Agro Resource Management

1. IBM Incubator - 2013 Agro Resource Management

2. Problem addressed • Widespread implementation of geospatial temporal data-gathering tools and massive deployment of remote sensors, combined with comprehensive analytical and optimization models, may provide a new landscape for agribusiness • Precision agriculture and production chain management are two prominent opportunities for the application of this technology • Collect and distribute information throughout farmers in a collaborative way, in order to enhance visibility of resource usage and consequently create a better management, it is still a challenge for small/medium producers. • Example: • Biofuels companies need to schedule sugarcane crop harvest efficiently • Cooperatives need to control shared resources and collect distributed information from farmers • This process is heavily dependent on the location and availability of moving resources (e.g., tractors) • The operation schedule and resource management (soil, fuel, power, equipment, etc) should be integrated in order to improve productivity and efficiency, and to reduce environmental impact • A similar situation can be observed in other industries, such as Mining 2 IBM confidential

3. Key Drivers for Smarter Agriculture and IT Role • Global population is headed toward 9 billion by 2050. To ensure that agriculture can meet future needs, the world will have to: • (1) reduce the millions of metric tons of post-harvest crops lost to pests, disease, and inferior storage methods each year; • (2) make better use of the crops and biomass already grown while using water and other inputs sparingly; and • (3) increase yields on existing land in ways that minimize the need to bring additional acres into production. • Given the necessity of feeding a growing planet, there is no choice but to increase our output with available resources. IT offers many opportunities for addressing challenges to increasing global agricultural output. The Bridge on Agriculture and Information Technology, Fall. 2011 • Information technology is increasingly impacting agriculture and the broader food system in myriad ways throughout the value chain. This ranges from the fundamental inputs, where genomics and computer modeling can help drive the next generation of seed and planting technology, all the way through to food distribution, where smarter logistics solutions can help deliver food to the point of consumption more quickly with less spoilage and using less fuel and machine resources-- Susan Wilkinson . Source:http://www.nae.edu/File.aspx?id=52553… 3 IBM confidential

4. Precision Agriculture: Integrated Operations and Management Information Technology Processes + + • Precision agriculture (PA) is a comprehensive approach to farm management which combines field instrumentation and analytics to achieve increased profitability and sustainability, improved product quality, effective pest management, energy, water and soil conservation, and surface and ground water protection. • PA takes advantage of GIS-based decision support applications and the huge amounts of geospatial temporal data collected by remote and vehicle-mounted sensors. • To enable repeatable solutions, a platform for integration of field data as well as analytical tools – addressing production planning, operations and asset management – is needed. Traditional agriculture: 1 analysis/Ha Precision agriculture: 15 analysis/500m2 Integrated Operations and Management Source: http://pubs.ext.vt.edu/442/442-500/442-500.html#L3 4 IBM confidential

5. Technical Solution • A platform for agriculture that will integrate data from resources (condition, usage, activities, anomalies), looking for easy data input (manually or systemic), with analytical / optimization / data mining models and geospatial temporal data visualization tools, leveraging the proposed Resources Management Platform, to improve agricultural productivity and make efficient use of resources (water, land, etc.), combined or not. Source:http://www.nae.edu/File.aspx?id=52553… 5 IBM confidential

6. GIS Scope KPIs Integration Mobile Visualization Reports Data Acquisition Web Agro Resource Mngt Analytics Irrigation Optimization Flexible Data Model Allow Simulated entries Pest Control Spatial Temporal designed IBM confidential

7. Potential market Association of farmers usually to share resources and administrative functions Cooperative Input Farmer Output Equipment Produces seeds, poison, fertilizer, fuel, etc. Responsible for producing vegetables or keeping livestock . Transforms Farmer’s results into consumable products. Provides equipments to farmers during production phases. IBM confidential

8. Big picture • Analytics Engine • Analyze data and present statistics for specific resource • Offline app • Register resource data • View Simple KPIs • View Activity Info • Register anomaly NextGen Mobile Agro Resource Management Analytics • Core • GIS • Cooperative/Farmers • Shared information KPIs • Indicators • KPIs about resource and its condition • Correlated info for a specific timeframe • Web app • Manage resources • View Analytics • Manage activities Reports IBM confidential

9. Integrations NextGen Mobile not considered yet IOC KPIs Agro Resource Management Maximo Tickets feed update IOC Events Maximo Assets create load IOC GIS Maximo Locations show Analytics Cognos Reports IBM confidential

10. Mobility Uses CSI (Tivoli) Mobile Next Gen Platform Input resource’s data Agro Resource Management KPIs, historical data, etc. Agronomist Register anomalies IBM confidential

11. … Technology … Crop Direct Indirect Geography Irrigation Agriculture Complexity Culture Size Market Resources Weather Essentially, all models are wrong, but some are useful. George E.P. Box IBM confidential

12. Analytics Flow Resource Data source Algorithm Results ASK relationships http://analytics40.watson.ibm.com:9080/ASK_WebSite/files/Analytics_Solution_Kit_Architecture_Document.pdf IBM confidential

13. Considerations • Agriculture is not a new science • Usually seen as a rough activity • Autonomic is expect but not responsible for take actions • Almost the available data is not public • High academic presence and consequently a continuous change of practices • IBM should provide ways to bring technology up to the field and do not turn itself into agro company • Productivity is more important that soil and planted area to define if a country is developed or not. • Incluir papel da Susan.. IBM confidential

14. What we are not doing • Asset management • Work management • Logistics • Supply chain IBM confidential

15. Interviews • COAMO – Largest cooperative in Latin America • Mobility • Pest Control • University of Sao Paulo • Irrigation optimization algorithm • University of Sao Carlos • Insights for Agricultural in general • EMBRAPA – Brazilian Public Agency for Agriculture • Insights for pest management IBM confidential

16. Team Gabriela Orpinelli Developer / Requirements Marcelo Esperandio Development Diego Moreira Manager Thiago Bianchi Development Ulisses Mello Research Collaborator Ned Bader Development Leucir Marin Development / Architect Alcantaro Jovanco Manager IBM confidential

17. Backup IBM confidential

18. Scope • This project covers 2 main areas: • Prove technology to facilitate end-user to input and visualize data • online (web) or offline (mobile) • Flexible data model for resource management • Anomaly management, GIS and SOPs from IOC • KPI and Reports • Demonstrate analytic capability for specific problems like: • Irrigation optimization • Improve response actions to avoid production losses according measurements and analytics over them • Pest control prediction • Predict potential risk of pest proliferation based on resource's information data • Additionally, this solution will rely on time-space data format, allowing geospatial analysis • Mobile applications using Tivoli NextGen platform • Datasets designed to support simulated entries IBM confidential

19. M M M M M * dynamic attributes Equip Human Water Power Fuel Land … $$ Contract Resource Type Cooperative Farmers Contractors Provider Anomaly Resource Reading point Date Value Resource Usage Event Condition Date Reason Resource Performance * IOC Core OOO Tags Activity * IOC Core Mobile app IBM confidential

20. Phases IBM confidential

21. Easy to… • Create new resource types • Create relation between resources • Register resource usage • Register resource performance • Register an activity / anomaly Think Mobile First! IBM confidential

22. Our challenges • Create mobile app to remote users using CSI NextGen • Create IOC apps, specific for Agriculture purpose • Extend/Reuse existent IOC apps • Identify one or two resources to deep dive in, and create at least one analytic’s model for it • Create KPIs and Reports for Resource Mngt perspective IBM confidential

23. Questions • How mobile is being used by farmers? • What is the concerns around resource management? • Any specific concern for Natural resources? • What would be the major areas to deep dive in? IBM confidential

24. ADD – Analytics Driven Development • Define and create data sources based on Analytics requirement. • What is the problem to be solved? • How can we get the data to analyze it? • Is it a real-time analysis, or time-based (periodically) based on historical data? • Single event or combined • Specific algorithms or a generic models? IBM confidential

25. Irrigation optimization IBM confidential

26. Pest prediction IBM confidential

27. GIS Future KPIs Visualization Reports Agro Resource Mngt Agro Resource Mngt Port Resource Mngt IBM confidential