Advertisement

Genetic Resources and Crop Evolution

, Volume 66, Issue 2, pp 491–512 | Cite as

Comparative assessment of einkorn and emmer wheat phenomes: I. Plant architecture

  • Abdullah A. JaradatEmail author
Research Article
  • 102 Downloads

Abstract

The domestication syndrome of wheat was based on genetically-mediated mutation(s) in reproductive traits with no due consideration to traits that comprise the plant “phenome” as an abstract expression of phenotypic architecture and a central component of the plant phenotype. The first of multi-part study reports on genetically and phenotypically variable germplasm source of diploid einkorn (Triticum monococcum L. subsp. monococcum) and tetraploid emmer wheat [Triticum turgidum subsp. dicoccon (Schrank) Thell.]. The germplasm was phenotyped for architectural (plant, tillers, leaves) and reproductive (spike, spikelet, and kernel) components of its phenome to quantify, describe, and contrast plant architecture of both species, and estimate the level of genetic divergence of emmer from einkorn due to polyploidy. Inter-specific variation for all architectural and reproductive components exhibited significant differences; while, intra-specific phenotypic variation estimates in both species decreased as follows: kernels > spikelets > spikes; and smaller values were estimated for leaves > plants > tillers. Diploid and tetraploid architectural modules, expressed as loadings on principal components, were significantly different; einkorn and emmer were 97.1 and 89.8 correctly classified, respectively, and were significantly separated at a multivariate level (Mahalanobis D2 = 127.5; p < 0.001). More traits exhibited larger magnitude and expressed larger genotypic variation due to polyploidy; however, phenotypic and genotypic variation, as well as heritability of these traits displayed different patterns, with large differences found for leaf area index and tillers per plant between species. Reduced major axis analysis of inter-specific functional relationships suggested that phenotypic and reproductive traits were divided almost equally between allometric and isometric patterns. Paired comparisons between species suggested that relationships between spikelet and kernel traits in each species were close (r = 0.54–0.97; p < 0.05). However, the magnitude of trait estimates in pairwise comparisons between ploidy levels were not in unison; the magnitude of almost all spikelet traits, but not kernel traits in emmer exceeded their counterparts in einkorn. Polyploidy caused phenotypic increases in traits that can support larger grain yield, including stems, peduncles, leaves and spikes, but not tillers; whereas, the species varied in the magnitude and distribution of genetic variances across their multivariate phenome space. Indicators were identified for the development of novel hulled wheat idiotypes with larger yield potential and wide adaptation.

Keywords

Architecture Functional relationships Hulled wheat Phenotypic variation Ploidy 

Abbreviations

CAI

Compact (or horizontal) architecture index

Do

Fractal dimension

ECa

Apparent soil electrical conductivity

FLA

Flag leaf area

FL SLW

Flag leaf specific leaf weight

FT

Number of fertile tillers per plant

GCV

Genotypic coefficient of variation

GDD

Growing degree days

GLM

General linear model

G(Sp)

Genotypes within species

h2

Heritability estimate (narrow sense)

LAI

Leaf Area Index

MATR

Major axis tests and routines

MSL

Main stem length

OLR

Ordinary linear regression

PCV

Phenotypic coefficient of variation

PL

Plant length

PLA

Penultimate leaf area

PL SLW

Specific leaf weight of penultimate leaf

PSPD

Percent significant pairwise mean comparison differences

PTQ

Photothermal quotient

RMA

Reduced major axis

SLW

Specific leaf weight (dry weight per unit leaf area)

T(Sp)

Traits within species

TTP

Total number of tillers per plant

VAI

Vertical architecture index

Y

Year

Notes

Acknowledgements

This research was supported by USDA Project Number: 5060-21220-005-D. Thanks are due to support staff at the North Central Soil Conservation Research Lab, Morris, MN. USDA is an equal-opportunity provider and employer.

Compliance with ethical standards

Conflict of interest

The author declares that he has no conflict of interest.

Supplementary material

10722_2018_729_MOESM1_ESM.docx (20 kb)
Supplementary material 1 (DOCX 19 kb)
10722_2018_729_MOESM2_ESM.docx (18 kb)
Supplementary material 2 (DOCX 18 kb)

References

  1. Baret F, de Solan B, Lopez-Lazano R, Ma K, Weiss M (2010) GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5 zenith angle: theoretical considerations based on 3D architecture models and application to wheat crops. Agric For Meteorol 150:1393–1401CrossRefGoogle Scholar
  2. Barthélémy D, Caraglio Y (2007) Plant architecture: a dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny. Ann Bot 99:375–407CrossRefGoogle Scholar
  3. Chateil C, Goldringer I, Tarall L, Kerbiriou C, Le Viol I, Ponge J-F, Salmon S, Gachet S, Porcher E (2013) Crop genetic diversity benefits farmland biodiversity in cultivated fields. Agric Ecosyst Environ 171:25–32CrossRefGoogle Scholar
  4. Chen D, Neumann K, Friedel S, Kilian B, Chen M, Altmann T, Klukas C (2014) Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. Plant Cell 26:4636–4655. www.plantcell.org/cgi/doi/.  https://doi.org/10.1105/tpc.114.129601
  5. Chomicki G, Coiro M, Renner S (2017) Evolution and ecology of plant architecture: integrating insights from the fossil record, extant morphology, developmental genetics and phylogenies. Ann Bot 120:855–891.  https://doi.org/10.1093/aob/mcx113 CrossRefGoogle Scholar
  6. Das Choudhury S, Bashyam S, Qiu Y, Samal A, Awada T (2018) Holistic and component plant phenotyping using temporal image sequence. Plant Methods 14:35.  https://doi.org/10.1186/s13007-018-0303-x CrossRefGoogle Scholar
  7. De Vita P, Riefolo C, Codianni P, Cattivelli L, Fares C (2006) Agronomic and qualitative traits of T. turgidum ssp. dicoccum genotypes cultivated in Italy. Euphytica 150:195–205CrossRefGoogle Scholar
  8. Donald C (1968) The breeding of crop ideotypes. Euphytica 17:385–403CrossRefGoogle Scholar
  9. Duan T, Chapman S, Holland E, Rebetzke G, Guo Y, Zheng B (2016) Dynamic quantification of canopy structure to characterize early plant vigor in wheat genotypes. J Exp Bot 67:4523–4534.  https://doi.org/10.1093/jxb/erw227 CrossRefGoogle Scholar
  10. Dubcovsky J, Dvorak J (2007) Genome plasticity a key factor in the success of polyploid wheat under domestication. Science 316:1862.  https://doi.org/10.1126/science.1143986 CrossRefGoogle Scholar
  11. Feldman M, Kislev M (2007) Domestication of emmer wheat and evolution of free-threshing tetraploid wheat. Isr J Plant Sci 55:207–221CrossRefGoogle Scholar
  12. Ferrante A, Cartelle J, Savin R, Slafer G (2017) Yield determination, interplay between major components and yield stability in a traditional and a contemporary wheat across a wide range of environments. Field Crops Res 203:114–127CrossRefGoogle Scholar
  13. Giraldo P, Royo C, González M, Carrillo J, Ruiz M (2016) Genetic diversity and association mapping for agro-morphological and grain Quality traits of a structured collection of durum wheat landraces including subsp. durum, t urgidum and dicoccon. PLoS ONE 11:0166577.  https://doi.org/10.1371/journal.pone.0166577 CrossRefGoogle Scholar
  14. Gonzalez-Navarro O, Griffiths S, Molero G, Reynolds M, Slafer G (2016) Variation in developmental patterns among elite wheat lines and relationships with yield, yield components and spike fertility. Field Crops Res 196:294–304CrossRefGoogle Scholar
  15. Guttieri M, Baenziger P, Frels K, Carver B, Arnall B, Waters B (2015) Variation for grain mineral concentration in a diversity panel of current and historical Great Plains hard winter wheat germplasm. Crop Sci 55:1035–1052CrossRefGoogle Scholar
  16. Hammer K (1984) Das Domestikationssyndrom. Kulturpflanze 32:11–34CrossRefGoogle Scholar
  17. Hinterthuer A (2017) Can ancient grains find their way in modern agriculture? CSA News Magazine, pp 4–8. Available at: https://dl.sciencesocieties.org/publications/csa/articles/62/4/4
  18. Iriondo J, Milla R, Volis S, Rubio de Casas R (2017) Reproductive traits and evolutionary divergence between Mediterranean crops and their wild relatives. Plant Biol.  https://doi.org/10.1111/plb.12640
  19. Jaradat A (2018) Statistical modeling of phenotypic plasticity under abiotic stress in Triticum durum L. and Triticum aestivum L. genotypes. Agronomy 8:139.  https://doi.org/10.3390/agronomy8080139 CrossRefGoogle Scholar
  20. Kissoudis C, van de Wiel C, Visser R, van der Linden G (2016) Future-proof crops: challenges and strategies for climate resilience improvement. Curr Opin Plant Biol 30:47–56CrossRefGoogle Scholar
  21. Konvalina P, Capouchova I, Stenho Z (2012) Agronomically important traits of emmer wheat. Plant Soil Environ 58:341–346CrossRefGoogle Scholar
  22. Krahmer J, Ganpudi A, Abbas A, Romanowski A, Halliday K (2018) Phytocrome, carbon sensing, metabolism, and plant growth plasticity. Plant Physiol 176:1039–1048CrossRefGoogle Scholar
  23. Kumar U, Lazaa M, Soulié J-C, Pascoa R, Mendeza K, Dingkuhn M (2017) Analysis and simulation of phenotypic plasticity for traits contributing to yield potential in twelve rice genotypes. Field Crops Res 202:94–107.  https://doi.org/10.1016/j.fcr.2016.04.037/ CrossRefGoogle Scholar
  24. Li P-F, Cheng Z-G, Ma B-L, Palta Kong H-Y, Mo F, Wang J-Y, Zhu Y, Lv G-C, Batool A, Bai X, Li F-M, Xiong C (2014) Dryland wheat domestication changed the development of aboveground architecture for a well-structured canopy. PLoS ONE 9:e95825.  https://doi.org/10.1371/journal.pone.0095825 CrossRefGoogle Scholar
  25. Li X, Wang X, Peng Y, Wei H, Zhu X, Chang S, Li M, Li T, Huang H (2017) Quantitative descriptions of rice plant architecture and their application. PLoS ONE 12(5):e0177669.  https://doi.org/10.1371/journal.pone.0177669 CrossRefGoogle Scholar
  26. Longin C, Würschum T (2016) Back to the future: tapping into ancient grains for food diversity. Trends Plant Sci 21:731–737.  https://doi.org/10.1016/j.tplants.2016.05.005 CrossRefGoogle Scholar
  27. Longin C, Ziegler J, Schweiggert R, Koehler P, Carle R, Würschum T (2015) Comparative study of hulled (einkorn, emmer, and spelt) and naked wheats (durum and bread wheat): agronomic performance and quality traits. Crop Sci 56:302–311.  https://doi.org/10.2135/cropsci2015.04.0242 CrossRefGoogle Scholar
  28. Marino S, Tognetti R, Alvino A (2009) Crop yield and grain quality of emmer populations grown in central Italy, as affected by nitrogen fertilizer. Eur J Agron 31:233–240CrossRefGoogle Scholar
  29. Mauro-Herrera M, Doust A (2016) Development and genetic control of plant architecture and biomass in the Panicoid grass, Setaria. PLoS ONE 11(3):e0151346.  https://doi.org/10.1371/journal.pone.0151346 CrossRefGoogle Scholar
  30. Messier J, Lechowicz M, McGill B, Violle C, Enquist B (2017) Interspecific integration of trait dimensions at local scales: the plant phenotype as an integrated network. J Ecol  https://doi.org/10.1111/1365-2745.12755
  31. Milla R, Osborne CP, Turcotte MM, Violle C (2015) Plant domestication through an ecological lens. Trends Ecol Evol 30:463–469.  https://doi.org/10.1016/j.tree.2015.06.006 CrossRefGoogle Scholar
  32. Mondini L, Grausgruber H, Pagnotta M (2014) Evaluation of European emmer wheat germplasm for agro-morphological, grain quality traits and molecular traits. Genet Resour Crop Evol 61:69–87.  https://doi.org/10.1007/s10722-013-0016-y CrossRefGoogle Scholar
  33. Münzbergová Z, Skuhrovec J (2016) Contrasting effects of ploidy level on seed production in a diploid-tetraploid system. AoB Plants 9: plw077.  https://doi.org/10.1093/aobpla/plw077
  34. Niklas KJ, Hammond ST (2014) Assessing scaling relationships: uses, abuses, and alternatives. Int J Plant Sci 175:754–763.  https://doi.org/10.1086/677238 CrossRefGoogle Scholar
  35. Oliveira H, Jones H, Leigh F, Lister D, Jones M, Pena-Chocarro L (2011) Phylogeography of einkorn landraces in the Mediterranean basin and Central Europe: population structure and cultivation history. Archaeol Anthropol Sci 3:327–341.  https://doi.org/10.1007/s12520-011-0076-x CrossRefGoogle Scholar
  36. Payne W (2014) Developments from analysis of variance through to generalized linear models and beyond. Ann Appl 164:11–17CrossRefGoogle Scholar
  37. Pigliucci M (2003) Phenotypic integration: studying the ecology and evolution of complex phenotypes. Ecology Lett 6:265–272CrossRefGoogle Scholar
  38. Poorter H, Niklas K, Reich P, Oleksyn J (2012) Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol 193:30–50.  https://doi.org/10.1111/j.1469-8137.2011.03952.x CrossRefGoogle Scholar
  39. Preece C, Livarda A, Christin P-A, Wallace M, Martin G, Charles M, Jones G, Rees M, Osborne C (2017) How did the domestication of Fertile Crescent grain crops increase their yields? Funct Ecol 31:387–397.  https://doi.org/10.1111/1365-2435.12760 CrossRefGoogle Scholar
  40. Qin X-L, Niklas K, Qi L, Yiong Y-C, Li F-M (2012) The effects of domestication on the scaling of below- vs. aboveground biomass in four selected wheat (Triticum; Poaceae) genotypes. Am J Bot 99:1112–1117CrossRefGoogle Scholar
  41. Rahman M, Ahsan M, Gillani Z, Chen M (2017) Digital biomass accumulation using high-throughput plant phenotype data analysis. J Integr Bioinform 20170028.  https://doi.org/10.1515/jib-2017-0028
  42. Robert C, Garin G, Abichou M, Houles V, Pradal C, Fournier C (2018) Plant architecture and foliar senescence impact the race between wheat growth and Zymoseptoria tritici epidemics. Ann Bot 121:975–989.  https://doi.org/10.1093/aob/mcx192 CrossRefGoogle Scholar
  43. Rötter R, Tao F, Höhn J, Palosuo T (2015) Use of crop simulation modelling to aid ideotype design of future cereal cultivars. J Exp Bot 66:3463–3476.  https://doi.org/10.1093/jxb/erv098 CrossRefGoogle Scholar
  44. Roucou A, Violle C, Fort F, Roumet P, Ecarnot M, Vile D (2018) Shifts in plant functional strategies over the course of wheat domestication. J Appl Ecol 55:25–37.  https://doi.org/10.1111/1365-2664.13029 CrossRefGoogle Scholar
  45. SAS Institute Inc (2016) JMP® Pro Version 13.2.0. SAS Institute Inc., CaryGoogle Scholar
  46. Schindlin J, Rueden C, Hiner M, Eliceiri K (2015) The ImageJ ecosystems: an open platform for biomedical image analysis. Mol Reprod Dev 82:518–529.  https://doi.org/10.1002/mrd.22489 CrossRefGoogle Scholar
  47. Semenov M, Stratonovitch P (2013) Designing high-yielding wheat ideotypes for a changing climate. Food Energy Secur 2:185–196.  https://doi.org/10.1002/fes3.34 CrossRefGoogle Scholar
  48. Soil Conservation Service (USDA-SCS) (1971) Soil survey stevens county, Minnesota. USDA SCS, Washington, DCGoogle Scholar
  49. StatCorp (2017) Stata statistical software, release 15.1. StatCorp LLC, College StationGoogle Scholar
  50. Teichmann T, Muhr M (2015) Shaping plant architecture. Front Plant Sci 6:233.  https://doi.org/10.3389/fpls.2015.00233 CrossRefGoogle Scholar
  51. Troccoli A, Codianni P (2005) Appropriate seeding rate of einkorn, emmer, and spelt grown under rainfed conditions in southern Italy. Eur J Agron 22:293–300CrossRefGoogle Scholar
  52. Unal G (2009) Some physical and nutritional properties of hulled wheats. Turkish J Agron 15:58–64Google Scholar
  53. Walter G, Aguirre J, Blows M, Ortiz-Barrientos D (2017) Evolution of genetic variance during adaptive radiation. bioRxiv. http://dx.doi.org/10.1101/097642
  54. Wang J, Turner N, Liu Y, Siddique K, Xiong Y (2017) Effects of drought stress on morphological, physiological and biochemical characteristics of wheat species differing in ploidy level. Funct Plant Biol 44:219–234.  https://doi.org/10.1071/FP16082 CrossRefGoogle Scholar
  55. Warton D, Duursma R, Falseter D, Taskinen S (2012) SMATR 3—an R package for estimation and inference about allometric lines. Methods Ecol Evol 3:257–259.  https://doi.org/10.1111/j.2041-210X.2011.00153.x CrossRefGoogle Scholar
  56. Xie Q, Mayes S, Sparkes D (2015) Spelt as a genetic resource for yield component improvement in bread wheat. Crop Sci 55:2753–2765CrossRefGoogle Scholar
  57. Zaharieva M, Monneveux P (2014) Cultivated einkorn wheat (Triticum monococcum L. subsp. monococcum): the long life of a founder crop of agriculture. Genet Resour Crop Evol  https://doi.org/10.1007/s10722-014-0084-7
  58. Zaharieva M, Ayana G, Al Hakimi A, Misra S, Monneveux P (2010) Cultivated emmer wheat (Triticum dicoccon Schrank), an old crop with promising future: a review. Genet Resour Crop Evol 57:937–962CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  1. 1.United States Department of Agriculture–Agricultural Research Service (USDA-ARS)MorrisUSA
  2. 2.Department of Agronomy and Plant GeneticsUniversity of MinnesotaMorrisUSA

Personalised recommendations