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Land suitability evaluation for multiple crop agroforestry planning using GIS and multi-criteria decision analysis: A case study in Fiji


We present novel methods that use geographic information systems (GIS) and multi-criteria decision analysis (MCDA) to evaluate land suitability for a set of seven agroforestry crops, with the aim to differentiate relative land suitability and its potential to achieve different benefit goals for the Sigatoka Valley, Fiji. Our first model (Land Suitability) identifies optimal areas for each crop based on readily available edaphic and environmental cultivation criteria and common GIS datasets. The second model (Weighted Maximum) uses objective approaches for weighting the relative importance of target crops to derive a multi-crop suitability map. We use the analytical hierarchy process (AHP) technique (a form of MCDA) to identify the relative importance of five benefit types (i.e. agroforestry initiative goals/AHP Objectives). AHP criterion weights were used to map the most important crops for different agroforestry goals. Our methods are unique among GIS-MCDA applications of land suitability analysis in that our aim was to investigate and spatially evaluate land use suitability for multiple crops on a per-crop basis, whereas the aim of most GIS-MCDA land suitability analyses is to evaluate relative suitability (e.g. low, medium, high), evaluate potential for different land uses (e.g. production, intensive, or multifunctional) or land suitability for a single crop. We conclude that the methods described can be adapted to agroforestry initiatives and other similar land use suitability applications in the Pacific region and other geographical settings.

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Availability of data and material

The datasets generated during and/or analysed during the current study are available in the Mendeley Data repository,

Code availability

Software requirement: ArcGIS Pro version 2.6 or later.


  1. Ahmad F, Goparaju L (2017) Geospatial approach for agroforestry suitability mapping: to enhance livelihood and reduce poverty, FAO based documented procedure (case study of Dumka District, Jharkhand, India) Biosciences. Biotechnol Res Asia 14:651–665.

    Article  Google Scholar 

  2. Ahmad F, Goparaju L, Qayum A (2017) FAO guidelines and geospatial application for agroforestry suitability mapping: case study of Ranchi, Jharkhand state of India. Agrofor Syst 93:531–544.

    Article  Google Scholar 

  3. Alkimim A, Sparovek G, Clarke KC (2015) Converting Brazil’s pastures to cropland: an alternative way to meet sugarcane demand and to spare forestlands. Appl Geogr 62:75–84.

    Article  Google Scholar 

  4. Ananda J, Herath G (2009) A critical review of multi-criteria decision making methods with special reference to forest management and planning. Ecol Econ 68:2535–2548.

    Article  Google Scholar 

  5. Barker G, Price R (2012) Environmental and biogeographic classifications as spatial frameworks for assessing representativeness in island archipelagos: a fijian case study. Landcare Research, Hamilton, New Zealand

  6. Chang K-T (2015) Introduction to geographic information systems. McGraw-Hill, New York

    Google Scholar 

  7. CIA (2019) The World Fact Book. US Central Intelligence Agency. Accessed 17/12/19

  8. Cóndor RD, Scarelli A, Valentini R (2010) Multicriteria decision aid to support multilateral environmental agreements in assessing international forestry projects. Int Environ Agreem Polit Law Econ 11:117–137.

    Article  Google Scholar 

  9. Cornelio DL (2015) Land use trends and agroforestry in Fiji. Fiji Agri J 55

  10. Dedeoğlu M, Dengiz O (2019) Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS. Comput Electron Agric.

    Article  Google Scholar 

  11. Diaz-Balteiro L, Romero C (2008) Making forestry decisions with multiple criteria: a review and an assessment. For Ecol Manage 255:3222–3241.

    Article  Google Scholar 

  12. Elaalem M, Comber A, Fisher P (2011) A comparison of fuzzy AHP and ideal point methods for evaluating land suitability. Trans GIS 15:329–346.

    Article  Google Scholar 

  13. Elevitch CR (1997–2019) Agroforestry Net, Inc. Accessed July, 2017

  14. Ellis EA, Nair PKR, Linehan PE, Beck HW, Blanche CA (2000) A GIS-based database management application for agroforesty planning and tree selection. Comput Electron Agric 27:41–55

    Article  Google Scholar 

  15. FBOS (2017) 2017 Population and housing census. Fiji Goverment, Suva

  16. FMS (2011) Changes and future climate of the Fiji Islands. Fiji Meterological Service, Suva

  17. Franzel S, Akinnifesi FK, Ham C (2008) Setting priorities among indigenous fruit trees in Africa: examples from Southern, Eastern and Western African regions. In: Akinnifesi FK, Leakey RRB, Ajay OC, Sileshi G, Tchoundjeu Z, Matakala P, Kwesiga FR (eds) Indigenous fruit trees in the Tropics: domestication, utilization and commercialization. CABI Publishing, Wallingford, UK,

  18. Greene R, Devillers R, Luther JE, Eddy BG (2011) GIS-based multiple-criteria decision analysis. Geogr Compass 5:412–432.

    Article  Google Scholar 

  19. Hamilton MC, Nedza JA, Doody P, Bates ME, Bauer NL, Voyadgis DE, Fox-Lent C (2016) Web-based geospatial multiple criteria decision analysis using open software and standards. Int J Geogr Inf Sci 30:1667–1686.

    Article  Google Scholar 

  20. Harrison S, Harrision R (2016) Priority tree species and potential agroforestry species mixtures for Fiji and Vanuatu. Aunstralian Centre for International Agricultural Research, Canberra, Australia

  21. Harrison S, Harrison R (2016) Agroforestry establishment and protection on degraded land in western Viti Levu. Australian Centre for International Agricultural Research, Canberra, Australia

  22. Harrison S, Karim MS (2016) Promoting sustainable agriculture and agroforestry to replace unproductive land use in Fiji and Vanuatu. Australian Centre for International Agricultural Research, Canberra, Australia

  23. Harrison S, Harrison R, Sullivan C, Karim S (2016) Non-market values of agroforestry systems and implications for Pacific island agroforestry. Australian Centre for International Agricultural Research, Canberra, Australia

  24. He J, Ho MH, Xu J (2015) Participatory selection of tree species for agroforestry on sloping land in North Korea. Mt Res Dev 35:318–327.

    Article  Google Scholar 

  25. Huang IB, Keisler J, Linkov I (2011) Multi-criteria decision analysis in environmental sciences: ten years of applications and trends. Sci Total Environ 409:3578–3594.

    CAS  Article  PubMed  Google Scholar 

  26. Jankowski P (1995) Integrating geographical information systems and multple criteria decision-making methods. Int J Geogr Inf Syst 9:251–273

    Article  Google Scholar 

  27. Jelokhani-Niaraki M (2020) Collaborative spatial multicriteria evaluation: a review and directions for future research. Int J Geogr Inf Sci.

    Article  Google Scholar 

  28. Jelokhani-Niaraki M, Malczewski J (2014) The decision task complexity and information acquisition strategies in GIS-MCDA. Int J Geogr Inf Sci 29:327–344.

    Article  Google Scholar 

  29. Kangas J, Kangas A (2005) Multiple criteria decision support in forest management—the approach, methods applied, and experiences gained. For Ecol Manage 207:133–143.

    Article  Google Scholar 

  30. Laskar A (2003) Integrating GIS and multicriteria decision making techniques for land resource planning. MSc Thesis, University of Twente

  31. Leslie DM, Seru VB (1998) Fiji soil taxonomic unit description handbook: supplement to the national map. Landcare Research Lincoln, New Zealand

  32. Mahmoody Vanolya N, Jelokhani-Niaraki M, Toomanian A (2019) Validation of spatial multicriteria decision analysis results using public participation. GIS Appl Geogr.

    Article  Google Scholar 

  33. Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, New York

    Google Scholar 

  34. Malczewski J (2006) GIS-based multicriteria decision analysis: a survey of the literature. Int J Geogr Inf Sci 20:703–726.

    Article  Google Scholar 

  35. Malczewski J, Rinner C (2015) Multicriteria decision analysis in geographic information science. Advances in geographic information science. Springer, New York

    Google Scholar 

  36. Mendas A, Delali A (2012) Integration of MultiCriteria Decision Analysis in GIS to develop land suitability for agriculture: application to durum wheat cultivation in the region of Mleta in Algeria. Comput Electron Agric 83:117–126.

    Article  Google Scholar 

  37. Mendoza GA, Martins H (2006) Multi-criteria decision analysis in natural resource management: a critical review of methods and new modelling paradigms. For Ecol Manage 230:1–22.

    Article  Google Scholar 

  38. Mighty MA (2015) Site suitability and the analytic hierarchy process: how GIS analysis can improve the competitive advantage of the Jamaican coffee industry. Appl Geogr 58:84–93.

    Article  Google Scholar 

  39. Modica G, Pollino M, Lanucara S, La Porta L, Pellicone G, Di Fazio S, Fichera CR (2016) Land suitability evaluation for agro-forestry: definition of a web-based multi-criteria spatial decision support system (MC-SDSS): preliminary results. In: Computational science and its applications -- ICCSA 2016. Lecture Notes in Computer Science. pp 399–413.

  40. Phua M-H, Minowa M (2005) A GIS-based multi-criteria decision making approach to forest conservation planning at a landscape scale: a case study in the Kinabalu Area, Sabah, Malaysia. Landsc Urban Plan 71:207–222.

    Article  Google Scholar 

  41. Quinta-Nova L, Natalia R (2018) An integrated agroforestal suitability model using a GIS-based multicriteria analysis method: a case study of Portugal. In: The Eurasia Proceedings of Science, Technology, Engineering & Mathematics, October 26–29, pp 11–20

  42. Reubens B et al (2011) Tree species selection for land rehabilitation in Ethiopia: from fragmented knowledge to an integrated multi-criteria decision approach. Agrofor Syst 82:303–330.

    Article  Google Scholar 

  43. Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw Hill, New York

    Google Scholar 

  44. Saaty TL (2005) The analytic hierarchy and analytic network process for the measurement of intangible criteria and for decision-making. In: Figueira J, Greco S, Ehrgott M (eds) Multiple criteria decision analysis: state of the art surveys. Springer, New York, pp 345–408

    Chapter  Google Scholar 

  45. Selim S, Koc-San D, Selim C, San BT (2018) Site selection for avocado cultivation using GIS and multi-criteria decision analyses: case study of Antalya. Turkey Comput Electron Agric 154:450–459.

    Article  Google Scholar 

  46. SPC (2012a) 1:50k Land use/land cover GIS data of Fiji. Secretariat of the Pacific Community, Suva, Fiji

  47. SPC (2012b) 1:50k Soils GIS data of Fiji. Secretariat of the Pacific Community, Suva, Fiji

  48. Thaman R (2008) Pacific Island agrobiodiversity and ethnobiodiversity: A foundation for sustainable pacific Island life. Biodiversity 8

  49. Thaman RR, Elevitch CR, Wilkinson KM (2006b) Multipurpose trees for agroforestry in the Pacific Ialand. In: Agroforestry Guides for Pacific Islands. Holualoa, HI

  50. Thaman R, Elevitch CR, Kennedy J (2006a) Urban and homegarden agroforestry in the Pacific Islands: current status and future prospects. In: Kumar BMN (ed) Tropical homegardens: a time-tested example of sustainable forestry. Springer, Netherlands

  51. Uhde B, Hahn WA, Griess VC, Knoke T (2015) Hybrid MCDA methods to integrate multiple ecosystem services in forest management planning: a critical review. Environ Manage 56:373–388.

    Article  PubMed  Google Scholar 

  52. Ullah KM, Mansourian A (2016) Evaluation of land suitability for urban land-use planning: case study Dhaka City. Trans GIS 20:20–37.

    Article  Google Scholar 

  53. Van Der Wolf J, Jassogne L, Gram GIL, Vaast P (2016) Turning local knowledge on agroforestry into an online decision-support tool for tree selection in smallholders’ farms. Exp Agric 55:50–66.

    Article  Google Scholar 

  54. Wairiu M (2016) Land degradation and sustainable land management practices in Pacific Island Countries. Reg Environ Change 17:1053–1064.

    Article  Google Scholar 

  55. Yalew SG, van Griensven A, van der Zaag P (2016) AgriSuit: A web-based GIS-MCDA framework for agricultural land suitability assessment. Comput Electron Agric 128:1–8.

    Article  Google Scholar 

  56. Zhang J, Su Y, Wu J, Liang H (2015) GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China. Comput Electron Agric 114:202–211.

    Article  Google Scholar 

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We are grateful to the anonymous reviewers whose comments helped improve this paper. Dean Wotlolan acknowledges with gratitude a scholarship from Australian Centre for International Agricultural Research (ACIAR) that made this research possible.


No funding was received for conducting this study.

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Conceptualization DW, JL, KG; Methodology DW, JL, Data curation DW, JL; Formal analysis DW, JL; Writing-original draft DW, JL; Writing-review & editing NW, KG; Supervision JL, NW.

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Correspondence to John H. Lowry.

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Wotlolan, D.L., Lowry, J.H., Wales, N.A. et al. Land suitability evaluation for multiple crop agroforestry planning using GIS and multi-criteria decision analysis: A case study in Fiji. Agroforest Syst 95, 1519–1532 (2021).

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  • Land suitability
  • Agroforestry spatial planning
  • Pacific island countries (PICs)
  • Analytical hierarchy process (AHP)
  • GIS models