Remote Sensing and Geographic Information System based Agroforestry Suitability Mapping and Area Identification in Part of Jharkhand

Remote Sensing and Geographic Information System based Agroforestry Suitability Mapping and Area Identification in Part of Jharkhand

Authors

  •   Firoz Ahmad   Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh
  •   Laxmi Goparaju   Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh
  •   Abdul Qayum   Department of Environment and Forest, Govt. of Arunachal Pradesh, Itanagar

DOI:

https://doi.org/10.36808/if/2018/v144i4/126555

Keywords:

Agroforestry, Digital Elevation Model, GIS, Jharkhand State, Remote Sensing, Spatial Modeling.

Abstract

Agroforestry has tremendous potential for poverty alleviation, alternate food security instrument and attempts to improve quality of fallow and abandoned land. Nonetheless, with advent of new technologies the practices of agroforestry can be scaled up as technology potentially predicts areas which have relatively higher suitability. Satellite data harnessed through remote sensing and maneuvered through GIS offers a better decision support system and prioritization of forest area for higher productivity. The present study aims for identification of suitable area for agroforestry projects towards maximizing the outcome in terms of agriculture output and carbon sequestration capacity by generating integrated maps for Chakardharpur sub-division, West Singhbhum district of state of Jharkhand. A weight matrix was derived based upon field data and related research works to produce nitrogen, phosphorous, potassium mapping along with soil pH, organic carbon and sulphur content. Later, it was superimposed with remote sensing imageries/images, topographic maps and climatic datasets for integrated mapping in GIS to develop agroforestry suitability. It was found that 21.6 % areas have high suitability and within watersheds 22 sample points corresponding to some village was generated for making buffers. It was done to establish relationship of nearness of watershed areas with high suitability. The study highlighted the scope of geo-spatial technology agroforestry practices and in estimating prominent factors for its optimal productivity. It demands for diversions of forestry projects of areas which plunge in high suitability zones to optimize the outcome. The use of ancillary data in GIS domain can have gigantic potential to map the land and emerging as one significant dimension in food security and carbon sequestration targets.

References

Ahmad F. and 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.doi: http://dx.doi.org/ 10.12775/ EQ.2017.006

Ahmad F., Goparaju L. and Qayum A. (2017). Studying malaria epidemic for vulnerability zones: Multi-criteria approach of geospatial tools. J. Geoscience and Environment Protection, 5(5): DOI: 10.4236/gep.2017.55003

Anderson S.H., Udawatta R.P., Seobi T. and Garrett H.E. (2009). Soil water content and infiltration in agroforestry buffer strips. Agrofor. Syst., 75: 5-16.

Asbjornsen H., Hernandez-Santana., Liebman M., Bayala J., Chen J., Helmers M., Ong C. and Schulte L. (2014). Targeting perennial vegetation in agricultural landscapes for enhancing ecosystem services. Renew. Agric. Food Syst., 29: 101-125.

Ayehu G.T. and Besufekad S.A. (2015). Land Suitability Analysis for Rice Production: A GIS Based Multi-Criteria Decision Approach. American J. Geographic Information System, 4(3): 95104, DOI: 10.5923/j.ajgis.20150403.02

Baig M.H.A., Zhang L., Shuai T. and Tong Q. (2014). Derivation of a tasseled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5(5): 423-431. doi:10.1080/2150704X.2014.915434

Bijalwan A., Upadhyay A.P. and Dobriyal M.J.R. (2015) Tree-crop Combinations, Biomass and Carbon Estimation in Conventional Agrisilviculture (Agroforestry) System along Altitude and Aspects in the Hills of Uttarakhand Himalaya, India . Int. J. Curr. Res. Biosci. Plant Biol., 2(6): 214-217.

Champion H.G. and Seth S.K. (1968). A Revised Survey of Forest Types of India, Govt. of India Press, New Delhi, 404.

Demmergues Y.R. (1987). The biological nitrogen fixation in agroforestry. In B.A. stappler and PKR Nair. ed. Agroforestry, a decade of development. Nairobi, ICRAF, 245-71

Ellis E.A., Nair P.K., Linehan P.E., Beck H.W. and Blanche C.A. (2000). A GIS-based database management application for agroforestry planning and tree selection. Computers and Electronics in Agriculture, 27: 41-55.

Franklin S.E. (2001). Remote sensing for sustainable forest management. CRC Press.

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

Indian Water Portal (2016).http://www.indiawaterportal.org/ met_data. Accessed 10 October 2016.

Jamal A., Moon Y. and Malik Z. (2010). Sulphur -a general overview and interaction with nitrogen. Australian J. of Crop Sc., 4(7): 523-29.

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

Kumar S. (2014). Contribution of Agroforestry based NTFPs as Livelihood options in Rural Areas of Jharkhand State of India. World Congress on Agroforestry, PP1.2.25, Delhi.

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

McHarg I. (1995). Design with Nature. John Wiley and Sons: New York, New York.

McNeely J.A. (2004). Nature vs. nurture: Managing relationships between forests, agroforestry and wild biodiversity. Agrofor. Syst., 61: 155-165.

Mukesh A.S.R.P. (2012). Progressive farmers in Ranchi, Seraikela-Kharsawan sow dwarf Taiwanese variety to reap tall profits. http://www.telegraphindia.com/1120419/jsp/jharkhand/story_1539 1773.jsp#.WCCW5y197IV Accessed 18 October 2016.

Negash M. and Kanninen M. (2015). Modeling biomass and soil carbon sequestration of indigenous agroforestry systems using CO2 FIX approach. Agric. Ecosyst. Environ, 203: 147-155.

Nguyen Q., Hoang M.H., Oborn I. and van Noordwijk M. (2013). Multipurpose agroforestry as a climate change resiliency option for farmers: An example of local adaptation in Vietnam. Clim. Change, 117: 241-257.

Qayum A., Arya R., Kumar P. and Lynn A.M. (2015). Socioeconomic, epidemiological and geographic features based GISintegrated mapping to identify malarial hotspot. Malaria J., 14(192). doi: 10.1186/s12936-015-0685-4.

Ramos N.C., Gastauer M. de., Cordeiro A.A.C. and Meira-Neto J.A.A. (2015). Environmental filtering of agroforestry systems reduces the risk of biological invasion. Agrofor. Syst., 89: 279289.

Reddy T.Y. and Reddy G.H.S., (2010). Principles of Agronomy. Kalyani Publishers, New Delhi, 527.

Tewari S., Banik R.L., Kausal R., Bhardwaj D.R. Chaturvedi, O.P. and Gupta A. (2015) Bamboo based agroforestry systems. ENVIS centre on forestry, National forest library and information centre forest research institute, ICFRE, Dehradun, 24 pp.

Thorlakson T., Neufeldt H. and Dutilleul F.C. (2012). Reducing subsistence farmers’ vulnerability to climate change: Evaluating the potential contributions of agroforestry in western Kenya. Agric. Food Secur., 1: 1-13.

Ustin S.L, Roberts D.A, Gamon J.A., Asner G.P. and Green R.O. (2004) Using imaging spectroscopy to study ecosystem processes and properties. Bioscience, 54: 523–534.

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Published

2018-04-01

How to Cite

Ahmad, F., Goparaju, L., & Qayum, A. (2018). Remote Sensing and Geographic Information System based Agroforestry Suitability Mapping and Area Identification in Part of Jharkhand. Indian Forester, 144(4), 343–353. https://doi.org/10.36808/if/2018/v144i4/126555

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