Spatial Information Research

, Volume 26, Issue 5, pp 517–526 | Cite as

Geospatial application for agroforestry suitability mapping based on FAO guideline: case study of Lohardaga, Jharkhand State of India

  • Firoz AhmadEmail author
  • Md Meraj Uddin
  • Laxmi Goparaju


In view of climate change scenario, the increasing population, higher food demand and deteriorating land productivity are the key issues which need to be addressed in present time frame because it will be more critical in the future. The scientific evaluation of land for agroforestry is a step towards sustainability for achieving the socio-economic and environmental goal of the community. The objective of the present study was to investigate the suitability of land use/land cover of Lohardaga district of state of Jharkhand, India for agroforestry use based on FAO land suitability criteria utilizing Landsat-8 images (NDVI/wetness), ASTER DEM (elevation/slope/drainage and watershed), ancillary data source (rainfall/organic carbon/pH and nutrient status). The analysis of our study for agroforestry suitability reveals that 50.5% area as highly suitable (S1), 28.2% area as moderately suitable (S2), 20% area as marginally suitable (S3) and 1.3% area as not suitable (NS). Only 2.9% of the total land area is dominated by two season crop which is a matter of serious concern. The statistical analysis of the results reveals that the lands have huge potentiality for harnessing agroforestry crops if utilized scientifically. Such results will greatly help to the state level policymakers for achieving the national agroforestry policy goal for extending it to the new areas in the districts of Jharkhand.


Agroforestry Nutrient status FAO LULC Remote sensing GIS Land suitability 



The authors are grateful to the USGS for free download of Landsat and DEM (ASTER) data which was used in the analysis.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

41324_2018_194_MOESM1_ESM.docx (1.3 mb)
Supplementary material 1 (DOCX 1353 kb)


  1. 1.
    ICRAF (1993). International centre for research in agroforestry: Annual report 1993. Nairobi, Kenya, pp 208Google Scholar
  2. 2.
    Verchot, L. V., Van Noordwijk, M., Kandji, S., et al. (2007). Climate change: Linking adaptation and mitigation through agroforestry. Mitigation and Adaptation Strategies for Global Change, 12, 901. Scholar
  3. 3.
    Nair, P. K. R. (1993). An introduction to agroforestry. Dordrecht: Kluwer.Google Scholar
  4. 4.
    Lipper, L., Thornton, P., Campbell, B. M., et al. (2014). Climate-smart agriculture for food security. Nature Climate Change, 4, 1068–1072.Google Scholar
  5. 5.
    Madusa, S. M. (2007). Role of agroforestry products in household income and poverty reduction in semi-arid areas of Misungwi District Mwanza Tanzania. Dissertation for award of Masters Degree, at Sokoine University of Agriculture. Morogoro, Tanzania.Google Scholar
  6. 6.
    Quli, S. M. S., Islam, M. A., & Singh, P. K. (2017). Mitigating livelihood crisis through agroforestry interventions in rural India. Jharkhand Journal of Development and Management Studies, 15(1), 7159–7178.Google Scholar
  7. 7.
    Islam, M. A., Rai, R., & Quli, S. M. S. (2015). Forest resources use for building livelihood resilience in ethnic communities of Jharkhand. Trends in Biosciences, 8(5), 1256–1264.Google Scholar
  8. 8.
    Albrecht, A., & Kandji, S. T. (2003). Carbon sequestration in tropical agroforestry systems. Agriculture, Ecosystems & Environment, 99, 15–27.Google Scholar
  9. 9.
    Mbow, C., van Noordwijk, M., Luedeling, E., et al. (2014). Agroforestry solutions to address food security and climate change challenges in Africa. Current Opinion in Environmental Sustainability, 6, 61–67.Google Scholar
  10. 10.
    Asbjornsen, H., Hernandez-Santana, V., Liebman, M., et al. (2014). Targeting perennial vegetation in agricultural landscapes for enhancing ecosystem services. Renewable Agriculture and Food Systems, 29, 101–125.Google Scholar
  11. 11.
    Jose, S. (2012). Agroforestry for conserving and enhancing biodiversity. Agroforestry Systems, 85, 1–8.Google Scholar
  12. 12.
    FAO (1976). A framework for land evaluation. Soils Bulletin 32. Food and Agriculture Organization of the United Nations, Rome, Italy. ISBN 92-5-100111-1. Accessed November 10, 2017.
  13. 13.
    Clarke, K. C. (2001). Getting started with geographic information systems (3rd ed.). Upper Saddle River: Prentice Hall.Google Scholar
  14. 14.
    Burrough, P. A. (1986). Principles of geographic information systems for land resource assessment., Monographs on soil and resources survey No. 12 New York: Oxford Science Publications.Google Scholar
  15. 15.
    ESRI. (1990). Understanding GIS: The ARC/INFO method. Redlands: ESRI.Google Scholar
  16. 16.
    Prakash, S., Sharma, M. C., Kumar, R., et al. (2016). Mapping and assessing land degradation vulnerability in Kangra district using physical and socio-economic indicators. Spatial Information Research, 24, 733. Scholar
  17. 17.
    Das, R. T., & Pal, S. (2017). Exploring geospatial changes of wetland in different hydrological paradigms using water presence frequency approach in Barind Tract of West Bengal. Spatial Information Research, 25, 467. Scholar
  18. 18.
    Das, S., Gupta, A., & Ghosh, S. (2017). Exploring groundwater potential zones using MIF technique in semi-arid region: A case study of Hingoli district, Maharashtra. Spatial Information Research, 25, 749. Scholar
  19. 19.
    Ahmad, F., Goparaju, L., & Qayum, A. (2018). Himalayan forest fire characterization in relation to topography, socio-economy and meteorology parameters in Arunachal Pradesh, India. Spatial Information Research. Scholar
  20. 20.
    Ploton, P., Pelissier, R., Proisy, C., et al. (2012). Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecological Applications, 22, 993–1003.Google Scholar
  21. 21.
    Bouma, J., Wagenet, R. J., Hoosbeek, M. R., & Hutson, J. L. (1993). Using expert systems and simulation modeling for land evaluation at farm level—a case study from New York State. Soil Use and Management, 9, 131–139.Google Scholar
  22. 22.
    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, Biotechnology Research Asia, 14, 651–665. Scholar
  23. 23.
    Ritung, S., Wahyunto, Agus F., & Hidayat, H. (2007). Land suitability evaluation with a case map of Aceh Barat District. Bogor: Indonesian Soil Research Institute and World Agroforestry Centre. ISBN 979-3198-36-1.Google Scholar
  24. 24.
    Reisner, Y., de Filippi, R., Herzog, F., et al. (2007). Target regions for silvoarable agroforestry in Europe. Ecological Engineering, 29, 401–418. Scholar
  25. 25.
    Yedage, A. S., Gavali, R. S., & Jarag, A. P. (2013). Land assessment for horticulture (pomegranate) crop using GIS and fuzzy decision analysis in the Sangolan Taluka of Solapur District. International Journal of Remote Sensing and GIS, 2(3), 104–113.Google Scholar
  26. 26.
    Ahmad, F., Goparaju, L., & Qayum, A. (2017). Agroforestry suitability analysis based upon nutrient availability mapping: A GIS based suitability mapping. AIMS Agriculture and Food, 2(2), 201–220. Scholar
  27. 27.
    Ahmad, F., Goparaju, L., & Qayum, A. (2017). FAO guidelines and geospatial application for agroforestry suitability mapping: Case study of Ranchi, Jharkhand state of India. Agroforestry Systems. Scholar
  28. 28.
    FSI (2001). Forest Resources of Ranchi, Gumla & Lohardaga districts of Jharkhand.,Gumla%20and%20Lohardaga%20Dist.pdf. Accessed on April 12, 2018.
  29. 29.
  30. 30.
    Mondal, S., Jeganathan, C., Sinha, N. K., Rajan, H., Roy, T., & Kumar, P. (2014). Extracting seasonal cropping patterns using multi-temporal vegetation indices from IRS LISS-III data in Muzaffarpur district of Bihar, India. The Egyptian Journal of Remote Sensing and Space Science, 17, 123–134. Scholar
  31. 31.
    Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185–201.Google Scholar
  32. 32.
    Meneses-Tovar, C. L. (2011). NDVI as indicator of degradation. Accessed on October 3, 2017.
  33. 33.
    Gomes, A. C. C., Bernardo, N., & Alcântara, E. (2017). Accessing the southeastern Brazil 2014 drought severity on the vegetation health by satellite image. Natural Hazards, 89, 1401. Scholar
  34. 34.
    Ahmad, F., & Goparaju, L. (2017). Soil and water conservation prioritization using geospatial technology—a case study of part of Subarnarekha Basin, Jharkhand, India. AIMS Geosciences, 3(3), 375–395. Scholar
  35. 35.
    Baig, M. H. A., Zhang, L., Shuai, T., & Tong, Q. (2014). Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5(5), 423–431. Scholar
  36. 36.
    Bohling, G.(2005). KRIGING. Accessed on February 3, 2018.
  37. 37.
    Ahmad, F., & Goparaju, L. (2017). Land evaluation in terms of agroforestry suitability, an approach to improve livelihood and reduce poverty: A FAO based methodology a geospatial solution: A case study of Palamu district, Jharkhand, India. Ecological Questions, 25, 67–84. Scholar
  38. 38.
    Bhan, S. (2013). Land degradation and integrated watershed management in India. International Soil and Water Conservation Research, 1(1), 49–57. Scholar
  39. 39.
    Dey, S. (2016). Jharkhand’s waterman gets Padma Shri for waging war against drought. Accessed on May 15, 2017.
  40. 40.
    Lal, R. (1999). Integrated watershed management in the global ecosystem. Soil and Water Conservation Society (U.S.). International Affairs Committee. Published. Boca Raton: CRC Press.Google Scholar

Copyright information

© Korean Spatial Information Society 2018

Authors and Affiliations

  1. 1.Vindhyan Ecology and Natural History FoundationMirzapurIndia
  2. 2.University Department of Mathematics, MCARanchi UniversityRanchiIndia
  3. 3.Vindhya Bachao SecretariatMirzapurIndia

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