Skip to main content

Advertisement

SpringerLink
Log in

Agroforestry suitability mapping of India: geospatial approach based on FAO guidelines

  • Published:
Agroforestry Systems Aims and scope Submit manuscript

A Correction to this article was published on 29 June 2018

This article has been updated

Abstract

Agroforestry system has the enormous capacity to achieve social, economic, and environmental goals by optimizing land productivity. The aim of the present study was to evaluate the land potentiality in India for agroforestry based on FAO land suitability criteria utilizing various land, soil, climate, and topographic themes. This was achieved in GIS Domain by integrating various thematic layers scientifically. The analysis of land potentiality in India for agroforestry suitability reveals 32.8% as highly suitable (S1), 40.4% moderately suitable (S2), 11.7% marginally suitable (S3), and 9.1% not suitable (NS). About 52% of land of India is under the cropland category. In addition, it revealed that the 46% of these cropland areas fall into high agroforestry suitable category “S1,” and provide huge opportunity for harnessing agroforestry practices. Furthermore, agroforestry suitability mapping in broad ecosystem and in different agroecological regions will assist various projects in India at the regional level. Such results will also boost the various objectives of the National Agroforestry Policy (2014, http://www.cafri.res.in/NAF_Policy.pdf) and policymakers of India where they need to extend it. The potential of geospatial technology can be exploited in the field of agroforestry for the benefit of rural poor people/farmers by ensuring food and ecological security, resilience in livelihoods, and can sustain extreme weather events such as droughts and climate change impact. Such type of research can be replicated in India at village level (local level) to state level (regional level) utilizing the significant themes which affect the agroforestry suitability. This will certainly fetch better results on ground and will significantly assist the management programs.

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

Similar content being viewed by others

Change history

  • 29 June 2018

    Unfortunately, in the original publication of the article, in Abstract and Results and discussion some percent values are written incorrectly.

References

  • 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 14:651–665. https://doi.org/10.13005/bbra/2491

    Google Scholar 

  • 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

    Google Scholar 

  • Ahmad F, Goparaju L, Qayum A (2017a) 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

    Google Scholar 

  • Ahmad F, Goparaju L, Qayum A (2017b) FAO guidelines and geospatial application for agroforestry suitability mapping: case study of Ranchi, Jharkhand state of India. Agrofor Syst. https://doi.org/10.1007/s10457-017-0145-y

    Google Scholar 

  • Albrecht A, Kandji ST (2003) Carbon sequestration in tropical agroforestry systems. Agric Ecosyst Environ 99:15–27

    CAS  Google Scholar 

  • Anderson SH, Udawatta RP, Seobi T, Garrett HE (2009) Soil water content and infiltration in agroforestry buffer strips. Agrofor Syst 75:5–16

    Google Scholar 

  • 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 29:101–125

    Google Scholar 

  • Assouline S, Ben-Hur A (2006) Effects of rainfall intensity and slope gradient on the dynamics of interrill erosion during soil surface sealing. CATENA 66:211–220

    Google Scholar 

  • Bandyopadhyay A, Bhadra A, Raghuwanshi NS, Singh R (2009) Temporal trends in estimates of reference evapotranspiration over India. J Hydrol Eng 14:508–515

    Google Scholar 

  • Bunyan M, Bardhan S, Aditya S, Jose S (2015) Effect of topography on the distribution of tropical montane forest fragments: a predictive modeling approach. J Trop For Sci 27(1):30–38

    Google Scholar 

  • Duran ZVH, Rodriguez PCR, Francia MJR, Carceles RB, Martinez RA, Perez GP (2008) Harvest intensity of aromatic shrubs vs. soil-erosion: an equilibrium for sustainable agriculture (SE Spain). CATENA 73(1):107–116

    Google Scholar 

  • 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 27:41–55

    Google Scholar 

  • Ellis EA, Bentrup G, Schoeneberger MM (2004) Computer-based tools for decision support in agroforestry: current state and future needs. Agrofor Syst 61(1):401–421

    Google Scholar 

  • Fairhead J, Leach M (1996) Misreading the African landscape: society and ecology in a forest-savanna mosaic. African Studies Series, 90. Cambridge University Press, Cambridge

    Google Scholar 

  • 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 http://www.fao.org/docrep/t0715e/t0715e06.htm. Accessed Nov 10 2017

  • FAO (1990) Guidelines for soil description. FAO, Rome

    Google Scholar 

  • FAO (1995)  Digital soil map of the world and derived soil properties (Version 3.5). CD-ROM, FAO, Rome, Italy

  • Fick SE, Hijmans RJ (2017) Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. http://worldclim.org/version2. Accessed 10 Nov 2017

  • Fischer G, Nachtergaele F, Prieler S, van Velthuizen HT, Verelst L, Wiberg D (2008) Global agro-ecological zones assessment for agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome

    Google Scholar 

  • Graveel JG, Tyler DD, Jones JR, McFee WW (2002) Crop yield and rooting as affected by fragipan depth in loess soils in the southeast USA. Soil Tillage Res 68:151–161

    Google Scholar 

  • Hatfield JL, Boote KJ, Kimball BA, Ziska LH, Izaurralde RC, Ort D, Thomson AM, Wolfe DW (2011) Climate impacts on agriculture: implications for crop production. Agron J 103(2011):351–370

    Google Scholar 

  • Hernandez G, Trabue S, Sauer T, Pfeiffer R, Tyndall J (2012) Odor mitigation with tree buffers: swine production case study. Agric Ecosyst Environ 149:154–163

    CAS  Google Scholar 

  • Iiyama M, Derero A, Kelemu K, Muthuri C, Kinuthia R, Ayenkulu E, Kiptot E, Hadgu K, Mowo J, Sinclair F (2016) Understanding patterns of tree adoption on farms in semi-arid and sub-humid Ethiopia. Agrofor Syst. https://doi.org/10.1007/s10457-016-9926-y

    Google Scholar 

  • Islam MA, Rai R, Quli SMS (2015) Forest resources use for building livelihood resilience in ethnic communities of Jharkhand. Trends Biosci 8(5):1256–1264

    Google Scholar 

  • Jose S (2012) Agroforestry for conserving and enhancing biodiversity. Agrofor Syst 85:1–8

    Google Scholar 

  • 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 2:265. https://doi.org/10.1186/2193-1801-2-265

    PubMed  PubMed Central  Google Scholar 

  • Lipper L, Thornton P, Campbell BM, Baedeker T, Braimoh A, Bwalya M, Caron P, Cattaneo A, Garrity D, Henry K, Hottle R, Jackson L, Jarvis A, Kossam F, Mann W, McCarthy N, Meybeck A, Neufeldt H, Remington T, Sen PT, Sessa R, Shula R, Tibu A, Torquebiau EF (2014) Climate-smart agriculture for food security. Nat Clim Change 4:1068–1072

    Google Scholar 

  • Madusa SM (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

  • Mbow C, van Noordwijk M, Luedeling E, Neufeldt H, Minang PA, Kowero G (2014) Agroforestry solutions to address food security and climate change challenges in Africa. Curr Opin Environ Sustain 6:61–67

    Google Scholar 

  • McNeely JA (2004) Nature vs. nurture: managing relationships between forests, agroforestry and wild biodiversity. Agrofor Syst 61:155–165

    Google Scholar 

  • 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 8:243–256

    Google Scholar 

  • Nair PKR (1993) An introduction to agroforestry. Kluwer, Dordrecht

    Google Scholar 

  • Nair PKR (1998) Directions in tropical agroforestry research: past, present and future. Agrofor Syst 38:223–245

    Google Scholar 

  • Nair PKR (2011) Agroforestry systems and environmental quality: introduction. J Environ Qual 40(3):784–790

    CAS  PubMed  Google Scholar 

  • National Agroforestry Policy (2014) http://www.cafri.res.in/NAF_Policy.pdf. Accessed 12 Jan 2018

  • Phillips-Howard KD, Lyon F (1994) Agricultural intensification and the threat to soil fertility in Africa: evidence from the Jos Plateau, Nigeria. Geogr J 160:252–265

    Google Scholar 

  • Prasad K, Kumar A, Dubey P, Mishra CM (2002) Significance and use of agro forestry system. Lab to land leaflet No. 10 p 50. http://www.fao.org/docrep/ARTICLE/WFC/XII/0931-B5.HTM#P28_106. Accessed 12 Jan 2018

  • Quandt A, Neufeldt H, McCabe JT (2017) The role of agroforestry in building livelihood resilience to floods and drought in semiarid Kenya. Ecol Soc 22(3):10. https://doi.org/10.5751/ES-09461-220310

    Google Scholar 

  • Ramos NC, Gastauer M, de Cordeiro AAC, Meira-Neto JAA (2015) Environmental filtering of agroforestry systems reduces the risk of biological invasion. Agrofor Syst 89:279–289

    Google Scholar 

  • Rani S, Rajiv Prawasi R (2015) Feature extraction using normalized difference vegetation index (NDVI): a case study of Panipat District. Int J Sci Eng Technol Res 11(4):3844–3848

    Google Scholar 

  • 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, Bogor. ISBN 979-3198-36-1

    Google Scholar 

  • Roy PS, Agrawal S, Joshi P, Shukla Y (2003) The land cover Map for Southern Asia for the Year 2000. GLC2000 database, European Commision Joint Research Centre, 2003. http://forobs.jrc.ec.europa.eu/products/glc2000/products.php

  • 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):1–13

    Google Scholar 

  • Tiffen M, Mortimore M, Gichuki F (1994) More people, less erosion. Environmental recovery in Kenya. Wiley, Chichester

    Google Scholar 

  • 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):20260–20264

    CAS  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the USGS, Food and Agriculture Organization of the United Nations (FAO), and the International Institute for Applied Systems Analysis (IIASA), the WorldClim-Global Climate Data, the European Commission’s science and knowledge service, and DIVA GIS for allowing free downloads of various datasets used in the analysis.

Funding

No funding in any form has been received by any of the authors for the current work.

Author information

Authors and Affiliations

  1. Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh, India

    Firoz Ahmad & Laxmi Goparaju

  2. University Department of Mathematics, MCA, Ranchi University, Ranchi, Jharkhand, India

    Md Meraj Uddin

Authors
  1. Firoz Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md Meraj Uddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laxmi Goparaju
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Firoz Ahmad.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmad, F., Uddin, M.M. & Goparaju, L. Agroforestry suitability mapping of India: geospatial approach based on FAO guidelines. Agroforest Syst 93, 1319–1336 (2019). https://doi.org/10.1007/s10457-018-0233-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10457-018-0233-7

Keywords

Search

Navigation