Advertisement

FAO guidelines and geospatial application for agroforestry suitability mapping: case study of Ranchi, Jharkhand state of India

  • Firoz Ahmad
  • Laxmi Goparaju
  • Abdul Qayum
Article

Abstract

Agroforestry has potential for achieving agricultural sustainability having capacity of optimizing its productivity by mitigating climate change impacts. The aim was to find such land patches which can be potentially mapped as suitable and fertile for agroforestry projects and to find out land-use systems which can play a pivotal role in poverty eradication and climate change mitigation. The study aims for applying geo-spatial technology towards visualizing various land, soil, climate and topographical data to reveal trends and interrelationships and to find a nutrient availability and agroforestry suitability map. FAO based land suitability criteria was adopted to generate agroforestry suitability maps based upon scientifically evaluated weight factors at GIS platform by integrating layers of LULC, NDVI, wetness factor, elevation, slope percent, drainage, watershed, rainfall, organic carbon, pH and nutrient status. About 6% of land is under cultivation of pure agriculture whereas the study area has agroforestry suitability of 32.8% of total area. Block wise agroforestry suitability reveals highly suitable percentage for Rahe, Bundu and Namkum blocks as 79.1, 56.5 and 1.1%, respectively. Based on high suitability percentage Rahe block among all should be prioritized. Therefore, if there is scientific planning with adequate technical inputs, the area can achieve tremendous scope for tribal and rural people in generating their livelihood. Such finding may work as guiding tool for the policymakers towards allocation of fund for agroforestry projects. The advance GIS modeling software has the potential to map such area logically and meaningfully.

Keywords

Agroforestry Digital elevation model (DEM) FAO GIS Land suitability Nutrient status Remote Sensing 

Notes

Acknowledgements

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

Funding

This is to declare that no fund was received from any source.

Authors’ contributions

FA proposed the idea and analyzed the satellite and ancillary data in GIS domain, LG supervised the analysis and drafted the manuscript. AQ made critical evaluation regarding GIS analysis and provided continuous feedbacks and added dimensions of metrological factors. All authors read and approved the final manuscript.

References

  1. Ahmad F, Goparaju L (2017a) Geospatial approach for agroforestry suitability mapping: to enhance livelihood and reduce poverty, FAO based documented procedure—case study of Dumka district, Jharkhand, India. Biosci Biotechnol Res Asia 14:651–665CrossRefGoogle Scholar
  2. Ahmad F, Goparaju L (2017b) 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. Ecol Quest 25:67–84.  https://doi.org/10.12775/EQ.2017.006 CrossRefGoogle Scholar
  3. Ahmad F, Goparaju L, Qayum A (2017) Agroforestry suitability analysis based upon nutrient availability mapping: a GIS based suitability mapping. AIMS Agric Food 2(2):201–220.  https://doi.org/10.3934/agrfood.2017.2.201 CrossRefGoogle Scholar
  4. Albert S (2013) Vegetable crop soil pH tolerances. (http://www.harvesttotable.com/2013/12/vegetable-crop-soil-ph-tolerances). Accessed 13 Jan 2017
  5. Asbjornsen H, Hernandez-Santana V, Liebman M, Bayala J, Chen J, Helmers M, Ong C, Schulte L (2014) Targeting perennial vegetation in agricultural landscapes for enhancing ecosystem services. Renew Agric Food Syst 29:101–125CrossRefGoogle Scholar
  6. Baig MHA, Zhang L, Shuai T, Tong Q (2014) Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sens Lett 5(5):423–431.  https://doi.org/10.1080/2150704X.2014.915434 CrossRefGoogle Scholar
  7. Bentrup G, Leininger T (2002) Agroforestry: mapping the way with GIS. J Soil Water Conserv 57(6):148a–152aGoogle Scholar
  8. Bouma J, Wagenet RJ, Hoosbeek MR, Hutson JL (1993) Using expert systems and simulation modeling for land evaluation at farm level—a case study from New York State. Soil Use Manag 9:131–139CrossRefGoogle Scholar
  9. Bydekerke L, Van Ranst E, Vanmechelen L, Groenemans R (1998) Land suitability assessment for cherimoya in southern Ecuador using expert knowledge and GIS. Agric Ecosyst Environ 69:89–98CrossRefGoogle Scholar
  10. Champion HG, Seth SK (1968) A Revised Survey of Forest Types of India. Govt. of India Press, New DelhiGoogle Scholar
  11. Creswell BR, Martin FW (1993) Dryland Farming: Crops and Techniques for Arid regions. Echo Technical NoteGoogle Scholar
  12. Dengiz O (2013) Land suitability assessment for rice cultivation based on GIS modeling. Turk J Agri For 37:326–334Google Scholar
  13. Ellis EA, Nair PK, Linehan PE, Beck HW, Blanche CA (2000) A GIS-based database management application for agroforestry planning and tree selection. Comput Electron Agric 27:41–55CrossRefGoogle Scholar
  14. Fairhead J, Leach M (1996) Misreading the African landscape: society and ecology in a forest-savanna mosaic. African Studies Series, 90. Cambridge University Press, CambridgeGoogle Scholar
  15. 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 10 April 2017. http://www.fao.org/docrep/t0715e/t0715e06.htm
  16. Hegde BR (1995) Drylandfarming : past progress and future prospects. In: Singh RP (ed) Sustainable development of dryland agriculture in India. Scientific Pub, Jodhpur, pp 7–12Google Scholar
  17. Hernandez G, Trabue S, Sauer T, Pfeiffer R, Tyndall J (2016) Odor mitigation with tree buffers: swine production case study. Agric Ecosyst Environ 149:154–163CrossRefGoogle Scholar
  18. Islam MA, Rai R, Quli SMS (2015) Forest resources use for building livelihood resilience in ethnic communities of Jharkhand. Trends Biosci 8(5):1256–1264Google Scholar
  19. Jose S (2012) Agroforestry for conserving and enhancing biodiversity. Agrofor Syst 85:1–8CrossRefGoogle Scholar
  20. Kihoro J, Bosco NJ, Murage H (2013) Suitability analysis for rice growing sites using a multicriteria evaluation and GIS approach in great Mwea region Kenya. Springerplus 2:265CrossRefPubMedPubMedCentralGoogle Scholar
  21. Lal R (2000) A modest proposal for the year 2001: we can control greenhouse gases and feed the world with proper soil management. J Soil Water Conserv 55(4):429–433Google Scholar
  22. McHarg I (1995) Design with nature. Wiley, New YorkGoogle Scholar
  23. McNeely JA (2004) Nature vs. nurture: managing relationships between forests, agroforestry and wild biodiversity. Agrofor Syst 61:155–165Google Scholar
  24. Montesano PM, Nelson R, Sun G, Margolis H, Kerber A, Ranson KJ (2009) MODIS tree cover validation for the circumpolar taiga-tundra transition zone. Remote Sens Environ 113:2130–2141CrossRefGoogle Scholar
  25. Mortimore M, Harris FMA, Turner B (1999) Implications of land use change for the production of plant biomass in densely populated Sahelo-Sudanian shrub-grasslands in northeast Nigeria. Glob Ecol Biogeogr 8:243–256CrossRefGoogle Scholar
  26. Phillips-Howard KD, Lyon F (1994) Agricultural intensification and the threat to soil fertility in Africa: evidence from the Jos Plateau, Nigeria. Geogr J 160:252–265CrossRefGoogle Scholar
  27. Ploton P, Pelissier R, Proisy C, Flavenot T, Barbier N, Rai SN, Couteron P (2012) Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecol Appl 22:993–1003CrossRefPubMedGoogle Scholar
  28. Ramos NC, Gastauer M, de Cordeiro AAC, Meira-Neto JAA (2015) Environmental filtering of agroforestry systems reduces the risk of biological invasion. Agrofor Syst 89:279–289CrossRefGoogle Scholar
  29. Rani S, Rajiv Prawasi R (2015) Feature extraction using normalized difference vegetation index (ndvi): a case stufy of Panipat district. Int J Sci Eng Technol Res 11(4):3844–3848Google Scholar
  30. Reij C, Scoone I, Toulmin C (1996) Sustaining the soil: indigenous soil and water conservation in Africa. Earthscan Publications, LondonGoogle Scholar
  31. Reisner Y, de Filippi R, Herzog F et al (2007) Target regions for silvoarable agroforestry in Europe. Ecol Eng 29:401–418.  https://doi.org/10.1016/j.ecoleng.2006.09.020 CrossRefGoogle Scholar
  32. Ritung S, Wahyunto Agus F, Hidayat H (2007) Land suitability evaluation with a case map of aceh Barat district. Indonesian Soil Research Institute and World Agroforestry Centre, BogorGoogle Scholar
  33. Thangataa PH, Hildebrand PE (2012) Carbon stock and sequestration potential of agroforestry systems in smallholder agroecosystems of sub-Saharan Africa: mechanisms for ‘reducing emissions from deforestation and forest degradation’ (REDD+). Agric Ecosyst Environ 158:172–183CrossRefGoogle Scholar
  34. Thorlakson T, Neufeldt H (2012) Reducing subsistence farmers vulnerability to climate change: evaluating the potential contributions of agroforestry in western Kenya. Agric Food Secur 1(15):1–13Google Scholar
  35. Tiffen M, Mortimore M, Gichuki F (1994) More people, less erosion. Environ recover Kenya, ChichesterGoogle Scholar
  36. Tilman D, Balzer C, Hill J, Befort BL (2011) Global food demand and the sustainable intensification of agriculture. Proc Natl Acad Sci USA (PNAS) 108(50):20260–20264CrossRefGoogle Scholar
  37. Yedage AS, Gavali RS, Jarag AP (2013) Land assessment for horticulture (pomegranate) crop using gis and fuzzy decision analysis in the Sangolan taluka of Solapur district. Int J Remote Sens GIS 2(3):104–113Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Vindhyan Ecology and Natural History FoundationMirzapurIndia
  2. 2.Department of Environment and ForestGovt. of Arunachal PradeshItanagarIndia

Personalised recommendations