Agroforestry suitability analysis based upon nutrient availability mapping: a GIS based suitability mapping

Firoz Ahmad; Laxmi Goparaju; Abdul Qayum; Firoz Ahmad; Laxmi Goparaju; Abdul Qayum

doi:10.3934/agrfood.2017.2.201

AIMS Agriculture and Food

2017, Volume 2, Issue 2: 201-220. doi: 10.3934/agrfood.2017.2.201

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Research article

Agroforestry suitability analysis based upon nutrient availability mapping: a GIS based suitability mapping

1.
Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh, India
2.
Department of Environment and Forest, Govt. of Arunachal Pradesh, Itanagar, India

Received: 04 March 2017 Accepted: 19 May 2017 Published: 23 May 2017

Agroforestry has drawn the attention of researchers due to its capacity to reduce the poverty and land degradation, improve food security and mitigate the climate change. However, the progress in promoting agroforestry is held back due to the lack of reliable data sets and appropriate tools to accurately map and to have an adequate decision making system for agroforestry modules. Agroforestry suitability being one special form of land suitability is very pertinent to study in the current times when there is tremendous pressure on the land as it is a limited commodity. The study aims for applying the geo-spatial tools towards visualizing various soil and environmental data to reveal the trends and interrelationships and to achieve a nutrient availability and agroforestry suitability map. Using weight matrix and ranks, individual maps were developed in ArcGIS 10.1 platform to generate nutrient availability map, which was later used to develop agroforestry suitability map. Watersheds were delineated using DEM in some part of the study area and were evaluated for prioritizing it and agroforestry suitability of the watersheds were also done as per the schematic flowchart. Agroforestry suitability regions were delineated based upon the weight and ranks by integrated mapping. The total open area was identified 42.4% out of which 21.6% area was found to have high suitability towards agroforestry. Within the watersheds, 22 village points were generated for creating buffers, which were further evaluated showing its proximity to high suitable agroforestry sites thus generating tremendous opportunity to the villagers to carry out agroforestry projects locally. This research shows the capability of remote sensing in studying agroforestry practices and in estimating the prominent factors for its optimal productivity. The ongoing agroforestry projects can be potentially diverted in the areas of high suitability as an extension. The use of ancillary data in GIS domain can have enormous ability to map the land for the benefit of rural people even up to the village level.
- agroforestry suitability,
- agroforestry modules,
- digital elevation model (DEM),
- integrated mapping,
- nutrient availability mapping
Citation: Firoz Ahmad, Laxmi Goparaju, Abdul Qayum. Agroforestry suitability analysis based upon nutrient availability mapping: a GIS based suitability mapping[J]. AIMS Agriculture and Food, 2017, 2(2): 201-220. doi: 10.3934/agrfood.2017.2.201

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Abstract

Agroforestry has drawn the attention of researchers due to its capacity to reduce the poverty and land degradation, improve food security and mitigate the climate change. However, the progress in promoting agroforestry is held back due to the lack of reliable data sets and appropriate tools to accurately map and to have an adequate decision making system for agroforestry modules. Agroforestry suitability being one special form of land suitability is very pertinent to study in the current times when there is tremendous pressure on the land as it is a limited commodity. The study aims for applying the geo-spatial tools towards visualizing various soil and environmental data to reveal the trends and interrelationships and to achieve a nutrient availability and agroforestry suitability map. Using weight matrix and ranks, individual maps were developed in ArcGIS 10.1 platform to generate nutrient availability map, which was later used to develop agroforestry suitability map. Watersheds were delineated using DEM in some part of the study area and were evaluated for prioritizing it and agroforestry suitability of the watersheds were also done as per the schematic flowchart. Agroforestry suitability regions were delineated based upon the weight and ranks by integrated mapping. The total open area was identified 42.4% out of which 21.6% area was found to have high suitability towards agroforestry. Within the watersheds, 22 village points were generated for creating buffers, which were further evaluated showing its proximity to high suitable agroforestry sites thus generating tremendous opportunity to the villagers to carry out agroforestry projects locally. This research shows the capability of remote sensing in studying agroforestry practices and in estimating the prominent factors for its optimal productivity. The ongoing agroforestry projects can be potentially diverted in the areas of high suitability as an extension. The use of ancillary data in GIS domain can have enormous ability to map the land for the benefit of rural people even up to the village level.

References

[1]	Nair PKR (1993) An Introduction to Agroforestry. Kluwer Academic Publishers: Dordrecht, The Netherlands.
[2]	Negash M, Kanninen M (2015) Modeling biomass and soil carbon sequestration of indigenous agroforestry systems using CO2 FIX approach. Agric Ecosyst Environ 203: 147-155. doi: 10.1016/j.agee.2015.02.004
[3]	Mbow C, Noordwijk MV, Luedeling E, et al. (2014) Agroforestry solutions to address food security and climate change challenges in Africa. Curr Opin Environ Sustain 6: 61-67. doi: 10.1016/j.cosust.2013.10.014
[4]	Albrecht A, Kandji ST (2003) Carbon sequestration in tropical agroforestry systems. Agric Ecosyst Environ 99: 15-27. doi: 10.1016/S0167-8809(03)00138-5
[5]	Thorlakson T, Neufeldt H, Dutilleul FC (2012) Reducing subsistence farmers' vulnerability to climate change: Evaluating the potential contributions of agroforestry in western Kenya. Agric Food Secur 1: 1-13. doi: 10.1186/2048-7010-1-1
[6]	Nguyen Q, Hoang MH, Oborn I, et al. (2013) Multipurpose agroforestry as a climate change resiliency option for farmers: An example of local adaptation in Vietnam. Clim Chang 117: 241-257. doi: 10.1007/s10584-012-0550-1
[7]	Jose S (2012) Agroforestry for conserving and enhancing biodiversity. Agrofor Syst 85: 1-8. doi: 10.1007/s10457-012-9517-5
[8]	McNeely JA (2004) Nature vs. nurture: Managing relationships between forests, agroforestry and wild biodiversity. Agrofor Syst 61: 155-165. doi: 10.1023/B:AGFO.0000028996.92553.ea
[9]	Ramos NC, Gastauer M, Cordeiro AAC, et al. (2015) Environmental filtering of agroforestry systems reduces the risk of biological invasion. Agrofor Syst 89: 279-289. doi: 10.1007/s10457-014-9765-7
[10]	Anderson SH, Udawatta RP, Seobi T, et al. (2009) Soil water content and infiltration in agroforestry buffer strips. Agrofor Syst 75: 5-16. doi: 10.1007/s10457-008-9128-3
[11]	Hernandez G, Trabue S, Sauer T, et al. (2012) Odor mitigation with tree buffers: Swine production case study. Agric Ecosyst Environ 149: 154-163. doi: 10.1016/j.agee.2011.12.002
[12]	Asbjornsen H, Hernandez-Santana V, Liebman M, et al. (2014) Targeting perennial vegetation in agricultural landscapes for enhancing ecosystem services. Renew Agric Food Syst 29: 101-125.
[13]	Book of Abstracts, 1st World Congress of Agroforestry Working Together for Sustainable Land Use Systems. University of Florida, 2004. Available from: http://conference.ifas.ufl.edu/WCA/Abstracts2.pdf
[14]	Jitendra, India becomes first country to adopt an agroforestry policy. 2014. Available from: http://www.downtoearth.org.in/news/india-becomes-first-country-to-adopt-an-agroforestry-policy-43518
[15]	India National Agroforestry Policy. 2014. Available from: http://iksa.in/gs3/india-national-agroforestry-policy/860
[16]	Ahmad F, Goparaju L (2016) Analysis of Urban Sprawl Dynamics Using Geospatial Technology in Ranchi City, Jharkhand, India. J Environ Geogr 9: 7-13. doi: 10.1515/jengeo-2016-0002
[17]	Ahmad F, Goparaju L (2016) Urban Forestry: Identification of Suitable sites in Ranchi city, India using Geospatial Technology. Ecol Quest 24: 45-58
[18]	Ahmad F, Goparaju L (2017) Spatio-temporal dynamics of mines in Singrauli, India: An analysis using geospatial technology. J Geomat 11: 53-59
[19]	Ahmad F, Goparaju L, Qayum A (2017) Natural Resource Mapping Using Landsat and Lidar towards Identifying Digital Elevation, Digital Surface and Canopy Height Models. Int J Environ Sci Nat Res 2: 555580. doi: 10.19080/IJESNR.2017.02.555580
[20]	Ahmad F, Goparaju L, Qayum A (2017) LULC analysis of urban spaces using Markov chain predictive model at Ranchi in India. Spat Inf Res DOI: 10.1007/s41324-017-0102-x. doi: 10.1007/s41324-017-0102-x
[21]	Ahmad F, Goparaju L, Qayum A (2017) Studying malaria epidemic for vulnerability zones: Multi-criteria approach of geospatial tools. J Geosci Environ Prot 5: 30-53.
[22]	Qayum A, Arya R, Kumar P, et al. (2015) Socio-economic, epidemiological and geographic features based GIS-integrated mapping to identify malarial hotspot. Malaria J 14: 192. doi: 10.1186/s12936-015-0685-4
[23]	Ritung S, Wahyunto FA, Hidayat H (2007) Land Suitability Evaluation with a case map of Aceh Barat District. Indonesian Soil R. Institute and World Agro Centre, Bogor, Indonesia.
[24]	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. doi: 10.1186/2193-1801-2-265
[25]	Dengiz O (2013) Land suitability assessment for rice cultivation based on GIS modeling. Turk J Agric For 37: 326-334.
[26]	Yedage AS, Gavali RS, Jarag AP (2013) Land Assessment for Horticulture (Pomegranate) Crop Using GIS and Fuzzy Decision Analysis in the Sangola Taluka of Solapur District. Int J Remote Sens GIS 2: 104-113
[27]	Sarkar A, Ghosh A, Banik P (2014) Multi-criteria land evaluation for suitability analysis of wheat: a case study of a watershed in eastern plateau region, India. Geospatial Inf Sci 17: 119-128. doi: 10.1080/10095020.2013.774106
[28]	Ayehu GT, Besufekad SA (2015) Land Suitability Analysis for Rice Production: A GIS Based Multi-Criteria Decision Approach. Am J Geogr Inf Syst 4: 95-104.
[29]	Reisner Y, de Filippi R, Herzog F, et al. (2007) Target regions for silvoarable agroforestry in Europe. Ecol Eng 29: 401-418. doi: 10.1016/j.ecoleng.2006.09.020
[30]	FAO, A framework for land evaluation. Soils Bulletin 32. Food and Agriculture Organization of the United Nations, Rome, Italy, 1976. Available from: http://www.fao.org/docrep/t0715e/t0715e06.htm
[31]	Champion HG, Seth SK (1968) A Revised Survey of Forest Types of India. Govt. of India Press, New Delhi, 404.
[32]	Sys C, Van-Ranst E, Debaveye J (1991) Land evaluation. Part 1: Principles in land evaluation and crop production calculations. Agricultural publications 7,1. General Administration of Development Cooperation of Belgium, Brussels. 273.
[33]	Sys C, Van-Ranst E, Debaveye J (1991) Land evaluation. Part 2: Methods in land evaluation. Agricultural publications 7,2. General Administration of Development Cooperation of Belgium, Brussels. 247.
[34]	McHarg I (1995) Design with Nature. John Wiley and Sons: New York, New York.
[35]	Ellis EA, Nair PK, Linehan PE, et al. (2000) A GIS-based database management application for agroforestry planning and tree selection. Comput Electron Agric 27: 41-55. doi: 10.1016/S0168-1699(00)00095-8
[36]	Franklin SE (2001) Remote sensing for sustainable forest management. CRC Press.
[37]	Indian Water Portal. 2016. Available from: http://www.indiawaterportal.org/met_data
[38]	Childs C, Interpolating surfaces in ArcGIS Spatial Analyst. Esri Education Services, 2004. Available from: https://www.esri.com/news/arcuser/0704/files/interpolating.pdf
[39]	Baig MHA, Zhang L, Shuai T, et al. (2014) Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sens Lett 5: 423-431. doi: 10.1080/2150704X.2014.915434
[40]	Nair PKR (1984) Soil productivity aspect of agroforestry, Nairobi. ICRAF, 85.
[41]	Jamal A, Moon Y, Malik Z (2010) Sulphur -a general overview and interaction with nitrogen. Aust J Crop Sci 4: 523-29.
[42]	Reddy TY, Reddy GHS (2010) Principles of Agronomy. Kalyani Publishers, New Delhi, 527.
[43]	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.
[44]	Demmergues YR (1987) The biological nitrogen fixation in agroforestry. In: Stappler-S and PKR Nair. ed. Agroforestry, a decade of development. Nairobi, ICRAF, 245-71.
[45]	Mukesh ASRP, Progressive farmers in Ranchi, Seraikela-Kharsawan sow dwarf Taiwanese variety to reap tall profits. 2012. Available from: http://www.telegraphindia.com/1120419/jsp/jharkhand/story_15391773.jsp#.WCCW5y197IV
[46]	Verma LR (1998) Indigenous technology knowledge for watershed management in upper north-west Himalayas of India. Participatory Watershed Management Training in Asia (PWMTA) Program, Kathmandu, Nepal.

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