Greening and Restoration of Wastelands with Agroforestry(G.R.O.W)

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TECHNICAL REPORT ON
Greening and Restoration
of Wastelands with Agroforestry
(G.R.O.W)
February 2024 2024
TECHNICAL REPORT ON
Greening and Restoration of
Wastelands with Agroforestry
(G.R.O.W) NITI Aayog (2024). Greening and Restoration of Wastelands
with Agroforestry (G.R.O.W).
Copyright@ NITI Aayog, 2024
ISBN No. 978-81-956821-3-3

Authors: Neelam Patel, Tanu Sethi, T Ravisankar, Shashikant Sharma,
Rajiv Kumar, Girish S Pujar, M. Arulraj, A Arunachalam, A.K. Handa,
Suresh Ramanan S., Lesslie A, Mobina Shaik, Shubham Das, K Sreenivas,
R J Bhanderi. Ravi Prabhu, Shiv Kumar Dhyani, Ruchika Singh, Parth
S Roy, Sushil Saigal, Avani Kumar and Rajiv Pandey
1
Disclaimer:
The thematic layer datasets used in study are provided by National
Remote Sensing Centre, Hyderabad and Space Application Centre
Ahmedabad. The datasets corresponds to scale and accuracy
commensurable with planning requirements only. NITI Aayog shall
not be liable for any loss or damage whatsoever, arising of, or in
connection with use of or reliance on the statistic, information found
in the document. The study methodology used is based on the
expert views of contributing scientists. Personal views and are open
or improvement that may be required over coming years.
1 Details of Authors and contributors are affiliation enclosed as Annexure- I Table of Contents
Acknowledgement Vii
Abbreviations and Acronyms Ix
Executive Summary Xi
1. Introduction 1
1.1 Objectives 3
2. Prospects for wastelands greening and restoration with Agroforestry 5
2.1 National Agroforestry Policy 5
2.2 Wastelands: Definition and types 7
2.3 Necessity for wastelands greening 9
2.4 Agroforestry interventions in transforming wastelands 11
2.5 Wastelands suitability mapping using geospatial approach 12
3. Methodology 15
3.1 Study Area 15
3.2 Approach and Methodology 15
3.3 Pre-processing of the input datasets 19
4. Results 29
4.1 National level mapping output 29
4.2 Case study of ASI of Madhya Pradesh State 33
5. Access to State, UTs and District level Suitability Maps for wastelands
greening via Bhuvan Geoportal 39
6. Way Forward 67
7. References 71
8. Annexures 75 List of Figures
Figure 1. Objectives of Sub-Mission on Agroforestry (SMAF) 6
Figure 2. Percentage of area under 23 classes of wastelands 8
Figure 3. Land Use Land Cover map of India 16
Figure 4. Wastelands map of India 17
Figure 5. Slope map of India 17
Figure 6. Waterbodies map of India (proximity distance in metres from waterbody) 18
Figure 7. Soil organic carbon map of India 18
Figure 8. Work flow for calculating Agroforestry Suitability Index (ASI) 19
Figure 9. Extraction of State boundary for Madhya Pradesh 23
Figure 10. Extraction of waterbody information for Madhya Pradesh 23
Figure 11. Preparation of waterbody proximity layer 24
Figure 12. Waterbody layers with classes for ASI computation 25
Figure 13. Map with sites suitable for greening with agroforestry 31
Figure 14. Statewise area under highly suitable category as per ASI 32
Figure 15. Statewise area under moderate suitable category as per ASI 32
Figure 16. Land Use Land Cover map of Madhya Pradesh 33
Figure 17. Wastelands map of Madhya Pradesh 34
Figure 18. Slope map of Madhya Pradesh 34
Figure 19. Waterbody map of Madhya Pradesh 35
Figure 20. Soil Organic Carbon map of Madhya Pradesh 35
Figure 21. Classified areas for greening in Madhya Pradesh 36
Figure 22. The ASI data of Madhya Pradesh 36
Figure 23. District wise classified area suitable for Agroforestry in Madhya Pradesh 37
Figure 24. Landing page on wastelands greening with Agroforestry- Suitability
Mapping on Bhuvan Geoportal 40
Figure 25. Agroforestry Mission Directorate 68 List of Tables
Table 1: Agroforestry area in 15 Agro-Climatic Zones (ACZs) of India 6
Table 2: Different classes of Wastelands as per Wastelands Atlas of India 8
Table 3: India imports of wood10
Table 4: Demand and supply estimates of dry and green forages (million tonnes) 11
Table 5: Different Land Use Land Cover classes with ranking 20
Table 6: Different wasteland classes and ranking 21
Table 7: Category of slope (in %) with ranking 22
Table 8: Proximity of waterbody (in meters) and ranking 22
Table 9: Soil Carbon (Kg/m
2
) with ranking 22
Table 10: Parameters and weightages used for ASI 27
Table 11: Ranking of classes used for Agroforestry Suitability Index 27
Table 12: Statewise distribution of potential areas for greening (as percent of TGA) 30
Table 13: District-wise area suitable for greening with Agroforestry 41 Acknowledgement
The technical report on Greening and Restoration of Wastelands with Agroforestry is an
outcome of one of the action plans assigned to NITI Aayog’s Agriculture Vertical, under
supervision of Hon’ble Member (Agriculture) Prof. Ramesh Chand. The authors are grateful to
Hon’ble Member (Agriculture) for his consistent guidance in the achieving objectives of the
task. Authors are thankful to Hon’ble Vice Chairman, Mr. Suman K Bery and CEO, Mr. B.V.R
Subrahmanyam for their encouragement and support for carrying out this project.
The authors would also like to express gratitude to the former Vice Chairman, NITI Aayog, Dr.
Rajiv Kumar and the former CEO Mr. Amitabh Kant for extending their support.
The authors are thankful to Director, National Remote Sensing Centre (NRSC), Deputy Director,
RSAA, NRSC and Director, Space Application Centre (SAC) and Deputy Director, EPSA, SAC for
their complete support in the successful completion of the study.
Since the inception of the study, subject matter experts, Research Institutions, State Government
officers, and State Agricultural Universities were involved in designing methodology for this
project. With the support of Head/Incharge of ICAR-Krishi Vigyan Kendra (KVK) viz., KVK
Sidhi, KVK Agra, KVK Bhavnagar and Vice Chancellors of State Agricultural University, namely,
Mahatma Phule Krishi Vidyapeeth, Swami Keshwanand Rajasthan Agricultural University, Tamil
Nadu Agricultural University, Assam Agricultural University, Dr YSR Horticultural University, Sher-
e-Kashmir University of Agricultural Sciences and Technology, Jammu and Odisha University
of Agriculture and Technology for successfully conducting Ground validation of the Suitability
Maps in selected districts.
We extend our sincere thanks to nominated officers that helped in ground validation of the
suitability maps in selected districts. The details of the officers are enclosed in Annexure-II.
We are thankful to International Centre for Research in Agroforestry (ICRAF) Head of Spatial
Data Science and Applied Learning Lab, Dr Tor-Gunnar Vagen and Dr Chandrashekhar Biradar,
Country Director India, Chief of Party Trees Outside Forest India; Network for Certification
and Conservation of Forests (NCCF) DG A. K. Srivastava, for providing valuable inputs for the
project.
I acknowledge the sincere efforts of Dr Tanu Sethi, Sr. Associate, NITI Aayog in developing
the first draft report and portal design for Bhuvan under GROW Project with NRSC Team. I
appreciate her in creating the acronym GROW.
Neelam Patel Abbreviations and
Acronyms
ACZAgro-Climatic Zones
AFOLUAgriculture, Forestry and other Landuse
AOIArea of Interest
ASIAgroforestry Suitability Index
ATMAAgricultural Technology Management Agency 
CAFRICentral Agroforestry Research Institute
CO
2
eCarbon dioxide Equivalent
DEMDigital Elevation Model
FAOFood and Agriculture Organisation of the United Nations
GHGGreenhouse Gases
GISGeographical Information System
GROWGreening and Restoration of Wastelands
Ha Hectare
GTGround truth
HLEGHigh Level Expert Group
HSAHighly Suitable Area
HYVHigh Yielding Variety
ICARIndian Council of Agricultural Research
IGFRIIndian Grassland and Fodder Research institute
IPCCIntergovernmental Panel on Climate Change
kmKilometre
KVKKrishi Vigyan Kendra
LULCLand Use Land Cover
mMetre MoA & FWMinistry of Agriculture and Farmers Welfare
MoEF&CCMinistry of Environment, Forest and Climate Change
MSAModerate suitable Area
NAPNational Agroforestry Policy
NbSNature-based solutions
NDCNationally Determined Contribution
NDVINormalized Difference Vegetation Index
NRSCNational Remote Sensing Centre
QGISQuantum Geographic Information System
SACSpace Application Centre
SDGSustainable Development Goals
SHGSelf-Help Group
SIS-DPSpace based Information Support for Decentralised Planning
SOCSoil Organic Carbon
SOISurvey of India
sq.kmSquare Kilometres
Kg/m
2
Kilograms per square metre
TGA Total Geographical Area
VEDASVisualisation of Earth Observation Data and Archival System
xii | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Executive Summary
India is the first country in the world to form and announce the National Agroforestry Policy in
2014, which focuses on enhancing productivity, profitability, diversity and ecosystem sustainability.
Agroforestry is an agroecological nature-based land use system that can simultaneously address
many ecological challenges of the current era viz. food, nutrition, energy, employment, natural
resources and environmental security. It includes both traditional & modern land use systems.
Integrating and optimising the interactions of the components of agroforestry i.e trees, crops
and/or livestock, can lead to improvements in the soil quality, greater vegetation and tree cover.
Agroforestry can simultaneously address the mitigation and adaptation needs of managing
climate change, along with many social and economic gains in the long term. Globally, empirical
evidence of beneficial socio-economic and ecological outcomes of agroforestry interventions,
in rural and urban areas, wastelands and degraded lands, have added momentum to harness
this set of practices for achieving targets under various National and International commitments
viz. Nationally Determined Contribution (NDC) as part of the Paris Agreement on Climate
Change, Bonn Challenge, UN Sustainable Development Goals, United Nations Convention on
Combating Desertification (UNCCD), Doubling Farmers Income, Green India Mission, National
Action Plan on Climate Change and Atmanirbhar Bharat. Due to the significance of goods and
services provided by agroforestry, the Union Budget of Government of India (FY-2022-23) has
underlined the promotion of agroforestry and private forestry as a priority.
India is the seventh largest country in the world, with an area of 328.73 million hectares and
has the second largest total arable area, after USA. Due to anthropogenic activities, many
regions have increased build up areas, degraded land, imbalanced natural resources that have
adversely impacted the environment and lives on the planet. There has been a concomitant
decline in per capita availability of land in the country. Hence, it is imperative to transform
land-use systems across the country, especially when it comes to classified wastelands that
need to be transformed to agricultural and other productive uses. About 55.76 million hectares
i.e. 16.96% of Total Geographical Area (TGA) of the country is wastelands and are currently
under-utilized and deteriorating due to a lack of appropriate resource management or on
account of natural causes.
Geospatial Technologies have been effectively used in mapping these regions/geographies and
activities of interest and development across various sectors. This technical report explores
the application of remote sensing datasets with GIS technology in prioritising wastelands in
the country suitable for greening with agroforestry intervention. To support this analysis, the Agroforestry Suitability Index (ASI) was derived to develop a national level area prioritisation
plan of wastelands for greening with agroforestry.
A multi-institutional team was formed by NITI Aayog to develop a Geographic Information
System based analysis to assess the agroforestry suitability regimes in wastelands across the
country. Multi-thematic datasets on wastelands, Land Use Land Cover, waterbodies, soil organic
carbon & slope at 1:50,000 scale were identified, and after appropriate weightages were applied,
these were used to carry out a national level overlay analysis. Ecological sensitive envelops,
such as natural grasslands, were identified and excluded in the methodology. Three area
prioritisation classes, i.e. highly suitable, moderately suitable and less suitable/not applicable
were used to stratify wastelands across districts of the country, excluding the Union Territories
of Lakshadweep, and Andaman and Nicobar Islands. Based on the analysis, 2,07,455.37
sq.km (6.31% of Total Geographical Area (TGA) area falls under ‘highly suitable’ category
and 1,62,372.33 sq.km (4.94% of TGA) under ‘moderately suitable’ category. Madhya Pradesh
(29,643.98 sq.km), Rajasthan (27,662.046 sq.km) and Maharashtra (24,228.03 sq.km) are the
top 3 states with significant extents of area under the highly suitable class. Most areas fall under
‘less suitable/not applicable’ category and these include cropped areas, forests and other land
use system, other than classified ‘wastelands’. This report furnishes state-wise and district-wise
area analysis of the results. Based on cumulative analysis of area under moderately suitable
and highly suitable area, Rajasthan (60,922.40 sq.km) holds the largest area for greening,
followed by Maharashtra (43,944.61 sq.km), Madhya Pradesh (41,474.16 sq.km), Andhra Pradesh
(24,7328.11 sq.km) and Gujarat (24,658.88 sq.km), respectively. States have variable wastelands
areas suited for taking up agroforestry for wasteland greening. The classification tool and
results of subsequent analysis can play a pivotal role in supporting Research Institutions/
Central/State Government Departments, wood-based Industries and others to prioritise and
initiate greening and restoration projects.
To enable Stakeholder to access the results of this analysis, a universal authorised access
portal called “Greening and Restoration of Wasteland with Agroforestry (GROW)- Suitability
Mapping” has been designed and developed that allows access to the results of state and
district level datasets. The suitability area statistics are available on Bhuvan- India’s Geoportal
at https://bhuvan-app1.nrsc.gov.in/asi_portal/. The database can be quite easily extended in
scope to bring in other related efforts to apply agroforestry to management and sustainability
challenges that will be useful for the Indian farming community. A special tool was added in
the system that provides flexibility to users to manipulate weightages and overlay criteria for
customised local prioritisation. The national area prioritization will be available as the standard
output against which the user can compare customised results, if required.
At present, total area under agroforestry is about 28.42 million hectares that covers about
8.65% of TGA of the country. The conversion of underutilised areas, esp. wastelands, can extend
multiple benefits of agroforestry across vast areas in the country.
This technical report on Greening and Restoration of Wastelands with Agroforestry (GROW)
will benefit for taking up restoration projects for achieving national commitments of Land
Degradation Neutrality and restoring 26 million hectares of degraded land by 2030, as well
as creating an additional carbon sink of 2.5 to 3 billion tonnes of carbon dioxide equivalent.
xiv | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Introduction1
India is the fifth largest global economy bestowed with rich natural resources. It is a
powerhouse of biological, cultural and economic diversity that lays apt foundation for growth
and development. India with 328.73 million hectares of geographical area holds the seventh
position in world area and ranked second in arable area (180.8 million hectares) and 10
th

(80.9 million hectares) in forest area. This “land-use patterns” plays a key role in influencing
economic growth, quality of life, natural ecosystem, goods and food supply. Globally and
regionally, changes in the land use pattern have emerged as a major contributor to climate
change, biodiversity loss and land degradation. The Intergovernmental Panel on Climate Change
(IPCC) (2021) mentioned that the global transitions in Landuse system i.e. Agriculture, Forestry
and Other Land Use (AFOLU) have contributed to about 23% of anthropogenic emissions of
Greenhouse gases and 11-14 percent (%) biodiversity loss. Around 12 million hectare of land
is annually lost due to degradation (IPCC Special Report, 2019; IPBES, 2018). Degradation of
Earth’s land surface through human activities is negatively impacting the well-being of at least
3.2 billion people, pushing the planet towards a sixth mass species extinction, and costing more
than 10% of the annual global gross product in loss of biodiversity and ecosystem services
(IPBES, 2018).
In India, Land use system is categorised in nine categories: (i) Forests area, (ii) Area under
Non-Agricultural use, (iii) Barren and Uncultured Land, (iv) Permanent Pastures and other
Grazing Land, (v) Land under Miscellaneous Tree Crops, (vi) Culturable Waste Land, (vii) Fallow
Land other than Current Fallows, (viii) Current Fallows, and (ix) Net Area Sown (Directorate
of Economics & Statistics, 2021). There was a significant change in India’s land use during
20
th
century. From the mid 1900’s onwards, there has been an extensive area expansion of
the agriculture sector, coupled with deforestation and urbanisation (Roy et al., 2015). Over the
years, these anthropogenic processes have led to an expansion in built-up area, environmental
degradation and imbalance in natural resources. Primarily due to rise in population, there is
a decline in per capita availability of land. Now, land under agriculture is decreasing at a
rate 0.03 million hectares per year (Handa et al., 2019). Further, pressure on land and natural
resources are rising due to population growth, urbanisation, low agri-productivity, excessive use
of synthetic fertilisers, depletion of natural resources, deforestation and demands of lifestyle
goods. The total Greenhouse Gas (GHG) emissions recorded in India during 2016 was 2,838.89
million tonne CO
2
e, excluding Land Use Land Use Change and Forestry (LULUCF) and 2,531.07
million tonne CO
2
e with inclusion of Land Use Land Use Change and Forestry (LULUCF)
(MoEF&CC, 2021). Emission of carbon dioxide was estimated at 2,231 million tonne (78.59%),
methane emissions at 409 million tonne CO
2
e (14.43%) and nitrous oxide emissions at 145 million tonne CO
2
e (5.12%), respectively. About 29.32% of India’s land (96.40 million ha) is
under degradation process (Desertification and Land Degradation Atlas of India, 2016). As per
Wastelands Atlas of India published by Ministry of Rural Development, 55.76 million hectares i.e.
16.96% of TGA are wastelands.
Land, being a finite resource, requires judicious planning and management measures to
foster sustainability and to avert the major crises that presently threaten ecology, society and
economy. India has declared its commitment to the Bonn Challenge, Nationally Determined
Contribution (NDC) to the Paris Agreement on Climate Change, the UN Sustainable
Development Goals, UN Decade of Ecosystem Restoration, Doubling Farmers Income,
the National Action Plan on Climate Change and Atmanirbhar Bharat that can significantly
be realised by transforming land use systems to deliver optimal benefits. A plethora of
schemes and programmes have been initiated by various government departments towards
mitigation of green house gases, facilitation of adaption strategies, reversing degradation and
improving biodiversity conservation. In all, 9.81 million hectares of area have been restored
across India from 2011 to 2016-17.
Best practices like Agroforestry are a productive and affordable pathway to enable India to
meet its land degradation neutrality and climate change adaptation commitments. Agroforestry
is an agroecological practice and followed in more than 130 countries in the world. India
is the leading nation to formalise and announce the National Agroforestry Policy in 2014,
defining it as a land use system which integrates woody perennials (trees and shrubs) on
farmlands and rural landscape to enhance productivity, profitability, diversity and ecosystem
sustainability (MoA&FW, 2014). In other words, agroforestry is the integration of trees, crops
and/or livestock on the same piece of land to enhance productivity and resilience of farms
and deliver numerous vital ecological services. Hence, it can act as a promising land-based
transformation solution with several co-benefits. Adoption of agroforestry can enhance farmers
income, increase in green cover, natural resource conservation, production of forest based
raw-materials, achieving NDC’s, rural development and scalability - all at the same time from
same land areas with different degrees. It is an ideal option to restore most of degraded and
wastelands in the country (Dhyani 2003; Dhyani et al., 2005 Chaturvedi et al., 2017 & 2018;
Dagar and Tiwari, 2016; Mishra and Rath, 2013; Handa et al, 2015; Planning Commission, 2001;
MoA&FW, 2014; Duguma et al., 2017).
Due to the significant goods and services provided by agroforestry, the Union Budget
of Government of India (FY-2022-23) has underlined the promotion of agroforestry and
private forestry; and financial support to farmers belonging to Scheduled Castes and
Scheduled Tribes, who want to take up agro-forestry (MoF, 2022). This announcement
has given impetus to scale-up agroforestry interventions in the country. Agroforestry
can be well ingrained with ecosystem restoration intervention across landscapes and
rehabilitation.
Present study is designed to harness the potential of agroforestry for Greening and Restoration
of Wastelands (GROW) by using geospatial analysis and remote sensing datasets. Certain
classes of existing country’s wastelands are opportune sites and can be transformed to
2 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) productive sites for multiple uses. Hence, prioritisation of these areas suitable for greening can
play a pivotal role for undertaking greening and restoration projects by Research Institutions/
Central/State Government Departments, wood based Industries etc.
1.1 OBJECTIVES
Greening of wastelands with agroforestry intervention can play a significant role in mitigating
climate change, bringing socio-economic welfare and achieving targets of the Bonn Challenge,
the Sustainable Development Goals (SDGs), the Paris Agreement and meeting national target
of 33% green cover. Before the potential of agroforestry can be harnessed in this way, it was
crucial to scientifically identify potential sites for greening, including those that were most
suitable and excluding sites that are required to be conserved, like grasslands, community
owned lands etc. from the purview of GROW. This was achieved in the present study through
complying with following objectives:
1. Deriving an Agroforestry Suitability Index (ASI) for delineating and prioritisation
of suitable areas across the country especially wastelands based on suitable bio-
geophysical parameters and geospatial technology, and
2. Developing a universal access platform for stakeholders to view suitability regions,
statistics, maps at district level for planning greening projects across States and
Districts.
A group of expert was consulted to achieve the study objectives and details are enclosed as
Annexure I.

Introduction  | 3 4 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Prospects for wastelands
greening and restoration
with agroforestry2
2.1 NATIONAL AGROFORESTRY POLICY
India is the first country in the world to develop and adopt an Agroforestry Policy. The National
Agroforestry Policy of Government of India (2014), seeks to enhance productivity, profitability,
diversity and ecosystem sustainability. It includes both traditional & modern land use systems
where woody perennials (trees, shrubs, bamboos and palms) are managed together with
crops and/or animals production system in agriculture settings (MoA&FW, 2014). The policy
was a cumulative result of many other policies and schemes emphasising on the importance
of agroforestry like the National Forest Policy (1988), the Planning Commission Task Force on
Greening India (2001), the National Bamboo Mission (2002), the National Policy on Farmers,
(2007) and the National Mission for a Green India (2010). Agroforestry is considered a nature-
based solution (NbS) that works within the functional limits of ecosystems to help societies
address a variety of environmental, social and economic challenges in sustainable ways
(Meybeck et al., 2020).
To operationalise the objectives envisioned in the policy, the Sub-Mission on
Agroforestry (SMAF) under National Mission for Sustainable Agriculture (NMSA) and
Har Medh Par Ped was launched in 2016-17 by Ministery of Agriculture and Farmers
Welfare to encourage tree plantation on farm land along with crops/ropping system
and make farming systems more climate resilient and adaptive. The Sub-Mission on
Agroforestry objectives were defined for stimulating the growth of agroforestry in India
(Figure 1).
The scheme was implemented in 20 States viz. Andhra Pradesh, Bihar, Chhattisgarh, Gujarat,
Haryana, Himachal Pradesh, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha,
Punjab, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, Mizoram, Meghalaya, Nagaland and
2 UTs viz. Jammu & Kashmir and Ladakh with funding pattern of 60:40 between Centre and
State Govt. for all States, excepting NE & Hilly states, where it is 90:10 and 100% in case
of UTs & National Level Agencies. Under the Mission, multipurpose tree species with short,
medium and long term returns are encouraged, so that farmers may get additional income
at regular intervals.
In India the total area under Agroforestry is about 28.42 million hectare (Mha) that covers about
8.65% of Total Geographical Area of the country (Arunachalam et al., 2022). The area is varied
across 15 Agro-Climatic Zones (ACZ) of the country and is highest in Upper Gangetic Plains
Region i.e ACZ-V. The area under agroforestry across different ACZ is mentioned in Table. 1. Figure 1. Objectives of Sub-Mission on Agroforestry (SMAF)
Table 1: Agroforestry area in 15 Agro-Climatic Zones (ACZs) of India
ACZ
no.
ACZ
Geographical area
(M ha)
Agroforestry
area (M ha)
Agroforestry
area (%)
I Northern Himalayan Region32.968 4.096 12.42
IIEastern Himalayan Region28.422 1.088 3.83
IIILower Gangetic Plains Region6.238 0.802 12.86
IVMiddle Gangetic Plains Region16.526 1.304 7.89
V Upper Gangetic Plains Region14.367 2.234 15.55
VITrans Gangetic Plains Region11.750 1.143 9.73
VIIEastern Plateau and Hill Region40.525 4.292 10.59
VIIICentral Plateau and Hill Region37.435 1.924 5.14
IXWestern Plateau and Hill Region32.539 1.556 4.78
X Southern Plateau and Hill Region39.294 2.976 7.57
XIEast Coast Plains and Hill Region 19.948 2.36 11.83
XIIWest Coast Plains and Hill Region11.69 1.632 13.96
XIIIGujarat Plains and Hill Region18.673 2.57 13.76
XIVWestern Dry Region17.587 0.431 2.45
XV The Island Region0.785 0.019 2.42
Total328.747 28.427 8.65
Source: Arunachalam et al., 2022
To encourage and expand tree plantation in complementary and integrated manner
with crops and livestock to improve productivity, employment opportunities, income
generation and livelihoods of rural households, especially the small farmers.
To popularise various Agroforestry practices/models suitable to different agro
ecological regions and land use conditions.
To ensure availability of quality planting material like seeds, seedlings, clones,
hybrids, improved varieties, etc.
To create database, information and knowledge support in the area of agroforestry.
To provide extension and capacity building support to agroforestry sector.
01
02
03
04
05
6 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) The National Agroforestry Policy (NAP) (2014) envisioned agroforestry interventions in
wastelands, described as “Non-forest wasteland barren community land to be encouraged for
plantation of agroforestry tree species to provide opportunities of economic returns as well as
contributing towards ecological benefits” (Statement from NAP, 2014). Empirical evidences have
showed agroforestry interventions in degraded/wastelands restoration can create economic
opportunities for the rural people and wood based industries (Planning Commission 2001;
FAO, 2017; Maji et al., 2010; Chavan et al., 2015). Over decades, considerable area has been
subjected to desertification or other types of degradation, that have resulted in depletion of
natural resources, urbanisation and other antheropogenic activities. India is on track to achieve
its national commitment of Land degradation neutrality and restoring 26 million hectares of
degraded land by 2030 and creating an additional carbon sink of 2.5 to 3 billion tonnes
of carbon dioxide equivalent, the wastelands are potential sites to initiate transformation to
productive use through agroforestry interventions.
2.2 WASTELANDS: DEFINITION AND TYPES
As per the erstwhile National Wastelands Development Board (NWDB), now Department
of Land Resources (DoLR) wastelands are defined as degraded land that can be brought
under vegetative cover with reasonable effort and which is currently under-utilised and is
deteriorating due to lack of appropriate water and soil management or on account of natural
causes. Further, wastelands were classified into cultural and non-cultural wastelands (http://
dolr.nic.in/wasteland_division.htm). Mishra et al. (2013), mentioned that wastelands are the
underutilised area and produce less than 20% of its biological productivity. Wastelands are
formed due to prolonged non-judicious and faulty land use practices. The key for land use
transformation can be achieved by reversing soil salinity, water logging, droughts, excessive
soil erosion caused due to deforestation, unscientific agricultural practices, over grazing etc.
In India, the National Remote Sensing Centre (NRSC) is the principal organisation to provide
National level geospatial information on wastelands using remote sensing technology at the
behest of the Department of Land Resources, Government of India. The estimated wastelands
area in the country was 55.76 million hectares i.e 16.96% of total geographical area (TGA) in
the year 2015-16 (Wastelands Atlas of India, 2019). The extent of variation in wastelands area
in the country was estimated to be about 63.85 Mha in Atlas 2000; 55.27 Mha in Atlas - 2005,
47.23 Mha in Atlas – 2010 and 46.70 Mha in Atlas – 2011, respectively (Wasteland Atlas of
India, DoLR, MoRD, GoI).
Wastelands is a consortium term that includes 23 categories such as gullied and/or ravinous
land, land with dense scrub, land with open scrub, waterlogged and marshy land, land
affected by salinity/alkalinity, degraded pastures/grazing land, under - utilised/degraded forest
(agriculture) etc. (Table. 2). The area under the 23 wastelands classes are varied, occupying
between 0.2% - 3.28 % of total 16.96 % wastelands of the country. The area under each category
of wastelands class is shown in figure 2.
Prospects for wastelands greening and restoration with agroforestry | 7 Table 2: Different classes of Wastelands as per Wastelands Atlas of India
1. Gullied and/or ravinous land (Medium)
2. Gullied and/or ravinous land (Deep)
3. Land with Dense Scrub
4. Land with Open Scrub
5. Waterlogged and Marshy land (Permanent)
6. Waterlogged and Marshy land (Seasonal)
7. Land affected by salinity/alkalinity (Medium)
8. Land affected by salinity/alkalinity (Strong)
9. Shifting Cultivation-Current Jhum
10. Shifting Cultivation-Abandoned Jhum
11. Under–utilised/degraded forest (Scrub
dominated)
12. Under–utilised/degraded forest (Agriculture)
13. Degraded pastures/grazing land
14. Degraded land under plantation crop
15. Sands-Riverine
16. Sands-Coastal
17. Sands-Desertic
18. Sands-Semi Stab. > 40m
19. Sands-Semi Stab. 15 - 40m
20. Mining Wastelands
21. Industrial Wastelands
22. Barren Rocky/Stony waste
23. Snow covered/Glacial area
Figure 2. Percentage of area under 23 classes of wastelands
8 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Wastelands are also classified as culturable and unculturable wastelands. The cultural wastelands
have potential for development and reclamation of its vegetative cover. These lands remain
unproductive for various reasons such as water-logging, salinity, non-availability of water or
aridity and unfavourable terrain, etc. that occurred due to faulty cultivation or other undesirable
land use practices. Large areas are found in north western Himalayan states like Jammu and
Kashmir, Himachal Pradesh, North Eastern states, Rajasthan, Uttar Pradesh, Bihar and Madhya
Pradesh for various reasons (Mishra and Rath, 2013).
However, most of the regions demarcated as wastelands such as naturally rocky areas, or
natural swamps – which are not ‘wastelands’ as most of these areas had favourable biophysical
parameters that supported certain types of ecosystem. However, these regions are currently
defined as wastelands because these were inappropriately managed in the past and are no
longer suitable for developing vegetative cover.
National Academy of Agricultural Sciences (NAAS) report (2010) on degraded and wasteland
provides state-wise analysis of land use systems and factors responsible for degradation and
formation of different types of wastelands. The report highlighted the finite nature of land
resources and need to take requisite measures to reclaim the degraded and wastelands through
effective crop management and agricultural development activities in the affected areas with
public and private investments (Maji et al., 2010; Planning Commission, 2001).
A country as diverse as India, can benefit by deploying most of these wastelands into productive
areas through agroforestry intervention. While myriad manifestation of agroforestry systems are
recorded across the globe and formalised through research. It is important to define a set of
suitable sites or geographical extents to promote them, in light of sustainability and climate
change. The suitability criteria can be derived from thematic information corresponding to
biophysical determinants like slope, organic carbon, water etc. that supports in farming of tree
with regular annual crops. Land use systems, related factors and appropriate spatial datasets
are required in selecting areas that are best suited for the greening.
2.3 NECESSITY FOR WASTELANDS GREENING
Due to conventional land use practices, rising population, industrialisation, food demand etc. are
exerting pressure on land resources that exceeded beyond its carrying capacity and resulting
into land degradation. India has 18% of the world’s population and only 2.4% of the global land
area. At same time, per capita availability of agriculture land in India has decreased over the
years. The per capita agriculture land in India is 0.12 ha whereas world per capita agriculture
land is 0.29 ha. 
Hence, it is imperative to transform these wastelands area into productive area to reduce
pressure on natural resources. With implementation of various Government of India schemes,
about 1.45 Mha wastelands are converted to productive aka non-wasteland categories from
2008-09 till 2015-16. These transformations was observed primarily in the categories of land
with dense scrub, waterlogged and marshy land, sandy areas, degraded pastures/grazing land
and gullied and/or ravinous land (Ministry of Rural Development, 2019; NRSC, 2019).
Prospects for wastelands greening and restoration with agroforestry | 9 Bringing areas, formerly classified as wastelands, into productive uses through agroforestry
can enhance avenues of employment, not just from primary production but also from
processing and other related value chain activities. These economic benefits come on top of
improving soil fertility and helping to meet the targets for increasing tree cover and other
environmental services. Tree cover increases, with the right species, can contribute in achieving
“Aatmanirbharta” in wood production and import substitution (HLEG, 2020).
As per the Fifteenth Finance Commission report, available underutilized land resources, like
cultivable wastelands, fallow lands can be deployed in achieving self-sufficiency in wood
and greening mission (HLEG, 2020). Wood and wood products imports (HS Code 44) have
increased due to rising demand (Table 3).
Table 3: India imports of wood by value (million USD), 2009-2019
Year
ITC HS Code
4403
Wood in
rough
4404
Hoop
wood
4407
Sawnwood
4408
Veneer
sheets
4411
Fibreboard
4412
Plywood &
panels
4703
Sulphate
pulp
4704
Sulphite
Pulp
2009 1,191.77 0.39 42.16 19.95 40.6 36.71 238.86 1.33
20101,334.26 0.38 57.43 27.02 77.25 52.32 394.41 2.13
20111,828.94 0.51 130.96 45.74 84.31 112.42 463.55 1.81
20122,004.68 1.61 159.73 55.8 91.67 90.24 414.67 1.41
20132,033.64 0.5 184.31 65.73 96.32 80.63 451.56 22.29
20142,010.89 0.7 205.37 91.19 87.63 84.9 461.77 22.39
20151,564.88 0.47 283.64 174.01 87.18 85.78 466.92 1.11
20161,277.53 0.25 275.44 200.19 88.46 79.84 445.38 0.65
20171,206.09 0.03 367.73 219.53 106.12 97.8 484.09 1.54
2018 1,117.66 0.04 423.05 234.34 121.9 121.41 561.3 3.21
2019 993.63 0.02 466.28 280.84 103.34 107.63 507.94 2.05
INDIA TIMBER SUPPLY AND DEMAND 2010-2030 (Dr Promode Kant and Raman Nautiyal)
Also, wastelands greening can support to meet the fodder supply deficit to boost growth of
dairy sector in the country. The country faces a net shortfall of 35.6% green fodder, 10.5% dry
crop leftovers, and 44% concentrate feed ingredients and land area under fodder cultivation
is very limited (Table 4) (Singh et. al., 2022; Singh, G., 2015; Dixit et al., 2012). The demand
for green and dry feed has been increased and by 2050, the demand will be 1012 and 631
million tonnes, respectively (IGFRI Vision, 2050). The non-arable and wastelands can be
utilized through viable fodder-producing agroforestry system to meet the fodder demand in
the country. Several forage trees such as Acacia eburnea, A. nilotica, A. leucophloea, Balanities
roxburghii, Cordarothii, Azadirachta indica, Pongamia pinnata, Dichrostachis cineria etc. are
suitable for wastelands.
As per Chand, R. (2023), there is a huge scope of raising trees and agroforestry on
fallow land, culturable waste and on field boundaries. India also has 12 million hectare
of culturable wastelands that can support in meeting domestic demand for wood along with
environment, ecology and sustainability. India has been importing large quantity of wood and
wood products, which has significantly increased import bill. However, there is a low interest
in tree plantations and agroforestry in India due to rigid restrictions on felling of trees grown
10 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) on non-forest private land and their inter-state movement. The reform in forest regulations
esp. removal of restrictions on tree felling on private lands, timber marketing encourage
participation of wood-based industry in initiating greening projects as well as raise employment
and income (Chand and Singh, 2023).
Table 4: Demand and supply estimates of dry and green forages (million tonnes)
Demand SupplyDeficit Deficit as %
Year Dry Green Dry Green Dry Green Dry Green
2010 508.99 816.83 453.28 525.51 55.72 291.32 10.95 35.66
2020 530.5 851.34 467.65 590.42 62.85 260.92 11.85 30.65
2030 568.1 911.67 500.03 687.46 68.07 224.21 11.98 24.59
2040 594.97 954.81 524.4 761.76 70.57 193.05 11.86 20.22
2050 631.05 1012.7 547.78 826.05 83.27 186.65 13.2 18.43
Source: The Working Group Report on Demand and Supply, NITI Aayog, February, 2018
2.4 AGROFORESTRY INTERVENTIONS IN TRANSFORMING WASTELANDS
Advance research has been carried out in developing empirical evidences on economics of
benefits, both ecological and economical, offered by various agroforestry systems. Several
agroforestry systems are developed for wastelands greening for different agroclimatic zones
of the country that are remunerative for local farmers/growers ( Dixit et al.,2012; ICAR-
CAFRI, 2016; Handa et al., 2019; Planning Commission 2001; Dagar et al,. 2014). For such
interventions, multipurpose tree species are integrated with crops and/or livestock (Chaturvedi
et al., 2017; Sarvade et al., 2017). Silvipasture systems that deploy multipurpose trees
(MPTs) and grass species such as Cenchrus setigerus, Andropogon gayanus, Bothriochloea
intermedia, Brachiaria decumbens, B. ruiziensis, Dichanthium annulatum, Panicum maximum,
Pennisetum pedicellatum etc. are considered suitable for the degraded lands in arid and
semi-arid regions. Aonla based agri-horticultural system, Subabool based agrisilvicultural
system, Shisham based silvi-pastoral system with Napier grass are few examples of species
arrangements that thrive well under wasteland classes such as salt-affected and ravine lands
with the minimum investments and higher economic return. Subabool based agri-silvicultural
systems are very useful for the improvement of degraded land and wastelands. As per Handa
et al. (2019) the estimated overall annual net income from this agroforestry system from
degraded grassland is `12000/- to `14000/- per ha in the initial years which increases up to
`50,000/- to `60,000/- per ha with the maturity of the system. Adoption of agroforestry in
wastelands restores landscapes and bridge gap between demand and supply of food, fodder
and timber too (Planning Commission, 2001; Sharma et al., 2017; Handa et al. 2019; https://
www.fao.org/forestry/agroforestry/80339/en/). The ICAR-Central Agroforestry Research
Institute (CAFRI) published a consortium of about 40 successfully tested well-researched
Prospects for wastelands greening and restoration with agroforestry | 11 agroforestry systems suitable for wastelands and degraded lands (Handa et al., 2019). Also,
successful agroforestry systems for income enhancement and ecosystems services are
documented at country level by ICAR-CAFRI (https://cafri.icar.gov.in/html/Technical_Bulletins/
Agroforestry-for-Income-enhancement-Climate-resilience-and-Ecosystem-services.pdf ).
Most preferred tree species in agroforestry systems are Populus spp., Eucalyptus spp., Tectona
grandis, Prosopis spp., Bamboo spp., Acacia spp., Gmelina spp., Grewia spp., Melia spp.,
Ailanthus spp., Dalbergia sissoo, Casuarina spp., Leucaena leucocephala, Azadirachta indica,
Anthocephalus cadamba, Albizia spp., Terminalia spp., Salix tetrasperma and Hardwickia binata
(https://cafri.icar.g ov.in/wp-content/u ploads/2024/02/2 5-Promising-agrof orestry-trees-in-
In dia.pdf). Plant species such as Suaeda salsa, Kalidium folium, Tetragonia tetragonioides,
Sesuvium portulacastrum, Arthrocnemum indicum, Suaeda frutica, S. portulacastrum, Atriplex
are identified for restoration of wastelands especially salt affected wastelands with high content
of soluble salt usually more than 0.2% (http://www.nbrienvis.nic.in/Database/1_2063.aspx)
Based on the type of wasteland class, geography and available natural resources, suitable
agroforestry systems can be planned and adopted based on local needs.
2.5 WASTELANDS SUITABILITY MAPPING USING GEOSPATIAL
APPROACH
Remote sensing technology plays a key role to assess the suitability sites of wastelands for
transformation through agroforestry. In the present study, suitable spatial data sets, coupled
with limited ground truthing, are used for identifying areas most amenable for taking up
cultivation along within existing land use patterns. Based on selected criteria, the result
provides a prioritised planning, that indicates where it would be easiest to get successful
transformation using agroforestry.
Till date, several studies are reported on national or regional level demarcation of agroforestry
suitability area using geospatial technology like Geographical Information System (GIS), Remote
Sensing (RS) and Geographical Positioning System (GPS). The integrated application of these
technologies can help in taking informed decisions in mapping and analysis of natural resources.
The geospatial technology is a valuable application for adoption and planning of land use and
agroforestry systems in the country. Similarly, suitability of India’s lands for various agroforestry
system have been evaluated by using remote sensing and GIS, varying from coarse to medium
resolution thematic layers. FAO land suitability criteria utilising Landsat-8 images (NDVI/wetness),
ASTER DEM (elevation/slope/drainage and watershed), ancillary data source (rainfall/organic
carbon/pH and nutrient status) were worked out to provide national level (Ahmad et al., 2019;
Ahmad et al., 2018) and district level (Lohardaga, Jharkhand, India) prioritisation, while Nath et
al. (2021) employed climate, soil, topography, socio-economic criteria along with remote sensing
derived parameters to prioritise area at coarse scale. An agroforestry land suitability analysis
study has been carried out in the Eastern Indian Himalayan region by Nath et al. (2021). It was
concluded that agroforestry land suitability can be assessed through a multi-criteria approach
too. Agroforestry suitability analysis study was carried out for part of Jharkhand area and was
based upon nutrient availability mapping for the area (Ahmad et al., 2017).
12 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) A pilot study was undertaken by NITI Aayog, wherein, Agroforestry Suitability Index (ASI)
was derived for wastelands suitability for agroforestry across selected districts by using GIS
Technology. A weighted index approach was adopted to integrate identified parameters viz.
Land Use Land Cover, wastelands, soil organic carbon, slope and waterbodies. The model was
further validated based on ground truth (GT) data collected in selected 17 districts with the help
of State Agricultural Universities and Krishi Vigyan Kendra’s. Details of Ground truth data can
be assessed at Visualisation of Earth Observation Data and Archival System (VEDAS) site using
link https://vedas.sac.gov.in/data-collection/. The study area was distributed across 17 districts
in 14 agroclimatic zones of India. Agroforestry Suitability was classified in 5 categories (High,
Moderate, Less, Very Less and Not Suitable). The GT points collected in High and Moderate
suitable wastelands regions showed good agreement with the model and registered an accuracy.
of about 86.60% (Patel et al., 2023).
Prospects for wastelands greening and restoration with agroforestry | 13 Methodology3
3.1 STUDY AREA
The study was conducted for Indian region, excluding the Union Territories of Lakshadweep
and Andaman and Nicobar Islands.
3.2 APPROACH AND METHODOLOGY
A multi-thematic GIS oriented weighted overlay analysis approach was adopted to analyse
suitable areas, keeping in view the requirement for identifying suitable areas dependent on
criteria that are available with Indian Space Research Organisation (ISRO). The methodology
used to assess agroforestry suitability in wastelands was categorised into 4 broader steps viz.
(1) Selection of appropriate parameters associated with Land Use Land Cover (2) Allocating
weights to individual parameter after logical evaluation, (3) Assignment of ranks to each
category within each parameter and (4) Integration of parameter (indicators) for spatial
representation for suitability and analysis of output data.
3.2.1 Datasets used
Five thematic datasets prepared under various national initiatives of ISRO for Land Use Land
Cover, wastelands, slope, water bodies and soil organic carbon have been used for this GIS
analysis-based prioritisation. The selection and number of parameters may vary based on local
site, its climate, community practices etc. Brief descriptions on the approach adopted to derive
each of the themes are as follows:
1. Land Use Land Cover (LULC): Under the ISRO’s National Natural Resource Census
programme of Land Use Land Cover mapping on 1:50,000 scale for entire country
has been taken up to study and understand the degree and magnitude of LULC
changes at every 5-year time intervals starting from 2005-06. For the current study,
LULC data for 2015-16 timeframe was used and feature level-2 classification covering
24 classes (Figure 3) is considered.
2. Wastelands: Under funding by Department of Land Resources, MoRD, GOI ISRO has
carried out wastelands mapping at 1:50000 scale for entire country. This layer was
used to identify wastelands classes. For the current study, wastelands data used is
for the 2015-16 timeframe and feature classification covering 23 classes of wastelands
(Figure 4). 3. Slope: Slope is described as the measurement of the rate of change of elevation of
the land per unit distance. Slope surface is calculated as percent slope using Digital
Elevation Model (DEM) of 30 m resolution derived from SIS-DP dataset (Figure 5).
4. Waterbody: All the information of the surface water, such as lakes, rivers, streams,
ponds etc., derived from 1:50000 scale LULC dataset (Figure 6).
5. Soil Organic Carbon (SOC): Soil Organic Carbon surface has been prepared using
random forests (RF) modeling based spatial prediction procedure, with climatic, land
cover, rock type, soil type, multi-year NDVI, irrigation status as independent input
variables. Models for predicting carbon density at 250 m spatial resolution developed
by NRSC has been deployed herewith. The spatial distribution indicates that majority
of the carbon stock resides in the northern part of India. The soil carbon stock of
eastern India has contribution from organic carbon, while the western portion has
contribution mainly from inorganic carbon (Figure 7).
Figure 3. Land Use Land Cover Map of India (2015-16)
16 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Figure 4. Wastelands Map of India (2015-16)
Figure 5. Slope Map of India
Methodology  | 17 Figure 6. Waterbodies Map of India (proximity distance in metres from waterbody)
Figure 7. Soil Organic Carbon Map of India (Kgs/m
2
)
18 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) 3.2.2 Methodology
The work flow adopted to calculate the Agroforestry Suitability Index (ASI) is shown in the
Figure 8. The methodology includes the pre-processing of input datasets related to generating
surface areas of water bodies, slope and soil organic carbon data for each state, proximity
analysis of the water bodies data, waterbody layers with classes for ASI computation,
assignment of ranks, computation of ASI and finally classification of land using ASI to derive
the output results.



Remot e Sensing Sources


Figure 8. Work flow for calculating the Agroforestry Suitability Index
3.3 PRE-PROCESSING OF THE INPUT DATASETS
The Land Use Land Cover and Wastelands datasets are vector datasets prepared using on-
screen visual interpretation technique at 1:50000 scale. The data volumes are very large and
require huge computational capabilities. Keeping data volume and computational constraints
in mind, the geo-spatial operations and analysis at state level was carried out. Further, it was
integrated to a single PAN India dataset.
LULCWastelands SOC
Agroforestry Suitability IndexWaterbody
Methodology  | 19 1. Land Use Land Cover (LULC) data: All the 24 classes in LULC are ranked from 0 to
4 depending on the LULC type and its role in the agroforestry suitability (Table. 5)
Table 5: Different Land Use Land Cover classes with ranking
LU Code Level 1 Land Use ClassLevel 2 LU Class LU Rank
1 BuiltupUrban0
2 BuiltupRural0
3 BuiltupMining0
4 AgricultureCrop land3
5 AgriculturePlantation1
6 AgricultureFallow4
7 AgricultureCurrent Shifting cultivation 4
8 ForestEvergreen/Semi Evergreen 0
9 ForestDeciduous1
10 ForestForest Plantation2
11 ForestScrub Forest4
12 ForestSwamp/Mangrove1
13 Grass/Grazing landGrass/Grazing land1
14 Barren/Unculturable wastelandsSalt affected land1
15 Barren/Unculturable wastelandsGullied/Ravinous2
16 Barren/Unculturable wastelandsScrub land3
17 Barren/Unculturable wastelandsSandy area1
18 Barren/Unculturable wastelandsBarren rocky0
19 Barren/Unculturable wastelandsRann1
20 Wetland/WaterbodiesInland wetland0
21 Wetland/WaterbodiesCoastal wetland0
22 Wetland/WaterbodiesRiver/Stream/Canals0
23 Wetland/WaterbodiesWaterbodies0
24 SnowSnow0
2. Wastelands data (WL): All the 23 classes in WL are ranked from 0 to 4 ranks
depending on the WL type and its role in the agroforestry suitability (Table. 6).
20 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Table 6: Different wasteland classes with ranking
WL CodeWastelandWL Rank
1 Gullied and/or ravinous land (Medium)2
2 Gullied and/or ravinous land (Deep)1
3 Land with Dense Scrub3
4 Land with Open Scrub4
5 Waterlogged and Marshy land (Permenant)0
6 Waterlogged and Marshy land (Seasonal)1
7 Land affected by salinity/alkalinity (Medium)2
8 Land affected by salinity/alkalinity (Strong)0
9 Shifting Cultivation (Current Jhum)2
10 Shifting Cultivation (Abadoned Jhum)4
11 Underutilised/degraded forest (Scrub dominated)4
12 Underutilised/degraded forest (Agriculture)4
13 Degraded pastures/grazing land4
14 Degraded land under plantation crop3
15 Sands Riverine1
16 Sands Coastal1
17 Sands-Desertic0
18 Sands-Semi Stab Stab>40m0
19 Sands-Semi Stab Stab 15-40m1
20 Mining Wastelands1
21 Industrial Wastelands0
22 Barren Rocky/Stony waste0
23 Snow covered/Glacial area0
3. Slope data: The slope map is derived from the Digital Elevation Model (DEM) and is
divided into 6 categories based on percentage of slope from 0 to 50% and above.
The ranks are given to the 6 categories with higher ranks to gentle slope and lower
value to steeper slope (Table. 7).
Methodology  | 21 Table 7: Category of slopes (in %) with ranking
Slope Code Slope (%) Description Slope Rank
1 0-3 Very Gentle4
2 3-8Gentle4
3 8-15Moderate3
4 15-35 Moderately Steep 2
5 35-50Steep1
6 >50Very Steep0
4. Waterbody (WB) data: All surface water bodies like rivers, lakes, reservoirs, ponds etc.
are considered within a single class as ‘waterbody’ and proximity analysis was carried
out for them. Priority ranking was assigned based on different buffer range like 0
to 500m, 500 to 1000m, 1000 to 2000m and greater than 2000m. The waterbody
ranks are given to the buffer range features from 0 to 4 (Table. 8).
Table 8: Proximity of waterbody (in meters) and its ranking
WBCode Waterbody Proximity (meter) WB Rank
10-5004
2500-10003
31000-20002
4>20001
5Waterbody0
5. Soil Organic Carbon (SOC) data: The soil organic carbon index map was used as
one of the indicators with index value ranging from 0 to greater than 12. Further, the
index was classified into 4 categories (Table. 9).
Table 9: Soil Carbon (Kg/sq.m) with ranking
Soil Organic Carbon
S. NoSOC DensityRank
10-31
23-72
37-123
4>124
22 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) 3.3.1 Generating waterody, slope and soil organic carbon data for each
state
The steps involved in generating waterbody, slope and soil organic carbon data are outlined
below (Figure 9 and 10). An illustration for state of Madhya Pradesh is provided.
1. Extraction of state bodies from SOI India/state boundaries dataset:
Figure 9. Extraction of State boundary for Madhya Pradesh
2. Clip operation was performed on the waterbody dataset using geo-processing tools
for vector data in QGIS. Waterbody data for India is used as input layer and state
polygon as overlay layer.
3. After getting the data for the waterbody layer for Madhya Pradesh state, Proximity
analysis was carried out prior to assignment of ranks to the waterbody data.

Figure 10. Extraction of waterbody related proximity information for Madhya Pradesh
The above example shows the extraction of waterbody for the state of Madhya Pradesh. In a
similar geo-processing approach, Slope and SOC data were extracted for the respective states.
Methodology  | 23 3.3.2 Proximity analysis of waterbody data.
Following steps are involved in proximity analysis of the waterbody data:
1. Considering the actual waterbody data, buffer range are created for different distance
values from the water-body, i.e. 500m, 1km and 2km and above.
2. The buffer operation was carried-out and different buffers are created for above
mentioned distances. Buffer tool under vector and geo-processing tools in QGIS was
used.
3. Erase operation was performed to eliminate the waterbody portion in the layer.
Difference tool under vector and geo-processing in QGIS are used.
After erasing the waterbody from the 500m buffer polygon, the output is shown in figure 12.
Figure 11. Preparation of waterbody proximity layer
4. Similarly, the areas for three different distances are obtained as below:
a. 500m to 1000m from the waterbody was obtained by erasing the buffer of 500
m from the buffer of 1000m.
b. 1000m to 2000m from the waterbody was obtained by erasing the buffer of
1000m from the buffer of 2000m.
c. Greater than 2000m from the waterbody was obtained by erasing the buffer of
2000m from the entire state. Note: The whole process of difference operation
was carried out because different area ranges are assigned with different ranks
(Figure 12).
d. Waterbody ranks are assigned to different layers as per the table 8.
e. Merge operation was performed to integrate 5 polygon layers i.e. original
waterbody, 0m to 500m, 500m to 1000m, 1000m to 2000m and greater than
2000m. Merge tool under vector and data management tool in QGIS was used.
After the merge operation of the buffers, the actual waterbody data is shown
in the figure 13.
24 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Figure 12. Waterbody layers with classes for ASI computation
Note: The ‘Buffer Analysis’ of the waterbody data is done using a model builder written in QGIS
software, which consists of all the operations listed above and takes in the India waterbody
data and the state name as the parameter and gives the waterbody data for that particular
state after the buffer analysis and ranks assignment. For some cases, where the waterbody is
near to a state boundary, the buffer analysis may lead to a final polygon which could exceed
the state boundary. For such cases, the final waterbody was clipped using the clip operation
to extract the state data.
3.3.3 Assignment of ranks
Each category was ranked within the parameters (LULC, Wastelands, Waterbody, Slope and
SOC) based on its suitability for transformation through agroforestry. In the present study, the
highest weightage was given to wastelands (Table 10). Based upon these ranks, the suitability
index was computed using following steps:
1. A new column for storing integer values, with a new name (<input_layer>_rank) is
created and rank values are calculated as per respective table mentioned above. The
process is carried out using editing the attribute table of a vector data layer.
2. The above-mentioned process was a manual step. So, for automating the process a
python script is written which edits the .dbf file (.dbf file forms the attribute table of
a vector data layer). The script adds a new column for rank and checks the column
for the code/description to assign the ranks accordingly. It takes in all the states data
at a time in a folder and updates the files.
3. LULC, Slope and Wastelands ranks are assigned using this python script.
Note: SOC data was given in raster format. The raster reclassification was done based upon
the classes given for different value ranges to SOC.
Methodology  | 25 Conversion of inputs from vector to raster
1. The coordinate system was converted from GCS to projected coordinate system i.e.
Albers equal area.
2. Vector to Raster conversion was carried out to reduce ASI computation process. During
conversion ranks are assigned as pixels value for each layer and spatial resolution of
the output raster is fixed to 20m.
3.3.4 Computation of Agroforestry Suitability Index (ASI)
Agroforestry Suitability Index (ASI) for particular parameter was calculated by multiplying
weightage of that parameters and ranks of each category within the parameter.
Calculation of Index for parameter-1
 Index
p1
= R
p1
* W
p1
 
Where, R
p1
: Rank of categories within parameter-1
W
p1
: Weightage of parameter-1
Similarly, index for other parameters was calculated.
3.3.5 Integration of parameters in GIS and Calculation of Total ASI
All parameters are integrated in GIS and Total ASI was calculated by summing up of individual
parameter index.
Total Index (ASI) = Index
p1
+ Index
p2
+ Index
p3
+ —- —-+ Index
pn
Here, Index
p1
: Index of parameter-1
ASI = [WLRank] * 0.4 + [LURank] * 0.3 + [SlopeRank] * 0.1 + [WBRank] * 0.1 + [SOC] *
0.1
For Built-up and waterbody, calculate ASI = 0
The raster datasets for all the input indicators of ASI was obtained and weighted sum operation
was performed to compute Agroforestry Suitability Index (ASI). The criteria weights and ranking
was adopted as per the expert-based standardisation are considered for ASI. The parameters
and critical weights are shown in the Table 9.
Table 10: Parameters and weightages used for ASI.
S. No.ParameterCriteria Weights
1 Land Use Land Cover0.3
2 Wastelands0.4
3 Slope0.1
4 Distance from Surface Waterbody0.1
5 Soil Organic Carbon0.1
Total1
26 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) The ASI raster data was obtained based on the performed weighted sum operation, where
each pixel was assigned with ASI value and calculated based on the assigned rank to each
five input data (LULC, Wastelands, Slope, Waterbody and Soil Organic Carbon ). The raster
datasets comprise of value ranging from 0 to 4 in scale. The ASI value with 4 representing
highly suitable and 0 as not suitable as shown in the Table. 11.
3.3.6 Classification using Agroforestry Suitability Index
The ASI datasets are a raster continuous dataset with pixel value ranging from 0 to 4. The
reclassify operation was carried out to classify ASI to discrete classified raster dataset comprising
of 3 classes ranging from highly suitable, moderate suitable and other/Not applicable as shown
in the Table 11.
Table 11: Ranking of classes used for Agroforestry Suitability Index
S. No. ASI valueASI ClassFinal Class
1 > 3 &<= 43Highly suitable
2 > 2 &<= 32Moderately Suitable
3 >= 0 &<= 21Others/Not Applicable
Note: The above-mentioned processes can be done using a customized model/tool in QGIS/ArcGIS.
Methodology  | 27 Results4
National level stratification of areas of wastelands suitable for transformation through
agroforestry was carried out using the Agroforestry Suitability Index (ASI) on a state wise and
district wise basis. Minor geographic units viz. Union Territories of Lakshadweep and Andaman
& Nicobar Islands are not covered in the study.
4.1 NATIONAL LEVEL MAPPING OUTPUT
As suitability area was represented in terms of percent of the total geographic area of the state,
results are collated in three size categories to retain comparability. States with more than 1 lakh
sq.km are considered as large, while those having area between 10000 sq.km up to 1 lakh sq.km
as medium and those below 10000 sq.km as small states (Table 12).
Amongst large size states, Madhya Pradesh, Telangana and Andhra Pradesh showed best extents
under highly suitable areas for agroforestry. Each of these states have atleast 69% area under others
category, which are equivalent to major cultivated tracts as such.
Tamil Nadu and Uttar Pradesh in the large state category showed only 3.6% and 1.5% land under
highly suitable category. Jammu & Kashmir having vast extents of cold deserts had only a small
area of wastelands considered highly suitable for transformation through agroforestry.
In medium sized states, four hill states viz., Manipur, Nagaland, Jharkhand & Mizoram had more
than 10% of area under highly suitable category, closely followed by Meghalaya (9.8%). As per this
analysis Punjab and West Bengal, showed lesser extents of highly suitable areas for wastelands
transformation through agroforestry.
In the case of small size states, Goa, Tripura and Delhi showed the highest area under highly
suitable category for taking up wasteland greening through agroforestry. Goa had dominant 45%
of area under less suitable category, while Sikkim and Puducherry recorded less area under highly
suitable category. This comparative analysis provides scope to understand states in terms of their
amenability to use agroforestry approaches, systems and tools to greening wastelands into more
productive uses, using multithematic criteria.
Although most areas fall under ‘low suitability’ as these are mostly cropped areas/sites which
fall outside this study and have a different sort of amenability for agroforestry. However, the
visualisation though portal on Bhuvan can resolve local level variation meaningfully so as to derive
assessment of amenable areas. Based on the analysis, 2,03,245.08 sq.km (6.18% of TGA area falls
under ‘highly suitable’ category and 1,61,366.65 sq.km (4.91% of TGA) under ‘moderately
suitable’ category. Most areas fall under ‘less suitable/not applicable’ category and these
include cropped areas, forests and other land use system, other than classified ‘wastelands’. Statewise
area, under highly suitable and moderate suitable category as per ASI, are mentioned in figure 14
and 15, respectively. Table 12: Statewise distribution of potential areas for greening (as percent of TGA)
Agroforestry Suitability Extent (in % of TGA of the State)
State Size State
Highly
Suitable
Moderately
Suitable
Others/Not
Applicable
Large
 (>1L sq.km)
Rajasthan 8.1 9.7482.16
Madhya Pradesh 9.65 3.8586.5
Maharashtra 7.97 6.4985.55
Uttar Pradesh 1.51 2.2896.22
Karnataka 5.05 1.9792.98
Gujarat 6.55 7.7485.71
Andhra Pradesh 8.66 6.6384.7
Ladakh1.53 0.7297.74
Odisha7.29 4.8887.83
Chhattisgarh 6.95 2.3590.7
Tamil Nadu 3.65 9.7686.6
Telangana 9.46 5.7184.82
Medium
(>10000 sq.km<
1 L sq.km)
Bihar3.01 3.0893.91
Jharkhand 11.55 7.5780.89
West Bengal 1.49 0.8797.64
Arunachal Pradesh 3.04 2.7194.26
Assam7.2 6.1386.67
Himachal Pradesh 6.33 4.1389.54
Jammu And
Kashmir
17.97 5.0377
Uttarakhand 2.26 4.3893.36
Punjab0.40.399.3
Haryana1.67 1.9696.38
Kerala 3.45 2.2794.28
Manipur 17.74 8.8373.43
Meghalaya 9.86 8.0682.08
Mizoram11.1 9.0479.86
Nagaland 17.04 14.1468.82
Small
 (<9999 sq.km)
Tripura 6.16 2.8191.03
Sikkim 0.93 0.7298.35
Goa8.87 4.486.73
Delhi3.52 0.6595.82
Puducherry 0.5 2.4797.03
Chandigarh 0.33 10.8888.79
Dadra & Nagar
Haveli
0.04 0.0799.89
Daman And Diu 0.00 0.00100
30 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Figure 13. Map with sites suitable for greening with Agroforestry
Results  | 31 0.00
5000.00
10000.00
15000.00
20000.00
25000.00
30000.00
35000.00
M AD H Y A P R A D E S H
RAJAS THAN
M AH AR A S H T R A
ANDH RA PR AD ESH
GU JARAT
O D IS H A
T E L A N G AN A
KARN ATAK A
CH HATTI SG ARH
J HA R K HA ND
J A M MU & K A SH MI R
ASS AM
T A M I L N AD U
M AN I P U R
U T T A R P RA D E S H
HI MACH AL PRADES H
N A G AL AN D
BI H A R
ARU NACHAL PRADES H
M EG HALAYA
M I Z OR A M
K E R AL A
UTTA RA KH AN D
WEST BENG AL
HARYAN A
T RI P U R A
GO A
P U NJ A B
S IK K IM
D E L HI
P O ND I C H E R R Y
C H AN D I G A R H
Area (in Sq.kms)
State
Highly Suitable Area across different States
Figure 14. State-wise area under the highly suitable category as per ASI
0
5000
10000
15000
20000
25000
30000
35000
RAJAS THAN
M AH AR A S H T R A
GU JARAT
T A M I L N AD U
M AD H Y A P R A D E S H
ANDH RA PR AD ESH
O D IS H A
T E L A N G AN A
J HA R K HA ND
U T T A R P RA D E S H
ASS AM
KARN ATAK A
CH HATTI SG ARH
BI H A R
J A M MU & K A SH MI R
N A G AL AN D
HI MACH AL PRADES H
UTTA RA KH AN D
ARU NACHAL PRADES H
M AN I P U R
M EG HALAYA
M I Z OR A M
K E R AL A
HARYAN A
WEST BENG AL
T RI P U R A
GO A
P U NJ A B
S IK K IM
C H AN D I G A R H
D E L HI
P O ND I C H E R R Y
Area (in Sq.kms)
State
Moderate Suitable area in States
Figure 15. State-wise area under the moderate suitable category as per ASI
Highly Suitable Area in different States/UT
Moderate Suitable Area in different States/UT
32 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Based on the study approach, State-wise modeling was done along with district level
characterisation across each State. A case study of Madhya Pradesh is presented in section
4.2 and similarly analysis was carried out for all states and UT’s.
4.2 CASE STUDY OF ASI OF MADHYA PRADESH
State and district-wise prioritised areas for Agroforestry Suitability GIS Modeling with Inputs
sets and result are as below:
i. Input data layers of five parameters of Madhya Pradesh used for ASI i.e LULC,
wastelands, slope, water bodies and soil organic carbon are shown in figures 16, 17,
18, 19, 20. The parameters a, b, c, d and e were analysed based on the weightages
approach to derive the ASI as mentioned in methodology section. The output i.e.
classified area suitable for agroforestry was obtained for Madhya Pradesh.
Based on the methodology adopted, district wise areas were classified for greening
with agroforestry (Figure 21). In Madhya Pradesh, 11830.18 sq.km area is under
moderately suitable class and 29643.98 sq.km area falls under highly suitable class
(Figure 22). The data for 52 Districts of Madhya Pradesh is shown in figure 23. The
Katni, Sagar, Mandla and Shahdol districts of Madhya Pradesh has potential area under
moderate suitable category i.e 541.77 sq.km. 474.34 sq.km, 436.02 sq.km and 423.37
sq.km, respectively. The Shivpuri, Sheopur, Khargone and Sagar have considerable
areas under highly suitable category i.e 1761.51 sq.km, 1488.23 sq.km, 1261.23 sq.km
and 1244.66 sq.km, respectively (Figure 23).
Figure 16. Landuse/Land Cover map of Madhya Pradesh
Results  | 33 Figure 17. Wastelands map of Madhya Pradesh
Figure 18. Slope map of Madhya Pradesh
34 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Figure 19. Waterbody map of Madhya Pradesh (in meters)
Figure 20. Soil Organic Carbon map of Madhya Pradesh (as per index)
Results  | 35 ii. Output Data: Agroforestry Suitability Index map of Madhya Pradesh
Figure 21. Classified areas for greening in Madhya Pradesh
11830.2
29644.0
265770.2
0
50000
100000
150000
200000
250000
300000
Moderately Suitable AreaH ig hl y Su itab le Ar eaN ot Ap pl icab le
Area (sq. km.)
Agroforestry suitable area extents in Madhya Pradesh
Figure 22. The ASI data of Madhya Pradesh
36 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Figure 23.
District-wise classified area suitable for Agroforestry in Madhya Pradesh
Results  | 37 5
Access to State,
UTs and District level
area suitability maps for
wastelands greening via
Bhuvan Geoportal
Based on the study approach, area suitable for greening with Agroforestry across districts are
calculated. The area statistic falling under different categories viz. highly suitable, moderate
and less suitable/not available is placed in Table. 13.
The Agroforestry suitability Maps of all States and districts are made available in the Bhuvan
Geoportal of ISRO under the project- Greening and Restoration of Wastelands with Agroforestry
(G.R.O.W) - Suitability Mapping System. The classified maps ranging from highly suitable to not
suitable category along with area statistics with legends, map viewer along with input datasets
can be assessed using the web browser https://bhuvan-app1.nrsc.gov.in/asi_portal/. All users
are required to create a log-in that will be authenticated by Bhuvan Team (Figure 24).
The maps can be accessed at different levels viz. Central, State and District level. The user
access manual is enclosed as Annexure-IV. Also, a special feature was incorporated in this
application that allows users to draw the area of interest and generate the statistics for the
area drawn on the fly like AOI. Also, stakeholders can find information on selected agroforestry
system and list of tree species exempted from transit and felling permit in the portal. The key
features of the system on Greening and Restoration Wastelands with Agroforestry-Suitability
Mapping are:
Provides District level information of wastelands area suitable for agroforestry;
Provides area prioritisation regime i.e. highly suitable area, moderate and less suitable
areas for agroforestry;
Provides State-wise & District-wise area analysis reports based on the suitability
regimes;
Provides information, such as potential agroforestry systems, list of tree species
exempted for transits and felling permit. Figure 24.
Landing page on Wastelands greening with Agroforestry- Suitability Mapping on Bhuvan Geoportal

40 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Table 13: District-wise area suitable for greening with Agroforestry
(area in sq.km)
State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
JAMMU &
KASHMIR
Mirpur1476.62106.951476.60
Muzaffarabad465.81126.423120.92
Punch351.67173.06 3373.38
Anantnag406.5679.542243.10
Baramula201.58168.41 1693.37
Kathua315.00208.381888.33
Udhampur152.3484.082045.01
Badgam239.8160.28950.44
Bandipura1007.15137.16 2901.32
Ganderbal583.6545.99991.26
Kulgam188.9173.461003.34
Kupwara334.65226.322184.57
Pulwama119.9321.16756.00
Rajauri539.30321.221776.69
Ramban365.7822.86900.56
Riasi168.04126.381639.41
Shupiyan36.7235.71433.52
Srinagar8.937.48265.37
Kishtwar1893.98270.965927.97
Doda245.6684.022020.03
Samba71.6176.78699.59
Jammu184.07162.081797.37
Total9357.77 2618.68 40088.15
LADAKH
Leh2269.31 1088.44 141826.75
Kargil172.1464.0813926.18
Total2441.451152.52 155752.94
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 41 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
HIMACHAL
PRADESH
Mandi470.09179.06 3305.78
Kangra446.64306.234961.47
Sirmaur379.06235.912186.95
Solan349.46231.061347.93
Shimla337.29445.794345.71
Bilaspur286.20102.25777.92
Chamba270.89217.72 5979.30
Kullu263.85163.51 5084.02
Una231.7477.071228.35
Kinnaur174.80122.73 5971.96
Hamirpur164.5054.76904.25
Lahaul & Spiti132.77149.7713512.91
Total3507.29 2285.86 49606.55
PUNJAB
Rupnagar56.2828.471283.74
Hoshiarpur35.3640.523281.61
S.A.S. Nagar(Mohali) 28.0214.051040.56
Faridkot15.450.121461.59
Pathankot14.6312.49832.88
S.B.S. Nagar13.137.111240.00
Patiala7.266.953296.41
Ludhiana4.873.253701.32
Bathinda3.861.183369.80
Fazilka2.602.162666.89
Tarn Taran2.445.352330.10
Sangrur2.380.733600.76
Muktsar2.130.562631.70
Mansa2.120.822163.16
Gurdaspur1.2716.392478.97
Kapurthala1.253.011625.73
Barnala1.110.091412.87
Ferozpur1.021.452422.70
Jalandhar0.790.472630.65
(area in sq.km)
42 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
PUNJAB
Amritsar0.752.472473.88
Moga0.572.152230.03
Fatehgarh Sahib0.240.261142.38
Total197.53150.07 49318.75
CHANDIGARH Chandigarh0.3812.69103.60
Total0.3812.69103.60
UTTARAKHAND
Pauri Garhwal188.24290.304953.84
Pithoragarh137.97417.04 5796.43
Tehri Garhwal118.80113.37 3637.88
Uttarkashi115.63375.447256.03
Bageshwar101.54115.66 2065.60
Dehradun101.36109.49 2850.34
Almora84.77105.902913.57
Nanital84.01182.58 3849.74
Chamoli77.05211.41 7187.29
Champawat66.0137.341426.83
Rudraprayag45.21112.52 1679.79
Haridwar30.30133.302131.37
Uddam S Nagar16.1460.982498.76
Total1167.03 2265.31 48247.47
HARYANA
Mahendragarh97.25103.241687.68
Gurugram81.7335.551123.62
Panchkula79.4044.45759.50
Faridabad74.1719.38627.25
Hisar66.1365.933932.54
Bhiwani42.4546.293140.40
Mewat41.4459.421388.13
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 43 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
HARYANA
Yamunanagar37.0226.151657.61
Rewari30.5046.931428.52
Charkhi Dadri30.4639.611308.62
Palwal24.1232.241310.06
Jhajjar21.8366.001837.87
Karnal18.5013.522437.70
Sonipat17.9756.042091.82
Ambala12.8031.241417.41
Rohtak10.6144.191623.59
Panipat9.7710.181230.33
Sirsa9.3227.564181.06
Kurukshetra7.206.081663.12
Kaithal6.5513.242258.79
Fatehabad6.5323.592483.08
Jind5.8147.092688.72
Total731.56857.93 42277.40
DELHI Delhi52.749.771434.75
Total52.749.771434.75
RAJASTHAN
Jaisalmer3975.33 7591.09 26423.18
Udaipur2431.29 1441.55 7900.52
Bhilwara1776.08 2948.875737.78
Chittorgarh1529.11 1036.58 8173.93
Baran1260.94413.435131.74
Karauli1248.07398.743799.11
Ajmer1076.431753.17 5692.49
Dungarpur1046.80426.692309.29
Jaipur1013.44 1195.24 9055.80
Sirohi918.65614.87 3620.93
Barmer906.631997.25 25257.40
Jodhpur871.811650.13 20345.04
(area in sq.km)
44 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
RAJASTHAN
Banswara866.75277.623904.31
Jhalawar853.37734.834652.61
Bundi820.21319.674442.12
Pali754.452625.35 8975.66
Kota660.94363.71 4482.51
Sikar574.27320.51 6798.78
Nagaur567.741028.58 16089.37
Rajsamand550.911348.24 2753.63
Bikaner509.86617.08 25874.57
Tonk487.95701.036061.38
Dholpur482.02378.132157.63
Sawai Madhopur475.89317.22 4227.40
Jhunjhunu384.26182.14 5350.78
Alwar298.03526.997465.94
Churu292.17252.64 16538.10
Jalore287.841147.16 9292.32
Bharatpur201.11249.21 4593.76
Dausa186.06236.052562.48
Pratapgarh180.7030.44754.10
Sri Ganganagar102.4051.38 10342.30
Hanumangarh70.5484.799835.11
Total27662.05 33260.36 280602.03
UTTAR
PRADESH
Jhansi278.39171.57 4504.97
Mirzapur266.08150.86 3980.96
Lalitpur243.6777.394602.51
Etawah229.85133.801945.99
Agra229.23156.95 3582.25
Sonbhadra223.25280.11 6338.42
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 45 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
UTTAR
PRADESH
Allahabad211.69102.10 5162.20
Lakhimpur139.39219.11 6690.65
Jalaun125.53241.464192.06
Chitrakoot122.13211.59 2729.45
Hamirpur105.38242.553861.84
Bahraich92.8578.644137.20
Mahoba80.17178.322631.45
Pilibhit61.5435.873057.99
Pratapgrah59.81109.37 3548.87
Firozabad55.2094.632219.29
Sitapur54.3086.585599.79
Budaun53.1885.104120.73
Chandauli52.1227.972457.39
Barabanki51.3261.693722.86
Balrampur47.2644.722839.73
Kanpur Dehat43.96125.93 3009.65
Azamgarh43.8453.994117.90
Gonda41.8935.753918.08
Amethi39.3079.563207.71
Sultanpur36.6742.992364.07
Auraiya35.5543.501939.28
Ballia34.3643.702884.55
Jaunpur33.4676.043926.99
J.p Nagar32.7922.032158.45
Faizabad30.3937.132679.76
Banda28.15272.364222.89
Raebareli27.1165.273183.08
Kanpur26.6166.682907.42
Hardoi25.93177.32 5788.75
Mahrajganj20.4121.762689.68
(area in sq.km)
46 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
UTTAR
PRADESH
Lucknow19.7974.752433.55
Shahjahanpur18.4545.264507.17
Gorakhpur17.6536.403284.29
Fatehpur16.98168.11 3962.13
Mathura16.1536.653260.02
Muzaffarnagar15.3746.212598.51
Bareilly14.0714.244045.77
Basti13.4541.432631.06
Ambedkar Nagar12.5622.002325.20
Unnao11.75143.19 4405.16
Farrukhabad10.3839.972130.79
Bulandshahr9.4822.013474.05
Kannauj8.8242.582040.35
Mainpuri8.5586.102648.73
Shravasti8.4016.711596.40
Ghazipur8.2930.053333.21
Varanasi8.0210.101510.23
Siddharthnagar7.8947.532543.75
Deoria7.3412.572485.82
Hathras7.1816.231757.50
Mau6.863.161691.99
Kansiramnagar6.7622.111929.24
Aligarh6.3424.843682.74
Gautam Budh Nagar 5.9937.201401.17
Bhimnagar4.3016.592378.33
St. Kabir Nagar3.7327.401593.74
Kaushambi3.4826.641751.37
St. Rabidas Nagar 3.087.841004.09
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 47 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
UTTAR
PRADESH
Etah2.7637.302443.41
Panchshil Nagar2.217.051095.96
Prabudhnagar2.0513.571312.79
Rampur2.0133.972288.98
Bijnor1.8463.784359.86
Ghaziabad1.5615.93850.39
Kushinagar1.2512.762840.02
Baghpat1.0416.491302.45
Meerut0.9919.232561.96
Saharanpur0.9890.263591.84
Moradabad0.1214.302254.86
Total3580.62 5396.95 228233.69
BIHAR
Jamui538.8469.482419.96
Banka514.7141.16 2483.03
Bhabhua411.17109.47 2833.03
Gaya295.44117.30 4532.73
Rohtas237.3172.663497.53
Pashchim Champaran 134.4678.714475.33
Nawada115.8948.572295.32
Aurangabad77.5527.323154.29
Katihar40.91324.39 2646.69
Chhapra31.9779.902522.53
Purba Champaran31.2678.763549.54
Bhagalpur29.41153.28 2348.69
Munger28.5648.301353.68
Gopalganj28.2769.571936.21
Muzaffarpur25.8866.773089.59
Bhojpur21.529.982462.47
Supaul18.4166.112121.05
Kishanganj16.6147.981538.95
Lakhisarai15.6344.601205.45
Purnia14.62172.54 3053.34
Patna13.7193.813074.26
(area in sq.km)
48 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
BIHAR
Jehanabad13.586.83898.97
Madhepura12.79144.801677.89
Siwan11.4834.692169.88
Madhubani8.8860.912878.21
Buxar8.0011.651553.52
Saharsa7.6668.611610.18
Vaishali7.4072.781923.90
Araria7.2452.112471.20
Arwal4.399.32618.40
Samastipur4.04141.04 2756.46
Kaimur4.040.4224.94
Begusarai3.5756.351876.33
Khagaria3.32138.12 1347.58
Sitamarhi3.3021.961966.23
Darbhanga1.87124.652159.70
Nalanda1.7120.462311.19
Sheohar0.392.21285.56
Jhanabad0.360.2663.66
Sheikhpura0.2629.04609.33
Total2746.40 2816.90 85796.78
SIKKIM
North23.5019.17 3652.03
East13.496.87915.27
South12.688.62708.47
West10.7512.021094.47
Total60.4346.686370.25
ARUNACHAL
PRADESH
West Kameng438.82225.573774.48
Tawang342.88212.921234.61
Longding178.98166.73502.07
Lower Subansiri148.03112.77 1033.93
Tirap144.3052.13817.84
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 49 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
ARUNACHAL
PRADESH
Dibang Valley123.7285.538714.43
Shi-Yomi103.3625.332682.24
Anjaw93.0845.855622.28
Kamle92.89125.551723.49
Changlang75.2589.103304.47
Upper Siang73.0780.196284.40
Papumpare69.6298.573209.03
East Siang69.27185.141566.18
Kurung Kumey57.62113.74 4145.30
East Kameng52.8269.484181.98
West Siang50.7632.101544.76
Upper Subansiri44.4945.095645.75
Lohit42.47103.30 2996.80
Lower Dibang Valley 33.5057.893508.55
Leparada29.8324.31751.70
Kra Daadi28.8723.132297.71
Siang28.4338.512898.75
Lower Siang25.2521.101792.22
Pakke-Kessang6.4117.991894.16
Namsai5.1749.301074.86
Total2358.882101.31 73201.99
NAGALAND
Mon538.44467.561140.72
Phek354.92159.961327.93
Zunheboto330.11188.321051.86
Tuensang327.40455.901365.32
Peren264.68128.881297.03
Mokokchung253.19265.981097.70
Kohima173.47118.93996.87
Longleng170.67102.78292.53
Kiphire162.96216.66744.31
(area in sq.km)
50 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
NAGALAND
Wokha153.54154.51 1302.08
Dimapur76.3268.01713.89
Total2805.70 2327.50 11330.24
MANIPUR
Churachandpur844.98203.831465.44
Senapati572.82186.221245.29
Tamenglong437.89224.32 2360.42
Kangpokpi399.5699.701046.49
Pherzawl384.07226.491609.11
Ukhrul347.43478.431391.55
Chandel318.85191.25 1225.45
Noney197.6463.47694.96
Tengnpoupal177.57100.62951.74
Kamjong97.8798.952094.34
Imphal East30.4917.18348.99
Kakching24.9510.64248.55
Imphal West19.136.63476.61
Thoubal11.547.94304.48
Bishnupur9.788.24426.07
Jiribam3.186.68159.18
Total3877.76 1930.59 16048.69
MIZORAM
Aizawl493.32294.642931.72
Champhai393.87439.641856.24
Lunglei384.49359.453346.96
Mamit224.77189.80 2594.24
Lawngtlai 205.32103.091403.78
Serchhip172.09155.54788.54
Kolasib110.2551.581169.75
Saiha94.3598.13859.40
Total2078.471691.87 14950.64
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 51 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
TRIPURA
Dhalai177.5833.411734.58
West Tripura68.6553.21670.49
North Tripura67.0215.18 1048.43
Gomati56.8933.991248.73
Sepahijala 53.2436.39910.26
South Tripura42.6431.20832.43
Khowai36.5431.05834.22
Unakoti 25.065.81517.57
Total527.61240.257796.71
MEGHALAYA
East Jaintia Hills 456.7073.631217.96
West Jaintia Hills 348.7330.581387.66
West Khasi Hills 302.73314.343174.85
West Garo Hills237.14362.292130.08
South West Khasi
Hills
176.36143.601010.68
East Khasi Hills147.1874.192401.95
East Garo Hills145.76170.891862.53
Ri Bhoi122.37350.851886.93
South Garo Hills 100.35137.36 1485.24
North Garo Hills 48.3630.37650.31
South West Garo
Hills
14.2829.00271.70
Total2099.941717.10 17479.90
ASSAM
Karbi Anglong1614.78 1202.09 7598.32
Nc Hills/Dima Hasao 1057.53 568.773219.89
Sonitpur536.13137.47 4552.56
Golaghat455.80172.41 2649.32
Kokrajhar337.15210.30 2543.94
Nagaon196.49351.99 3487.43
Chirang178.0693.991590.63
Tinsukia130.2295.643591.77
Hailakandi127.2055.321139.45
(area in sq.km)
52 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
ASSAM
Cachar101.2586.843333.84
Kamrrup Rural94.49233.342747.46
Baksa93.42119.50 2198.09
Sivasagar92.0976.222460.14
Goalpara75.87146.581723.39
Jorhat73.7266.452940.25
Udalguri72.15133.501775.21
Dibrugarh57.8359.873263.28
Karimganj56.0055.081209.88
Lakhimpur55.45108.90 2720.09
Dhemaji35.90167.90 2299.90
Kamrup Metro33.7954.52933.71
Bongaigaon13.2869.991021.35
Darrang10.46133.851436.12
Morigaon8.9378.651379.66
Dhubri4.7047.501467.00
Barpeta3.50100.602155.38
Nalbari1.1773.82976.56
Total5517.36 4701.08 66414.60
WEST BENGAL
Bankura348.81106.61 6434.95
Purulia308.34143.97 5735.62
Jhargram198.4346.522827.50
Paschim Burdwan103.4448.696133.55
Birbhum80.5032.724403.86
Paschim Bardhhaman 76.2042.321491.57
Alipurduar13.228.192690.16
Jalpaiguri10.3914.983168.31
Darjeeling10.1223.111631.18
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 53 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
WEST BENGAL
Kalimpong8.112.481055.90
Purba Burdwan5.2724.545377.45
Cooch Bihar0.7336.792922.50
Murshidabad0.5420.884870.21
Nadia0.3326.083036.18
Dakshin Dinajpur0.135.182054.00
Uttar Dinajpur0.112.602987.14
South 24 Parganas 0.089.925312.04
Malda0.0113.733520.43
North 24 Parganas 0.0017.282485.67
Hoogly0.0021.693130.12
Howrah0.0013.11 1422.77
Kolkata0.000.0094.66
Purba Medinipur0.0022.903629.13
Total1164.77 684.30 76414.90
JHARKHAND
Chatra1078.58289.21 2369.50
Giridih1007.53183.293771.07
Hazaribag714.52305.043769.91
Latehar664.11509.733081.34
Palamu605.39298.673462.78
Simdega579.80503.092665.04
Ranchi561.39523.856531.31
Garhwa532.41363.793188.03
Bokaro424.19215.552126.86
Pashchimi
Singhbhum
395.01270.686509.37
Kodarma388.63164.97 2079.50
Deoghar343.74372.451714.35
Purbi Singhbhum299.60354.672875.65
Gumla273.36596.674480.16
Dumka244.87122.71 3378.44
Saraikela216.68192.19 2217.24
(area in sq.km)
54 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
JHARKHAND
Godda206.57170.991853.97
Dhanbad162.28123.50 1840.00
Jamtara158.9934.841573.78
Pakur119.41175.851498.11
Sahibganj109.74114.981919.94
Lohardaga78.23119.14 1299.61
Khunti0.400.877.02
Total9165.42 6006.71 64212.98
ODISHA
Malkangiri799.87564.484376.72
Gajapati779.74370.81 2945.67
Ganjam729.97396.187257.27
Nabarangpur668.03273.09 4478.00
Kalahandi660.45482.346760.12
Kandhamal
(Phulbani)
586.35748.79 6687.40
Keonjhar (Kendujhar) 561.26451.32 7287.48
Sambalpur554.16234.955817.85
Balangir526.74278.835747.68
Angul516.67223.39 5633.04
Nuapada484.36108.353247.72
Bargarh458.2593.715275.73
Sundargarh457.09257.19 8979.92
Nayagarh410.10279.473207.84
Mayurbhanj397.53164.91 9833.37
Koraput359.39967.22 7060.78
Raygada341.79453.486529.76
Dhenkanal328.42222.603906.71
Boudh277.87113.392711.52
Deogarh (Debagarh) 276.07106.822570.87
Khordha 268.45111.75 2500.94
Sonepur224.26108.68 2029.63
Cuttack181.43137.15 3393.98
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 55 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
ODISHA
Jharsuguda159.8358.891897.08
Jajpur147.5572.442676.65
Balasore69.8639.173592.42
Puri21.82101.50 3298.50
Kendrapada19.0267.092260.63
Jagatsinghpur13.1049.041789.45
Bhadrak2.2816.282179.53
Total11281.71 7553.31 135934.27
CHHATTISGARH
Bijapur998.30119.998253.11
Balrampur819.06186.055210.24
Surajpur669.08150.52 4620.84
Koriya582.1899.175899.46
Korba539.4693.145972.32
Rajnandgaon514.22273.647277.59
Surguja485.08142.71 3396.83
Gariaband479.68120.03 4233.56
Raigarh420.32145.856471.48
Bilaspur380.08119.98 5036.73
Sukma344.5690.394947.46
Balodabazar298.97148.364219.14
Kondagaon283.4357.924746.96
Jashpur278.14143.70 5420.36
Kanker276.13121.98 6799.59
Mahasamund255.34284.294211.60
Narayanpur240.5735.033431.73
Raipur214.89144.862546.16
Kawardha191.1373.713916.42
Janjgir-Champa188.65108.393567.01
Bastar181.1076.425123.72
Balod181.0370.723109.16
Dhamtari167.0961.013855.05
Durg117.25139.75 2042.21
(area in sq.km)
56 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
CHHATTISGARH
Dantewada106.8957.472704.71
Bemetra93.6776.982693.41
Mungeli77.3523.602679.77
Total9383.603165.67 122386.62
MADHYA
PRADESH
Shivpuri1761.51 346.627811.84
Sheopur1488.24268.354757.16
Khargone1261.24127.07 6618.26
Sagar1244.66474.348505.18
Barwani1231.77 310.59 3858.20
Damoh1030.43332.11 5940.16
Neemuch1011.33 109.01 2928.69
Guna919.09236.665148.69
Mandla843.29436.026225.28
Panna837.06380.245887.32
Morena816.99345.923829.36
Chhatarpur816.53266.887593.32
Singrauli767.84391.74 4638.75
Dhar763.87173.55 7239.43
Shahdol748.21423.37 4489.20
Balaghat710.79373.568151.74
Gwalior710.29134.083712.42
Dewas677.75121.47 6205.60
Satna642.76239.406571.97
Dindori583.56373.584787.68
Tikamgarh563.79101.35 4318.04
Katni562.52541.77 3975.45
Mandsaur557.24156.724719.91
Jhabua549.52189.39 2694.56
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 57 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
MADHYA
PRADESH
Raisen524.73193.98 7729.68
Rajgarh521.49157.55 5473.77
Umaria509.76349.74 3704.39
Ratlam461.11209.194141.75
Jabalpur456.49306.444337.73
Khandwa454.46111.12 6880.21
Ashoknagar416.8070.914250.69
Rewa394.94203.635694.81
Agarmalwa383.68136.702190.51
Chhindwara377.93293.4811107.49
Betul365.07349.309332.75
Vidisha363.18162.92 6794.43
Anuppur362.73285.613134.76
Sidhi358.94347.484037.57
Alirajpur328.06236.57 2766.30
Sehore322.61169.55 6078.87
Burhanpur294.59113.14 2794.46
Seoni294.36183.408284.73
Indore254.0789.463567.95
Ujjain219.47272.33 5608.86
Bhopal201.9194.672473.47
Datia175.7154.172780.90
Shajapur164.3492.513206.70
Narsinghpur106.27120.054913.47
Hoshangabad104.9162.056526.19
Harda80.1188.723162.16
Bhind46.02221.724187.42
Total29643.98 11830.18 265770.23
GUJARAT
Kutch2590.705279.12 29788.32
Surendranagar757.71981.03 7503.62
Rajkot699.60324.556728.33
Amreli624.12309.73 6303.96
(area in sq.km)
58 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
GUJARAT
Morbi442.67331.12 4158.91
Bhavnagar436.67633.085613.97
Jamnagar433.89372.524782.01
Dahod400.00588.462628.32
Banaskantha381.21474.239781.61
Chhota Udaipur376.60197.31 2875.81
Mahisagar335.87181.301981.60
Valsad326.75164.481841.87
Sabarkantha320.96308.053574.10
Narmada320.12152.79 2332.05
Arvalli287.99145.20 2703.58
Junagadh250.17128.674197.96
Navsari213.9855.981640.45
Bharuch203.49156.893684.10
Ahmedabad202.94264.876599.12
Gir Somnath201.21121.462167.51
Vadodara195.77272.393597.98
Dang194.95102.841459.12
Botad159.66137.832182.53
Panchmahal158.26224.372896.17
Patan154.35559.25 5060.08
Tapi128.51100.26 2885.42
Mahesana98.80135.29 4180.93
Kheda97.4554.413290.18
Surat87.04104.693737.02
Devbhumi Dwarka68.77267.292414.79
Gandhinagar59.07101.50 1942.89
Porbandar52.8066.85753.38
Anand34.0764.922621.67
Total11296.14 13362.73 147909.35
(area in sq.km
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 59 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
MAHARASHTRA
Ahmednagar1835.36512.52 14706.14
Nashik1783.02795.76 12972.43
Pune1766.51 1845.92 12043.26
Dhule1759.36 368.685054.27
Satara1467.63 926.358112.82
Beed1072.43 469.649503.26
Amravati1012.40716.18 10465.93
Chandrapur979.42549.499769.51
Yavatmal917.01832.4011779.31
Kolhapur888.37602.666167.94
Jalgaon811.53603.6910350.41
Raigad798.671223.71 4245.71
Gadchiroli796.32355.65 13263.62
Sangli786.55737.38 7032.07
Solapur729.92931.02 13235.21
Nandurbar666.66202.615017.47
Nanded655.61469.859456.30
Buldhana640.59565.248543.34
Ratnagiri583.39962.946075.52
Aurangabad532.22791.42 8792.20
Hingoli357.67192.79 4139.80
Palghar355.18400.983586.10
Washim350.46196.99 4648.67
Sindhudurg327.20633.183942.11
Jalna324.50482.826962.93
Nagpur314.28541.30 9052.67
Thane288.72589.812781.72
Parbhani229.93131.94 5976.04
Gondia228.98444.574790.51
Wardha223.66354.465731.27
Osmanabad221.78747.336588.18
Latur207.63218.22 6320.86
Akola189.02146.345071.49
Bhandara123.48158.31 3575.45
Mumbai Suburban2.5914.47270.52
Mumbai City0.000.0041.01
Total24228.03 19716.58 260066.06
(area in sq.km)
60 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
ANDHRA
PRADESH
Kadapa2686.361293.91 11174.09
Chittoor2206.841523.9 11326.78
Anantapur2007.711275.4 15858.19
Prakasam1605.221324.15 14713.44
Visakhapatnam1228.41 1507.79 8617.91
Nellore1184.061114.48 10723.87
Kurnool978.91788.9315917.53
Guntur566.62234.75 10548.77
Srikakulam547.65380.225285.02
Vizianagaram402.49512.28 4635.68
Krishna274.96227.427872.86
East Godavari260.95371.0512783.3
West Godavari56.98166.68 7485.85
Total14007.16 10720.96 136943.29
KARNATAKA
Belgaum870.00234.78 12250.57
Chitradurga607.71176.32 7637.54
Uttar Kannada550.87133.86 9076.45
Tumkur507.11303.17 9746.59
Bangalkote477.8173.976021.38
Shimoga465.57162.80 7849.42
Gulbarga455.6365.8610410.51
Davangere397.8478.055447.05
Raichur394.0543.987997.13
Bidar392.8713.494994.54
Bellary384.58266.897786.71
Ramanagara324.4570.493115.14
Chikkaballapur322.44451.67 3444.86
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 61 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
KARNATAKA
Hassan308.04126.926377.12
Chikkamagaluru269.36109.53 6824.25
Dakshina Kannada 260.5832.844244.58
Koppal259.03191.49 5126.06
Kolar254.97259.293444.72
Mysore245.8675.905979.96
Udupi242.6258.293473.50
Yadgir239.7198.694931.10
Chamarajanagar233.47172.08 5233.87
Mandya229.06108.96 4624.65
Dharwad180.7853.624023.90
Vijayapura163.6291.53 10218.40
Haveri159.5156.554608.61
Gadag150.2775.394429.16
Bangalore Rural110.1891.382105.87
Kodagu103.2654.783925.03
Bangalore Urban80.1127.61 2076.60
Total9641.36 3760.16 177425.23
GOA
North Goa178.0471.541386.70
South Goa131.5582.101641.40
Total309.60153.64 3028.10
KERALA
Kasaragod264.9126.601639.82
Palakkad243.4078.104133.51
Idukki202.96371.88 4414.54
Thiruvananthapuram 134.897.832015.88
Kannur116.9934.332772.90
Malappuram95.6547.923405.51
Kottayam51.9042.372116.42
Thrissur48.3135.222929.18
Pathanamthitta43.8165.972532.83
(area in sq.km)
62 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
KERALA
Ernakulam41.0721.572344.77
Kozhikode35.3523.162261.46
Wayanad29.7076.822011.52
Kollam23.2722.232429.92
Alappuzha0.0322.701393.36
Total1332.24876.71 36401.63
TAMIL NADU
Tiruvannamalai436.63501.28 5244.98
Vellore427.08279.27 5325.64
Erode276.99139.385333.91
Krishnagiri273.20323.354516.09
Tiruppur270.22401.86 4499.44
Tirunelveli258.41736.75 5659.95
Dharmapuri238.81247.81 4011.50
Villupuram228.93954.875986.02
Dindigul225.32650.965167.99
Coimbatore209.24178.844314.32
Kanchipuram195.89182.64 4065.01
Salem162.09180.534895.12
Thiruvallur158.99112.05 3028.46
Thoothukkudi139.16 444.884119.30
Madurai128.99481.32 3098.32
The Nilgiris121.5566.972343.15
Virudhunagar119.16 422.043680.61
Theni112.97376.742367.02
Tiruchirappalli99.64362.88 4049.66
Kanniyakumari98.5462.921494.74
Sivaganga97.621156.82 2985.78
Pudukottai97.49749.063808.42
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 63 State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
TAMIL NADU
Perambalur89.54223.231442.66
Karur80.28150.542667.65
Namakkal63.75142.27 3206.96
Ariyalur34.7880.741823.89
Cuddalore34.67168.44 3463.59
Ramanathapuram14.091350.81 2369.83
Thanjavur5.32734.492647.05
Thiruvarur4.53649.301569.52
Nagapattinam2.4181.642422.12
Chennai0.660.40174.76
Total4706.97 12595.08 111783.44
PUDUCHERRY
Puducherry1.494.20268.33
Karaikal0.003.1119.10
Yanam0.000.000.04
Total1.497.31287.47
TELANGANA
Bhadradri1487.84173.645235.16
Nagarkurnool763.06258.955210.02
Mahabubabad675.43256.122621.07
Jayashankar638.93194.86267.17
Adilabad562.49185.713144.11
Nalgonda553.37493.566161.28
Komarambhem538.36253.053671.15
Kamareddy535.13366.52771
Nizamabad436.44292.233430.12
Sangareddy413.44316.623711.76
Vikarabad359.9147.62 3089.94
Mancherial328.16208.81 3348.29
Medak297.61173.53 2296.57
Mahabubnagar295.44224.974575.59
Siddipet251.7264.43118.87
Nirmal246.62214.263237.07
Rangareddy243.24363.34362.04
Khammam227.63113.37 4008.04
(area in sq.km)
64 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) State Name District Name
Highly Suitable
Area (HS)
Moderate Suitable
Area (MS)
Others/Not
applicable
TELANGANA
Yadadri213378.792666.01
Jagtial211.03149.19 2491.27
Peddapalle182.63101.88 1923.02
Rajanna182.3695.911619.64
Suryapet182.35141.253192.91
Wanaparthy147.56102.961916.37
Warangal (R)132.96118.32 1889.27
Jangaon107.2249.971929.18
Medchal96.53126.84849.98
Warangal (UR)72.02116.431106.22
Karimnagar70.23214.26 1840.45
Jogulamba67.8354.892440.05
Hyderabad1.433.43209.95
Total 10521.92 6355.56 94333.57
GROW report and portal can prove to be a key tool in identifying wastelands suitable for
greening through agroforestry, that can help the country in achieving its commitments and
empowerment of the locals. These transformations of wastelands into more productive land
use systems can help in meeting the demand for fodder, food and for substitution of wood
imports, while, providing new income sources for people living in or around those areas.
Under GROW project, scientifically wasteland areas are identified that are most likely to deliver
successful sites with high-performance potential, in terms of adopting greening strategies based on
agroforestry interventions and in terms of outcomes for restoration and agroecological amelioration.
The suitability area statistics mentioned in Table 13 serve as an operational baseline for the first level
planning of state-wise requirements for greening and restoration of wastelands through agroforestry.
Stakeholders including Government (Central/State), Research Organisation/Institution, Universities,
Industries etc. can use the suitability data for initiating greening projects based on local needs
by accessing maps and data at Bhuvan geo-portal https://bhuvan-app1.nrsc.gov.in/asi_portal/.
Also, few agroforestry systems are proposed for wasteland in Annexure III.
This project interface provides a mechanism for user driven ranking and overlay analysis as well.
With that application, a wide number of users can get customised outcomes. As the outputs can
at times suggest overlapping outcomes, it is important that due diligence is practiced to ensure
that appropriate conclusions for action drawn.
(area in sq.km)
Access to State, UTs and District level area suitability maps for wastelands greening via Bhuvan Geoportal | 65 66 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) 6
Way Forward:
Scaling-up GROW in a
Mission Mode
1. The GROW analysis presents a novel opportunity for advancement in agroforestry, especially
in wastelands greening. Certain wasteland categories possess sufficient quantity and quality
of soil organic content and water resources that can support agriculture and forestry
plantations. The present study on mapping and prioritisation regime of wastelands can
play an instrumental role in planning greening and restoration projects with agroforestry
interventions. The prioritised area regime across various districts was based on geospatial
analysis of 5 critical parameters i.e. Soil Organic Carbon, water proximity, Land Use Land
Cover, Wastelands and Slope. In this project, limited ground-truthing was carried out in
selected districts across different agroclimatic zones. With use of GROW datasets and portal
by state agencies, researchers, universities and other stakeholders, their shared feedback
and partnership, the database can be updated and improved at intervals. This will add
robustness in GROW datasets. This study will open avenues for advancement in mapping
and prioritisation of wastelands/other challenged regions with additional bio-geographical
inputs at local or national level and advanced methods of multi-criteria evaluation.
2. To upscale agroforestry in wastelands, an improved mechanism for convergence of
government schemes/programmes like MGNREGA, National Bamboo Mission (NBM),
Rashtriya Krishi Vikas Yojana (RKVY), National Horticulture Mission (NHM), Sub-Mission of
Agroforestry, Watershed Development Component of the Pradhan Mantri Krishi Sinchayee
Yojana, National Biofuel Policy, Aspirational District Programme, One District One Product,
State programmes, etc. can be developed.
3. Special provisions can be created to encourage participations of women, especially, rural
women in restoration projects. There is evidence of success of women centric wastelands
restoration and rehabilitation projects such as the model for Panchmahals, District of Gujarat
State (Sarin, M., 1993). The Panchamahal project had raised awareness amongst rural women
towards rehabilitation of degraded common land that enabled them to meet their needs
for biomass in an economicaly and ecologicaly manner.
4. A hybrid scheme/programme implementation protocol can be developed to increase
efficiency for adopting agroforestry in wastelands, as it involves diverse components viz.
livestock, agriculture, forestry, land resources. Agriculture as well as land are state subjects
and hence procedure and regulations may be streamlined for co-ordination amongst relevant
departments to implement schemes/programmes. Forest on the other hand is in concurrent
list. A hybrid implementation framework for achieving is suggested in figure 25. 5. India is a country with a strong institutional framework for protecting forests. The Forest
Department and the National Green Tribunal, are empowered by the Indian Forest Act (1927),
Wildlife Protection Act (1972), Forest Conservation Act (1980), Forest Rights Act (2006),
Environment Protection Act (1986) and many other forest regulations. States have enacted
their own relevant acts and policies, specifically related to trees outside forests (TOFs),
tree preservation and conservation and production of trees. As in many situations, trees
on private lands/ farmlands are not fully regarded as property of the landowners which
has been a bottleneck in marketing of trees grown as agroforestry. The relaxation provided
by the States in forest rules esp. exemption in transit and felling permits for trees have
encouraged tree plantation and agroforestry. However, the list of tree species are limited and
varied extensively amongst States that restrict the scale of agroforestry adoption. Under the
Sub-Mission of Agroforestry (SMAF), around 28 different species of trees are exempted from
transit and felling permits by certain States. Also, prospective state-specific agroforestry
tree species, developed agro climate-specific models by ICFRE supplemented with state-
specific felling and transit regulations was published by Indian Council of Forestry Research
and Education (https://moef.gov.in/wp-content/uploads/2023/08/FAQs-on-Agroforestry_
Released_compressed.pdf) (Annexure-V). Exemption of more tree species from forest rules,
ease in marketing of agroforestry goods can promote agroforestry across States.
6. Public private partnerships (PPPs) can be a beneficial model for scaling agroforestry in
wastelands. The types of activities for which a PPP model could be implemented are timber
production, non-timber-based forest products, fodder production, handicrafts, ecosystem
Figure 25. Agroforestry Mission Directorate
68 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) services, food, Biofuel etc. The NAP (2014) also endorses to encourage agroforestry
in PPP models for barren community land/other non-forest waste lands that provides
opportunities of economic returns and ecological services. Suitable agroforestry systems
with multipurpose tree species can be planted in wastelands that are remunerative to the
growers while providing environmental services. Bamboo based systems, energy farms
(fuel plantations and shelterbelts), silvopastoral systems, agrosilvopastoral systems with
plantation crops, fish culture in dammed sites, and the use of multipurpose trees etc.
are other examples of models that can be adopted based on local needs.
7. Extension activities are the key for imbibing an integrated development of agriculture and
forestry sector. This is important for penetration of agroforestry package of practices at
grassroot level that can support in achieving SDG-1, 13, 16 targets for socio-economic and
environmental sustainability. Concerted efforts in capacity building of extension personal
of Agricultural Technology Management Agency (ATMA) and Krishi Vigyan Kendra (KVK)
through crash courses and training modules on agroforestry practices in wastelands or
degraded lands are required. The community awareness programmes in collaboration
of SHGs, women SHGs, stewardship initiatives on agroforestry based land management
systems, and forest product value chain systems etc. can be encouraged.
8. The Centre(s) for Excellence for Human Resource Deployment in Agroforestry and its
monitoring can be established in selected ACZ. These centres can ensure imparting of
capacity building training/modules, advanced research towards land degradation issues
and stewardship over land. Strengthening agroforestry products marketing, value chain
systems and access to quality planting material can promote adoption of agroforestry.
9. A national-level survey study to understand farmers’ interests and socio-economic factors
influencing the level of adoption of agroforestry can be conducted by the Central and
State Governments.
10. A unified portal on agroforestry can be developed to enhance the scope of agroforestry
adoption in the country. It will act as a one-stop solution for all agroforestry related
information i.e programmes, state wise forest rule, updated list of tree species exempted
from transits and felling permit, etc. required by various stakeholders. This will provide
transparency, monitoring as well as ease of doing business.
Way Forward: Scaling-up GROW in a Mission Mode | 69 7References
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74 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Annexure
ANNEXURE-I: DETAILS OF AUTHORS /CONTRIBUTORS
InstitutionDetails
NITI Aayog
Dr Neelam Patel, Sr Adviser (Agriculture and Allied Sectors)
Dr Tanu Sethi, Sr Associate, (Agriculture and Allied Sectors)
National Remote Sensing Centre, ISRO,
Hyderabad
Dr T Ravisankar, Former Deputy Director, BWGS
Sh. M. Arulraj, Group Head, Bhuvan Web Services Group
Sh. Lesslie A, Head, BWS Div
Sh. Mobina Shaik, SC, BWS Div, BWSG
Sh.Shubham Das, SC, BWS Div, BWSG
Dr K Sreenivas, Deputy Director, RSAA
Sh. Rajiv Kumar, Group Head, SRLUMG, RSA
Dr Girish S Pujar, Head, LUCMD, SRLUMG, RSA
Space Application Centre, ISRO,
Ahmadabad
Sh. Shashikant Sharma, Scientists G & Group Director,
VEDAS Research Group–EPSA
Sh. R J Bhanderi, Scientist SF, CGDD/VRG/EPSA
Central Agroforestry Research Institute
Jhansi (ICAR-CAFRI)
Dr A Arunachalam, Director
Dr A.K. Handa, Principal Scientist
Sh. Suresh Ramanan S, Scientist
Indian Council of Forestry Research and
Education (ICFRE)
Dr Rajiv Pandey, Head and Scientist, Division of Forestry
Statistics
International Centre for Research in
Agroforestry, Nairobi and New Delhi
(ICRAF)
Dr Ravi Prabhu, Director- Innovation, Investment and
Impact
Dr Shiv Kumar Dhyani, Country Director India, Principle
Scientist, ICRAF
World Resource Institute, India (WRI)
Dr Ruchika Singh, Director
Dr Parth S Roy, Senior Fellow
The Nature Conservancy- India Dr Sushil Saigal
Network for Certification and
Conservation of Forests
Sh. Avani Kumar, Chairman
Sh. A. K. Srivastava, Director General ANNEXURE-II: INSTITUTIONS SUPPORTED IN GROUND TRUTHING
S. No. District Institution/ UniversitiesContributor
1 Agra
Krishi Vigyan Kendra,
Agra, UP
1. Dr. Rajendra Singh Chauhan
Sr. Scientist & Head
Krishi Vigyan Kendra, Bichpuri Campus,
Raja Balwant Singh College, Agra
283105
2. Shri Anupam Dubey, Subject Matter
Specialist, Horticulture
2 Ahmednagar
Mahatma Phule Krishi
Vidyapeeth Rahuri, Dist.
Ahmednagar
1. Dr BT Sinare, Associate Professor of
Agronomy & Officer Incharge, NARP
Agroforestry
2. Dr. K.L. Jadhav, Senior Research Asstt.
Dept. of Agril. Economics, MPKV, Rahuri
3. Shri. Ankush Ramnath Kakad, Agril.
Assistant, Dept. of Agril. Economics,
MPKV, Rahuri
4. Shri. Haribhau Sakharam Aaynar, Agril.
Assistant, Dept. of Agril. Economics,
MPKV, Rahuri
5. Shri. Ashok Muktaji Aaynar, Agril.
Assistant, Dept. of Agril. Economics,
MPKV, Rahuri
6. Shri. Vijay Trimbak Palave, Agril.
Assistant, Dept. of Agril. Economics,
MPKV, Rahuri
7. Shri. Vishal Raghunath Kedar, Agril.
Assistant, Dept. of Agril. Economics,
MPKV, Rahuri
8. Shri. Nikhil Dilip Chaubhare, Agril.
Assistant, Dept. of Agril. Economics,
MPKV, Rahuri
9. Shri. Shivaji Baban Khedkar, Agril.
Assistant, Dept. of Agril. Economics,
MPKV, Rahuri
10. Shri. Ganga Manjabapu Mane, Agril.
Assistant, Dept. of Agril. Economics,
MPKV, Rahuri
11. Shri. Raosaheb Sabhaji Godage, Agril.
Assistant, Dept. of Agril. Economics,
MPKV, Rahur
76 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) S. No. District Institution/ UniversitiesContributor
3 Bhavnagar
KVK Bhavnagar,
Sanosara
1. Dr. Nigam Shukla, Sr. Scientist & Head
2. Mr. P. M. Kyada, Subject Matter
Specialists (Ag. Engineering)
3. Mr. V. B. Savani, Prog. Assistant (Farm
Manager)
4. Dr Shashikant A Sharma (Space
Applications Centre, ISRO, Ahmedabad)
5. Dr RJ Bhanderi (Space Applications
Centre, ISRO, Ahmedabad)
4 Bikaner
Swami Keshwanand
Rajasthan Agricultural
University, Beechwal,
Bikaner, Rajasthan
1. Prof. NK Sharma, Additional Director
Research (Seeds), Swami Keshwanand
Rajasthan Agricultural University, Bikaner.
2. Dr. RS Rathore, Associate Professor
(Horticulture), Swami Keshwanand
Rajasthan Agricultural University, Bikaner
3. Dr. AS Godara, Associate Professor
(Agronomy), Swami Keshwanand
Rajasthan Agricultural University, Bikaner
4. Dr. AS Godara, Associate Professor
(Agronomy), Swami Keshwanand
Rajasthan Agricultural University, Bikaner
5 Coimbatore
Tamil Nadu Agricultual
University
Department of Soil Science & Agricultural
Chemistry. Tamil Nadu Agricultural University,
Coimbatore
6 Erode
Tamil Nadu Agricultural
University
S. Radhakrishnan, Professor (Forestry),
Department of Silviculture, Forest College
& Research Institute, (FC&RI). Tamil Nadu
Agricultural University, Mettupalayam–641301
7 Sidhi
Krishi Vigyan Kendra,
Sidhi
1. Dr. Dhananjai Singh, Scientist, Agronomy,
KVK, Sidhi
2. Smt. Priya Chouksey, Programme
Assistant, Computer Science, KVK, Sidhi
8 Kamrup
Assam Agricultural
University
1. Mrs. Ranjita Bezbaruah, Junior Scientist &
OIC, AICRP on Agroforestry. Horticultural
Research Station, Kahikuchi, Assam
Agricultural University
Annexure  | 77 S. No. District Institution/ UniversitiesContributor
9 Kadapa
Dr YSR Horticultural
University, Andhra
Pradesh
1. Dr T Suseela, Sr Scientist & Head
2. Banana Research Station, Pulivendru
3. Dr M Raja Nail, Assciate Professor
4. College of Horticulture, Anantharajupeta
5. Dr VNP Sivarama Krishna, Assiatnat
Professor, College of Horticulture,
Anantharapeta
6. Dr N Srividya Rani, Scientist & Head,
KVK, Vonipenta
7. Dr G Sandeep Naik, Scientist,
Horticultural Reaserch Station,
Anantharajupeta
8. 6. Dr. D. Sreedhar, Senior Scientist
(horticulture), Dr. Y.S.R Horticultural
University.Horticultural Research Station,
Anantharajupeta
10 Jammu
Sher-e-Kashmir
University of
Agricultural Sciences
and Technology of
Jammu
1. Dr. Sanjay Khajuria, Senior Scientist
Agroforestry. S.K. University of
Agricultural Sciences &Technology of
Jammu-180009
2. Dr. Meenakshi Gupta, Assistant Professor,
Agroforestry, Sher-e- Kashmir University
of Agricultural Sciences and Technology,
Jammu,
3. Lalit Upadhyay, Scientist Agroforestry,
SKUAST Jammu
11 Malkanagiri
Odisha University
of Agriculture and
Technology
1. Dr SR Dash, Sr. Scientist and Head,, KVK,
OUAT, Malkangiri, Odisha
2. Mr Nigamananda Behera, Subject
Matter Specialst, Agronomy, Scientist (
Agronomy)KVK, OUAT, Malkangiri, Odisha
3. Tanmaya Kumar Behera, Farm Manager (
Horticulture), KVK, Malkangiri
78 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) ANNEXURE-III: AGROFORESTRY SYSTEMS SUITABLE FOR

WASTELAND GREENING IN DIFFERENT AGROCLIMATIC ZONES
S. No.
Agro-climatic zones
State
District
Potential MPTS
Potential Agroforestry System
I
Western Himalayan division
Jammu & Kashmir

(J& K), Himachal Pradesh and Uttarakhand
Jammu
Populus deltoides, Salix alba, Melia azedarach, Toona ciliata, Grewia spp.
1.
Mulberry based silvipastoral system (Napier-Bajra hybrid/
Seteria
),
2.
Apple based agroforestry system (fodder and agricultural crops like beans and vegetables),
3.
Apricot based agroforestry system (Peas, Barley and mustard)
II
Eastern Himalayan division
Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, Tripura, West Bengal
Kamrup (Assam)
Bamboo, Parkia roxburghii, Gmelina arborea, Arecanut, Acacia mangium
1.
Bamboo based agroforestry system (Pineapple, Banana, Papaya and lime)
III
Lower Gangetic plain region
West Bengal
Bankura (WB)
Anthocephalus cadamba, Eucalyptus tereticornis, Acacia auriculiformis, Gmelina arborea, Tectona grandis, Zizyphus spp., Azadirachta indica, Dalbergia sissoo, Shorea robusta, Swietenia mahogoni, Diospyrus, Melanoxylon, Madhuca indica, Terminalia arjuna, D. latifolia, Pongamia pinnata,
2.
Kadamb based agroforestry system (Mustard, Lentil and Vegetables)
IV
Middle Gangetic plain region
Uttar Pradesh, Bihar
Banda, Sonbhadra (UP)
Tectona grandis, Dalbergia sissoo, Acacia senegal, Acacia nilotica, Bamboo, Emblica officinalis, Azadirachta indica, Pongamia pinnata, Anogeissus latifolia, Melia dubia, Leucaena leucocephala,
1.
Teak based agroforestry system (Wheat, Gram, Sesamum or fodder crops)
2.
Neem based agroforestry system (fodder crops)
3.
Aonla-based agri-horticulture system
Annexure  | 79 S. No.
Agro-climatic zones
State
District
Potential MPTS
Potential Agroforestry System
V
Upper Gangetic plain region
Uttar Pradesh
Lucknow (UP)
Tectona grandis, Dalbergia sissoo, Anthocephalus cadamba, Bamboo, Emblica officinalis, Azadirachta indica, Pongamia pinnata, Terminalia arjuna, Melia dubia, Leucaena leucocephala,
1.
Aonla-based agri-horticulture system, Poplar-based agri-silviculture system,
2.
Eucalyptus-based agroforestry system
VI
Trans-Gangetic plain region
Chandigarh, Delhi, Haryana, Punjab, Rajasthan
-
Tectona grandis, Dalbergia sissoo, Anthocephalus cadamba, Populus spp, Bamboo, Emblica officinalis, Azadirachta indica, Pongamia pinnata, Terminalia arjuna, Melia dubia, Leucaena leucocephala, Casuarina equisetifolia
1.
Silvopastoral with
L. leucocephala

+ Napier grass; and
Albizia

amara

+ grass+ stylo; Popular based agroforestry system (wheat and mustard)
VII
Eastern plateau and hill region
Chhattisgarh, Jharkhand, Madhya Pradesh, Maharashtra, Odisha, West Bengal
Sidhi (MP)
Tectona grandis, D. sissoo, A. senegal, A. nilotica, A. cadamba, L. leucocephala, A. excelsa, G. arborea, Emblica officinalis,
1.
Amla based agroforestry system,
2.
Boundary plantation of timber species
VIII
Central plateau and hill region
Madhya Pradesh, Rajasthan, Uttar Pradesh
Bikaner (Rajasthan)
Acacia tortilis, D. sissoo, A. nilotica, P. cineraria, Azadirachta indica, Tecomella undulata, Pithecelobium dulci, Salvadora persica, Zyziphus nummularia
1.
P. cineraria
+ pearl millet/cluster
bean/mothbean/medicinal plants/groundnut (Agri-silviculture system)
2.
Ber + kharif crops (Agri-horticulture)
80 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) S. No.
Agro-climatic zones
State
District
Potential MPTS
Potential Agroforestry System
IX
Western plateau and hill region
Madhya Pradesh, Maharashtra
Ahmednagar (Maharashtra)
T. grandis, Terminalia paniculata, T. chebula, Madhuca indica, M. dubia, L. leucocephala, T. arjuna, Eucalyptus, D. sissoo, D. latifolia, P. pinnata, Bamboo spp.
1.
Bamboo based agroforestry system; Pongamia based agroforestry (Chickpea, Onion)
2.
Boundary plantation of timber species
X
Southern plateau and hill region
Andhra Pradesh, Karnataka, Tamil Nadu
Erode (TN)

Casuarina equisetifolia, Eucalyptus, Anthocephalus cadamba, Melia dubia, Bombax ceiba, Azadirachta indica, Leucaena leucocephala, Albizia lebbeck, Pongamia pinnata, Terminalia arjuna, Tamarindus indica
1.
Bund planting of
Albizia

lebbeck
,
Ailanthus

excelsa
,
Hardwickia

binate
2.
Intercropping tapioca, groundnut, sesame with
E. tereticornis

Woodlots of
Ceiba

pentandra

Silvipasture consists of
Acacia

leucophloea
with fodder sorghum,
Cenchrus
spp.
Melia dubia
based
Agroforestry system.
Annexure  | 81 S. No.
Agro-climatic zones
State
District
Potential MPTS
Potential Agroforestry System
XI
Southern plateau and hill region
Andhra Pradesh, Karnataka, Tamil Nadu
YSR Kadapa (AP)
Casuarina equisetifolia, Eucalyptus, Melia dubia, Dalbergia sissoo, Azadirachta indica
,
Albizia
procera, Acacia nilotica, Azadirachta indica, Capparis deciduas, D. latifolia, Pongamia pinnata, L. leucocephala
Chitradurga (Kar) Karnataka
Tectona grandis, Melia dubia, Anthocephalus cadamba, Dalbergia latifolia, Casuarina equisetifolia, Acacia mangium, Acacia auriculiformis, Leucaena leucocephala, Sesbania grandiflora, Acacia nilotica, Pongamia pinnata, Terminalia arjuna, Eucalyptus, D. sissoo, P. pinnata
Trees with fruit crops (
Mangifera indica,
Manilkara zapota, Psidium guajava, Citrus limon
)
XII
East coast plain and hill region
Andhra Pradesh, Odisha, Puducherry, Tamil Nadu
Malkangiri (Odisha)
A. mangium, T. grandis, Eucalyptus spp., C. equisetifolia, Sesbania grandiflora, G. arborea, D. sissoo, D. latifolia, P. pinnata, T. arjuna
Acacia mangium
based agroforestry
(Maize, Ragi, Seasamum and Vegetables); Homegardens
82 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) S. No.
Agro-climatic zones
State
District
Potential MPTS
Potential Agroforestry System
XIII
West coast plain and hill region
Goa, Karnataka, Kerala, Maharashtra, Tamil Nadu
Coimbatore (TN)
Casuarina equisetifolia, Eucalyptus, Anthocephalus cadamba, Melia dubia, Bombax ceiba, Artocarpus fraxinifolius, Azadirachta indica, Leucaena leucocephala, Albizia lebbeck, Hibiscus tiliaceaous, Calophyllum ionophyllum, Pongamia pinnata, Terminalia arjuna, Tamarindus indica, Prosopis juliflora, Acacia leucophloea
1.
Bund planting of
Albizia

lebbeck
,
Ailanthus

excelsa
,
Hardwickia

binata
2.
Intercropping tapioca, groundnut, sesame with
E
.
tereticornis

Woodlots of
Ceiba

pentandra
3.
Silvipasture consists of
Acacia

leucophloea
with fodder sorghum,
Cenchrus spp.
4.
Melia dubia
based agroforestry system
XIV
Western plain and hill region
Rajasthan
Jodhpur
Prosopis juliflora, Acacia tortilis, D. sissoo, A. leucophloea, A. nilotica, P. cineraria, Azadirachta indica, Tecomella undulata, Pithecelobium dulci, Salvadora persica, Zyziphus nummularia
1.
P. cineraria
+ pearl millet/cumin/
chilies/moth/cluster bean (Agri-silviculture system)
2.
Ber + kharif crops (Agri-horticulture).
3.
C. ciliaris
dominant silvi-pastoral
system
Annexure  | 83 ANNEXURE IV: HELP DOCUMENT FOR “WASTELANDS GREENING WITH
AGROFORESTRY - SUITABILITY MAPPING” PORTAL
1. User Roles
There are three user roles provided for login in the portal:
a. Central
i. The user can visualise all states/districts data.
ii. The user can view the statistics for State-wise, District-wise as well as Area to
Class-wise for district level.
b. State
i. The user can visualise all districts data of the user’s authorised state.
ii. The user can view the statistics for District-wise as well as Area to Class-wise
for district level.
c. District
i. The user can visualise only the district’s data for which the user is authorized.
ii. The user can view as Area to Class-wise for the district.
2. Login
The login page on the portal will look like given below.
1. Upon click on “Login” the login page appears on the screen.
1
1
84 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) 2. After the authorised user logins, the user will be able to view the Agro Forestry
Suitability Map can be visualised. Along with this, user will be able to perform Query
of ASI and fetch statistics for Area of Interest (AOI).
2
3. Visualisation of statistics data
?For user with role as “Central”, statistics of all states can be viewed.
1. Without selection of any state/district pan-India statistics can be obtained.
2. Click on statistics for viewing the statistics on pan India state-wise.
1
2
?For user with role as “State”, statistics of all districts of the state the user belonging
to, can be viewed. Annexure  | 85 1. Upon selection of any state and district kept as “All”, the complete State’s
suitability area statistics can be obtained by clicking of “view statistics”. Here
Gujarat state is selected and district value is kept as “All”.
2. Click on statistics for viewing the statistics of the state district-wise.
1
2
For user with role as “District”, statistics of the district the user belonging to, can be
viewed.
1. Select any state and a district, and click “view statistics” to obtain the statistics
under each suitability category in the district. Here Bihar state is selected and
Bhojpur is selected.
2. Click on statistics for viewing the statistics of district class-wise.
86 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) 1
2
4. Map visualisation
1. For visualising state data, select a state from the dropdown and keep district value
as “All”. Here, Bihar state has been selected and district value is kept as “All”.
2. Upon clicking on “View Map” button, agro forestry suitability map can be visualized
on the map.
3. The agro forestry suitability map of Bihar state appears on the map.
4. “View Map” button changes to “Remove Map” button. Once the user clicks on “Remove
Map” the map gets removed from the map.
5. Legend appears on the left panel.
Annexure  | 87 1
2
4
3
5
6. For visualising district data, select a state as well as a district from the dropdown.
Here, Bihar state and Begusarai district has been selected.
7. Upon click on the “View Map” button, the agro forestry map appears on the map
8. “View Map” button changes to “ Remove Map”.
9. District boundary will be highlighted when map for district is to be visualised.
88 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) 5
6
7
8
5. Statistics for Area of Interest (AOI) of the user.
1. User can draw AOI on the map. Upon clicking on “Start to identify fetch area”, the
user will be able draw an AOI upon the map. And to finish the drawing click on
“Finish Drawing”.
2. Below an example of AOI is drawn on the map.
3. Upon clicking on the “Analyze” button, the analysis is performed.
4. Once the analysis is over, it will be displayed in the popup.
Annexure  | 89 2
3
4
1
6. Pilot Study – This module is available for all authorized user roles.
1. State, district and layers (ground truth points, agroforestry suitability map, district
boundary) can be selected for visualising the data. Here, Rajasthan state, Bikaner
district and Point of Interests layer has been selected.
1
2
2. On click on the point data upon the map, information related to agro forestry suitability
for that location comes in the popup.
3. Similarly, other layers also can be visualised. Here, Rajasthan state, Bikaner district
and District AFSI layer has been selected.
4. The agroforestry suitability map as per pilot study will appear on the map.
90 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) 3
4
5. Complete data for the layers for the pilot study can also be viewed by selecting the
check boxes.
6. On click of “i” button; user can enable viewing information on click in the ground
truth point.
5
6
7
7. Information of the point data appears as a popup.
8. Similarly pan India agro forestry suitability pilot study maps can be visualised by
clicking on the check box.
Annexure  | 91 89
9. Agroforestry suitability maps user pilot study appears on the map.
92 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) S.No. Name of
States/UTs
Status
1. Andaman
& Nicobar Islands
• Transit Permit is required for transit of forest produce in A & N
Islands and no exemption for any sps. has been provided in the
Regulation.
2. Assam• No Felling Permission (FP) is required for home grown bamboo.
• No Transit Pass (TP) is required. Certificate from Gram
Panchayat is required.
3. Andhra Pradesh • No FP. All spp exempted.
• No TP, All spp exempted.
4. Arunachal Pradesh • No FP is for bonafide use except commercial use
• No TP is required except commercial and other use
5. Bihar• Tree sps.currently exempted from Transit Regulations (as on
27.02.2009) Poplar, Eucalyptus, Kadamb, Gamhar, Mango,
Litchi, Toddy palm, Khajur, Bamboo sps (Except Dandrocalamus
strictus), Semul.
• Some more sps. are in process to be exempted.
6. Chandigarh• No interstate transit permit is being issued by Forest
Department as no forest check posts have been established.
• The permission for felling of trees on private /non-forest land is
given only in two cases, i.e either for any development work or
trees are dangerous to human life or property. As such no tree
sps. is exempted under this.
7. Chhattisgarh• Timber sps. that have been exempted from transit regulations
are Poplar, Casuarina, Su-babul, Israili babul, Vilayati babul,
Manzium , Nilgiri
8. Delhi• Since land is a premium commodity in Delhi, farmers generally do
not practice agroforestry here. Sps. like Poplar, Kikar and
Eucalyptus have been proposed for exemption. So FP is required.
9. Goa• No FP. Omitted Bamboo from the definition of tree.
• No TP. All types of bamboo grown in private areas (non forest
areas) will not fall under the purview of forest produce and hence
transit permit for bamboo felled from private areas are exempted
10. Gujarat• Nilgiri, Subabul, Saru, Champa, Laxmanfal, Ramfal, Sitafal,
Asopalav, Pendula, Nagkesar, Nagchampha, Falsa, Ingorio/Angarea,
Kamrakh, Kadhipatta, Limbu, Chikotru, Bijoru/Turanj, Narangi,
Mausambi, Maharuk, Rukhdo, Motoarduso, Limdo,Neem, Bakan,
Bakan, nim, Irani nim, Nimbara, Limbara, Mahanim, Mahogony,
Bordi, Bor, Khati bor, Ghulbor, Liehi, Lilchi, Aritha, Aritha, Amba,
Kadvo Saragavo, Saragavo, Agathin, Segto, Agastin, Desi Baval,
Goras amlili, Gando baval, Ganda baval , Botlle Brush, Jamphal,
Dadam, Chikoo, Boralli/Mursal/Vakal/ Varasd/ Bakul, Saptaparni,
Champo, Safed champo, Liar/ Nani/Gundi/ Nagod, Nirgund/
Nargundi, Lingur Nirgudi, Ambla, Fanas, Pipli/papri, Shetur, Haredo,
Harero, Poplar, Golden cane palm, Oilplam
ANNEXURE - V GREENING AND RESTORATION OF WASTELAND (GROW) -
SUITABILITY MAPPING
Annexure  | 93 11. Himachal
Pradesh
• Kala Siris/Ohi/Sriris, Kachnar/Karial, Safeda, Kimu/ Chirmu/Shahtoot/
Tut/Mulberry, Poplar, Indian Willow/Biuns,Kuth, Kala Zira, Japanese She-
hoot/paper mulberry, Paik/Koi/ Kosh/Kunis/ Kunish/Nyun, Khirk/ Khad-
ki, Darark/Bakin, Fagoora/ Phagoora/Tiamble/timla/ tirmal/anjiri/ clus-
ter fig/goolar, Toon, Jamun, Teak/Sagun/Sagwan, Arjun, Semal,
Shalmaltas, Bihul/Beul/Bhimal/Bhiunal/Dhaman, Paza/Padam, Kamala/
Raini/Rohan/Rohini/Sinduri, Aam (Mango wild variety), Rishtak/Ritha/
Dode
12. Haryana • Some sps. are exempted from regulations under Punjab Land Preservation
Act, 1900. These are Eucalyptus, Poplar, Ailanthus, Eucalyptus and Acacia
tortilis. There is no transit rules applied for timber sps.
13. Jharkhand • Eucalyptus (Safeda), Poplar, Casuarina, Maha Neem, Baken Kadmb,
Subabool, Silver Oak, Israeli Babool Vilayati Babool, Babool, Plam, Ber,
Munga, Mulberry, Guava, Nimboo, Santra, Mosambi, Ashok.
14. Jammu
& Kashmir
• Kikar, Bel, Siris, Champ, Neem, Malugarh, Kakrad, Palas, Amaltus/
Karangal, Sisoo/Tali, Dhamman, Nili Gulmohar, Akhrot (khod), Kehbal
jhingar, Baronkal, Bilati Kikar, Safeda, Poplar, Robin, Chitta banddha,
Rondu banddha, Sagwan, Arjun, Beheda, Tun/Toon, Bana, Dhoi.
15. Karnataka • Acacia hybrid, Acacia mangium, Tree of Heaven, Rain tree, All Cassias
except Golden Rain tree, Cashew, Christmas tree, Arecanut, Casuarina,
India Beef wood, Lemon, Ornage, Coconut, Coffee, Mayflower, Indian
coral tree, Eucalyptus, Glyceridia/Quick stick, Silver Oak, Rubber,
Jacaranda, Sausage tree, Subabul, Umbrella tree, Sapota/Chikoo fruit,
Melia, Indian Cork tree, Drumstick, Mulberry, Curry leaf tree, Peltoform,
Purple bauhinia, Pagoda tree, False Ashoka, Guava, Sesbania,
Hummingbrid tree, Paradise tree, African tulip , Tabebula, Trumpet tree.
16. Kerala • Species for Ply wood Vellappine, Kurangandi/Narivenga/Mundani,
Karakily/Kalpine, Kulamavu/Kulirmavu/Ooravu, Pali/Palendinjan, Kulavu,
Red Cedar, Thellipine/Undapine, Poon/Punna/Punnappa, WVediplavu/
Mullampali, Charu, Pothundi/Perunthondi, Cheeni, Nedunar, Vallabham/
Varangu, Chorapine, Chemmaram, Champakam, Cherukonna, Mulliam,
Neeramruthu, Peenary, Kumbil, Veembu, Gnavel, Kattunelli, Vakka,
Thavala,
• Species for Matchwood : Aspin/Kanala/Nasakam, Elavu/Poola, Pala/
Mukkampala,
• Species for Bobbin wood : Vellakil, Manjakdambu,
• Species for pencil wood: Venkotta, Perumtholi/Poochakadmbu,
Attuthekku/Cadambu,
• Species for packing wood: Kara/ Bhadraksham, Amazham, Aval,
Arayanjili, Kalaveppu/Malaveppu, Vatta/Uppathi,
• Fire wood : Palvu (Jack), Parankimavu (cashew), Kattadi (Casuarina),
Poovarasu (Poovarasu), Mavu (Mango tree), Puli (Tamarind tree),
Nattupunna (Nattupunna), Aanjili (Aanjili), Vaka (Vaha- species), Poovam,
(Poovam), Konna, Thanni (Thanni), Uthi (Uthi), Aal Jatikal (Ficus species),
Matti, Murukku, Elappu (IIoia) and Kodamuli (Koadampuli).
17. Lakshadweep• No FP. IFA or any Forest Act is not enforced in Lakshadweep. Also,
Bamboo is not grown anywhere in Lakshadweep. Therefore amendment
in IFA or any Forest Act does not arise in this state.
• No TP.
94 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) 18. Madhya
Pradesh
yNeelgiri, Casuarina , Poplar, Subabul, Israili Babul, Vilayati Babul, Australian
Babul, Babul, Khamer, Maharukh, Kadamb, Cassia siamea, Gulmohar,
Jacaranda, Silver oak, Plam, Ber, Mulberry, Katahal, Amrood, Nimbu, Santra,
Mussambi, Munga, Molshri, Ashok, Putranjiva, Imli, Jamun, Mango,
Saptparni, Kaitha, Jungle Jalebi, Petltaphorum, Neem, Bakain, Sissoo,
Karanj, Palash, Safed Sirus, Pipal, Bargad, Gular, Rubber, Semal, Kapok,
Chirol, Gliricidea, Rimjha, meithi Neem, Gurhal, Jasoun, Conifers, imported
Timber Species.
19. Maharashtra yNilgiri trees, Babhul, Subabhul, Prosopis, Ashok, Drumstick, Sindi, Orange,
Chiku, Bhendi, Acacia, Poplar, Lac, Casuarina equisetifolia, Rubberwood
20. Meghalaya yMeghalaya being a Hilly state, there is no Agroforestry at all, since
percentage of states land covered by agriculture is very small. If any blank
inter- state movement o timber is permitted, state will lose meagre resource
of forests under control of the State Government.
21. Mizoram yKothal, Tung, Eucalyptus spp., Mulberry, Neem, Rubber tree, Imli, Silver
Oak, Subabul, Mango, Guava, Coconut, Citrus, Areca nut
22. Manipur yNo Felling Permission (FP) is required
yNo Transit Pass (TP) is required for home grown within state.
yTP is required outside state
23. Nagaland yAam, Korei, Walnut, Neem, Alder, Manipur Sim, Kadam, Hollock, Khokan,
Teak, Gamari
24. Odisha yBada chakunda, Sana Chakunda, Jhaun, Sliver Oak, Patas/Nilgiri, Sunajhari/
Acacia, Subabul, Kaitha, Ambada, Batapi, Oau, Sajana, Karamanga, Sahada,
Plam tree, Debadaru, Bhersunga, Gohira, Giliricidia, Paladhua, Coconut
25. Punjab y“Forest produce” shall specifically mean timber (converted or otherwise),
firewood, charcoal, katha and resin, but shall not include Non Timber
Forest Produces (NTFPs) like bamboos and agro-forestry species such as
Populus spp., Eucalyptus spp., Melia azedarach (Drek), Morus alba
(Mulberry), Leucaena leucocephala (Subabul), Casuarina spp., Grevillea
robusta (Silver Oak), Acacia mangium, Melia dubia (Malabar Neem),
Prosopis cineraria (Khejri), Salix alba (Indian willow), Gmelina arborea
(Gamari) or any other species declared by the State/authorized agency as
agro-forestry species from time to time.
26. Rajasthan yCasuarina, Australian babul, Khamer, Caaia Siamea, Gulmohar, Jaccaranda,
Silver oak, Plam, Ber, Mulberry, Katahal, Amrood, Sehjana, Molshri, Ashok,
Putranjiva, Imli, Jamun, Saptarni, Kaitha, Jungle Jalebi, Petaphorum,
Bakain, Karanj, Safed Sirus, Semal, Kapok, Churel, Mithi neem
27. Sikkim yNo permission for felling of trees on any private or Forest land has been
granted.If anyone wishes, he have to apply to Block Officer.
28. Tamil Nadu yMesquite, Casuarina, Subabul, Palmyrah, Dadops, Umbrella thom, White
Back Acacia/Panicled Acacia, Maharuch, Maharukh/East India Walnut/
Siris, Cashew, Kadam, Jack, Neem/Margosa, Red silk cotton/Kapok,
Sappan, Cassia, white silk cotton tree/kapok, Sissoo, Coral tree, Eucalyptus,
Gamari, Rubber, Sea Hibiscus, Mohua, Mango, Persian Lilac, Malabar
Neem, Morinda/Suranji, Manila/Tamarind, Pongam/Indian Beach, Rain
tree, Mahogeny, Jamun/Indian cherry, Tamarind, Esperanaza, Indian Portia
tree/Indian Tulip, Red Cedar/Toon, Silver Oak.
Annexure  | 95 Source: FAQs on Agroforestry. Published by Indian Council of Forestry Research and Education on 16-08-2023.
29. Telangana yEucalyptus, Neelagiri, Jama oil, Casurina, Sarugudu, Sarvi, Saru
Poplar, Subabul, Israeli Babool, Seema, Thumma, Australian babul
Gummaadi teak, Pddamanu, Kadamb, Seema/ Tangedu, Jacaranda, Silver
oak, Regu, Ber Mulberry, Jama, Guava, Orange and related species,
Mungam, Ashok/Naramamidi, Mahaputrajivi/Putrajeevi, Edakulapala,
Turakavepa, Kanuga, Rubber/ Seemamarri, Tella Tumma , Gliricidea/
Seema/Kanuga, Tella Tumma, Kaivepaku, Mandara, Conifers (chir, Kail,
Deodar, Pine species), Tati, Tadi, Palmyrah, Sapota, Coconut, Kobbari,
Tenkai, Cashew, Jeedimamidi, Semma, Chinta, Raint ree, Nidragannreru,
Mango, Mamidi, Panasa, Jackfruit.
30. Tripura yTree species like Mango, Litchi, Drumstick, Guava, Rubber and bamboo
are exempted from extraction from private land. Bamboo sps. have been
exempted from transit permits both from Private and Forest land.
Transport of Timber is also permitted.
31. Uttar
Pradesh
yAru, Casuarina, Jangal Jalebi, Poplar, Babool, Vilayati Babool, Rabania,
Siris, Su-babool, Kathber, Jamun, Eucalyptus, Dhak Palas, Paper Mulberry,
Ber, Sainjana, Shah toot, Mango (Desi, Tukhmi or Kalmi)
32. Uttarakhand y27 tree species have been exempted from the provision of Tree protection
Act, 1976. This includes fodder and small timber species that are being
used in small scale industries, animal husbandry, agricultural implements
and allied activity. Other 07 tree species like Walnut , Neem, Oak , Ficus
(Peepal and Banyan) and Deodar have been placed in the restricted
category and felling permission can be granted only in case of dead or
dangerous trees.
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FAQS ON AGROFORESTRY
96 | Greening and Restoration of Wastelands with Agroforestry (G.R.O.W) Designed by: Printed by
Communication Cell, NITI Aayog