1. Geospatial Knowledge Management in Agriculture N H Rao National Academy of Agricultural Research Management Hyderabad

2. NAARM 2 outline  knowledge management and GIS  Geospatial knowledge management systems  Applications

3. NAARM increasing importance of knowledge as a factor of production in agriculture significant value addition results from timely knowledge interventions at all links in the agri-supply chain: Knowledge and agriculture Information Technology : improves the nature and speed of knowledge sharing and management

4. NAARM Knowledge management Knowledge Management (KM): activities designed to manage, exchange and create or enhance intellectual assets of an organization to improve decision-making KM amplifies knowledge created by individuals and crystallizes it as shared knowledge in a network or organization The data–to–wisdom pyramid is the key challenge of KM – capabilities to turn large volumes of data into information and knowledge (and wisdom ?) Information technology is key enabler of KM knowledge management broadly focuses on five areas: Connect people with information Enable conversion of information to knowledge Connect people with other knowledgeable people Encapsulate knowledge, to make it easier to transfer Disseminate knowledge

5. NAARM 5 Geographic Information System (GIS) GIS = G + IS spatial coordinates of locations on the surface of the earth for corresponding map locations (spatial data) Database (attribute data - relational database) Geographic reference Information System + Where ? What ? GIS is computer hardware and software system designed to store, manage, analyze and display spatially (geographically) referenced data - links geographic information (where things are) with their description (what they are) GIS consists of: Spatial data of coordinates Data-base of attributes Some way to link the two with strong spatial data visualization capabilities

6. NAARM The map – data connection in GIS Spatial data – district map of rice production (kharif) Spatial Feature - district Source: NAARM GIS Database - Feature attribute table

7. NAARM 7 building blocks: map layers and associated Tables data structure: set of independent map layers - one layer for one feature with associated Feature Attribute Table that can be connected to other attribute data common locational reference (lat/long grid – geographic coordinate system): allows integration of layers by fixing them to a common coordinate system. thematic layers: can be made visible - all at the same time or selectively - and linked by common location Overlay: different layers can be overlaid to get homogenous land units and other types of information Collation: data can be collated from several layers for any location Spatial analysis: buffering, kriging, Thiessen polygons, models, etc. GIS characteristics

8. NAARM 8 vital linkages to be made between apparently unrelated activities, to reveal trends and patterns that are not apparent with tabular databases (knowledge generation) …..leading to fundamental changes in the way we visualize and analyze information for decision-making GIS is a knowledge integrator Bringing together data from different sources, with a common locational component, allows --------

9. NAARM 9 GIS and KM Level-3 Level-2 Level-1 GIS based Decision Support Systems Integration of Different Layers (Overlay) Resource Inventory (Data Base Management) with its strong spatial data management, analysis and visualization capabilities, GIS provides a very natural and effective platform for knowledge management in agriculture Levels of GIS use

10. NAARM An example: GIS based DSS for rainfed sorghum management

11. NAARM Natural resources and agricultural production processes vary spatially. Geographical Information Systems (GIS) provide an effective platform for storing and mapping spatial data and integrating data with models of relevant processes for decision support. Huge volumes and variety of geospatial data on specific themes such as land use, soils, water, climate, pests, etc., are being collected by public and private agencies. However, currently it is difficult for most users to access and share the data and integrate them with models to derive useful information and knowledge for management decisions. There is a need to create geospatial data and knowledge management systems that can provide efficient, on-demand and remote access of spatial data to users to enable integration with models for a variety of applications in agricultural and natural resources management. Need for geospatial KM systems in agriculture

12. NAARM 12 Issues finding and accessing real-world spatial data accessing spatial analysis tools and learning resources integrating spatial analysis tools into domain knowledge of agricultural systems and processes Enabling geospatial knowledge management The Geospatial Library is a useful concept for implementing and enabling geospatial knowledge management

13. State Services (AP) SOILS Soil Type Soil Slope Soil Depth Soil Erosion Soil Drainage Soil AWC Soil Quality WATERSHEDS Watershed Sub Watershed Mini Watershed Micro Watershed District Services WATERSHEDS Watershed Sub Watershed Mini Watershed Micro Watershed Village Services Attribute Data (.mdb) Learning Resources Training Manuals Knowledge Resources GIS Products Spatial Data (Levels of Extraction) State Mandals Census Livestock Agriculture Infrastructure Meteorological Data Markets data Fields Farmers/households Districts (22) Mandals SOILS Soil Type Soil Slope Soil Depth Soil Erosion Soil Drainage Soil AWC Soil Quality Mandal Services (Nalgonda Dist) SOILS Soil Type Soil Slope Soil Depth Soil Erosion Soil Drainage Soil AWC Soil Quality Watershed Sub Watershed Mini Watershed Micro Watershed Villages WATERSHEDS Districts 1. NAARM GEOSPATIAL LIBRARY - STRUCTURE Villages/ fields Survey number Soils Micro Watershed Soil health Resources Contours Settlements Roads Drainage

15. NAARM 15 NGSL interfaces

16. NAARM Accessing spatial data select and view the layer as an image in an html viewer and download the image View the image in a java viewer (downloadable from the main web page) Download the required spatial data layers as shape files from the service directory for integrating with other layers and use in his specific application An important feature of the download option is that with every download two shape files are downloaded  the feature selected and  the boundary of the region of the selected feature (The advantage of the simultaneous download of the boundary is that it can be used as a template for any other feature that may need to be created by the user to address his specific problem) Accessing Attribute data The data consists of crops, livestock, census, land use, infrastructure, and meteor- ological Data. The user can download the data in the form of MS Access (.mdb) files by selecting the appropriate theme Accessing Learning Resources The Learning Resources link on the main page leads to a repository for TRAINING MANUALS, GIS APPLICATIONS IN AGRICULTURE and the GIS PRODUCTS Accessing data from NGSL

17. NAARM The sign of things to come; big data analytics and geospatial knowledge management in agriculture …….

18. NAARM Emerging KM technologies – Big data/Data Science Big Data: characterized by variety, volume, velocity Data science: generalizable extraction of knowledge from data. Example: Monsanto’s Integrated Farming Systems platform identify genetic variations within a crop species by sequencing and comparing genomes of hundreds of thousands of different varieties, both wild and domestic. assess which genetic variations control or influence which traits (grow seedlings both in controlled laboratory conditions and in the field, using an automated process and photographing them on a regular basis- phenotyping) Big Data = phenotype information, images representing roughly 100 terabytes of unstructured data, integrated with t database of sequenced genetic information of all the existing varieties. Analytical programs sift through this massive data set to determine which minute differences among the billions of nucleotides in each genome are associated with which traits Next, incorporate data of how genomes perform across a wide range of environments to identify exact hybrid best for a particular area FieldScripts, a computer programme uses Big Data and inputs from the farmer (detailed information about each field — boundaries, yield data, soil test results) to deliver a variable rate seeding plan directly to the FieldView iPad app, which the farmer connects to the monitor in the planter cab so that the machine can execute the script. Over time, the farmer will capture the results of each harvest and feed this additional information back into the system. In 2013, Monsanto purchased Climate Corporation for $930 million to combine hyper-local weather monitoring, agronomic data modeling and high-resolution weather simulations with genetic information.

19. NAARM Variable rate NPK based on management zones In-season fertility & disease management Yield monitoring advances IFS -Monsanto

20. NAARM 20