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

  • Pablo Gonzalez-Barrios 1 UdelaR, Facultad de Agronomía, Dept. Biometría, Estadística y Cómputo. Av. Garzón 780, 12900 Montevideo, Uruguay. 2 University of Wisconsin at Madison, Agronomy Dept. 1575 Linden Dr., 53705 Madison, USA.
  • Marina Castro INIA, Estación Experimental La Estanzuela. Ruta 50 km 11, 70006 Colonia
  • Osvaldo Pérez INIA, Estación Experimental La Estanzuela. Ruta 50 km 11, 70006 Colonia
  • Diego Vilaró DuPont Pioneer. Av. Fulvio S. Pagani 47, 2434 Córdoba
  • Lucía Gutiérrez 1 UdelaR, Facultad de Agronomía, Dept. Biometría, Estadística y Cómputo. Av. Garzón 780, 12900 Montevideo, Uruguay 2 University of Wisconsin at Madison, Agronomy Dept. 1575 Linden Dr., 53705 Madison, USA
Keywords: genotype by environment interaction, multi-environment trials, sunflower, network efficiency, yield stability


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.


Download data is not yet available.


Andrade FH, 1995. Analysis of growth and yield of maize, sunflower and soybean grown at Balcarce, Argentina. Field Crop Res 41: 1-12.

Annicchiarico P, 2002. Genotype × environment interactions: Challenges and opportunities for plant breeding and cultivar recommendations. FAO Plant Prod & Protect Paper No. 174. FAO, Rome.

Arief VN, DeLacy IH, Crossa J, Payne T, Singh R, Braun HJ, Tian T, Basford KE, Dieters MJ, 2015. Evaluating testing strategies for plant breeding field trials: Redesigning a CIMMYT International Wheat Nursery. Crop Sci 55: 164-177.

Balalić I, Zorić M, Branković G, Terzić S, Crnobarac J, 2012. Interpretation of hybrid × sowing date interaction for oil content and oil yield in sunflower. Field Crops Res 137: 70-77.

Bange MP, Hammer GL, Rickert KG, 1997. Environmental control of potential yield of sunflower in the subtropics. Aust J Agric Res 48: 231-240.

Beard BH, Geng S, 1982. Interrelationship of morphological and economic characters of sunflower. Crop Sci 22: 817-822.

Ceretta S, van Eeuwijk F, 2008. Grain yield variation in malting barley cultivars in Uruguay and its consequences for the design of a trials network. Crop Sci 48: 167-180.

Chapman SC, Crossa J, Edmeades GO, 1997. Genotype by environment effects and selection for drought tolerance in tropical maize. I. Two-mode pattern analysis of yield. Euphytica 95:1-9.

Chapman SC, Cooper M, Butler D, Henzell R, 2000. Genotype by environment interactions affecting grain sorghum. I. Characteristics that confound interpretation of hybrid yield. Aust J Agric Res 51: 197-207.

Connor DJ, Hall AJ, 1997. Sunflower physiology. In: Sunflower Technology and Production; Schneiter AA (ed.). Agron Monogr 35, pp. 113-182. ASA, CSSA & SSSA, Madison, WI, USA.

Cooper M, Delacy IH, 1994. Relationship among analytical methods used to study genotypic variation en genotype by environment interaction in plant breeding multi-environment experiments. Theor Appl Genet 88: 561-572.

Cooper M, Byth DE, 1996. Understanding plant adaptation to achieve systematic applied crop improvement—A fundamental challenge. In: Plant adaptation and crop improvement; Cooper M & Hammer GL (ed.), pp: 5-23. CAB Int., Wallingford, UK.

Cooper M, Rajatasereekul S, Fukai S, Basnayaky L, 1999. Rainfed lowland rice breeding strategies for Northeast Thailand. I: Genotypic variation and genotype x environment interactions for grain yield. Field Crops Res 64: 131-151.

Craufurd P, Wheeler T, 2009. Climate change and the flowering time of annual crops. J Exp Bot 60 (9): 2529-2539.

Cullis BR, Thomson FM, Fisher JA, Gilmour AR, Thompson R. 1996. Analysis of the NSW wheat variety database. II. Variance component estimation. Theor Appl Genet 92: 28-39.

de la Vega AJ, Chapman SC, Hall AJ, 2001. Genotype by environment interaction and indirect selection for yield in sunflower. I. Two-mode pattern analysis of oil and biomass yield across environments in Argentina. Field Crop Res 72: 17-38.

de la Vega AJ, Hall A, 2002. Effects of planting date, genotype, and their interactions on sunflower yield: I. determinants of oil-corrected grain yield. Crop Sci 42: 1991-1201.

de la Vega AJ, DeLacy IH, Chapman SC, 2007. Progress over 20 years of sunflower breeding in central Argentina. Field Crops Res 100: 61-72.

de León N, Jannink JL, Edwards JW, Kaeppler S, 2016. Introduction to a special issue on genotype by environment interaction. Crop Sci 56 (5): 2081-2089.

Easterling DR, Meehl GA, Parmesan C, Changnon SA, Karl TR, Mearns LO, 2000. Climate extremes: Observations, modeling, and impacts. Science 289: 2068-2074.

Fernandez-Martinez JM, Pérez-Vich B, Velasco L, 2010. Sunflower. In: Oil Crops; Vollmann J, & Rajcan I (eds.), pp: 155-233. Springer, NY.

Finlay KW, Wilkinson GN, 1963. The analysis of adaptation in a plant breeding programme. Aust J Agric Res 14: 742-754.

Fischer RA, Edmeades GO, 2010. Breeding and cereal yield progress. Crop Sci 50: 85-98.

Foucteau A, El Daouk M, Baril C, 2001. Interpretation of genotype by environment interaction in two sunflower experimental networks. Theor Appl Genet 102: 327-334.

Gabriel KR, 1971. The biplot graphic display of matrices with applications to principal components analysis. Biometrika 58: 453-467.

Hall AJ, Chimenti CA, Vilella F, Freier G, 1985. Timing of water stress effects on yield components in sunflower. Proc. 11th Int. Sunflower Conj, Int Sunflower Assoc, Mar del Plata, pp: 131-36.

Hanson CE, Palutikof JP, Livermore MTJ, Barring L, Bindi M, Corte-Real J, Durao R, Giannakopoulos C, Good P, Holt T, et al., 2007. Modelling the impact of climate extremes: an overview of the MICE Project. Climate Change 81: 163-177.

IBP Breeding View, 2015. The IBP Breeding Management System Version 3.0.9 (December 2015). The integrated Breeding Platform.

Izquierdo NG, Aguirrezabal LA, Andrade FH, Geroudet C, Valentinuz O, Pereyra Iraola M, 2009. Intercepted solar radiation affects oil fatty acid composition in crop species. Field Crops Res 114: 66-74.

Jambunathan R, Raju M, Barde SP, 1985. Analysis of oil content of groundnuts by nuclear magnetic resonance spectrometry. J Sci Food Agric 36 (3): 162-166.

Lado B, González Barrios P, Quincke M, Silva P, Gutiérrez L, 2016. Modeling genotype by environment interaction for genomic selection with unbalanced data from a wheat breeding program. Crop Sci 56: 1-15.

Leon AJ, Andrade FH, Lee M, 2003. Genetic analysis of seed-oil concentration across generations and environments in sunflower. Crop Sci 43: 135-140.

Patterson HD, Silvey V, Talbot M, Weatherup STC, 1977. Variability of yields of cereal varieties in U.K. trials. J Agric Sci 89: 238-245.

Piepho HP, Buchse A, Truberg B, 2006. On the use of multiple lattice designs and α-designs in plant breeding trials. Plant Breeding 125 (5): 523-528.

Qiao CG, Basford KE, DeLacy IH, Cooper M, 2000. Evaluation of experimental designs and spatial analyses in wheat breeding trials. Theor Appl Genet 100 (1): 9-16.

Rondanini D, Savin R, Hall AJ, 2003. Dynamics of fruit growth and oil quality of sunflower (Helianthus annuus L.) exposed to brief intervals of high temperature during grain-filling. Field Crops Res 83: 71-90.

Rondanini D, Mantese A, Savin R, Hall AJ, 2006. Response of sunflower yield and grain quality to alternating day/night high temperature regimes during grain filling: effects of timing, duration and intensity of exposure to stress. Field Crops Res 96: 48-62.

Rose LW, Das MK, Taliaferro CM, 2008. A comparison of dry matter yield stability assessment methods for small numbers of genotypes of Bermuda grass. Euphytica 164: 19-25.

Sadras VO, Reynolds M, de la Vega AJ, Petrie P, Robinson R, 2009. Phenotypic plasticity of yield and phenology in wheat, sunflower and grapevine. Field Crops Res 110: 242-250.

SAS Institute Inc., 2011. SAS system for Windows v. 9.3. Cary, NC, USA.

Schneiter AA, Miller JF, 1981. Description of sunflower growth stages. Crop Sci 21: 901-903.

Smith A, Cullis B, Thompson R, 2001. Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trends. Biometrics 57: 1138-1147.

Talbot M, 1984. Yield variability of crop varieties in the U.K. J Agric Sci 102: 315-321.

van Eeuwijk FA, Denis JB, Kang MS, 1996. Incorporating additional information on genotypes and environments in models for two-way genotype by environment tables. In: Genotype-by-environment interaction: New perspectives; Kang MS & Gauch HG, Jr. (eds.), pp: 15-49. CRC Press, Boca Raton, FL, USA.

Vargas M, Crossa J, 2000. El análisis AMMI y la gráfica del biplot en SAS. 2000. 42 pp. CIMMYT, Mexico DF.

Vargas M, Crossa J, van Eeuwijk F, Sayre K, Reynolds MP, 2001. Interpreting treatment x environment interaction in agronomy trials. Agron J 93: 949-960.

Williams ER, Matheson AC, Harwood CE, 2002. Experimental design and analysis for tree improvement, 2nd edn. CSIRO, Canberra.

Wold S, Sjostrom M, Eriksson L, 2001. PLS-regression: a basic tool of chemometrics. 58: 109-130.

Yan W, Cornelius PL, Crossa J, Hunt LA, 2001. Two types of GGE biplots for analyzing multi-environment trial data. Crop Sci 41: 656-663.

Yan W, Kang MS, 2003. GGE biplot analysis: A graphical tool for breeders, geneticists and agronomists. CRC Press, Boca Raton, FL, USA.

Yau SK, 1997. Efficiency of alpha-lattice designs in international variety yield trials of barley and wheat. J Agric Sci 128: 5-9.

Zegada-Lizarazu W, Monti A, 2011. Energy crops in rotation. A review. Biomass Bioenerg 35: 12-25.

How to Cite
Gonzalez-Barrios, P., Castro, M., Pérez, O., Vilaró, D., & Gutiérrez, L. (2018). Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency. Spanish Journal of Agricultural Research, 15(4), e0705.
Plant breeding, genetics and genetic resources