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Rural-Informatics in Decision Making J. Adinarayana Agro-Informatics Division

Rural-Informatics in Decision Making J. Adinarayana Agro-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. Rural Planning. Prescriptive planning.

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Rural-Informatics in Decision Making J. Adinarayana Agro-Informatics Division

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  1. Rural-Informatics in Decision Making J. Adinarayana Agro-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

  2. Rural Planning Prescriptive planning Executive Approach (Top-down) • Client : Decision-maker at district/sub-district • level(Rural Extension Community) DA-IICT Workshop, 16-17 December 2004

  3. Part of UNDP/DST Joint Program on ‘GIS based technologies for local level development planning’ • Spatial Decision Support System for • rural Land Use Planning (SDSS/LUP) • ___________________________________________________________________ • 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). DA-IICT Workshop, 16-17 December 2004

  4. Applications Area selection for schemes • Watersheds for interventions • Priority sub-watersheds • Critical sectors within sub-watershed Site selection for infrastructure • Conservation infrastructures • Water resources infrastructures 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 DA-IICT Workshop, 16-17 December 2004

  5. Module Description 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 (logical) Database Design Application Description Dataflow Diagram GIS Function List Entity-Relationship-Diagram DA-IICT Workshop, 16-17 December 2004

  6. User Interface • SDSS/LUP developed in ArcView • (Vector-based model) • Series of views ‘input maps • and tables; derived maps; ratings’ DA-IICT Workshop, 16-17 December 2004

  7. Kolar district DA-IICT Workshop, 16-17 December 2004

  8. Watersheds DA-IICT Workshop, 16-17 December 2004

  9. Meters DA-IICT Workshop, 16-17 December 2004

  10. DA-IICT Workshop, 16-17 December 2004

  11. DA-IICT Workshop, 16-17 December 2004

  12. DA-IICT Workshop, 16-17 December 2004

  13. Scenarios / Multi-criteria Priority ratings NWDPRA Watershed Physical characteristics Socio-economic characteristics 2 1 Bairasagara 4 3 Chalamena Halli 3 1 Chonduru 2 4 Peresandra Priority ratings Sub-watershed Sediment yield index Extent of degraded lands Soil erosion intensity 3 1 2 RP_E 1 2 3 RP_N 2 3 1 RP_W DA-IICT Workshop, 16-17 December 2004

  14. DA-IICT Workshop, 16-17 December 2004

  15. 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 sensing • Example : Crop growth model – • soil series > soil texture and thickness > available water capacity DA-IICT Workshop, 16-17 December 2004

  16. Land Evaluation for changes in Land Use • 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 DA-IICT Workshop, 16-17 December 2004

  17. 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 useAdvanced 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) DA-IICT Workshop, 16-17 December 2004

  18. 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

  19. Advances in Agro-Informatics in JapanOn-Site Data Input by Mobile Phone Mobile Phone with Web browser and e-Mail Slide from Dr Seishi Ninomiya, NARC, Japan

  20. Advances in Agro-Informatics in JapanField Monitoring Server with Wireless LAN Slide from Dr Seishi Ninomiya, NARC, Japan

  21. Advances in Agro-Informatics in JapanEfficient Data Acquisition e.g. Growth Model Slide from Dr Seishi Ninomiya, NARC, Japan

  22. Advances in Agro-Informatics in JapanAg 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 Slide from Dr Seishi Ninomiya, NARC, Japan

  23. Web Marketing with Remote Camera • Live Growth Process to Consumers • Virtual Farming by Consumers Slide from Dr Seishi Ninomiya, NARC, Japan

  24. Advances in Agro-Informatics in JapanProduct Identification System • Identification of Production Area and Farmer • Reliability on Quality and Tight Relationship • Link to Farm Diary (Agro-chemicals, Organic Information etc.) Slide from Dr Seishi Ninomiya, NARC, Japan

  25. 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. DA-IICT Workshop, 16-17 December 2004

  26. 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) DA-IICT Workshop, 16-17 December 2004

  27. Lessons Learnt / Experiences • Develop tool/package in conjunction with the • user community • Develop simple SDIs / DIs and assist the users in • their own existing decision making processes • Identify the clients / users – involve/train them • Conceptualize the problems (needs assessment) • Integrate IT with Knowledge-based systems • for technology transfer DA-IICT Workshop, 16-17 December 2004

  28. Shift GST from Doers to Users Thank you adi@iitb.ac.in www.csre.iitb.ac.in/adi/ DA-IICT Workshop, 16-17 December 2004

  29. SDSS/LUP An 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 DA-IICT Workshop, 16-17 December 2004

  30. 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 DA-IICT Workshop, 16-17 December 2004

  31. 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 DA-IICT Workshop, 16-17 December 2004

  32. Degree of Confidence in Data Source DA-IICT Workshop, 16-17 December 2004

  33. Mock-Up of LUSA Framework DA-IICT Workshop, 16-17 December 2004

  34. 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. DA-IICT Workshop, 16-17 December 2004

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