Strategies in the Genetic Breeding of Jatropha curcas for Biofuel Production in Brazil

  • Bruno Galvêas LaviolaEmail author
  • Erina Vitório Rodrigues
  • Larissa Pereira Ribeiro
  • Lidiane Aparecida Silva
  • Leonardo de Azevedo Peixoto
  • Leonardo Lopes Bhering


The global challenge is to increase food production in a sustainable way, given that most of the energy used comes from fossil fuels, which causes unsustainable damage to the environment, such as the greenhouse gas emissions. Aiming at diversifying the Brazilian energy matrix, the use of biofuels emerged as a promising alternative. In this context, it is important to emphasize that soybean sustains most of the biodiesel and biokerosene markets (79.1%), so it is highly dependent on this crop, which constitutes a threat concerning economical security issues. In this way, it is the need of the hour to invest in diversification of potential raw materials for biofuel production, such as Jatropha, which has been identified to present a high content of quality oil suitable for biofuels. However, the seed and oil yields per hectare of Jatropha are still too low to be economically sustainable for farmers. This situation requires the development of improved cultivars. Several research efforts with this crop have already been initiated in Brazil. However, there is still much to be done in order to bring Jatropha to the level of a commercial crop able to deliver a suitable return on farming. Considering that it presents long breeding cycles, it is important to adopt strategies for increasing the selection efficiency and genetic gain, as well as for decreasing the cultivar generation time. In view of the considerations given above, the purpose of this chapter is to integrate the information available in the literature and to report on the most promising approaches of genetics and biotechnology for the selective breeding of improved Jatropha cultivars in Brazil.


Breeding populations Genome-wide selection Recurrent selection Renewable energy 


  1. Achten WM, Nielsen LR, Aerts R et al (2010) Towards domestication of Jatropha curcas. Biofuels 1(1):91–107Google Scholar
  2. Aguilera-Cauich EA, Pérez-Brito D, Yabur AN et al (2015) Assessment of phenotypic diversity and agronomic contrast in American accessions of Jatropha curcas L. Ind Crop Prod 77:1001–1003Google Scholar
  3. Alves AA, Laviola BG, Formighieri EF et al (2015) Perennial plants for biofuel production: bridging genomics and field research. Biotechnol J 10(4):505–507PubMedGoogle Scholar
  4. Anggraeni TDA, Satyawan D, Kang YJ et al (2018) Genetic diversity of Jatropha curcas collections from different islands in Indonesia. Plant Genet Resour 1–9Google Scholar
  5. Azevedo CF, de Resende MDV, e Silva FF et al (2015) Ridge, Lasso and Bayesian additive-dominance genomic models. BMC Genet 16(1):1Google Scholar
  6. Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48(5):1649–1664Google Scholar
  7. Bernardo R, Yu J (2007) Prospects for genome wide selection for quantitative traits in maize. Crop Sci 47(3):1082–1090Google Scholar
  8. Bhering L, Cruz C, Laviola B (2011) Biometria aplicada ao melhoramento de espécies alternativas para produção de biodiesel. In: Cardoso DL, da Luz LN, Pereira TNS (eds) Estratégias em melhoramento de plantas. Arka, Viçosa, pp 90–129Google Scholar
  9. Borém A, Miranda GV (2013) Melhoramento de Plantas, vol 6. UFV, ViçosaGoogle Scholar
  10. Carvalho CR, Clarindo WR, Praca MM et al (2008) Genome size, base composition and karyotype of Jatropha curcas L., an important biofuel plant. Plant Sci 174(6):613–617Google Scholar
  11. Cavalcanti JJV, Resende MDV, FHCd S et al (2012) Simultaneous prediction of the effects of molecular markers and genome wide selection in cashew. Rev Bras Frutic 34(3):840–846Google Scholar
  12. Changwei L, Kun L, You C et al (2007) Pollen viability, stigma receptivity and reproductive features of Jatropha curcas L.(Euphorbiaceae). Acta Bot Bor Occ Sin 27(10):1994–2001Google Scholar
  13. Cremonez PA, Feroldi M, Nadaleti WC et al (2015) Biodiesel production in Brazil: current scenario and perspectives. Renew Sust Energ Rev 42:415–428Google Scholar
  14. Crossa J, de Los Campos G, Pérez P et al (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186(2):713–724PubMedPubMedCentralGoogle Scholar
  15. Daetwyler HD, Villanueva B, Woolliams JA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3(10):e3395PubMedPubMedCentralGoogle Scholar
  16. Daetwyler HD, Pong-Wong R, Villanueva B et al (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185(3):1021–1031PubMedPubMedCentralGoogle Scholar
  17. De Los Campos G, Gianola D, Rosa G (2009a) Reproducing kernel Hilbert spaces regression: a general framework for genetic evaluation. J Anim Sci 87(6):1883–1887PubMedGoogle Scholar
  18. De Los Campos G, Naya H, Gianola D et al (2009b) Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182(1):375–385Google Scholar
  19. de Oliveira EJ, de Resende MDV, Santos VD et al (2012) Genome-wide selection in cassava. Euphytica 187(2):263–276. Google Scholar
  20. Dharma S, Masjuki H, Ong HC et al (2016) Optimization of biodiesel production process for mixed Jatropha curcas–Ceiba pentandra biodiesel using response surface methodology. Energy Convers Manag 115:178–190Google Scholar
  21. Divakara B, Upadhyaya H, Wani S et al (2010) Biology and genetic improvement of Jatropha curcas L.: a review. Appl Energy 87(3):732–742Google Scholar
  22. Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4(3):250–255Google Scholar
  23. Falconer D, Mackay T (1996) Introduction to quantitative genetics. Longman Scientific & Technical, HarlowGoogle Scholar
  24. Goddard ME, Hayes B (2007) Genomic selection. J Anim Breed Genet 124(6):323–330PubMedGoogle Scholar
  25. Grattapaglia D, Resende MD (2011) Genomic selection in forest tree breeding. Tree Genet Genome 7(2):241–255Google Scholar
  26. Habier D, Fernando R, Dekkers J (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177(4):2389–2397PubMedPubMedCentralGoogle Scholar
  27. Habier D, Fernando RL, Kizilkaya K et al (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinforma 12(1):1Google Scholar
  28. Hallauer AR, Carena MJ, Miranda Filho JD (2010) Quantitative genetics in maize breeding, vol 6. Springer, New YorkGoogle Scholar
  29. Hayes BJ, Bowman PJ, Chamberlain AJ et al (2009) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92(2):433–443PubMedGoogle Scholar
  30. Horbach MA, Malavasi UC, de Matos Malavasi M (2014) Propagation methods for physic nut (Jatropha curcas). Adv For Sci 1(1):53–57Google Scholar
  31. Hull FH (1945) Recurrent selection for specific combining ability in corn 1. Agron J 37(2):134–145Google Scholar
  32. Iwata H, Hayashi T, Terakami S et al (2013) Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia). BMC Genet 14(1):1Google Scholar
  33. Jaccoud D, Peng K, Feinstein D et al (2001) Diversity arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Res 29:e25PubMedPubMedCentralGoogle Scholar
  34. Kumar A, Sharma S (2008) An evaluation of multipurpose oil seed crop for industrial uses (Jatropha curcas L.): a review. Ind Crop Prod 28(1):1–10Google Scholar
  35. Kumar S, Chagné D, Bink MC et al (2012) Genomic selection for fruit quality traits in apple (Malus× domestica Borkh.). PLoS One 7(5):e36674PubMedPubMedCentralGoogle Scholar
  36. Kumar Y, Ringenberg J, Depuru SS et al (2016) Wind energy: trends and enabling technologies. Renew Sust Energ Rev 53:209–224Google Scholar
  37. Laviola B, Rocha RB, Kobayashi AK et al (2010a) Genetic improvement of Jatropha for biodiesel production. Ceiba 51(1):1–10. Google Scholar
  38. Laviola BG, Rosado TB, Bhering LL et al (2010b) Genetic parameters and variability in physic nut accessions during early developmental stages. Pesq Agrop Bras 45(10):1117–1123Google Scholar
  39. Laviola BG, dos Anjos SD, Juhász ACP et al (2014) Desempenho agronômico e ganho genético pela seleção de pinhão-manso em três regiões do Brasil. Pesq Agrop Bras 49(5):356–363Google Scholar
  40. Limón J, Rodriguez MA, Sánchez J et al (2012) Metodología bayesiana para la optimización simultánea de múltiples respuestas. Inf Tecnol 23(2):151–166 SpanishGoogle Scholar
  41. Lucena AMA, Vasconcelos GCL, de Lucena Medeiros KAA et al (2014) Características morfológicas de peças reprodutivas de acessos de Jatropha curcas L. Scientia Plena 10(4):1–9Google Scholar
  42. Maghuly F, Jankowicz-Cieslak J, Pabinger S et al (2015) Geographic origin is not supported by the genetic variability found in a large living collection of Jatropha curcas with accessions from three continents. Biotechnol J 10(4):536–551PubMedGoogle Scholar
  43. Massman JM, Jung HJG, Bernardo R (2013) Genome wide selection versus marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize. Crop Sci 53(1):58–66Google Scholar
  44. Meuwissen TH (2007) Genomic selection: marker assisted selection on a genome wide scale. J Anim Breed Genet 124(6):321–322PubMedGoogle Scholar
  45. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829PubMedPubMedCentralGoogle Scholar
  46. Moniruzzaman M, Yaakob Z, Khatun R (2016) Biotechnology for Jatropha improvement: a worthy exploration. Renew Sust Energ Rev 54:1262–1277Google Scholar
  47. Montes JM, Melchinger AE (2016) Domestication and breeding of Jatropha curcas L. Trends Plant Sci 21(12):1045–1057PubMedGoogle Scholar
  48. Neale DB, Kremer A (2011) Forest tree genomics: growing resources and applications. Nat Rev Genet 12(2):111–122PubMedGoogle Scholar
  49. Paramathma M, Venkatachalam (2007) Jatropha improvement, management and production of biodiesel. Centre of excellence in biofuels. Agricultural Engineering College and Research Institute, CoimbatoreGoogle Scholar
  50. Peixoto LA, Laviola BG, Bhering LL et al (2016) Oil content increase and toxicity reduction in Jatropha seeds through family selection. Ind Crop Prod 80:70–76Google Scholar
  51. Peixoto LA, Laviola BG, Alves AA et al (2017) Breeding Jatropha curcas by genomic selection: a pilot assessment of the accuracy of predictive models. PLoS One 12(3):e0173368Google Scholar
  52. Punia M (2007) Current status of research and development on Jatropha (Jatropha curcas) for sustainable biofuel production in India. In: USDA global conference on agricultural biofuels: research and economics. pp 20–22Google Scholar
  53. Ramalho M, Abreu AF, Jd S et al (2012) Aplicações da genética quantitativa no melhoramento de plantas autógamas. UFLA, Portuguese, LavrasGoogle Scholar
  54. Resende MDV (2002) Genética biométrica e estatística no melhoramento de plantas perenes. Embrapa, Brasília, p 975 ISBN-10:8573831618, PortugueseGoogle Scholar
  55. Resende MD (2007) SELEGEN-REML/BLUP: sistema estatístico e seleção genética computadorizada via modelos lineares mistos. Embrapa, Brasília 359 p, ISBN-10: 8589281167, PortugueseGoogle Scholar
  56. Resende MDV, Lopes PS, Silva RL et al (2008) Seleção genômica ampla (GWS) e maximização da eficiência do melhoramento genético. Pesq Flores Bras 56:63–77Google Scholar
  57. Resende MDV, Resende MFR, Sansaloni CP et al (2012a) Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol 194(1):116–128. PubMedGoogle Scholar
  58. Resende MFR, Munoz P, Acosta JJ et al (2012b) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193(3):617–624PubMedGoogle Scholar
  59. Resende M, Silva F, Azevedo C (2014) Estatística matemática, biométrica e computacional. Suprema, Visconde do Rio Branco 881 pGoogle Scholar
  60. Rocha RB, Ramalho AR, Teixeira AL et al (2012) Eficiência da seleção para incremento do teor de óleo do pinhão-manso. Pesq Flores Bras 47(1):44–50Google Scholar
  61. Rosado TB, Laviola BG, Faria DA et al (2010) Molecular markers reveal limited genetic diversity in a large germplasm collection of the biofuel crop Jatropha curcas L. in Brazil. Crop Sci 50(6):2372–2382Google Scholar
  62. Sanou H, Angulo-Escalante MA, Martínez-Herrera J et al (2015) Loss of genetic diversity of Jatropha curcas L. through domestication: implications for its genetic improvement. Crop Sci 55(2):749–759Google Scholar
  63. Santos D, Ferreira J, Pasqual M et al (2016) Population structure of Jatropha and its implication for the breeding program. Genet Mol Res 15(1)Google Scholar
  64. Silitonga A, Atabani A, Mahlia T et al (2011) A review on prospect of Jatropha curcas for biodiesel in Indonesia. Renew Sust Energ Rev 15(8):3733–3756Google Scholar
  65. Silva M, Peternelli L, Nascimento M et al (2013) Modelos mistos na seleção de famílias de cana-de-açúcar aparentadas sob o enfoque clássico e bayesiano. Rev Bras Biomet 31:1–12Google Scholar
  66. Soontornchainaksaeng P, Jenjittikul T (2003) Karyology of Jatropha (Euphorbiaceae) in Thailand. Thai For Bull 31:105–112Google Scholar
  67. Spinelli VM, Rocha RB, Ramalho AR (2010) Componentes primários e secundários do rendimento de óleo de pinhão-manso. Ciênc Rural 40(8):1752–1758Google Scholar
  68. Surwenshi A, Kumar V, Shanwad U (2011) Critical review of diversity in Jatropha curcas for crop improvement: a candidate biodiesel crop. Res J Agric Sci 2(2):193–198Google Scholar
  69. Takase M, Zhao T, Zhang M (2015) An expatiate review of neem, Jatropha, rubber and karanja as multipurpose non-edible biodiesel resources and comparison of their fuel, engine and emission properties. Renew Sust Energ Rev 43:495–520Google Scholar
  70. Tiwari AK, Kumar A, Raheman H (2007) Biodiesel production from jatropha oil (Jatropha curcas) with high free fatty acids: an optimized process. Biomass Bioenergy 31(8):569–575Google Scholar
  71. Trebbi D, Papazoglou EG, Saadaoui E et al (2015) Assessment of genetic diversity in different accessions of Jatropha curcas. Ind Crop Prod 75:35–39Google Scholar
  72. Viana AP, Resende Md (2014) Genética Quantitaiva no Melhoramento de Fruteiras, vol 1Google Scholar
  73. Viana AP, MDVd R, Riaz S et al (2016) Genome selection in fruit breeding: application to table grapes. Sci Agric 73(2):142–149Google Scholar
  74. Weyhrich RA, Lamkey KR, Hallauer AR (1998) Responses to seven methods of recurrent selection in the BS11 maize population. Crop Sci 38(2):308–321Google Scholar
  75. Wong CK, Bernardo R (2008) Genome wide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theor Appl Genet 116(6):815–824PubMedGoogle Scholar
  76. Zhang Z, Guo X, Liu B, Tang L, Chen F (2011) Genetic diversity and genetic relationship of Jatropha curcas between China and Southeast Asian revealed by amplified fragment length polymorphisms. Afr J Biotechnol 10(15):2825–2832Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Bruno Galvêas Laviola
    • 1
    Email author
  • Erina Vitório Rodrigues
    • 2
  • Larissa Pereira Ribeiro
    • 3
  • Lidiane Aparecida Silva
    • 3
  • Leonardo de Azevedo Peixoto
    • 4
  • Leonardo Lopes Bhering
    • 3
  1. 1.Laboratório de Genética e Biotecnologia, Empresa Brasileira de Pesquisa Agropecuária (Embrapa)Embrapa AgroenergiaBrasíliaBrazil
  2. 2.Ciências da Vida e da TerraUniversidade de Brasília – Faculdade de Planaltina (UnB-FUP)BrasíliaBrazil
  3. 3.Laboratório de BiometriaUniversidade Federal de Viçosa (UFV)ViçosaBrazil
  4. 4.Monsanto Brasil, CENUSão Paulo/SPBrazil

Personalised recommendations