Research Article
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EVALUATION OF SUNFLOWER HYBRIDS IN MULTI-ENVIRONMENT TRIAL (MET)

Year 2019, Volume: 24 Issue: 2, 202 - 210, 15.12.2019
https://doi.org/10.17557/tjfc.645276

Abstract

Sunflower has been proposed as a potential crop model for an adaptation to a changing environment and
special attention should be paid to testing hybrids under different environments. Additive main effects and
multiplicative interaction model (AMMI) supplemented with genotype main effects and genotype by
environment interaction effects (GGE) were used for dissection of genotype by environment interaction and
evaluation of hybrids and testing environments. The research included 24 sunflower hybrids grown across
twelve environments. AMMI analysis identified four significant interaction principal components (IPC), while
in GGE biplot the first two IPCs accounted together for 44.59%. Environmental factors contributed the
largest proportion in the total variation of seed yield (67.40%), followed by interaction and genotypes. High
yielding hybrids H1, H14 and H11 showed specific adaptation to environments E10 and E1, respectively. The
average environment coordination (AEC) view of GGE biplot indicated H17 as the most desirable genotype
regarding seed yield. From the results of this study it can be concluded that MET trials are important not just
for evaluation of stability and choosing the most stable genotypes, but also the genotypes that will perform well
in low yielding environments and be able to take advantage of the favourable environmental conditions. 

Supporting Institution

Ministry of Education, Science and Technological Development of the Republic of Serbia

Project Number

TR-31025 /114-451-2126/2016-03 / 451-03- 01732/2017-09/3

Thanks

This research is part of the project TR-31025, funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, provincial Secretariat for Higher Education and Science of Vojvodina, project 114-451-2126/2016-03 as well as a bilateral project between Serbia and Germany 451-03- 01732/2017-09/3 and COST Action CA 16212. Special thanks to Mr. Ilija Radeka and his team for efforts in carrying out the experiment.

References

  • Branković, G., I. Balalić, M. Zorić, V. Miklič, S. Jocić and G. Šurlan-Momirović. 2012. Characterization of sunflower testing environments in Serbia. Turkish Journal of Agriculture and Forestry 36 (3): 275-283.
  • Casadebaig, P., E. Mestries and P. Debakey. 2016. A modelbased approach to assist variety evaluation in sunflower crop. European Journal of Agronomy 81: 92-105.
  • Ceccareli, S. 1994. Specific adaptation and breeding for marginal conditions. Euphytica 77: 205-219.
  • da Silveira, L.C.I., V. Kist, T.O.M. de Paula, M.H.P. Barbosa, L.A. Peternelli and E. Daros. 2013. Ammi analysis to evaluate the adaptability and phenotypic stability of sugarcane genotypes. Scientia Agricola 70 (1): 27-32.
  • De La Vega, A.J. and S.C. Chapman. 2001. Genotype by environment interaction and indirect selection for seed yield in sunflower, II. Three-mode principal component analysis of oil and biomass yield across environments in Argentina. Field Crops Research 72 (1): 39-50.
  • De La Vega, A.J. and S.C. Chapman. 2006. Defining sunflower selection strategies for a highly heterogeneous target population of environments. Crop Sci. 46 (1): 136-144.
  • De La Vega, A.J. and S.C. Chapman. 2010. Mega-environment differences affecting genetic progress for yield and relative value of component traits. Crop Sci. 50 (2): 574-583.
  • Del Gatto, A., C. Mengarelli, E. Foppa Pedretti, D. Duca, S. Pieri, L. Mangoni, M. Signor, S.A. Raccuia and M.L. Melilli. 2015. Adaptability of sunflower (Helianthus annuus L.) high oleic hybrids to different Italian areas for biodiesel production. Industrial Crops and Products, 75A: 108-117.
  • FAO. 2019: http://www.fao.org/faostat/en/#data/QC(accessed 14.04.2019)
  • Fox, P.N., J. Crossa and I. Ramagossa. 1997. Multienvironmental testing and genotype x environment interaction. In: Statistical Methods for Plant Variety Evaluation, ed. Kempton, R.A. and Fox, P.N., 117-138, Chapman & Hall, London.
  • Gauch, H.G. and R.W. Zobel. 1997. Identifying megaenvironments and targeting genotypes. Crop Sci. 37 (2): 311- 326.
  • Gauch, H.G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46 (4): 1488-1500.
  • Gauch, H.G. 2013. A Simple Protocol for AMMI Analysis of Yield Trials. Crop Sci. 53 (5): 1860-1869.
  • Hassani, M., B. Heidari, A. Dadkhodaie and P. Stevanato. 2018. Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.). Euphytica 214 (4): 79.
  • Hristov, N., N. Mladenov, V. Đurić, A. Kondic-Spika, A. Marjanović-Jeromela and D. Simić. 2010. Genotype by environment interactions in wheat quality breeding programs in southeast Europe. Euphytica 174: 315–324.
  • Jandong, E.A., M.I. Uguru and B.C. Oyiga. 2011. Determination of stability of Soybean (Glycine max) genotypes across diverse soil pH levels using GGE Biplot analysis. Journal of applied Biosciences 43: 2924-2941.
  • Kaya, Y. 2014. Sunflower Production in Balkan Region: Current Situation and Future prospects. Agriculture and Forestry 60 (4): 95-101.
  • Marinković, R., M. Jocković, A. Marjanović-Jeromela, S. Jocić, M. Ćirić, I. Balalić and Z. Sakač. 2011. Genotype by environment interactions for seed yield and oil content in sunflower (H. annuus L.) using AMMI model. Helia 34(54):79-88.
  • Marjanović-Jeromela, A., R. Marinković, A. Mijić, M. Jankulovska, Z. Zdunić and N. Nagl. 2008. Oil Yield Stability of Winter Rapeseed (Brassica napus L.) Genotypes. Agriculturae Conspectus Scientificus 73 (4): 217-220.
  • Marjanović-Jeromela, A., N. Nagl, J. Gvozdanović-Varga, N. Hristov, A. Kondić-Špika, M. Vasić and R. Marinković. 2011. Genotype by environment interaction for seed yield per plant in rapeseed using AMMI model. Pesquisa Agropecuária Brasileira 46 (2): 174-181.
  • Mijić, A., I. Liović, A. Sudarić, D. Gadžo, Z. Jovović, M. Jankulovska, A. Markulj-Kulundžić and T. Duvnjak. 2017. The effect of environment on the phenotypic expression of grain yield, oil content and oil yield in sunflower hybrids. Agriculture and Forestry 63 (1): 309-318.
  • Moghaddam, M.J. and S.S. Pourdad. 2011. Genotype x environment interactions and simultaneous selection for high oil yield and stability in rainfed warm areas rapeseed (Brassica napus L.) from Iran. Euphytica 180 (3): 321–335.
  • Mohammadi, R. and A. Amri. 2013. Genotype x environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192 (2): 227-249.
  • Nowosad, K., A. Liersch, W. Poplawska and J. Bocianowski. 2016. Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica 208 (1): 187-194.
  • Oliveira, E.J., J.P.X. Freitas and O.N. Jesus. 2014. AMMI analysis of the adaptability and yield stability of yellow passion fruit varieties. Scientia Agricola 71 (2): 139-145.
  • Piepho, H.P. 1998. Methods for comparing the yield stability of cropping system – a review. Journal of Agronomy and Crop Science 190: 193-21.
  • Radanović, A., D. Miladinović, S. Cvejić, M. Jocković and S. Jocić. 2018. Sunflower Genetics from Ancestors to Modern Hybrids - a review. Genes 9 (11): 1-19.
  • Romay, M.C., R.A. Malvar, L. Campo, A. Alvarez, J. MorenoGonzales, A. Ordas and P. Revilla. 2010. Climatic and Genotypic Effects for Grain Yield in Maize under Stress Conditions. Crop Sci. 50 (1): 51-58.
  • Seiler, G. and C.C. Jan. 2010. Basic information. In: Genetics, genomics and breeding of sunflower, ed. Hu, J. Seiler, G. and Kole, C., 1-40, Enfield, New Hampshire, USA, CRC Press.
  • Solonechnyi, P., N. Vasko, A. Naumov, O. Solonechnaya, O. Vazhenina, O. Bondareva and Y. Logvinenko. 2015. GGE biplot analysis of genotype by environment interaction of spring barley varieties. Zemdirbyste-Agriculture 102 (4): 431-436.
  • Stojaković, M., B. Mitrović, M. Zorić, M. Ivanović, D. Stanisavljević, A. Nastasić and D. Dodig. 2015. Grouping pattern of maize test locations and its impact on hybrid zoning. Euphytica 204 (2): 419-431.
  • Temesgen, A., K. Mammo and L. Dagnachew. 2014. Genotype by Environment Interaction (G x E) and Grain Yield Stability Analysis of Ethiopian Linseed and Niger Seed Varieties. Journal of Applied Biosciences 80: 7093-7101.
  • Yan, W. 2001. GGE biplot: a windows application for graphical analysis of multi environment trial data and other types of two-way data. Agron J. 93: 1–11.
  • Yan, W. and C. Rajcan. 2002. Biplot analysis of test sites and trait relations of Soybean in Ontario. Crop Sci. 42: 11–20.
  • Yan, W. and M.S. Kang. 2003. GGE biplot analysis: A graphical tool for breeders, geneticists and agronomists. CRC Press, Boca Raton, FL, USA, p. 1-271.
  • Yan, W. and N.A. Tinker. 2006. Biplot analysis of multienvironment trial data principles and application. Canadian Journal of Plant Science, 86(3): 623–645.
  • Yan, W., M.S. Kang, B. Ma, S. Woods and P.L. Cornelius. 2007. GGE Biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47: 643–655.
  • Yan, W. 2014. Crop variety trials: data management and analysis. Wiley-Blackwell, New York, 360 p.
  • Zobel, R.W., M.J. Wright and H.G. Gauch. 1988. Statistical analysis of yield trial. Agron J. 80: 388–393.
Year 2019, Volume: 24 Issue: 2, 202 - 210, 15.12.2019
https://doi.org/10.17557/tjfc.645276

Abstract

Project Number

TR-31025 /114-451-2126/2016-03 / 451-03- 01732/2017-09/3

References

  • Branković, G., I. Balalić, M. Zorić, V. Miklič, S. Jocić and G. Šurlan-Momirović. 2012. Characterization of sunflower testing environments in Serbia. Turkish Journal of Agriculture and Forestry 36 (3): 275-283.
  • Casadebaig, P., E. Mestries and P. Debakey. 2016. A modelbased approach to assist variety evaluation in sunflower crop. European Journal of Agronomy 81: 92-105.
  • Ceccareli, S. 1994. Specific adaptation and breeding for marginal conditions. Euphytica 77: 205-219.
  • da Silveira, L.C.I., V. Kist, T.O.M. de Paula, M.H.P. Barbosa, L.A. Peternelli and E. Daros. 2013. Ammi analysis to evaluate the adaptability and phenotypic stability of sugarcane genotypes. Scientia Agricola 70 (1): 27-32.
  • De La Vega, A.J. and S.C. Chapman. 2001. Genotype by environment interaction and indirect selection for seed yield in sunflower, II. Three-mode principal component analysis of oil and biomass yield across environments in Argentina. Field Crops Research 72 (1): 39-50.
  • De La Vega, A.J. and S.C. Chapman. 2006. Defining sunflower selection strategies for a highly heterogeneous target population of environments. Crop Sci. 46 (1): 136-144.
  • De La Vega, A.J. and S.C. Chapman. 2010. Mega-environment differences affecting genetic progress for yield and relative value of component traits. Crop Sci. 50 (2): 574-583.
  • Del Gatto, A., C. Mengarelli, E. Foppa Pedretti, D. Duca, S. Pieri, L. Mangoni, M. Signor, S.A. Raccuia and M.L. Melilli. 2015. Adaptability of sunflower (Helianthus annuus L.) high oleic hybrids to different Italian areas for biodiesel production. Industrial Crops and Products, 75A: 108-117.
  • FAO. 2019: http://www.fao.org/faostat/en/#data/QC(accessed 14.04.2019)
  • Fox, P.N., J. Crossa and I. Ramagossa. 1997. Multienvironmental testing and genotype x environment interaction. In: Statistical Methods for Plant Variety Evaluation, ed. Kempton, R.A. and Fox, P.N., 117-138, Chapman & Hall, London.
  • Gauch, H.G. and R.W. Zobel. 1997. Identifying megaenvironments and targeting genotypes. Crop Sci. 37 (2): 311- 326.
  • Gauch, H.G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46 (4): 1488-1500.
  • Gauch, H.G. 2013. A Simple Protocol for AMMI Analysis of Yield Trials. Crop Sci. 53 (5): 1860-1869.
  • Hassani, M., B. Heidari, A. Dadkhodaie and P. Stevanato. 2018. Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.). Euphytica 214 (4): 79.
  • Hristov, N., N. Mladenov, V. Đurić, A. Kondic-Spika, A. Marjanović-Jeromela and D. Simić. 2010. Genotype by environment interactions in wheat quality breeding programs in southeast Europe. Euphytica 174: 315–324.
  • Jandong, E.A., M.I. Uguru and B.C. Oyiga. 2011. Determination of stability of Soybean (Glycine max) genotypes across diverse soil pH levels using GGE Biplot analysis. Journal of applied Biosciences 43: 2924-2941.
  • Kaya, Y. 2014. Sunflower Production in Balkan Region: Current Situation and Future prospects. Agriculture and Forestry 60 (4): 95-101.
  • Marinković, R., M. Jocković, A. Marjanović-Jeromela, S. Jocić, M. Ćirić, I. Balalić and Z. Sakač. 2011. Genotype by environment interactions for seed yield and oil content in sunflower (H. annuus L.) using AMMI model. Helia 34(54):79-88.
  • Marjanović-Jeromela, A., R. Marinković, A. Mijić, M. Jankulovska, Z. Zdunić and N. Nagl. 2008. Oil Yield Stability of Winter Rapeseed (Brassica napus L.) Genotypes. Agriculturae Conspectus Scientificus 73 (4): 217-220.
  • Marjanović-Jeromela, A., N. Nagl, J. Gvozdanović-Varga, N. Hristov, A. Kondić-Špika, M. Vasić and R. Marinković. 2011. Genotype by environment interaction for seed yield per plant in rapeseed using AMMI model. Pesquisa Agropecuária Brasileira 46 (2): 174-181.
  • Mijić, A., I. Liović, A. Sudarić, D. Gadžo, Z. Jovović, M. Jankulovska, A. Markulj-Kulundžić and T. Duvnjak. 2017. The effect of environment on the phenotypic expression of grain yield, oil content and oil yield in sunflower hybrids. Agriculture and Forestry 63 (1): 309-318.
  • Moghaddam, M.J. and S.S. Pourdad. 2011. Genotype x environment interactions and simultaneous selection for high oil yield and stability in rainfed warm areas rapeseed (Brassica napus L.) from Iran. Euphytica 180 (3): 321–335.
  • Mohammadi, R. and A. Amri. 2013. Genotype x environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192 (2): 227-249.
  • Nowosad, K., A. Liersch, W. Poplawska and J. Bocianowski. 2016. Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica 208 (1): 187-194.
  • Oliveira, E.J., J.P.X. Freitas and O.N. Jesus. 2014. AMMI analysis of the adaptability and yield stability of yellow passion fruit varieties. Scientia Agricola 71 (2): 139-145.
  • Piepho, H.P. 1998. Methods for comparing the yield stability of cropping system – a review. Journal of Agronomy and Crop Science 190: 193-21.
  • Radanović, A., D. Miladinović, S. Cvejić, M. Jocković and S. Jocić. 2018. Sunflower Genetics from Ancestors to Modern Hybrids - a review. Genes 9 (11): 1-19.
  • Romay, M.C., R.A. Malvar, L. Campo, A. Alvarez, J. MorenoGonzales, A. Ordas and P. Revilla. 2010. Climatic and Genotypic Effects for Grain Yield in Maize under Stress Conditions. Crop Sci. 50 (1): 51-58.
  • Seiler, G. and C.C. Jan. 2010. Basic information. In: Genetics, genomics and breeding of sunflower, ed. Hu, J. Seiler, G. and Kole, C., 1-40, Enfield, New Hampshire, USA, CRC Press.
  • Solonechnyi, P., N. Vasko, A. Naumov, O. Solonechnaya, O. Vazhenina, O. Bondareva and Y. Logvinenko. 2015. GGE biplot analysis of genotype by environment interaction of spring barley varieties. Zemdirbyste-Agriculture 102 (4): 431-436.
  • Stojaković, M., B. Mitrović, M. Zorić, M. Ivanović, D. Stanisavljević, A. Nastasić and D. Dodig. 2015. Grouping pattern of maize test locations and its impact on hybrid zoning. Euphytica 204 (2): 419-431.
  • Temesgen, A., K. Mammo and L. Dagnachew. 2014. Genotype by Environment Interaction (G x E) and Grain Yield Stability Analysis of Ethiopian Linseed and Niger Seed Varieties. Journal of Applied Biosciences 80: 7093-7101.
  • Yan, W. 2001. GGE biplot: a windows application for graphical analysis of multi environment trial data and other types of two-way data. Agron J. 93: 1–11.
  • Yan, W. and C. Rajcan. 2002. Biplot analysis of test sites and trait relations of Soybean in Ontario. Crop Sci. 42: 11–20.
  • Yan, W. and M.S. Kang. 2003. GGE biplot analysis: A graphical tool for breeders, geneticists and agronomists. CRC Press, Boca Raton, FL, USA, p. 1-271.
  • Yan, W. and N.A. Tinker. 2006. Biplot analysis of multienvironment trial data principles and application. Canadian Journal of Plant Science, 86(3): 623–645.
  • Yan, W., M.S. Kang, B. Ma, S. Woods and P.L. Cornelius. 2007. GGE Biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47: 643–655.
  • Yan, W. 2014. Crop variety trials: data management and analysis. Wiley-Blackwell, New York, 360 p.
  • Zobel, R.W., M.J. Wright and H.G. Gauch. 1988. Statistical analysis of yield trial. Agron J. 80: 388–393.
There are 39 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Milan Jockovıć This is me

Sandra Cvejıć This is me

Siniša Jocıć This is me

Ana Marjanovıć-jeromela This is me

Dragana Mıladınovıć This is me

Bojan Jockovıć This is me

Vladimir Mıklıč This is me

Velimir Radıć This is me

Project Number TR-31025 /114-451-2126/2016-03 / 451-03- 01732/2017-09/3
Publication Date December 15, 2019
Published in Issue Year 2019 Volume: 24 Issue: 2

Cite

APA Jockovıć, M., Cvejıć, S., Jocıć, S., Marjanovıć-jeromela, A., et al. (2019). EVALUATION OF SUNFLOWER HYBRIDS IN MULTI-ENVIRONMENT TRIAL (MET). Turkish Journal Of Field Crops, 24(2), 202-210. https://doi.org/10.17557/tjfc.645276
AMA Jockovıć M, Cvejıć S, Jocıć S, Marjanovıć-jeromela A, Mıladınovıć D, Jockovıć B, Mıklıč V, Radıć V. EVALUATION OF SUNFLOWER HYBRIDS IN MULTI-ENVIRONMENT TRIAL (MET). TJFC. December 2019;24(2):202-210. doi:10.17557/tjfc.645276
Chicago Jockovıć, Milan, Sandra Cvejıć, Siniša Jocıć, Ana Marjanovıć-jeromela, Dragana Mıladınovıć, Bojan Jockovıć, Vladimir Mıklıč, and Velimir Radıć. “EVALUATION OF SUNFLOWER HYBRIDS IN MULTI-ENVIRONMENT TRIAL (MET)”. Turkish Journal Of Field Crops 24, no. 2 (December 2019): 202-10. https://doi.org/10.17557/tjfc.645276.
EndNote Jockovıć M, Cvejıć S, Jocıć S, Marjanovıć-jeromela A, Mıladınovıć D, Jockovıć B, Mıklıč V, Radıć V (December 1, 2019) EVALUATION OF SUNFLOWER HYBRIDS IN MULTI-ENVIRONMENT TRIAL (MET). Turkish Journal Of Field Crops 24 2 202–210.
IEEE M. Jockovıć, S. Cvejıć, S. Jocıć, A. Marjanovıć-jeromela, D. Mıladınovıć, B. Jockovıć, V. Mıklıč, and V. Radıć, “EVALUATION OF SUNFLOWER HYBRIDS IN MULTI-ENVIRONMENT TRIAL (MET)”, TJFC, vol. 24, no. 2, pp. 202–210, 2019, doi: 10.17557/tjfc.645276.
ISNAD Jockovıć, Milan et al. “EVALUATION OF SUNFLOWER HYBRIDS IN MULTI-ENVIRONMENT TRIAL (MET)”. Turkish Journal Of Field Crops 24/2 (December 2019), 202-210. https://doi.org/10.17557/tjfc.645276.
JAMA Jockovıć M, Cvejıć S, Jocıć S, Marjanovıć-jeromela A, Mıladınovıć D, Jockovıć B, Mıklıč V, Radıć V. EVALUATION OF SUNFLOWER HYBRIDS IN MULTI-ENVIRONMENT TRIAL (MET). TJFC. 2019;24:202–210.
MLA Jockovıć, Milan et al. “EVALUATION OF SUNFLOWER HYBRIDS IN MULTI-ENVIRONMENT TRIAL (MET)”. Turkish Journal Of Field Crops, vol. 24, no. 2, 2019, pp. 202-10, doi:10.17557/tjfc.645276.
Vancouver Jockovıć M, Cvejıć S, Jocıć S, Marjanovıć-jeromela A, Mıladınovıć D, Jockovıć B, Mıklıč V, Radıć V. EVALUATION OF SUNFLOWER HYBRIDS IN MULTI-ENVIRONMENT TRIAL (MET). TJFC. 2019;24(2):202-10.

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