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Theoretical and Applied Genetics

, Volume 130, Issue 11, pp 2445–2461 | Cite as

Selection for water-soluble carbohydrate accumulation and investigation of genetic × environment interactions in an elite wheat breeding population

  • Ben Ovenden
  • Andrew Milgate
  • Chris Lisle
  • Len J. Wade
  • Greg J. Rebetzke
  • James B. Holland
Original Article

Abstract

Key message

Water-soluble carbohydrate accumulation can be selected in wheat breeding programs with consideration of genetic × environmental interactions and relationships with other important characteristics such as relative maturity and nitrogen concentration, although the correlation between WSC traits and grain yield is low and inconsistent.

Abstract

The potential to increase the genetic capacity for water-soluble carbohydrate (WSC) accumulation is an opportunity to improve the drought tolerance capability of rainfed wheat varieties, particularly in environments where terminal drought is a significant constraint to wheat production. A population of elite breeding germplasm was characterized to investigate the potential for selection of improved WSC concentration and total amount in water deficit and well-watered environments. Accumulation of WSC involves complex interactions with other traits and the environment. For both WSC concentration (WSCC) and total WSC per area (WSCA), strong genotype × environment interactions were reflected in the clear grouping of experiments into well-watered and water deficit environment clusters. Genetic correlations between experiments were high within clusters. Heritability for WSCC was larger than for WSCA, and significant associations were observed in both well-watered and water deficit experiment clusters between the WSC traits and nitrogen concentration, tillering, grains per m2, and grain size. However, correlations between grain yield and WSCC or WSCA were weak and variable, suggesting that selection for these traits is not a better strategy for improving yield under drought than direct selection for yield.

Notes

Acknowledgements

The authors gratefully acknowledge the Grains Research and Development Corporation (GRDC) of Australia funding for this project (ICF00007) and a Grains Industry Research Scholarship for Ben Ovenden. The American Australian Association is gratefully acknowledged for the ConocoPhillips Education Fellowship awarded to Ben Ovenden. The authors thank Kerry Schirmer and Aaron Hutchison for their expert technical contributions and data collection. The authors also thank the reviewers for their improvements to the manuscript. The breeding lines used in this study were kindly provided by Australian Grains Technologies, InterGrain, HRZ Wheats (now Dow Seeds), Sydney University, LongReach Plant Breeders and CIMMYT.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Supplementary material

122_2017_2969_MOESM1_ESM.docx (92 kb)
Supplementary material 1 (DOCX 91 kb)
122_2017_2969_MOESM2_ESM.xlsx (88 kb)
Supplementary material 2 (XLSX 87 kb)

References

  1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723CrossRefGoogle Scholar
  2. AOAC (1990) Method 990.03—protein (crude) in animal feed—combustion method. In: Horwitz W (ed) Official methods of analysis of AOAC International, 15th edn. Association of Official Analytical Chemists, Arlington, pp 18–19Google Scholar
  3. Araus J, Slafer G, Reynolds M, Royo C (2002) Plant breeding and drought in C3 cereals: what should we breed for? Ann Bot 89(7):925CrossRefPubMedPubMedCentralGoogle Scholar
  4. Australian Government Bureau of Meteorology (2017). Climate Data Online. http://www.bom.gov.au/climate/data/. Accessed 5 May 2017
  5. Batten GD, Blakeney AB, McGrath VB, Ciavarella S (1993) Non-structural carbohydrate: analysis by near infrared reflectance spectroscopy and its importance as an indicator of plant growth. Plant Soil 155–156(1):243–246CrossRefGoogle Scholar
  6. Beeck CP, Cowling WA, Smith AB, Cullis BR (2010) Analysis of yield and oil from a series of canola breeding trials. Part I. Fitting factor analytic mixed models with pedigree information. Genome 53(11):992–1001CrossRefPubMedGoogle Scholar
  7. Bennett D, Izanloo A, Reynolds M, Kuchel H, Langridge P, Schnurbusch T (2012) Genetic dissection of grain yield and physical grain quality in bread wheat (Triticum aestivum L.) under water-limited environments. Theor Appl Genet 125(2):255–271CrossRefPubMedGoogle Scholar
  8. Bidinger F, Musgrave RB, Fischer RA (1977) Contribution of stored pre-anthesis assimilate to grain yield in wheat and barley. Nature 270(5636):431–433CrossRefGoogle Scholar
  9. Blum A (1998) Improving wheat grain filling under stress by stem reserve mobilisation. Euphytica 100(1):77–83CrossRefGoogle Scholar
  10. Butler D, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual. Queensland Department of Primary Industries and Fisheries, BrisbaneGoogle Scholar
  11. Chapman S (2008) Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials. Euphytica 161(1–2):195–208CrossRefGoogle Scholar
  12. 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):1743–1755CrossRefPubMedGoogle Scholar
  13. Chenu K, Deihimfard R, Chapman SC (2013) Large-scale characterization of drought pattern: a continent-wide modelling approach applied to the Australian wheatbelt—spatial and temporal trends. New Phytol 198(3):801–820CrossRefPubMedGoogle Scholar
  14. Coombes NE (2002) The reactive tabu search for efficient correlated experimental designs. (PhD thesis), Liverpool John Moores University, Liverpool, UKGoogle Scholar
  15. Cooper M, Woodruff D, Eisemann R, Brennan P, DeLacy I (1995) A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: managed-environments for selection among genotypes. Theor Appl Genet 90(3–4):492–502PubMedGoogle Scholar
  16. Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11(4):381–393CrossRefGoogle Scholar
  17. 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):1002–1016CrossRefPubMedGoogle Scholar
  18. Dreccer M, van Herwaarden A, Chapman S, Shorter R (2008) Spring wheat lines contrasting in water soluble carbohydrate concentration: growth; nitrogen absorption and partitioning. In: Appels R, Eastwood R, Lagudah E, Langridge P, Mackay M, McIntyre L, Sharp P (eds) The 11th International wheat genetics symposium. Australia, Brisbane, pp 24–29Google Scholar
  19. Dreccer MF, Chapman SC, Rattey AR, Neal J, Song Y, Christopher JT, Reynolds M (2013) Developmental and growth controls of tillering and water-soluble carbohydrate accumulation in contrasting wheat (Triticum aestivum L.) genotypes: can we dissect them? J Exp Bot 64(1):143–160CrossRefPubMedGoogle Scholar
  20. Ehdaie B, Alloush GA, Waines JG (2008) Genotypic variation in linear rate of grain growth and contribution of stem reserves to grain yield in wheat. Field Crops Res 106(1):34–43CrossRefGoogle Scholar
  21. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman, EssexGoogle Scholar
  22. Fischer RA (1979) Growth and water limitation to dryland wheat yield in Australia: a physiological framework. J Aust Inst Agric Sci 45:83–94Google Scholar
  23. Fischer RA, Byerlee DR, Edmeades GO (2014) ACIAR Monograph No. 158: crop yields and global food security. Australian Centre for International Agricultural Research, CanberraGoogle Scholar
  24. Foulkes MJ, Snape JW, Shearman VJ, Reynolds MP, Gaju O, Sylvester-Bradley R (2007) Genetic progress in yield potential in wheat: recent advances and future prospects. J Agric Sci 145(1):17–29CrossRefGoogle Scholar
  25. Gebbing T (2003) The enclosed and exposed part of the peduncle of wheat (Triticum aestivum): spatial separation of fructan storage. New Phytol 159(1):245–252CrossRefGoogle Scholar
  26. Gebbing T, Schnyder H (1999) Pre-anthesis reserve utilization for protein and carbohydrate synthesis in grains of wheat. Plant Physiol 121(3):871–878CrossRefPubMedPubMedCentralGoogle Scholar
  27. Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2(3):269–293CrossRefGoogle Scholar
  28. Goggin DE, Setter TL (2004) Fructosyltransferase activity and fructan accumulation during development in wheat exposed to terminal drought. Funct Plant Biol 31(1):11–21CrossRefGoogle Scholar
  29. Holland JB (2006) Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Sci 46(2):642–654CrossRefGoogle Scholar
  30. Howard A (2005) Australia and New Zealand, climate of. In: Oliver J (ed) Encyclopedia of World climatology. Springer, Netherlands, pp 137–154Google Scholar
  31. Isik F, Holland J, Maltecca C (2017) Genetic data analysis for plant and animal breeding. Springer International, Berlin (in press) CrossRefGoogle Scholar
  32. Kenward MG, Roger JH (1997) Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53(3):983–997CrossRefPubMedGoogle Scholar
  33. Kiniry JR (1993) Nonstructural carbohydrate utilization by wheat shaded during grain growth. Agron J 85(4):844–849CrossRefGoogle Scholar
  34. Lopes MS, Rebetzke GJ, Reynolds M (2014) Integration of phenotyping and genetic platforms for a better understanding of wheat performance under drought. J Exp Bot 65(21):6167–6177CrossRefPubMedGoogle Scholar
  35. Ludlow MM, Muchow RC (1990) A critical evaluation of traits for improving crop yields in water-limited environments. In: Brady NC (ed) Advances in agronomy, vol 43. Academic Press, New York, pp 107–153Google Scholar
  36. McRae F, McCaffery D, Matthews P (2009) Winter crop variety sowing guide. NSW Department of Primary Industries, Orange, p 108Google Scholar
  37. Mitchell JH, Chapman SC, Rebetzke GJ, Bonnett DG, Fukai S (2012) Evaluation of a reduced-tillering (tin) gene in wheat lines grown across different production environments. Crop Pasture Sci 63(2):128–141CrossRefGoogle Scholar
  38. Passioura JB (1996) Drought and drought tolerance. Plant Growth Regul 20(2):79–83CrossRefGoogle Scholar
  39. Pheloung P, Siddique K (1991) Contribution of stem dry matter to grain yield in wheat cultivars. Funct Plant Biol 18(1):53–64Google Scholar
  40. Piltz J, Law D (2007) AFIA-laboratory methods manual. Australian Fodder Industry Association Inc, BalwynGoogle Scholar
  41. R Development Core Team (2012) R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/
  42. Rattey A, Shorter R, Chapman S, Dreccer F, van Herwaarden A (2009) Variation for and relationships among biomass and grain yield component traits conferring improved yield and grain weight in an elite wheat population grown in variable yield environments. Crop Pasture Sci 60(8):717–729CrossRefGoogle Scholar
  43. Rebetzke GJ, van Herwaarden AF, Jenkins C, Weiss M, Lewis D, Ruuska S, Tabe L, Fettell NA, Richards RA (2008) Quantitative trait loci for water-soluble carbohydrates and associations with agronomic traits in wheat. Aust J Agric Res 59(10):891–905CrossRefGoogle Scholar
  44. Rebetzke GJ, Chapman SC, McIntyre CL, Richards RA, Condon AG, Watt M, van Herwaarden AF (2009) Grain yield improvement in water-limited environments. In: Carver BF (ed) Wheat: science and trade. Wiley-Blackwell, Ames, pp 215–249CrossRefGoogle Scholar
  45. Rebetzke GJ, Chenu K, Biddulph B, Moeller C, Deery DM, Rattey AR, Bennett D, Barrett-Lennard EG, Mayer JE (2012) A multisite managed environment facility for targeted trait and germplasm phenotyping. Funct Plant Biol 40(1):1–13CrossRefGoogle Scholar
  46. Rebetzke GJ, Chenu K, Biddulph B, Moeller C, Deery DM, Rattey AR, Bennett D, Barrett-Lennard G, Mayer JE (2013) A multisite managed environment facility for targeted trait and germplasm phenotyping. Funct Plant Biol 40:13Google Scholar
  47. Reynolds M, Tattaris M, Cossani CM, Ellis M, Yamaguchi-Shinozaki K, Pierre CS (2015) Exploring Genetic Resources to Increase Adaptation of Wheat to Climate Change. In: Ogihara Y, Takumi S, Handa H (eds.) Advances in Wheat Genetics: From Genome to Field: Proceedings of the 12th International Wheat Genetics Symposium (pp. 355-368). Springer Japan, TokyoGoogle Scholar
  48. Richards RA, Rebetzke GJ, Condon AG, van Herwaarden AF (2002) Breeding opportunities for increasing the efficiency of water use and crop yield in temperate cereals. Crop Sci 42(1):111–121CrossRefPubMedGoogle Scholar
  49. Richards RA, Rebetzke GJ, Watt M, Condon AG, Spielmeyer W, Dolferus R (2010) Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment. Funct Plant Biol 37(2):85–97CrossRefGoogle Scholar
  50. Richards RA, Rebetzke GJ, Condon AG, Watt M, Spielmeyer W, Ellis MH, Bonnett DG, Dolferus R (2008) Genetic improvement of wheat for dry environments–a trait based approach. In: Appels R, Eastwood R, Lagudah E, Langridge P, Mackay M, McIntyre L, Sharp P (eds) The 11th International wheat genetics symposium. Australia, Brisbane, pp 24–29Google Scholar
  51. Ruuska SA, Rebetzke GJ, van Herwaarden AF, Richards RA, Fettell NA, Tabe L, Jenkins CLD (2006) Genotypic variation in water-soluble carbohydrate accumulation in wheat. Funct Plant Biol 33(9):799–809CrossRefGoogle Scholar
  52. Sadras VO, Rodriguez D (2007) The limit to wheat water-use efficiency in eastern Australia. II. Influence of rainfall patterns. Aust J Agric Res 58(7):657–669CrossRefGoogle Scholar
  53. Schnyder H (1993) The role of carbohydrate storage and redistribution in the source-sink relations of wheat and barley during grain filling—a review. New Phytol 123(2):233–245CrossRefGoogle Scholar
  54. Shearman VJ, Sylvester-Bradley R, Scott RK, Foulkes MJ (2005) Physiological processes associated with wheat yield progress in the UK. Crop Sci 45(1):175–185Google Scholar
  55. Sissons M, Ovenden B, Adorada D, Milgate A (2014) Durum wheat quality in high-input irrigation systems in south-eastern Australia. Crop Pasture Sci 65(5):411–422CrossRefGoogle Scholar
  56. Smith A, Cullis B, Thompson R (2001) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57(4):1138–1147CrossRefPubMedGoogle Scholar
  57. Smith A, Lim P, Cullis BR (2006) The design and analysis of multi-phase plant breeding experiments. J Agric Sci 144(5):393CrossRefGoogle Scholar
  58. Snape JW, Foulkes MJ, Simmonds J, Leverington M, Fish LJ, Wang Y, Ciavarrella M (2007) Dissecting gene x environmental effects on wheat yields via QTL and physiological analysis. Euphytica 154(3):401–408CrossRefGoogle Scholar
  59. Stefanova KT, Smith AB, Cullis BR (2009) Enhanced diagnostics for the spatial analysis of field trials. J Agric Biol Environ Stat 14(4):392–410CrossRefGoogle Scholar
  60. Stram DO, Lee JW (1994) Variance components testing in the longitudinal mixed effects model. Biometrics 50:1171–1177CrossRefPubMedGoogle Scholar
  61. Takahashi T, Chevalier PM, Rupp RA (2001) Storage and remobilization of soluble carbohydrates after heading in different plant parts of a winter wheat cultivar. Plant Prod Sci 4(3):160–165CrossRefGoogle Scholar
  62. van Herwaarden AF, Angus JF, Richards RA, Farquhar GD (1998) ‘Haying-off’, the negative grain yield response of dryland wheat to nitrogen fertiliser II. Carbohydrate and protein dynamics. Aust J Agric Res 49(7):1083–1094CrossRefGoogle Scholar
  63. Virgona JM, Barlow EWR (1991) Drought stress induces changes in the non-structural carbohydrate composition of wheat stems. Funct Plant Biol 18(3):239–247Google Scholar
  64. Wardlaw IF, Willenbrink J (1994) Carbohydrate storage and mobilisation by the culm of wheat between heading and grain maturity: the relation to sucrose synthase and sucrose-phosphate synthase. Funct Plant Biol 21(3):255–271Google Scholar
  65. Xue G-P, McIntyre CL, Rattey AR, van Herwaarden AF, Shorter R (2009) Use of dry matter content as a rapid and low-cost estimate for ranking genotypic differences in water-soluble carbohydrate concentrations in the stem and leaf sheath of Triticum aestivum. Crop Pasture Sci 60(1):51–59CrossRefGoogle Scholar
  66. Xue G-P, Drenth J, Glassop D, Kooiker M, McIntyre CL (2013) Dissecting the molecular basis of the contribution of source strength to high fructan accumulation in wheat. Plant Mol Biol 81(1–2):71–92CrossRefPubMedGoogle Scholar
  67. Yang D-L, Jing R-L, Chang X-P, Li W (2007) Identification of quantitative trait loci and environmental interactions for accumulation and remobilization of water-soluble carbohydrates in wheat (Triticum aestivum L.) stems. Genetics 176(1):571–584CrossRefPubMedPubMedCentralGoogle Scholar
  68. Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14(6):415–421CrossRefGoogle Scholar
  69. Zhu L, Li S, Liang Z, Zhang Z, Xu X (2010) Relationship between yield, carbon isotope discrimination and stem carbohydrate concentration in spring wheat grown in Ningxia Irrigation Region (North-west China). Crop Pasture Sci 61(9):731–742CrossRefGoogle Scholar
  70. Zila CT, Samayoa LF, Santiago R, Butrón A, Holland JB (2013) A genome-wide association study reveals genes associated with fusarium ear rot resistance in a maize core diversity panel. G3 Genes| Genomes| Genetics 3(11):2095–2104CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.NSW Department of Primary IndustriesYanco Agricultural InstituteYancoAustralia
  2. 2.NSW Department of Primary IndustriesWagga Wagga Agricultural InstituteWagga WaggaAustralia
  3. 3.National Institute for Applied Statistics Research AustraliaUniversity of WollongongWollongongAustralia
  4. 4.Charles Sturt UniversityGraham CentreAustralia
  5. 5.School of Agriculture and Food SciencesThe University of QueenslandBrisbaneAustralia
  6. 6.CSIRO Agriculture and FoodCanberraAustralia
  7. 7.USDA-ARS Plant Science Research Unit and Department of Crop and Soil SciencesNorth Carolina State UniversityRaleighUSA

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