Skip to main content
SpringerLink
Log in

Meta-analyses of oil yield in Cuphea PSR23

  • Published:
Euphytica Aims and scope Submit manuscript

Abstract

Oil content and composition of Cuphea seed are of special economic value as raw materials for industrial and food applications. The inherent unpredictability in determining and predicting Cuphea’s oil yield is attributed, in part, to the indeterminate growth habit and the persistence of the domestication syndrome of this semi-domesticated potential oilseed crop. Meta-analysis using multivariate statistical modeling, computer simulations, and custom profiling was carried out on a database collated from several studies carried out in growth chamber, greenhouse and field experiments. Meta-analyses identified the importance of, and quantified direct and indirect relationships and tradeoffs between and within functional traits classified within five interrelated plant modules. Several multivariate statistical analyses procedures were employed in predicting oil content and oil yield, as performance measures of Cuphea at the plant and population levels of integration. The most parsimonious partial least squares regression model identified plant-, capsule-, and seed-based traits that can be used in reconstructing the best configuration needed for high agronomic performance at the individual plant and population levels. Variance-based structural equation modeling suggested that the variation in relative growth rate was strongly linked to differences in specific leaf area and leaf mass ratio; both traits expressed large positive direct and indirect effects on oil yield, but not oil content. Results of custom profiling suggested that seed yield, oil% and oil yield can be optimized by trait adjustments within the phenotypic and metabolic modules. Adjustments to thousand-seed weight and protein content would influence seed yield, oil yield and oil%, in a decreasing order. Improvements in eco-physiological traits, nutrient ratios and structural traits would lead to a slightly higher oil% and eventually higher oil yield.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

AIC:

Akaike information criterion

ANOVA:

Analysis of variance

CanCor:

Canonical correlation

C:N:

Carbon:nitrogen ratio

C:P:

Carbon:phosphorus ratio

C.V.:

Coefficient of variation

DM:

Dry matter

Do :

Fractal dimension

GAI:

Green area index

DF:

Days to flowering in GDD

DM:

Days to maturity in GDD

GDD:

Growing degree days base 10 °C

GCV:

Genotypic coefficient of variation

h2 :

Broad-sense heritability

HI:

Harvest index

HSW:

Hundred-seed weight

LAP:

Leaf area per plant

LAR:

Leaf area ratio

LDW:

Leaf dry weight

LMR:

Leaf mass ratio

LNC:

Leaf nitrogen content

MVA:

Multivariate analysis

NAR:

Net assimilation rate

N:P:

Nitrogen:phosphorus ratio

N:S:

Nitrogen:sulfur ratio

PB:

Primary branches

PCA:

Principal components analysis

PCV:

Phenotypic coefficient of variation

PLS:

Partial lest squares regression

PVL:

Plant volume

Q2 :

Validation coefficient of determination

R2 :

Calibration coefficient of determination

REML:

Restricted maximum likelihood

RGR:

Relative growth rate

RMR:

Root mass ratio

RNC:

Root nitrogen content

R:S:

Root:shoot ratio

SB:

Secondary branches

SD:

Standard deviation

SEM:

Structural equation modeling

SLA:

Specific leaf area

SMR:

Stem mass ratio

SY:

Seed yield, kg ha−1

SYP:

Seed yield, g plant−1

VCA:

Variance components analysis

References

  • Adunga W, Labuschagne MT (2003) Association of linseed characters and its variability in different environments. J Agric Sci 140:285–296

    Article  Google Scholar 

  • Basalma D (2008) The correlation and path analysis of yield and yield components of different winter rapeseed (Brassic napus ssp. oleifera L.) cultivars. Res J Agric Biol Sci 4:120–125

    Google Scholar 

  • Belete YS (2011) Genetic variability, correlation and path analysis studies in Ethiopian mustard (Brassica carinata A. Brun) genotypes. Intl J Plant Breed Genet 5:328–338

    Article  Google Scholar 

  • Bizzi S, Surridge B, Lerner D (2013) Structural equation modeling: a novel statistical framework for exploring the spatial distribution of benthic macroinvertebrates in riverine ecosystems. River Res Appl 29:743–759

    Article  Google Scholar 

  • Blackshaw RE, Johnson EN, Gan Y, May WE, McAndrew DW, Barthet V, McDonald T, Wispiski D (2011) Alternative oilseed crops for biodiesel feedstock on the Canadian prairies. Can J Plant Sci 91:889–896

    Article  Google Scholar 

  • Blonder B, Vasseur F, Violle C, Shipley B, Enquist BJ, Vile D (2015) Testing models for the leaf economics spectrum with leaf and whole-plant traits in Arabidopsis thaliana. AOB Plants. doi:10.1093/aobpla/plv049

    PubMed  PubMed Central  Google Scholar 

  • Bonser SP, Ladd B, Monroe K, Hall MD, Forster MA (2010) The adaptive value of functional and life-history traits across fertility treatments in an annual plant. Ann Bot 106:979–988

    Article  PubMed  PubMed Central  Google Scholar 

  • Cai G, Yang Q, Chen H, Yang Q, Zhang C, Fan C, Zhou Y (2016) Genetic dissection of plant architecture and yield-related traits in Brassica napus. Sci Report 6:21625. doi:10.1038/srep21625

  • Chen X, Min D, Yasir TA, Hu Y-G (2012) Evaluation of 14 morphological, yield-related and physiological traits as indicators of drought tolerance in Chines winter bread wheat revealed by analysis of the membership function value of drought tolerance (MFVD). Field Crops Res 137:195–201

    Article  Google Scholar 

  • Chen D, Neumann K, Friedel S, Kilian B, Chen M, Altman 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ciaccio MF, Finkle JD, Xue AY, Bagheri N (2014) A systems approach to integrative biology: an overview of statistical methods to elucidate association and architecture. Integr Comp Biol. doi:10.1093/icb/icu037

    PubMed  PubMed Central  Google Scholar 

  • Copur O, Gur MA, Karakus M, Demirel U (2006) Determination of correlation and path analysis among yield components and seed yield in oil flax varieties (L. usitatissimum). J Biol Sci 6:738–743

    Article  Google Scholar 

  • Dell Inc. (2015) Dell Statistica (data analysis software systems) version 13. https://Software.dell.com

  • Dwivedi SL, Upadhyaya HD, Hegde DM (2005) Development of core collection using geographic information and morphological descriptors in safflower (Carthamus tinctorius L.) germplasm. Genet Resour Crop Eol 52:821–830

    Article  Google Scholar 

  • Fikreselassie M, Zeleke H, Alemayehu N (2012) Correlation and path analysis in Ethiopian fenogreek (Trigonella foenum-graecum L.) landraces. Crown Res Educ 2:132–142

    Google Scholar 

  • González-Paleo L, Ravetta DA (2015) Carbon acquisition strategies uncoupled from predictions derived from species life-cycle. Flora 212:1–9

    Article  Google Scholar 

  • Grace JB, Anderson TM, Olff H, Scheiner SM (2010) On the specification of structural equation models for ecological systems. Ecol Monogr 80:67–87

    Article  Google Scholar 

  • Grace JB, Schoolmaster DR, Guntenspergen GR, Little AM, Mitchell BR, Miller KM, Schwelger EW (2012) Guidelines for a graph-theoretic implementation of structural equation modeling. Ecosphere 3:73. doi:10.1890/ES12-00048.1

    Article  Google Scholar 

  • Granier C, Vile D (2014) Phenotyping and beyond: modeling the relationships between traits. Curr Opin Plant Biol 18:96–102

    Article  PubMed  Google Scholar 

  • Hirsinger F, Knowles PF (1984) Morphological and agronomic description of selected Cuphea germplasm. Econ Bot 38:439–451

    Article  Google Scholar 

  • Huang Q, Zhao Y, Liu C, Zou X, Cheng Y, Fu G, Xu J, Zhan X, Lu G (2015) Evaluation of and selection criteria for drought resistance in Chines semi-winter rapeseed varieties at different developmental stages. Plant Breed 134:542–550

    Article  CAS  Google Scholar 

  • Jaradat AA (2012) Reproductive allocation and nutrient relationships in Cuphea: a semi-domesticated oilseed crop. J Plant Nutr 35:1579–1599

    Article  CAS  Google Scholar 

  • Jaradat AA (2016a) The integrated phenotype and plasticity of Cuphea PSR23: a semi-domesticated oilseed crop. Commun Biom Crop Sci 11:10–30

    Google Scholar 

  • Jaradat AA (2016b) Evolution of Cuphea under cultivation. Euphytica 210(1):41–55. doi:10.1007/s10681-0.16-1694-2

    Article  CAS  Google Scholar 

  • Jaradat AA, Rinke JL (2008a) Phenotypic divergence and population variation in Cuphea. J Agron 7(1):25–32

    Article  Google Scholar 

  • Jaradat AA, Rinke JL (2008b) Flowering, capsule and seed characteristics in Cuphea. Euphytica 161:447–459

    Article  Google Scholar 

  • Jaradat AA, Rinke JL (2009) Dynamics of macro- and micronutrients in Cuphea. J Plant Nutr 32:1383–1406

    Article  CAS  Google Scholar 

  • Jaradat AA, Rinke JL (2010) Nutrient homeostasis, C:N:S ratios, protein, and oil content in Cuphea seed. Seed Sci Biotechnol 4(1):1–9

    Google Scholar 

  • Jaradat AA, Rinke JL (2014) Empirical modeling of the impact of Mollisol soils variation on performance of Cuphea: a potential oilseed crop. Aust J Crop Sci 8:1093–1113

    CAS  Google Scholar 

  • Jöreskog K, Sörbom D (2015) LISREL 9.20. Scientific Software International, Inc

  • Kahlon CS, Board JE, Kang MS (2011) An analysis of yield component changes for new vs. old soybean cultivars. Agron J 103:13–22

    Article  Google Scholar 

  • Knapp SJ, Crane JM (2000) Registration of reduced shattering Cuphea germplasm PSR23. Crop Sci 41:299–300

    Article  Google Scholar 

  • Kumar A, Pathak RK, Gupta SM, Guar VS, Pandey D (2015) Systems biology for smart crops and agricultural innovation: filling the gaps between genotype and phenotype for complex traits linked with robust agricultural productivity and sustainability. OMICS 19:581–601

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lamb EG, Shritliffe SJ, May WE (2011) Structural equation modeling in the plant sciences: an example using yield components in oat. Can J Plant Sci 91:603–619

    Article  Google Scholar 

  • Lefcheck JS (2015) piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol Evol. doi:10.1111/2041-210X.12512

    Google Scholar 

  • Ma B-L, Biswas DK, Herath AW, Whalen JK, Ruan SQ, Caldwell C, Earl H, Vanasse A, Scott P, Smith DL (2015) Growth, yield, and yield components of canola as affected by nitrogen, sulfur, and boron application. J Plant Nutr Soil Sci 178:658–670

    Article  CAS  Google Scholar 

  • Madry W, Studnicki M, Rozbicki J, Golba J, Gozdowski D, Pecio A, Olesky A (2015) Ontogenetic-based sequential path analysis of grain yield and its related traits in several winter wheat cultivars. Acta Agric Scand Sect B 65:605–618

    CAS  Google Scholar 

  • Mahbub MM, Rahman MM, Hossain MS, Mahmud F, Mir kabir MM (2015) Genetic variability, correlation and path analysis for yield and yield components in soybean. Am-Eurasian J Agric Environ Sci 15:231–236

    CAS  Google Scholar 

  • Malik MFA, Qurashi AS, Ashraf M, Ghafoor A (2006) Genetic variability of the main yield related characters in soybean. Intl J Agric Biol 8:815–819

    Google Scholar 

  • Marsh HW, Morin A, Parker P, Kaur G (2014) Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis. Ann Rev Clin Psychol 10:85–110

    Article  Google Scholar 

  • Mason CM, Goolsby EW, Humphreys DP, Donovan LA (2016) Phylogenetic structural equation modelling reveals no need for an “origin” of the leaf economics spectrum. Ecol Lett 19:54–61

    Article  PubMed  Google Scholar 

  • Mehmood T, Ahmed B (2016) The diversity in the applications of partial least squares: an overview. J Chemom 30:4–17

    Article  CAS  Google Scholar 

  • Oguchi R, Ozaki H, Hanada K, Hikosaka K (2016) Which plant trait explains the variations in relative growth rate and its response to elevated carbon dioxide concentration among Arabidopsis thaliana ecotypes derived from a variety of habitats? Oecologia 180:865–876

    Article  PubMed  Google Scholar 

  • Onogi A, Ideta O, Yoshioka T, Ebana K, Yamasaki M (2016) Uncovering a nuisance influence of a phenological traits of plants using a nonlinear structural equation: application to days to heading and culm length in Asian cultivated rice (Oryza sativa L.). PLoS ONE 11(2):e0148609. doi:10.1371/journal.pone.0148609

    Article  PubMed  PubMed Central  Google Scholar 

  • Rasband WS (2016) ImageJ v 1.49u: Image processing and analysis in Java. U.S. National Institute of Health, Bethesda, MD. http://Imagej.nih.gov/ij. Accessed 12 May 2016

  • Rayan (2013) VARIAN-Rapid measurements of major, minor and trace elements in plant and animal materials using Varian 730-ES, www.varianinc.com. Accessed 12 May 2016

  • Reichmann LG, Schwinning S, Polley HW, Fay PA (2016) Traits of an invasive grass conferring an early growth advantage over native grasses. J Plant Ecol 9(6):672–681. doi:10.1098/rstb.2015.0273

    Article  Google Scholar 

  • Sabaghnia N, Dehghani H, Alizadeh B, Mohghaddam M (2010) Interrelationships between seed yield and 20 related traits of 49 canola (Brassica napus L.) genotypes in non-stressed and water-stressed environments. Spanish J Agric Res 8:356–370

    Article  Google Scholar 

  • SAS Institute Inc. (2016) JMP® 12.1.0, Cary, NC, 1989-2016

  • Selvarasu A, Kandhasamy R (2013) Analysis of variability, correlation and path coefficients in induced mutants of Glory Lily (Gloriosa superba L.). Intl J Plant Breed 7:69–75

    Google Scholar 

  • Setotaw TA, Belayneh SG, Gwinner R, Ferreira JL (2014) Developing selection criteria based on an ontogenetic path analysis approach to improve yield in barley. Genet Mol Res 13:4635–4646

    Article  CAS  PubMed  Google Scholar 

  • Severino LS, Auld DL (2013a) A framework for the study of the growth and development of castor plant. Ind Crops Prod 46:25–38

    Article  Google Scholar 

  • Severino LS, Auld DL (2013b) Seed yield and yield components of castor influenced by irrigation. Ind Crops Prod 49:52–60

    Article  CAS  Google Scholar 

  • Tadesse T, Singh H, Weyssa B (2016) Correlation and path coefficient analysis among seed yield traits and oil content in Ethiopian linseed germplasm. Int J Sustain Crop Prod 4:8–16

    Google Scholar 

  • Vanhaeren H, Inze D, Gonzalez N (2016) Plant growth beyond limits. Trends Plant Sci 21:102–109

    Article  CAS  PubMed  Google Scholar 

  • Vilela A, Cariaga R, Gonzalez-Paleo L, Ravetta D (2008) Trade-offs between reproductive allocation and storage in species of Oenothera L. (Onagraceae) native to Argentina. Acta Oecologia 33:85–92

    Article  Google Scholar 

  • Virot M, Tomao V, Colnagui G, Visinoni F, Chemat F (2007) New microwave-integrated Soxhlet extraction: an advantageous tool for the extraction of lipids from food products. J Chromatogr 1174:138–144

    Article  CAS  Google Scholar 

  • Zhang T, Lamb EG, Soto-Cerda B, Duguid S, Cloutier S, Rowland G, Diederichsen A, Booker HM (2014) Structural equation modeling of the Canadian flax (Linum usitatissimum L.) core collection for multiple phenotypic traits. Can J Plant Sci 94:1325–1332

    Article  Google Scholar 

  • Zhang Y, Wang Z, Wang Y (2016) Multi-hierarchical profiling: an emerging and quantitative approach to characterizing diverse biological networks. Brief Bioinform. doi:10.1093/bib/bbv112

    Google Scholar 

Download references

Acknowledgements

Thanks to Jana Rinke who managed field experiments, performed digital measurements, and carried out chemical analyses; and to Jay Hanson who performed part of the chemical analyses. USDA is an equal-opportunity provider and employer.

Author information

Authors and Affiliations

  1. ARS-USDA & U Minnesota, Morris, MN, USA

    Abdullah A. Jaradat

Authors
  1. Abdullah A. Jaradat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdullah A. Jaradat.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jaradat, A.A. Meta-analyses of oil yield in Cuphea PSR23. Euphytica 213, 210 (2017). https://doi.org/10.1007/s10681-017-1993-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10681-017-1993-2

Keywords

Search

Navigation