Abstract
Selecting high yielding genotypes with stable performance is the breeders’ priority but is constrained by genotype × environment (G×E) interaction. We investigated canola yield of 35 genotypes and its stability in multiple environment trials (MET) in south-western Australia and the possibility to breed broadly-adapted high yielding genotypes. The Finlay–Wilkinson (F–W) regression and factor analytic (FA) model were used to investigate the G×E interaction, yield and genotype stability and adaptability. The cross-over response in the F–W regression, substantial genetic variance heterogeneity, and the genetic correlations in the FA model demonstrated substantial G×E interaction for yield. Cluster analysis suggests low, medium and high rainfall mega-environments. F–W regression indicated that genotypes with high stability (e.g. low regression slope values) produced relatively low yield and vice versa, but also identified broadly adapted genotypes with high intercepts and steep regression slopes. The FA model provided a more detailed analysis of performance, dividing genotypes by positive, flat or negative responses to environment. In general, early flowering genotypes responded negatively to favourable environments and vice versa for late flowering genotypes. More importantly, a few early flowering hybrids with long flowering phases were consistently productive in both low and high yielding environments, showing broad adaptability. These productive hybrids were consistent with those identified earlier by high F–W intercept and slope values. Hybrids were higher yielding and more stable than open-pollinated canola, as was Roundup-Ready® canola compared to the three other herbicide tolerance groups (Clearfield®, Triazine tolerant, conventional). We conclude that yield stability and high yield are not mutually exclusive and that breeding for broadly adapted high yielding canola is possible.
Similar content being viewed by others
References
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723
Becker HC, Leon J (1988) Stability analysis in plant-breeding. Plant Breed 101(1):1–23. doi:10.1111/j.1439-0523.1988.tb00261.x
Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual. Department of Primary Industries and Fisheries, Brisbane. www.vsn-intl.com/products/asreml
Chenu K, Cooper M, Hammer GL, Mathews KL, Dreccer MF, Chapman SC (2011) Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern Australia. J Exp Bot 62(6):1743–1755. doi:10.1093/jxb/erq459
Cullis BR, Smith AB, Beeck CP, Cowling WA (2010) Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysis. Genome 53(11):1002–1016. doi:10.1139/G10-080
Cullis BR, Jefferson P, Thompson R, Smith AB (2014) Factor analytic and reduced animal models for the investigation of additive genotype-by-environment interaction in outcrossing plant species with application to a Pinus radiata breeding programme. Theor Appl Genet 127(10):2193–2210. doi:10.1007/s00122-014-2373-0
de Figueiredo AG, Von Pinho RG, Silva HD, Balestre M (2015) Application of mixed models for evaluating stability and adaptability of maize using unbalanced data. Euphytica 202(3):393–409. doi:10.1007/s10681-014-1301-3
Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6(1):36
Finlay KW, Wilkinson GN (1963) Analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14(6):742–754
Gunasekera CP, Martin LD, Siddique KHM, Walton GH (2006) Genotype by environment interactions of Indian mustard (Brassica juncea L.) and canola (B. napus L.) in Mediterranean-type environments I. Crop growth and seed yield. Eur J Agron 25(1):1–12. doi:10.1016/j.eja.2005.08.002
Hu XY (2014) Combined yield comparison and stability evaluation of rape genotypes using the mixed model. Field Crops Res 167:11–18. doi:10.1016/j.fcr.2014.07.001
Hu XY, Spilke J (2011) Variance–covariance structure and its influence on variety assessment in regional crop trials. Field Crops Res 120(1):1–8. doi:10.1016/j.fcr.2010.09.015
Kelly AM, Smith AB, Eccleston JA, Cullis BR (2007) The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials. Crop Sci 47(3):1063–1070. doi:10.2135/cropsci2006.08.0540
Kirkegaard JA, Lilley JM, Morrison MJ (2016) Drivers of trends in Australian canola productivity and future prospects. Crop Pasture Sci. doi:10.1071/Cpv67n4_Fo
Lawes RA, Huth ND, Hochman Z (2016) Commercially available wheat cultivars are broadly adapted to location and time of sowing in Australia’s grain zone. Eur J Agron 77:38–46. doi:10.1016/j.eja.2016.03.009
Lin CS, Binns MR, Lefkovitch LP (1986) Stability analysis—where do we stand? Crop Sci 26(5):894–900
Moghaddam MJ, Pourdad SS (2011) Genotype × 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. doi:10.1007/s10681-011-0371-8
Muhleisen J, Piepho HP, Maurer HP, Longin CFH, Reif JC (2014a) Yield stability of hybrids versus lines in wheat, barley, and triticale. Theor Appl Genet 127(2):309–316. doi:10.1007/s00122-013-2219-1
Muhleisen J, Piepho HP, Maurer HP, Zhao YS, Reif JC (2014b) Exploitation of yield stability in barley. Theor Appl Genet 127(9):1949–1962. doi:10.1007/s00122-014-2351-6
Piepho HP (1999) Stability analysis using the SAS system. Agron J 91(1):154–160
R Core Team (2012) R: a language and environment for statistical anlaysis. R Foundation for Statistical Computing, Vienna
R Development Core Team (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Viena
Shafii B, Mahler KA, Price WJ, Auld DL (1992) Genotype × environment interaction effects on winter rapeseed yield and oil content. Crop Sci 32(4):922–927
Shukla GK (1972) Some statistical aspects of partitioning genotype environmental components of variability. Heredity 29:237. doi:10.1038/Hdy.1972.87
Si P, Walton GH (2004) Determinants of oil concentration and seed yield in canola and Indian mustard in the lower rainfall areas of Western Australia. Aust J Agric Res 55(3):367–377. doi:10.1071/Ar03151
Si P, Mailer RJ, Galwey N, Turner DW (2003) Influence of genotype and environment on oil and protein concentrations of canola (Brassica napus L.) grown across southern Australia. Aust J Agric Res 54(4):397–407. doi:10.1071/Ar01203
Smith AB, Ganesalingam A, Kuchel H, Cullis BR (2015) Factor analytic mixed models for the provision of grower information from national crop variety testing programs. Theor Appl Genet 128(1):55–72. doi:10.1007/s00122-014-2412-x
Sylvester-Bradley R, Makepeace RJ (1984) A code for the stages of development in oilseed rape (Brassica napus L.). Aspects of applied biology 6: agronomy, physiology, plant breeding and crop protection of oilseed rape. Association of Applied Biologists, Churchill College, Cambridge
Tollenaar M, Lee EA (2002) Yield potential, yield stability and stress tolerance in maize. Field Crops Res 75(2–3):161–169. doi:10.1016/S0378-4290(02)00024-2
Welham S, Gogel B, Kelly A, Cullis BA, Butler DG, Thompson R (2013) Analysis of linear mixed models by ASReml-R with application in plant breeding: course notes
Zhang H, Flottmann S (2016a) Seed yield of canola (Brassica napus L.) is determined primarily by biomass in a high-yielding environment. Crop Pasture Sci 67(4):369–381. doi:10.1071/cp15236
Zhang HP, Flottmann S (2016b) Genotypic variation in the accumulation of water-soluble carbohydrate in canola and its potential contribution to seed yield in different environments. Field Crops Res 196:124–133. doi:10.1016/j.fcr.2016.06.014
Zhang H, Turner NC, Poole ML (2010) Source-sink balance and manipulating sink-source relations of wheat indicate that the yield potential of wheat is sink-limited in the high-rainfall zone of South-western Australia. Crop Pasture Sci 61:852–861
Zhang HP, Berger JD, Milroy SP (2013) Genotype × environment interaction studies highlight the role of phenology in specific adaptation of canola (Brassica napus) to contrasting Mediterranean climates. Field Crops Res 144:77–88. doi:10.1016/j.fcr.2013.01.006
Zhang H, Berger JD, Seymour M, Brill R, Herrmann C, Quinlan R, Knell G (2016) Relative yield and profit of Australian hybrid compared with open-pollinated canola is largely determined by growing-season rainfall. Crop Pasture Sci 67(4):323–331. doi:10.1071/cp15248
Acknowledgments
We thank Mr Sam Flottmann and Mr Adam Brown for field work and collecting the data. We acknowledge that seed supply was provided by Pacific Seeds, Nuseed, and Pioneer Hybrid Australia. This study received financial support from the Australian Grains Research and Development Corporation (GRDC) (CSP00169). We acknowledge a number of growers generously provided their land to host the trials. We thank Drs Roger Lawes and Julianne Lilley for comments that have improved the paper. We also thank an anonymous reviewer for his constructive comments that have improved the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, H., Berger, J.D. & Herrmann, C. Yield stability and adaptability of canola (Brassica napus L.) in multiple environment trials. Euphytica 213, 155 (2017). https://doi.org/10.1007/s10681-017-1948-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10681-017-1948-7