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Leaf area estimation in Jatropha curcas (L.): an update

1 Plant Physiology Laboratory, Federal University of Pernambuco, Department of Botany, CCB, Recife, PE, Brazil, 50670901
2 Faculty of Agricultural Science, University of Córdoba, Montería, Colombia

Special Issues: Biofuels: how they can improve the world

We aimed to propose a reliable and accurate model using non-destructive measurements of leaf length (L) and/or width (W) for estimating leaf area (LA) of three genotypes of purging nut (Jatropha curcas L.), i.e. Astrea and Barranca, both Colombian and a Mexican genotype. For model construction, ~500 healthy leaves were collected from at least 20 healthy plants naturally grown at each locations, and encompassed the full spectrum of measurable leaf sizes. Equations with L and/or W were generated; however, the best fit were made when product between L and W (LW). To validate these models, independent data set of 450 leaves were used. Thus, we developed a single model (Yi = β0 * LWβ1) with high precision and accuracy, random dispersion pattern of residuals and unbiased. In spite of this, a single equation, joining all the studied genotypes, was developed [LA = 0.822 * (LW)0.983; standard errors: β0 = 0.020, β1 = 0.004; R2a = 0.976], which greatly simplifies the process of measuring the leaf area of the genotypes, a fact that should be highlighted.
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Keywords purging nut; estimate model; leaf length; leaf width; reevaluation

Citation: Marcos André dos Santos, Alfredo Jarma-Orozco, Flavio Lozano-Isla, José Nailson S. Barros, Jesús Rivera, Carlos Espitia-Romero, Ángela Castillejo-Morales, Betty Jarma-Arroyo, Marcelo Francisco Pompelli. Leaf area estimation in Jatropha curcas (L.): an update. AIMS Environmental Science, 2018, 5(5): 353-371. doi: 10.3934/environsci.2018.5.353


  • 1. Pecina-Quintero V, Anaya-López JL, Zamarripa-Colmenero A, et al. (2014) Genetic structure of Jatropha curcas L. in Mexico and probable centre of origin. Biomass Bioenerg 60: 147–155.
  • 2. Silitonga AS, Hassan MH, Ong HC, et al. (2017) Analysis of the performance, emission and combustion characteristics of a turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends using kernel-based extreme learning machine. Environ Sci Pollut Res 24: 25383–25405.    
  • 3. Mardhiah HH, Ong HC, Masjuki HH, et al. (2017) Investigation of carbon-based solid acid catalyst from Jatropha curcas biomass in biodiesel production. Energy Convers Manag 144: 10–17.    
  • 4. Alburquerque N, García-Almodóvar RC, Valverde JM, et al. (2017) Characterization of Jatropha curcas accessions based in plant growth traits and oil quality. Ind Crops Prod 109: 693–698.    
  • 5. Corte-Real N, Endres L, Santos KPO, et al. (2016) Morphoanatomy and ontogeny of the fruit and seeds of Jatropha curcas L.: A promising biofuel plant. In: Segura-Campos MR, Betancur-Ancova D, editors. The Promising Future of Jatropha curcas: Proprieties and potential applications. Hauppauge, NY: Nova Science Publishers, Inc. pp. 141–158.
  • 6. Heller J (1996) Physic nut. Jatropha curcas L. Promoting the conservation and use of underutilized and neglected crops. Rome: International Plant Genetic Resources Institute (IPGRI). 66 p.
  • 7. Pandey VC, Singh K, Singh JS, et al. (2012) Jatropha curcas: A potential biofuel plant for sustainable environmental development. Renew Sust Energy Rev 16: 2870–2883.    
  • 8. Pompelli MF, Ferreira DTRG, Cavalcante PPGS, et al. (2010) Environmental influence on the physico-chemical and physiological properties of Jatropha curcas L. seeds. Aust J Bot 58: 421–427.    
  • 9. Contran N, Chessa L, Lubino M, et al. (2013) State-of-the art of the Jatropha curcas productive chain: From sowing to biodiesel and by-products. Ind Crops Prod 42: 202–215.    
  • 10. Divakara BN, Upadhyaya HD, Wani SP, et al. (2010) Biology and genetic improvement of Jatropha curcas L. A review. Appl Energ 87: 732–742.    
  • 11. Pompelli MF, Orozco AJ, Oliveira MTO, et al. (2011) Crise energética mundial e o papel do Brasil na problemática de biocombustíveis. Agronomía Colombiana 29: 361–371.
  • 12. Openshaw K (2000) A review of Jatropha curcas: an oil plant of unfulfilled promise. Biomass Bioenerg 19: 1–15.    
  • 13. Chel A, Kaushik G (2011) Renewable energy for sustainable agriculture. Agron Sustain Dev 31: 91–118.    
  • 14. Gutiérrez-Antonio C, Romero-Izquierdo AG, Gómez-Castro FI, et al. (2016) Simultaneous energy integration and intensification of the hydrotreating process to produce biojet fuel from Jatropha curcas. Chem Eng Process 110: 134–145.    
  • 15. Beaver A, Castaño AG, Díaz MS (2016) Life cycle analysis of Jatropha curcas as a sustainable biodiesel feedstock in Argentina. Chem Engin Trans 50.
  • 16. Galati A, Crescimanno M, Gristina L, et al. (2016) Actual provision as an alternative criterion to improve the efficiency of payments for ecosystem services for C sequestration in semiarid vineyards. Agric Systems 144: 58–64.    
  • 17. Keesstra SD, Bouma J, Wallinga J, et al. (2016) The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals. Soil 2: 111–128.    
  • 18. Brittaine R, Lutaladio N (2010) Jatropha: A Smallholder Bioenergy Crop The Potential for Pro-Poor Development. Rome Italy: Plant Production and Protection Division FAO. 96 p.
  • 19. Díaz S, Kattge J, Cornelissen JHC, et al. (2016) The global spectrum of plant form and function. Nature 529: 167–171.    
  • 20. Cristofori V, Rouphael Y, Mendoza-de Gyves E, et al. (2007) A simple model for estimating leaf area of hazelnut from linear measurements. Sci Hort 113: 221–225.    
  • 21. Pompelli MF, Antunes WC, Ferreira DTRG, et al. (2012) Allometric models for non-destructive leaf area estimation of the Jatropha curcas. Biomass Bioenerg 36: 77–85.    
  • 22. Blanco FF, Folegatti MV (2005) Estimation of leaf area for greenhouse cucumber by linear measurements under salinity and grafting. Sci Agr 62: 305–309.    
  • 23. Antunes WC, Pompelli MF, Carretero DM, et al. (2008) Allometric models for non-destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora). Ann Appl Biol 153: 33–40.    
  • 24. Keramatlou I, Sharifani M, Sabouri H, et al. (2015) A simple linear model for leaf area estimation in Persian walnut (Juglans regia L.). Sci Hort 184: 36–39.    
  • 25. Liu Z, Zhu Y, Li F, et al. (2017) Non-destructively predicting leaf area, leaf mass and specific leaf area based on a linear mixed-effect model for broadleaf species. Ecol Indic 78: 340–350.    
  • 26. Peksen E (2007) Non-destructive leaf area estimation model for faba bean (Vicia faba L.). Sci Hortic-Amsterdam 113: 322–328.    
  • 27. Thomas B (2017) Leaf Development. In: Thomas B, Murray BG, Murphy DJ, editors. Encyclopedia of Applied Plant Sciences (Second Edition). San Diego: Elsevier. pp. 191–197.
  • 28. Kirkman LK, Sharitz RR (1994) Vegetation disturbance and maintenance of diversity in intermittently flooded Carolina bays in South Carolina. Ecol Appl 4: 177–188.    
  • 29. Collins SL (1987) Interaction of disturbance in tallgrass prairie: a field experiment. Ecology 68: 1243–1250.    
  • 30. Steel M, Penny D (2000) Parsimony, likelihood, and the role of models in molecular phylogenetics. Mol Biol Evol 17: 839–850.    
  • 31. Cumming G, Fidler F, Vaux DL (2007) Error bars in experimental biology. J Cell Biol 177: 7–11.    
  • 32. Walther BA, Moore JL (2005) The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography 28: 815–829.    
  • 33. Messier J, McGill BJ, Lechowicz MJ (2010) Ecol Lett 13: 838–848.
  • 34. Pinheiro J, Bates D, DebRoy S, et al. (2017) nlme:Linear and Nonlinear Mixed Effects Models. R package version 3.1–131. Viena, Austria: R Foundation for Statistical Computing.
  • 35. Achten WMJ, Maes WH, Reubens B, et al. (2010) Biomass production and allocation in Jatropha curcas L. seedlings under different levels of drought stress. Biomass Bioenerg 34: 667–676.
  • 36. Severino LS, Vale LS, Beltrão NEM (2007) A simple method for measurement of Jatropha curcas leaf area. Rev Bras Ol Fibros 11: 9–14.
  • 37. Ahmed N, Khan D (2011) Leaf area estimation in Jatropha curcas L. Int J Biol Biotech 8: 401–407.
  • 38. Zuur AF, Elena NI, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Method Ecol Evol 1: 3–14.    
  • 39. Kandiannan K, Parthasarathy U, Krishnamurthy KS, et al. (2009) Modeling individual leaf area of ginger (Zingiber officinale Roscoe) using leaf length and width. Sci Hortic-Amsterdam 120: 532–537.    
  • 40. Souza MC, Amaral CL (2015) Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less. Braz J Biol 75: 152–156.    
  • 41. Villar R, Ruiz-Robleto J, Ubera JL, et al. (2013) Exploring variation in leaf mass per area (LMA) from leaf to cell: an anatomical analysis of 26 woody species. Am J Bot 100: 1969–1980.    
  • 42. Zhang L, Pan L (2011) Allometric models for leaf area estimation across different leaf-age groups of evergreen broadleaved trees in a subtropical forest. Photosynthetica 49: 219–226.    
  • 43. Tondjo K, Brancheriau L, Sabatier SA, et al. (2015) Non-destructive measurement of leaf area and dry biomass in Tectona grandis. Trees 29: 1625–1631.    


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