rural-informatics in decision making j. adinarayana agro-informatics division

Rural-Informatics in Decision Making

J. AdinarayanaAgro-Informatics Division

Centre of Studies in Resources Engineering

Indian Institute of Technology - Bombay Powai, Mumbai - 400 076, India

DA-IICT Workshop, 16-17 December 2004

Executive Approach (Top-down)

Prescriptive planning

• Client : Decision-maker at district/sub-district

level (Rural Extension Community)

Rural Planning


• Spatial Decision Support System for rural Land Use Planning (SDSS/LUP)

Part of UNDP/DST Joint Program on ‘GIS based technologies for local level

development planning’

___________________________________________________________________

A spatial decision support system for land use planning at district level in India, J. Adinarayana, S. Maitra and David Dent, The Land : Journal of the International Society of Land Use, FAO/NRI-UK, 4.2, 111-130 (2000).


Land evaluation for changes in landuse Economic, Conservation and Management (implementable) options for the existing LUTs (land use types) – minor changes New LUTs and infrastructures (radical options) – major changes

Area selection for schemes Watersheds for interventions Priority sub-watersheds Critical sectors within sub-watershed

Applications

Site selection for infrastructure Conservation infrastructures Water resources infrastructures


Data model : vector/raster Input requirement : maps (polygon/line/point) & rational tables their codes, entities, attributes, source of data, method to generate the map/table, determination of attributes, etc. Processing : derivation of related attributes, maps using different physical methods/criteria

Module Description

(logical) Database DesignApplication DescriptionDataflow DiagramGIS Function ListEntity-Relationship-Diagram


SDSS/LUP developed in ArcView (Vector-based model)• Series of views ‘input maps and tables; derived maps; ratings’

User Interface


Kolar district


Watersheds D

A-IIC

T W

orkshop, 16-17 D

ecember 2004

Meters

DA

-IICT

Work sh

o p, 1 6-17 Decem

b er 20 04

Priority ratingsNWDPRAWatershed Physical

characteristicsSocio-economic characteristics

Bairasagara

Chalamena Halli

Chonduru

Peresandra

Priority ratingsSub-watershed

RP_E

Soil erosion intensity

Sediment yieldindex

Extent of degraded lands

RP_N

RP_W

1432

2

314

3

1

2

1

2

3

2

3

1

Scenarios / Multi-criteria


Land Evaluation for changes in Land Use

Immediate and useful service

• Land Use Sustainability Assessment (LUSA) Identify Threats and their Indicators Rank indicators according to ease of obtaining data Arrive at a six-fold land use capability classification Results with three degrees of confidence:

<C (1) – where, C is land use capability class, and 1 is degree of confidence

Threat Identification and Management (TIM) concept

• Transfer Functions Modeling large scale soil and land data from small

scale survey data and remote sensingExample : Crop growth model – soil series > soil texture and thickness > available water capacity


Better service in future

• Automated Land Evaluation Systems - ALES (Rossiter & van Wambeke, 1997)

• WebLUP – for efficient and easy way to handle the spatial data in Internet

for rural development

Land Evaluation for changes in Land Use


WebLUP – Web based rural land use planning Proposed System

selection of watersheds for schemes displaying maps making useful queries/decisions

Using HTML image maps

Demo for Kolar District, Karnataka watershed/sub-watershed selection LUSA (Land Use Sustainability Assessment)

Dynamic/interactive mapping/queries with web-components of GRAM++ GIS (http://www.csre.iitb.ac.in/gram++/)

Proposed to use Advanced Network (Example: APAN http://apan.net/) for reaching the Rural Extension Community

Mock-Up (http://www.csre.iitb.ac.in/adi/dummy-webpage/choosescheme.htm)


http://www.csre.iitb.ac.in/gram++/

http://apan.net/

GIS-based decentralized planning at district/sub-district level

• Sponsored Research from the National Informatics Centre, Ministry of Information & Communication Technology, Government of India under their DISNIC Program – ‘turning data into information’

• Main Tasks :

(1) Generation of district-level spatial information system

(2) Generation of Village-Level Information System (VLIS)

- integration of census-data with spatial information

(3) Views/Scenarios for decentralized planning

___________________________________________________________

Village level information system – a tool for decentralized planning at district level in India, J Adinarayana and F Joseph Raj , Paper to be appeared in the Journal of Environmental Informatics, International Society for Environmental Information Sciences, Canada DA-IICT Workshop, 16-17 December 2004

Advances in Agro-Informatics in Japan On-Site Data Input by Mobile Phone

Mobile Phone with Web browser and e-Mail

Slide from Dr Seishi Ninomiya, NARC, Japan

Advances in Agro-Informatics in Japan Field Monitoring Server with Wireless LAN

SOHO

Greenhouse

Greenhouse

Greenhouse

Weather station

home/office home/office

Internet provider

Giga-bit network

CATV

Weather station

Mountain

Weather station

Slid

e fr

om D

r Se

ishi

Nin

omiy

a, N

AR

C, J

apan

Advances in Agro-Informatics in Japan Efficient Data Acquisition

e.g. Growth Model


Advances in Agro-Informatics in Japan Ag e-commerce

• B to C

– A farmer to consumers

– Most of Web direct marketing in Japan are not successful

– Too many to find out

– Fragile and weak supply management

– Virtual Mall may be promising

• B to B promising?

– e-market place to bridge farmers and wholesalers or retailers

– Several big companies have now taken part in


Web Marketing with Remote Camera

• Live Growth Process to Consumers

• Virtual Farming by Consumers


Advances in Agro-Informatics in Japan Product Identification System

• Identification of Production Area and Farmer

• Reliability on Quality and Tight Relationship

• Link to Farm Diary (Agro-chemicals, Organic Information etc.)


Precision Agriculture

• SMART Farming Technologies (Scientific, Marketable, Affordable, Reliable & Time-saving)

- Towards this end, the integration of remote sensing, global positioning system, geographic information system, ground sensors, and machinery systems are the core technologies for database generation, analysis and information extraction for decision support.


Challenges in GST for Rural Sector

• Voluminous RS Data• Automation

• Metadata Services

• Distributed Collaboration

• More real-time applications

• Open GIS (Internet makes the GIS an open system)


Lessons Learnt / Experiences

1) Develop tool/package in conjunction with theuser community

2) Develop simple SDIs / DIs and assist the users in their own existing decision making processes

3) Identify the clients / users – involve/train them

4) Conceptualize the problems (needs assessment)

5) Integrate IT with Knowledge-based systems for technology transfer


Thank you

[email protected]/adi/

Shift GST from Doers to Users


SDSS/LUPAn automated system applied to the spatial problems at the district/sub-district level, that would assist the decision-maker at these levels to make zoning (designating uses in different land areas) and interventions decisions


Fundamental data availability in the districts

Digital 1:50 000 scale Survey of India topographic maps, contour interval 20m An overlay of district and block boundaries with village centres identified as points Social, economic and agricultural census data (e.g. proportion of irrigated land) held in tabular format by administrative unit Agro-climatic data, held in tabular format by point. There is an India Meteorological Department station in each district and a much more intensive network of rainfall stations. At a more generalized level, the country has been divided into agro-ecological zones that are matched with crop requirements. Geological Survey and, sometimes, geomorphological maps at 1:250 000 Land cover interpretation of 1:250 000 satellite imagery All India Soil Survey maps at 1:250 000, sometimes at 1:100 000 Nation wide Census of India data of 1991 in digital form by the NIC. Latest 2001 Census data is available in some pockets from the NIC.

Census GIS - an interactive thematic census data (of 2001) on demographic details online for district and state level


Precision Agriculture

• SMART Farming Technologies (Scientific, Marketable, Affordable, Reliable & Time-saving)

- Towards this end, the integration of remote sensing, global positioning system, geographic information system, ground sensors, and machinery systems are the core technologies for database generation, analysis and information extraction for decision support.

• Asian Conference on Precision Agriculture http://www.macres.gov.my/acpa/index.htm


Degree of Confidence in Data Source


Mock-Up of LUSA Framework


Procedure to allocate the land class to a particular patch of land

Start at the top left corner with the first limiting factor, length of growing season, slope. Scan horizontally to locate the appropriate limiting value, say growing season is 250 days, stay in Column A. Now move down to second limitation slope, so that if the slope in question is 2, stop in the second column. The land class cannot be better than B-Arable. Now move down to the third limitation, past erosion¸, which might be assessed as ‘nil’. This favorable characteristic does not improve the capability class; slope remains limiting. Now move down to the third limitation, wetness, which might be assessed as ‘wet for short periods during the growing season’. Scan horizontally to find the appropriate degree of limitation, which is in the third column so the capability class cannot be better than C. Continue stepwise downwards, considering each limiting factor in turn. The final classification is determined by the single most limiting factor. The sub class may denote the more limiting factors, say C slope and available water capacity.


rural-informatics in decision making j. adinarayana agro-informatics division

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