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower ( Helianthus annuus L. ). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage.  An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars.

【 授权许可】

Free