Skip to main content
SpringerLink
Log in

Location Assessment of Jatropha Cultivation for Biofuel Production in Fars Province, Iran: A Hybrid GIS-Based Fuzzy Multi-criteria Framework

  • Original Paper
  • Published:
Waste and Biomass Valorization Aims and scope Submit manuscript

Abstract

Biofuels, which are known to be a beneficial solution to control the destructive environmental effects of fossil fuels, can be produced from a wide range of feedstocks. Jatropha (Jatropha Curcas L.) is known as one of the most appropriate feedstocks used for the biofuel production. Since finding the optimal Jatropha cultivation site is the first step to produce biofuel, this study proposes a hybrid approach based on Geographic Information System (GIS) and decision-making techniques to assess most suitable Jatropha cultivation sites under uncertain conditions, to produce methyl-based biofuel in Fars province, Iran. In order to consider uncertain conditions in this research, fuzzy theory is used in decision-making techniques. To this aim, fuzzy best–worst method (FBWM) is employed to calculate the weight of sub-criteria, and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy multi-objective optimization on the basis of ratio analysis (MOORA) methods are used to rank suitable locations. The results indicate that 4% of the total study area, equal to 490,432 ha, mostly located in the southern zones of the study area, which can provide 59% of total produced Jatropha, is highly suitable for Jatropha planting. This research also indicates that Larestan and Zarrindasht, located in the southern half of Fars province, are the optimal places to cultivate Jatropha. These two counties have mostly warm and dry climate with more sunshine, which makes them appropriate for growing Jatropha. It is determined that approximately 3.9 million tons of Jatropha seeds can be harvested annually, which leads to produce 1.18 million tons of biofuel in Fars province per year. Ultimately, the variability of the final results in relation to the changes in the input data is examined through five different scenarios and the amount of Jatropha and biofuel produced in each scenario is reported.

Graphical Abstract

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

The data that support the finding of this study are available from the corresponding author on reasonable request.

References

  1. Zhu, T., Zhang, L., Li, Z., Wei, G., Xin, Z., Xiong, D., et al.: Partial hydrogenation of Jatropha oil biodiesel catalyzed by nickel/bentonite catalyst. Waste Biomass Valor. 12, 465–474 (2020). https://doi.org/10.1007/s12649-020-00977-8

    Article  Google Scholar 

  2. Zhou, X., Wang, F., Hu, H., Yang, L., Guo, P., Xiao, B.: Assessment of sustainable biomass resource for energy use in China. Biomass Bioenerg. 35, 1–11 (2011). https://doi.org/10.1016/j.biombioe.2010.08.006

    Article  Google Scholar 

  3. Afkhami, P., Zarrinpoor, N.: The energy-water-food-waste-land nexus in a GIS-based biofuel supply chain design: a case study in Fars Province. Iran. J. Clean. Prod. (2022). https://doi.org/10.1016/j.jclepro.2022.130690

    Article  Google Scholar 

  4. Lovett, A.A., Sünnenberg, G.M., Riche, A.B., Karp, A.: Land use implications of increased biomass production identified by GIS-based suitability and yield mapping for Miscanthus in England. Bioenergy Res. 2, 17–28 (2009). https://doi.org/10.1007/s12155-008-9030-x

    Article  Google Scholar 

  5. Thushari, I., Babel, S.: Biodiesel production from waste palm cooking oil using solid acid catalyst derived from coconut meal residue. Waste Biomass Valor. 11, 4941–4956 (2020). https://doi.org/10.1007/s12649-019-00820-9

    Article  Google Scholar 

  6. Zarrinpoor, N., Khani, A.: Designing a sustainable biofuel supply chain by considering carbon policies: a case study in Iran. Energy, Sustainability and Society 11, 1–22 (2021). https://doi.org/10.1186/s13705-021-00314-4

    Article  Google Scholar 

  7. Hui, W., Hassim, M.H., Ng, D.K.S.: Review of evolution, technology and sustainability assessments of biofuel production. J. Clean. Prod. 71, 11–29 (2014). https://doi.org/10.1016/j.jclepro.2014.01.006

    Article  Google Scholar 

  8. Bhuiya, M.M.K., Rasul, M.G., Khan, M.M.K., Ashwath, N., Azad, A.K.: Prospects of 2nd generation biodiesel as a sustainable fuel—part: 1 selection of feedstocks, oil extraction techniques and conversion technologies. Renew. Sustain. Energy Rev. 55, 1109–1128 (2016). https://doi.org/10.1016/j.rser.2015.04.163

    Article  Google Scholar 

  9. Afkhami, P., Zarrinpoor, N.: Optimization design of a supply chain for Jatropha-based biofuel from a sustainable development perspective considering international resources and demand: a case study. Ind. Eng. Chem. Res. 60, 6188–6207 (2021). https://doi.org/10.1021/acs.iecr.0c06209

    Article  Google Scholar 

  10. Mostafa, A., Pishvaee, M.S., Mohseni, S.: Third-generation biofuel supply chain: A comprehensive review and future research directions. J. Clean. Prod. (2021). https://doi.org/10.1016/j.jclepro.2021.129100

    Article  Google Scholar 

  11. Babazadeh, R., Razmi, J., Pishvaee, M.S.: Sustainable cultivation location optimization of the Jatropha curcas L. under uncertainty: a unified fuzzy data envelopment analysis approach. Measurement 89, 252–260 (2016). https://doi.org/10.1016/j.measurement.2016.03.063

    Article  Google Scholar 

  12. Corral, S., Nuez, D.L., De, L.D.R.: Integrated assessment of biofuel production in arid lands: Jatropha cultivation on the island of Fuerteventura. Renew. Sustain. Energy Rev. 52, 41–53 (2015). https://doi.org/10.1016/j.rser.2015.07.070

    Article  Google Scholar 

  13. Jain, S., Sharma, M.P.: Prospects of biodiesel from Jatropha in India: a review. Renew. Sustain. Energy Rev. 14, 763–771 (2010). https://doi.org/10.1016/j.rser.2009.10.005

    Article  Google Scholar 

  14. Achten, W.M., Verchot, L., Franken, Y.J., Mathijs, E., Singh, V.P., Aerts, R., et al.: Jatropha bio-diesel production and use. Biomass Bioenerg. 32, 1063–1084 (2008). https://doi.org/10.1016/j.biombioe.2008.03.003

    Article  Google Scholar 

  15. Kumar, A., Sharma, S.: An evaluation of multipurpose oil seed crop for industrial uses (Jatropha curcas): a review. Ind. Crops Prod. 28, 1–10 (2008). https://doi.org/10.1016/j.indcrop.2008.01.001

    Article  Google Scholar 

  16. The World Bank: 2020. https://www.worldbank.org/en/country/iran/overview

  17. Eghtesadifard, M., Afkhami, P., Bazyar, A.: An integrated approach to the selection of municipal solid waste landfills through GIS, K-means and multi-criteria decision analysis. Environ. Res. 185, 109348 (2020). https://doi.org/10.1016/j.envres.2020.109348

    Article  Google Scholar 

  18. Bahrani, S., Ebadi, T., Ehsani, H., Yousefi, H., Maknoon, R.: Modeling landfill site selection by multi-criteria decision making and fuzzy functions in GIS, case study: Shabestar, Iran. Environ. Earth Sci. 75, 1–14 (2016). https://doi.org/10.1007/s12665-015-5146-4

    Article  Google Scholar 

  19. Juan, D., Villacreses, G., Gaona, G., Martínez-g, J.: Wind farms suitability location using geographical information system (GIS), based on multi-criteria decision making (MCDM) methods: the case of continental Ecuador. Renew. Energy 109, 275–286 (2017). https://doi.org/10.1016/j.renene.2017.03.041

    Article  Google Scholar 

  20. Kaya, Ö., Tortum, A., Alemdar, K.D., Çodur, M.Y.: Site selection for EVCS in Istanbul by GIS and multi-criteria. Transp. Res. Part D 80, 102271 (2020). https://doi.org/10.1016/j.trd.2020.102271

    Article  Google Scholar 

  21. Tercan, E., Dereli, M.A.: Development of a land suitability model for citrus cultivation using GIS and multi-criteria assessment techniques in Antalya province of Turkey. Ecol. Ind. 117, 106549 (2020). https://doi.org/10.1016/j.ecolind.2020.106549

    Article  Google Scholar 

  22. Taddese, H.: Suitability analysis for Jatropha curcas production in Ethiopia—a spatial modeling approach. Environ. Syst. Res. 3, 25 (2014). https://doi.org/10.1186/s40068-014-0025-7

    Article  Google Scholar 

  23. Zyadin, A., Natarajan, K., Latva-Käyrä, P., Igli, A., Trishkin, M., Pelkonen, P., et al.: Estimation of surplus biomass potential in southern and central Poland using GIS applications. Renew. Sustain. Energy Rev. 89, 204–215 (2018). https://doi.org/10.1016/j.rser.2018.03.022

    Article  Google Scholar 

  24. Gorsevski, P.V., Donevska, K.R., Mitrovski, C.D., Frizado, J.P.: Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: a case study using ordered weighted average. Waste Manage. 32, 287–296 (2012). https://doi.org/10.1016/j.wasman.2011.09.023

    Article  Google Scholar 

  25. Voivontas, D., Assimacopoulos, D., Koukios, E.G.: Assessment of biomass potential for power production: a GIS based method. Biomass Bioenerg. 20, 101–112 (2001)

    Article  Google Scholar 

  26. Zhuang, D., Jiang, D., Liu, L., Huang, Y.: Assessment of bioenergy potential on marginal land in China. Renew. Sustain. Energy Rev. 15, 1050–1056 (2011). https://doi.org/10.1016/j.rser.2010.11.041

    Article  Google Scholar 

  27. Zambelli, P., Lora, C., Spinelli, R., Tattoni, C., Vitti, A., Zatelli, P., et al.: A GIS decision support system for regional forest management to assess biomass availability for renewable energy production. Environ. Model. Softw. 38, 203–213 (2012). https://doi.org/10.1016/j.envsoft.2012.05.016

    Article  Google Scholar 

  28. Natarajan, K., Latva-käyrä, P., Zyadin, A., Pelkonen, P.: New methodological approach for biomass resource assessment in India using GIS application and land use/land cover (LULC) maps. Renew. Sustain. Energy Rev. 63, 256–268 (2016). https://doi.org/10.1016/j.rser.2016.05.070

    Article  Google Scholar 

  29. Guerrero, A., Aguado, P., Sánchez, J., Curt, M.: GIS-based assessment of banana residual biomass potential for ethanol production and power generation : a case study. Waste Biomass Valor. 7, 405–415 (2016). https://doi.org/10.1007/s12649-015-9455-3

    Article  Google Scholar 

  30. Guilhermino, A., Lourinho, G., Brito, P., Almeida, N.: Assessment of the use of forest biomass residues for bioenergy in Alto Alentejo, Portugal: logistics, economic and financial perspectives. Waste Biomass Valor. 9, 739–753 (2018). https://doi.org/10.1007/s12649-017-9830-3

    Article  Google Scholar 

  31. Abbasi, M., Pishvaee, M.S., Bairamzadeh, S.: Land suitability assessment for Paulownia cultivation using combined GIS and Z-number DEA: a case study. Comput. Electron. Agric. 176, 105666 (2020). https://doi.org/10.1016/j.compag.2020.105666

    Article  Google Scholar 

  32. Lozano, F.J., Fabi, D., Flores-tlacuahuac, A.: GIS-based modeling of residual biomass availability for energy and production in Mexico B. Renew. Sustain. Energy Rev. 120, 109610 (2020). https://doi.org/10.1016/j.rser.2019.109610

    Article  Google Scholar 

  33. Babazadeh, R., Khalili, M., Toloo, M.: A data envelopment analysis method for location optimization of microalgae cultivation: a case study. Waste Biomass Valor. 11, 173–186 (2020). https://doi.org/10.1007/s12649-018-0371-1

    Article  Google Scholar 

  34. Hernández, F., Dirk Jaeger, U., Samperio, J.I.: Modeling forest woody biomass availability for energy use based on short-term forecasting scenarios. Waste Biomass Valor. 11, 2137–2151 (2020). https://doi.org/10.1007/s12649-018-0511-7

    Article  Google Scholar 

  35. Battuvshin, B., Matsuoka, Y., Shirasawa, H., Toyama, K., Hayashi, U.: Supply potential and annual availability of timber and forest biomass resources for energy considering inter-prefectural trade in Japan. Land Use Policy 97, 104780 (2020). https://doi.org/10.1016/j.landusepol.2020.104780

    Article  Google Scholar 

  36. Jusakulvijit, P., Bezama, A., Thrän, D.: The availability and assessment of potential agricultural residues for the regional development of second-generation bioethanol in Thailand. Waste Biomass Valori. (2021). https://doi.org/10.1007/s12649-021-01424-y

    Article  Google Scholar 

  37. Tapia, J.F.D., Doliente, S.S., Samsatli, S.: How much land is available for sustainable palm oil? Land Use Policy 102, 105187 (2021)

    Article  Google Scholar 

  38. Gamaralalage, D., Kanematsu, Y., Denny, K.S.N., Foong, S.Z.Y., Andiappan, V., Foo, D.C.Y., et al.: Life cycle assessment of international biomass utilization: a case study of Malaysian Palm Kernel shells for biomass power generation in Japan. Waste Biomass Valor. (2022). https://doi.org/10.1007/s12649-021-01643-3

    Article  Google Scholar 

  39. Sunil, N., Sivaraj, N., Anitha, K., Abraham, B., Kumar, V., Sudhir, E., et al.: Analysis of diversity and distribution of Jatropha curcas L. germplasm using Geographic Information System (DIVA-GIS). Genet. Resour. Crop Evol. 56, 115–119 (2009). https://doi.org/10.1007/s10722-008-9350-x

    Article  Google Scholar 

  40. Babazadeh, R., Razmi, J., Pishvaee, M.S., Rabbani, M.: A non-radial DEA model for location optimization of Jatropha curcas L. cultivation. Ind. Crops Prod. 69, 197–203 (2015). https://doi.org/10.1016/j.indcrop.2015.02.006

    Article  Google Scholar 

  41. Naja, F., Sedaghat, A., Mostafaeipour, A., Issakhov, A.: Location assessment for producing biodiesel fuel from Jatropha curcas in Iran. Energy 236, 121446 (2021). https://doi.org/10.1016/j.energy.2021.121446

    Article  Google Scholar 

  42. SCAR: Shiraz city annual report 2018–2019. Shiraz Municipality, Iran (2019). https://portal.shiraz.ir/Modules/ShowFramework.aspx?FrameworkPageType=SEC&RelFacilityId=1115&TabID=247. Accessed 21 June 2020.

  43. Qasim, S., Qasim, M., Shrestha, R.P., Phongaksorn, N.: GIS based land suitability assessment for Jatropha curcas L. cultivation in Phetchaburi Province, Thailand. J. Sci. Technol. 4, 10–13 (2015)

    Google Scholar 

  44. Jingura, R.M., Matengaifa, R., Musademba, D.: Characterisation of land types and agro-ecological conditions for production of Jatropha as a feedstock for biofuels in Zimbabwe. Biomass Bioenerg. 35, 2080–2086 (2011). https://doi.org/10.1016/j.biombioe.2011.02.004

    Article  Google Scholar 

  45. Taddese, H.: Application of geographic information systems in identifying accessible sites for Jatropha curcas production in Ethiopia. Energy Procedia 93, 82–88 (2016). https://doi.org/10.1016/j.egypro.2016.07.153

    Article  Google Scholar 

  46. Weiguang, W.U., Jikun, H., Xiangzheng, D.: Potential land for plantation of Jatropha curcas as feedstocks for biodiesel in China. Sci. China Ser. D Earth Sci. 53, 120–127 (2010). https://doi.org/10.1007/s11430-009-0204-y

    Article  Google Scholar 

  47. Guo, S., Zhao, H.: Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl. Based Syst. 121, 23–31 (2017). https://doi.org/10.1016/j.knosys.2017.01.010

    Article  Google Scholar 

  48. Rezaei, J.: Best-worst multi-criteria decision-making method. Omega 53, 49–57 (2015). https://doi.org/10.1016/j.omega.2014.11.009

    Article  Google Scholar 

  49. Hafezalkotob, A., Hafezalkotob, A.: A novel approach for combination of individual and group decisions based on fuzzy best-worst method. Appl. Soft Comput. J. 59, 316–325 (2017). https://doi.org/10.1016/j.asoc.2017.05.036

    Article  MATH  Google Scholar 

  50. Brauers, W.K.M., Zavadskas, E.K.: The MOORA method and its application to privatization in a transition economy by a new method: the MOORA method. Control. Cybern. 35, 445–469 (2006)

    MATH  Google Scholar 

  51. Arabsheybani, A., Paydar, M.M., Safaei, A.S.: An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier’s risk. J. Clean. Prod. (2018). https://doi.org/10.1016/j.jclepro.2018.04.167

    Article  Google Scholar 

  52. Siddiqui, Z.A., Tyagi, K.: Application of fuzzy-MOORA method : Ranking of components for reliability estimation of component-based software systems application of fuzzy-MOORA method: ranking of components for reliability estimation of component-based software systems component-bas. Decis. Sci. Lett. 5, 116–188 (2016). https://doi.org/10.5267/j.dsl.2015.6.005

    Article  Google Scholar 

  53. Akkaya, G., Turano, B., Özta, S.: An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing. Expert Syst. Appl. 42, 9565–9573 (2015). https://doi.org/10.1016/j.eswa.2015.07.061

    Article  Google Scholar 

  54. Baležentis, A., Baležentis, T., Brauers, W.K.: Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert Syst. Appl. 39, 7961–7967 (2012). https://doi.org/10.1016/j.eswa.2012.01.100

    Article  MATH  Google Scholar 

  55. Hwang, C.-L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Springer, New York (1981). https://doi.org/10.1007/978-3-642-48318-9

    Book  MATH  Google Scholar 

  56. Rajak, M., Shaw, K.: Evaluation and selection of mobile health (mHealth) applications using AHP and fuzzy TOPSIS. Technol. Soc. 59, 101186 (2019). https://doi.org/10.1016/j.techsoc.2019.101186

    Article  Google Scholar 

  57. Chen, C.: Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114, 1–9 (2000)

    Article  Google Scholar 

  58. Samaie, F., Meyar-naimi, H., Javadi, S., Feshki-farahani, H.: Comparison of sustainability models in development of electric vehicles in Tehran using fuzzy TOPSIS method. Sustain. Cities Soc. 53, 101912 (2020). https://doi.org/10.1016/j.scs.2019.101912

    Article  Google Scholar 

  59. Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980). https://doi.org/10.1016/0377-2217(90)90057-I

    Book  MATH  Google Scholar 

  60. Babazadeh, R., Razmi, J., Pishvaee, M.S., Rabbani, M.: A sustainable second-generation biodiesel supply chain network design problem under risk. Omega 66, 258–277 (2017). https://doi.org/10.1016/j.omega.2015.12.010

    Article  Google Scholar 

  61. Ghelichi, Z., Saidi-mehrabad, M., Pishvaee, M.S.: A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: a case study. Energy 156, 661–687 (2018). https://doi.org/10.1016/j.energy.2018.05.103

    Article  Google Scholar 

  62. https://fars.bonyadmaskan.ir/Pages/zarindasht.aspx (2020). Accessed 7 Mar 2022

Download references

Funding

The authors did not receive support from any organization for the submitted work and no funds, grants, or other supports was received.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Shiraz University of Technology, Shiraz, Iran

    Payam Afkhami & Naeme Zarrinpoor

Authors
  1. Payam Afkhami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naeme Zarrinpoor
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors voluntarily agree to take part in this study.

Corresponding author

Correspondence to Naeme Zarrinpoor.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Informed Consent

All authors have been personally and actively involved in substantial work leading to the paper, and will take public responsibility for its content.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Afkhami, P., Zarrinpoor, N. Location Assessment of Jatropha Cultivation for Biofuel Production in Fars Province, Iran: A Hybrid GIS-Based Fuzzy Multi-criteria Framework. Waste Biomass Valor 13, 4511–4532 (2022). https://doi.org/10.1007/s12649-022-01809-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12649-022-01809-7

Keywords

Search

Navigation