GIS-based assessment of land-agroforestry potentiality of Jharkhand State, India

doi:10.1016/j.regsus.2022.10.004

Abstract

Abstract:

Scaling-up agroforestry areas in various forms is a scientific path towards achieving various sustainable development goals (SDGs), especially improving livelihood, reducing poverty, conserving environment and biodiversity, and transforming climate change. In this study, the effort was made to investigate the land potentiality for agroforestry at the district level in Jharkhand State, India by applying geographic information system (GIS) modeling technology using climate (temperature and precipitation), topography (slope and elevation), ecology (percent tree cover and normalized difference vegetation index (NDVI)), and social economics (poverty rate and tribal dominance) factors. The results revealed that six districts of Jharkhand State had agroforestry potential greater than 60.00%. The highest agroforestry suitability was found in Simdega District (78.20%), followed by Pakur (76.25%), West Singhbhum (72.70%), Dumka (68.84%), Sahibganj (64.63%), and Godda (63.43%) districts. Additionally, we identified 513 out of 32,620 villages of Jharkhand State potentially suitable (agroforestry suitability ≥80.00%) for agroforestry with the objective of life improvement among marginalized society. Under the outside forest area, 8.58% of the total geographical land of Jharkhand State was wasteland, much of which was found suitable for agroforestry practices. The agroforestry setups in those wastelands can absorb 637 t carbon annually in long run and can provide direct economic benefits to the locals besides additional income for carbon emission reduction. This study concluded that Jharkhand State has plenty of high potential land for agroforestry, and adoption of agroforestry at the village level must be given high priority. This study could guide the nodal authorities to prepare appropriate strategies for scaling the tree cover in agroforestry systems in village-level landscape planning which needs policy attention and investment for achieving 9 out of the 17 SDGs.

Key words: Agroforestry suitability, Land potential, Climate change, Topography, Food security, GIS Modeling, Poverty alleviation, Sustainable development goals (SDGs)

Firoz AHMAD, Nazimur Rahman TALUKDAR, Laxmi GOPARAJU, Chandrashekhar BIRADAR, Shiv Kumar DHYANI, Javed RIZVI. GIS-based assessment of land-agroforestry potentiality of Jharkhand State, India[J]. Regional Sustainability, 2022, 3(3): 254-268.

Figures/Tables 20

Fig. 1.

Table 1

Fig. 2.

Fig. 3.

Fig. 4.

Table S1

Fig. 5.

Fig. 6.

Table S2

Fig.7.

Fig. 8.

Fig. 9.

Table S3

Fig. 10.

Fig. 11.

Table S4

Fig. 12.

Table 2

Table 3

Fig. 13.

References 84

[1]	Ahmad, F., Uddin, M.M., Goparaju, L., 2018. An evaluation of vegetation health and the socioeconomic dimension of the vulnerability of Jharkhand state of India in climate change scenarios and their likely impact: a geospatial approach. Environ. Socio-Econ. S. 6(4), 39-47.
[2]	Ahmad, F., Uddin, M.M., Goparaju, L., 2019. Agroforestry suitability mapping of India: geospatial approach based on FAO guidelines. Agroforest. Syst. 93(4), 1319-1336. doi: 10.1007/s10457-018-0233-7
[3]	Ahmad, F., Uddin, M.M., Goparaju, L., et al., 2020. Quantification of the land potential for scaling agroforestry in South Asia. KN-Journal of Cartography and Geographic Information. 70, 71-89. doi: 10.1007/s42489-020-00045-0
[4]	Ahmad, F., Uddin, M.M., Goparaju, L., et al., 2021. Tree suitability modeling and mapping in Nepal: a geospatial approach to scaling agroforestry. Model. Earth Syst. Env. 7(1), 169-179. doi: 10.1007/s40808-020-00922-7
[5]	Asbjornsen, H., Hernandez-Santana, V., Liebman, M., et al., 2014. Targeting perennial vegetation in agricultural landscapes for enhancing ecosystem services. Renew. Agr. Food Syst. 29 (2), 101-125. doi: 10.1017/S1742170512000385
[6]	Baja, S., Chapman, D.M., Dragovich, D., 2002. A conceptual model for defining and assessing land management units using a fuzzy modeling approach in GIS environment. Environ. Manage. 29(5), 647-661. pmid: 12180179
[7]	Baraik, V.K., 2019. Dimensions of tribal education and employment in Jharkhand: linkage of educational level and industrial category. Jharkhand Journal of Development and Management Studies. 17(3), 8159-8174.
[8]	Bathgate, S., 2011. Ecological Site Classification. [2022-03-12]. https://cdn.forestresearch.gov.uk/2022/02/esc_manual.pdf.
[9]	Brown, M.E., Funk, C.C., 2008. Food security under climate change. Science. 319(5863), 580-581. doi: 10.1126/science.1154102
[10]	Carlson, K.M., Gerber, J.S., Mueller, N.D., et al., 2017. Greenhouse gas emissions intensity of global croplands. Nat. Clim. Change. 7, 63-68. doi: 10.1038/NCLIMATE3158
[11]	Census of India, 2022. Jharkhand Population 2011-2022. [2022-04-28]. https://www.census2011.co.in/census/state/jharkhand.html.
[12]	Chandniha, S.K., Meshram, S.G., Adamowski, J.F., et al., 2017. Trend analysis of precipitation in Jharkhand State, India. Theor. Appl. Climatol. 130(1-2), 261-274. doi: 10.1007/s00704-016-1875-x
[13]	Chen, X.P.S., 2016. An analysis of climate impact on landscape design. Atmospheric and Climate Sciences. 6, 475-481. doi: 10.4236/acs.2016.63037
[14]	Climate data, 2022. [2022-04-10]. https://en.climate-data.org/asia/india/jharkhand-772/.
[15]	Dağıstanlı, C., Turan, İ.D., Dengiz, O., 2018. Evaluation of the suitability of sites for outdoor recreation using a multi-criteria assessment model. Arab. J. Geosci. 11(17), 492, doi: 10.1007/s12517-018-3856-0. doi: 10.1007/s12517-018-3856-0
[16]	Didan, K., 2021. MOD13A3 v061 MODIS/Terra Vegetation Indices Monthly L3 Global 1km SIN Grid. [2022-02-08]. https://doi.org/10.5067/MODIS/MOD13A3.061.
[17]	Dollinger, J., Jose, S., 2018. Agroforestry for soil health. Agroforest. Syst. 92(2), 213-219. doi: 10.1007/s10457-018-0223-9
[18]	Everest, T., 2021. Suitable site selection for pistachio (Pistacia vera) by using GIS and multi-criteria decision analyses (a case study in Turkey). Environ. Dev. Sustain. 23, 7686-7705. doi: 10.1007/s10668-020-00941-5
[19]	Everest, T., Sungur, A., Ozcan, H., 2021. Determination of agricultural land suitability with a multiple-criteria decision-making method in Northwestern Turkey. Int. J. Environ. Sci. Te. 18(5), 1073-1088. doi: 10.1007/s13762-020-02869-9
[20]	Evugin, S., 2021. Poverty Least in Kerala; Highest In UP, Bihar, Jharkhand. [2020-08-18]. https://www.deshabhimani.com/english/news/national/least-poverty-in-kerala-highest-in-up-bihar-jharkhand/5414.
[21]	FAO (Food and Agriculture Organization of the United Nations), 1976. A Framework for Land Evaluation. Rome: FAO, 20.
[22]	FAO, 2007. Land evaluation. Towards a revised framework. [2022-03-14]. https://www.fao.org/fileadmin/templates/nr/images/resources/pdf_documents/lman_070601_en.pdf.
[23]	FAO, 2018. Agroforestry. [2022-04-16]. https://www.fao.org/sustainable-forest-management/toolbox/modules/agroforestry/basic-knowledge/en/?type=111.
[24]	FAO, IFAD (International Fund for Agricultural Development), WFP (World Food Programme), 2015. The state of food insecurity in the world 2015.Meeting the 2015 international hunger targets:taking stock of uneven progress. [2022-03-16]. http://www.fao.org/3/i4646e/i4646e.pdf.
[25]	FD Report, 2012. Wastelands of Jharkhand. [2022-05-16]. http://forest.jharkhand.gov.in/About_us/pdf/wastelands.pdf.
[26]	Fick, S.E., Hijmans, R.J., 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J. Climatol. 37, 4302-4315. doi: 10.1002/joc.5086
[27]	Gerassis, S., Albuquerque, M.T.D., Roque, N., et al., 2021. Future habitat suitability for species under climate change-lessons learned from the strawberry tree case study. Forest. Ecol. Manag. 491, 119150, doi: 10.1016/j.foreco.2021.119150. doi: 10.1016/j.foreco.2021.119150
[28]	GHG Greenhouse Gas Platform India, 2014. Trend analysis of GHG emissions in Jharkhand. [2022-05-16]. http://www.ghgplatform-india.org/Images/Publications/GHGPI-PhaseIII-Trend%20Analysis%20State-Jharkhand-Dec'19.pdf.
[29]	GoJ, 2022. Minerals in Jharkhand. [2022-05-16]. https://www.jharkhand.gov.in/home/AboutMinerals.
[30]	Goparaju, L., Ahmad, F., Uddin, M., et al., 2020. Agroforestry: An effective multi-dimensional mechanism for achieving sustainable development goals. Ecol. Quest. 31(3), 63-71.
[31]	Han, J.W., Kamber, M., Pei, J., 2012. Data Mining:Concepts and Technique, (3^rdedition). Amsterdam: Elsevier Inc., 279-323.
[32]	Hansen, M.C., Potapov, P.V., Moore, R., et al., 2013. High-resolution global maps of 21st-century forest cover change. Science. 342(6160), 850-853. doi: 10.1126/science.1244693 pmid: 24233722
[33]	Hernandez, R.R., Debenport, S.J., Leewis, M.-C.C.E., et al., 2015. The native shrub, Piliostigma reticulatum, as an ecological “resource island” for mango trees in the Sahel. Agr. Ecosyst. Environ. 204, 51-61. doi: 10.1016/j.agee.2015.02.009
[34]	Herzberg, R., Pham, T.G., Kappas, M., et al., 2019. Multi-Criteria decision analysis for the land evaluation of potential agricultural land use types in a hilly area of Central Vietnam. Land-Basel. 8(6), 90, doi: 10.3390/land8060090. doi: 10.3390/land8060090
[35]	ICRAF (International Council for Research in Agroforestry), 2010. Agroforestry Policy Initiative. [2022-03-06]. http://old.worldagroforestry.org/downloads/publications/PDFs/RP09004.DOC.
[36]	India Today, 2011. Not poor if you earn Rs 32 a day: Planning Commission https://www.indiatoday.in/india/north/story/planning-commission-bpl-earn-rs-25-a-day-india-141619-2011-09-21.
[37]	IISD (International Institute for Sustainable Development), 2018. Report Shows Agroforestry’s Contribution to Several SDGs. [2022-07-13]. https://sdg.iisd.org/news/report-shows-agroforestrys-contribution-to-several-sdgs/.
[38]	Jose, S., 2009. Agroforestry for ecosystem services and environmental benefits: an overview. Agroforest. Syst. 76(1), 1-10. doi: 10.1007/s10457-009-9229-7
[39]	Khan, M.A., 2022. Ownership Scheme: Why tribals are opposing drone surveys in Jharkhand. [2022-03-08]. https://www.downtoearth.org.in/news/governance/ownership-scheme-why-tribals-are-opposing-drone-surveys-in-jharkhand-81562.
[40]	Kho, R.M., 2000. A general tree-environment-crop interaction equation for predictive understanding of agroforestry systems. Agr. Ecosyst. Environ. 80(1-2), 87-100. doi: 10.1016/S0167-8809(00)00136-5
[41]	Kumar, A., Chandra, G., 2017. Insect pest infestation on Gmelina arborea Roxb. in different agroclimatic zones of Jharkhand, India. Arthropods. 6(1), 8-20
[42]	Lang, A.C., von Oheimb, G., Scherer-Lorenzen, M., et al., 2014. Mixed afforestation of young subtropical trees promotes nitrogen acquisition and retention. J. Appl. Ecol. 51(1), 224-233. doi: 10.1111/1365-2664.12157
[43]	Leakey, R.R.B., 1996. Definition of agroforestry revisited. Agroforestry Today. 8(1), 5-7.
[44]	Mahato, S., Dasgupta, S., Todaria, N.P., et al., 2016. Agroforestry mapping and characterization in four districts of Garhwal Himalaya. Energy. Ecol. Environ. 1(2), 86-97.
[45]	Maji, A.K., Reddy, G.P.O., Sarkar, D., 2010. Degraded and Wastelands of India, Status and Spatial Distribution. New Delhi: ICAR (Indian Council of Agricultural Research), 14.
[46]	Malezieux, E., Crozat, Y., Dupraz, C., et al., 2009. Mixing plant species in cropping systems: concepts, tools and models. A review. Agron. Sustain. Dev. 29(1), 43-62. doi: 10.1051/agro:2007057
[47]	Mandal, V.P., Rehman, S., Ahmed, R., et al., 2020. Land suitability assessment for optimal cropping sequences in Katihar district of Bihar, India using GIS and AHP. Spat. Inf. Res. 28(5), 589-599. doi: 10.1007/s41324-020-00315-z
[48]	Meneses-Tovar, C.L., 2011. NDVI as indicator of degradation. [2022-02-16]. https://www.fao.org/3/i2560e/i2560e07.pdf.
[49]	Mertz, O., Halsnaes, K., Olesen, J.E., et al., 2009. Adaptation to climate change in developing countries. Environ. Manage. 43(5), 743-752. doi: 10.1007/s00267-008-9259-3 pmid: 19184576
[50]	Montagnini, F., 2020. The contribution of agroforestry to restoration and conservation:biodiversity islands in degraded landscapes. n: Dagar. J.C., Gupta, S.R., Teketay, D., (eds). Agroforestry for Degraded Landscapes. Springer: Singapore, 445-479.
[51]	Morton, J.F., 2007. The impact of climate change on smallholder and subsistence agriculture. P. Natl. Acad. Sci. USA. 104(50), 19680-19685.
[52]	Musakwa, W., 2018. Identifying land suitable for agricultural land reform using GIS-MCDA in South Africa. Environ. Dev. Sustain. 20(5), 2281-2299. doi: 10.1007/s10668-017-9989-6
[53]	Nair, P.K.R., 1984. Soil Productivity Aspect of Agroforestry. Nairobi: ICRAF, 85.
[54]	Nair, P.K.R., 2011. Agroforestry systems and environmental quality: introduction. J. Environ. Qual. 40(3), 784-790. doi: 10.2134/jeq2011.0076 pmid: 21546663
[55]	Nair, P.K.R., Toth, G.G., 2016. Measuring agricultural sustainability in agroforestry systems. In: Lal, R., Kraybill, D., Hansen, D.O., (eds). Climate Change and Multi-Dimensional Sustainability in African Agriculture. Springer: Cham, 365-394.
[56]	Nath, A.J., Kumar, R., Devi, N.B., et al., 2021. Agroforestry land suitability analysis in the Eastern Indian Himalayan region. Environmental Challenges. 4, 100199, doi: 10.1016/j.envc.2021.100199. doi: 10.1016/j.envc.2021.100199
[57]	NITI Aayog, 2021. National Multidimensional Poverty Index. Baseline Report Based on NFHS-4 (National Family Health Survey-4)(2015-16). [2022-04-10]. https://www.niti.gov.in/sites/default/files/2021-11/National_MPI_India-11242021.pdf.
[58]	Oliver, M.A., Gregory, P.J., 2015. Soil, food security and human health: a review. Eur. J. Soil Sci. 66(2), 257-276. doi: 10.1111/ejss.12216
[59]	Opanda, S., 2022. How Many Trees Needed to Offset Your Carbon Emissions? [2022-05-16]. https://8billiontrees.com/carbon-offsets-credits/reduce-co2-emissions/how-many-trees-offset-carbon-emissions/.
[60]	Oraon, P.R., Singh, B.K., Kumar, A., et al., 2018. Traditional agroforestry practices of Jharkhand: a viable options for livelihood upliftment. Multiloginic in Science. 8(special C), 66-67.
[61]	Pardon, P., Reubens, B., Mertens, J., et al., 2018. Effects of temperate agroforestry on yield and quality of different arable intercrops. Agr. Syst. 166, 135-151. doi: 10.1016/j.agsy.2018.08.008
[62]	Perles-Garcia, M.D., Kunz, M., Fichtner, A., et al., 2021. Tree species richness promotes an early increase of stand structural complexity in young subtropical plantations. J. Appl. Ecol. 58(10), 2305-2314. doi: 10.1111/1365-2664.13973
[63]	Ramos, N.C., Gastauer, M., Cordeiro, A.D.C., 2015. Environmental filtering of agroforestry systems reduces the risk of biological invasion. Agroforest. Syst. 89(2), 279-289. doi: 10.1007/s10457-014-9765-7
[64]	Rao, U., 2022. Andhra Pradesh farmers reap carbon credits from agriculture. [2022-05-16]. https://article.wn.com/view/2022/03/24/Andhra_Pradesh_farmers_reap_carbon_credits_from_agriculture/.
[65]	Rosenstock, T.S., Lamanna, C., Chesterman, S., et al., 2016. The scientific basis of climate-smart agriculture:A systematic review protocol. In: CCAFS (Climate Change, Agriculture and Food Security) Working Paper No. 136. CGIAR. Copenhagen, Denmark.
[66]	Roy, P.S., Meiyappan, P.K., Joshi, M.P., et al., 2016. Decadal land use and land cover classifications across India, 1985, 1995, 2005. [2022-03-12]. https://daac.ornl.gov/VEGETATION/guides/Decadal_LULC_India.html.
[67]	Sandeep, D., Sharan, A., 2017. In Jharkhand, using an old technique for sustainable water. [2022-05-15]. https://news.climate.columbia.edu/2017/05/24/in-jharkhand-using-an-old-technique-for-sustainable-water/.
[68]	Scholten, T., Goebes, P., Kuhn, P., et al., 2017. On the combined effect of soil fertility and topography on tree growth in subtropical forest ecosystems-a study from SE China. J. Plant. Ecol. 10(1), 111-127. doi: 10.1093/jpe/rtw065
[69]	Seeberg-Elverfeldt, C., 2010. Carbon Finance Possibilities for Agriculture, Forestry and Other Land Use Projects in a Smallholder Context. Rome: FAO, 1-24.
[70]	Smith, J., Pearce, B.D., Wolfe, M.S., 2013. Reconciling productivity with protection of the environment: Is temperate agroforestry the answer? Renew Agr. Food Syst. 28(1), 80-92. doi: 10.1017/S1742170511000585
[71]	Talukdar, N.R., Choudhury, P., Ahmad, F., et al., 2020. Habitat suitability of the Asiatic elephant in the trans-boundary Patharia Hills Reserve Forest, northeast India. Model. Earth Syst. Environ. 6, 1951-1961.
[72]	Tewari, V.P., Dagar, J.C., 2017. Introduction. In: Dagar, J.C., Tewari, V.P., (eds). Agroforestry. Singapore: Springer, 1-11.
[73]	Tirkey, A.S., Ghosh, M., Pandey, A.C., et al., 2018. Assessment of climate extremes and its long term spatial variability over the Jharkhand State of India. Egypt. J. Remote. Sens. 21(1), 49-63.
[74]	Verheye, W., Koohafkan, P., Nachtergaele, F., 1982. Land Use, Land Cover and Soil Sciences-Vol. II-The FAO Guidelines for Land Evaluation. Paris: UNESCO-EOLSS (United Nations Educational, Scientific, and Cultural Organization-Encyclopedia of Life Support Systems), 1-23.
[75]	Wadood, A., Kumari, P., 2009. Impact of climate change on Jharkhand agriculture:mitigation and adoption. In: ISPRS ArchivesXXXVIII-8/W 3 Workshop Proceedings:Impact of Climate Change on Agriculture. Hanover: ISPRS (International Society for Photogrammetry and Remote Sensing),207-210.
[76]	Walpole, M., Smith, J., Rosser, A., et al., 2013. Smallholders, Food Security, and the Environment. Rome: IFAD, UNEP (United Nations Environment Programme), 6-22.
[77]	WFP, 2011. A food security and vulnerability atlas of Indonesia. [2022-03-06]. https://reliefweb.int/report/indonesia/food-security-and-vulnerability-atlas-indonesia-2009.
[78]	Wheeler, T., von Braun, J., 2013. Climate change impacts on global food security. Science. 341(6145), 508-513. doi: 10.1126/science.1239402 pmid: 23908229
[79]	Wojtkowski, P.A., 1998. The Theory and Practice of Agroforestry Design. A Comprehensive Study of The Theories, Concepts and Conventions That Underlie The Successful Use of Agroforestry. Enfield: Science Publishers, 282.
[80]	WorldData, 2022. The Climate in India. Average Daytime and Nighttime Temperatures. [2022-04-10]. https://www.worlddata.info/asia/india/climate-jharkhand.php.
[81]	Yohannes, H., Soromessa, T., 2018. Land suitability assessment for major crops by using GIS-based multi-criteria approach in AnditTid watershed, Ethiopia. Cogent. Food Agr. 4(1), doi: 10.1080/23311932.2018.1470481. doi: 10.1080/23311932.2018.1470481
[82]	Zhang, C.S., Li, X.Y., Chen, L., et al., 2016. Effects of topographical and edaphic factors on tree community structure and diversity of subtropical mountain forests in the Lower Lancang River Basin. Forests. 7(10), 222, doi: 10.3390/f7100222. doi: 10.3390/f7100222
[83]	Zolekar, R.B., Bhagat, V.S., 2015. Multi-criteria land suitability analysis for agriculture in hilly zone: Remote sensing and GIS approach. Comput. Electron. Agr. 118, 300-321. doi: 10.1016/j.compag.2015.09.016
[84]	Zomer, R.J., Bossio, D.A., Trabucco, A., et al., 2007. Trees and water: smallholder agroforestry on irrigated lands in Northern India. International Water Management Institute. Colombo, Sri Lanka.

Factor	Indicator	Data source	Year	Reference
Climatic factor	Precipitation	https://worldclim.org/data/worldclim21.html	1970-2000	Fick and Hijmans (2017)
Climatic factor	Temperature	https://worldclim.org/data/worldclim21.html	1970-2000	Fick and Hijmans (2017)
Topographical factor	Slope	ASTER DEM: https://earthexplorer.usgs.gov/	2000
Topographical factor	Elevation	ASTER DEM: https://earthexplorer.usgs.gov/	2000
Ecological factor	NDVI	Terra vegetation indices monthly 2021 MODIS: https://lpdaac.usgs.gov/products/myd13a3v006/	2003-2021	Didan (2021)
Ecological factor	Percent tree cover	https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html	2000	Hansen et al. (2013)
Socio-economic factor	Poverty rate	https://doi.org/10.2478/environ-2018-0026	2001	Ahmad et al. (2018)
Socio-economic factor	Tribal dominance	https://doi.org/10.2478/environ-2018-0026	2001	Ahmad et al. (2018)

District	Climatic suitability of land for agroforestry (%)
District	Minimum	Maximum	Mean	Standard deviation
Simdega	63.99	100.00	82.32	9.13
Pakur	66.21	89.82	80.31	5.44
East Singhbhum	69.08	97.47	79.97	5.02
Dumka	53.87	89.70	73.05	7.92
Saraikela-Kharsawan	52.05	76.01	71.37	4.84
Jamtara	58.23	80.38	70.36	5.44
West Singhbhum	48.10	86.51	68.41	6.69
Sahibganj	46.63	76.61	61.65	6.33
Godda	48.79	72.63	59.78	5.22
Deoghar	49.59	70.72	59.49	4.90
Dhanbad	45.77	67.45	59.18	3.82
Khunti	48.97	72.95	57.46	5.10
Bokaro	37.13	72.31	57.19	6.40
Ramgarh	44.23	63.87	53.47	4.75
Ranchi	36.04	69.46	53.05	6.92
Gumla	4.94	81.23	51.75	15.97
Giridih	23.91	61.24	43.85	8.71
Palamu	30.22	47.56	39.04	3.60
Latehar	1.50	50.96	37.57	9.70
Chatra	21.04	46.67	37.23	5.95
Lohardaga	16.09	48.57	35.32	8.29
Garhwa	0.00	42.30	34.94	4.17
Hazaribagh	16.19	51.11	33.17	8.27
Koderma	17.39	34.69	24.80	4.08

District	Topographical suitability of land for agroforestry (%)
District	Minimum	Maximum	Mean	Standard deviation
Pakur	60.68	96.93	89.39	7.38
Sahibganj	56.71	100.00	87.85	8.64
Jamtara	78.11	94.01	87.27	2.09
Godda	59.67	95.34	86.92	7.10
Dumka	60.86	95.44	85.34	5.03
Dhanbad	39.11	94.73	84.51	6.31
Deoghar	48.47	88.60	84.19	3.76
East Singhbhum	42.09	94.50	83.19	10.46
Palamu	48.48	91.46	82.43	5.38
Saraikela-Kharsawan	34.44	92.38	80.75	11.94
Bokaro	27.36	89.68	80.26	8.97
Giridih	0.00	89.52	79.99	5.66
Garhwa	26.08	91.34	78.29	8.95
Koderma	39.02	92.21	77.87	6.00
Chatra	51.84	90.89	75.40	6.75
Ramgarh	32.54	84.81	74.67	7.77
Simdega	49.56	85.76	72.34	6.32
Hazaribagh	47.68	87.56	72.00	6.18
West Singhbhum	34.99	88.62	70.67	10.62
Ranchi	32.57	87.49	68.01	8.21
Latehar	22.42	84.69	67.47	12.82
Khunti	51.71	80.26	66.43	4.29
Gumla	28.37	73.38	59.84	10.01
Lohardaga	28.98	76.74	57.90	11.32

District	Ecological suitability of land for agroforestry (%)
District	Minimum	Maximum	Mean	Standard deviation
Latehar	22.26	95.42	46.37	18.14
West Singhbhum	22.51	94.33	44.25	18.10
Koderma	22.22	76.17	39.90	16.28
Lohardaga	19.20	89.75	39.10	19.22
Chatra	23.03	84.41	38.12	12.24
Hazaribagh	0.00	84.76	36.24	13.55
Garhwa	10.51	80.22	35.92	12.72
Khunti	24.03	61.31	35.74	9.78
Gumla	21.66	89.75	35.18	14.45
Simdega	22.49	59.09	34.02	7.64
Sahibganj	1.97	63.47	33.91	8.21
Palamu	7.10	80.64	33.34	10.07
Godda	24.23	66.74	32.19	7.45
East Singhbhum	18.04	88.24	31.57	9.47
Pakur	16.97	55.23	30.93	5.95
Ranchi	6.34	75.30	30.58	8.64
Saraikela-Kharsawan	5.94	78.26	30.41	10.46
Bokaro	1.19	85.67	29.90	10.77
Ramgarh	19.11	71.26	29.04	7.23
Dumka	0.17	58.66	28.53	6.43
Giridih	21.03	100.00	27.90	8.42
Dhanbad	1.33	61.87	27.63	5.29
Jamtara	0.29	33.72	25.41	2.71
Deoghar	20.75	44.66	24.91	1.92

District	Socio-economic suitability of land for agroforestry (%)
District	Minimum	Maximum	Mean	Standard deviation
Simdega	29.98	97.46	91.77	7.03
Gumla	37.38	96.07	86.83	4.28
West Singhbhum	22.48	100.00	85.52	10.39
Lohardaga	68.40	92.11	81.04	4.45
Khunti	64.73	92.49	79.18	5.88
Latehar	45.57	92.80	76.49	9.10
Pakur	31.79	82.30	76.39	4.71
Dumka	34.65	85.81	73.88	8.41
Garhwa	49.45	81.30	73.78	4.82
Godda	51.95	77.49	70.41	4.38
Sahibganj	38.16	74.72	68.34	4.05
Palamu	49.74	77.59	64.46	5.47
Jamtara	24.49	71.66	54.35	11.86
Ranchi	11.01	86.14	54.15	13.07
Saraikela-Kharsawan	23.47	73.46	53.97	7.64
Chatra	24.69	64.61	46.55	8.18
Deoghar	30.85	68.79	43.45	7.93
East Singhbhum	0.01	58.19	39.39	7.02
Giridih	19.24	41.47	29.97	5.03
Ramgarh	20.77	45.10	28.54	5.67
Hazaribagh	19.20	53.69	28.31	7.53
Dhanbad	10.95	47.50	23.56	8.76
Bokaro	10.39	29.64	23.19	2.53
Koderma	16.37	27.32	19.55	1.97