Project Overview
RICESTATS addressed a key gap in global rice research and policymaking by developing a centralized, searchable, and publicly accessible database of rice socio-economic data. Using AWS cloud infrastructure and Qlik Sense analytics, the project created a standardized platform for rice statistics, indicators, and evidence-based decision-making across Asia and beyond.
🌾 Global Food Security Impact
This platform supports researchers, policymakers, and educators by providing standardized rice data critical for addressing food security challenges and agricultural development across Asia.
AWS Cloud Infrastructure
Data Storage & Processing
AWS S3 for data lake storage with Athena for serverless SQL queries and data analysis
ETL Pipeline
AWS Glue for automated extract, transform, and load operations from multiple survey sources
Data Visualization
Qlik Sense dashboards for interactive analytics and user-friendly data exploration
Ontology Integration
SEOnt socio-economic ontology for standardized variable mapping across datasets
Public Access Interface
Web-based platform for researchers and policymakers to access rice statistics
Real-time Analytics
Dynamic indicators for yield gap, genetic diversity, and fertilizer efficiency
☁️ Amazon Web Services Technology Stack
Comprehensive cloud infrastructure powering the RICESTATS platform:
Rice Analytics & Indicators
🌾 Grain Yield
Production metrics and yield gap analysis across regions and varieties
🧪 Fertilizer Efficiency
Nutrient use efficiency and input optimization strategies
🧬 Genetic Diversity
Variety distribution and biodiversity indicators for sustainable farming
🌱 Crop Care Practices
Agricultural management techniques and farming methodologies
👩🌾 Gender Participation
Women's involvement in rice production and decision-making processes
📊 Economic Metrics
Cost-benefit analysis and socio-economic impact assessments
Data Processing Pipeline
Data Collection
Aggregation of rice survey data from LOOP and other key agricultural studies spanning decades of research.
Ontology Mapping
Standardization using SEOnt socio-economic ontology to map variables across diverse datasets.
AWS Processing
ETL pipeline using AWS Glue and Athena for data transformation and structured schema creation.
Visualization
Qlik Sense dashboard development for interactive analytics and stakeholder testing.
Key Achievements & Impact
🚀 Platform Development Success
- Five Surveys Integrated: LOOP survey and others loaded into AWS Athena with Qlik Sense visualization
- 15 Indicators Mapped: Comprehensive analytics including yield, fertilizer efficiency, and gender participation
- Functional Dashboard: Fully operational user interface tested by stakeholders across research institutions
- Ontology Implementation: SEOnt-based standardization enabling seamless dataset integration
💡 Capacity Building Excellence
100% Team Training Achievement: Complete AWS cloud training delivered through webinars, online summits, and tailored sessions for IRRI, PhilRice, and international partners.
Knowledge Transfer: 25+ participants trained in cloud technologies and analytics, building local expertise in agricultural data science.
🌐 Public Engagement Impact
Strong visibility through press releases, online events, and CGIAR network integration, driving interest across academic and policy audiences for evidence-based agricultural decision-making.
🦠 COVID-19 Resilience
Successfully adapted project delivery during pandemic disruptions, completing data pipeline and interface development while transitioning from planned MSc/PhD recruitments to consultancy models with SENTI AI partnership.
Strategic Partnership Network
Led by the International Rice Research Institute (IRRI) with comprehensive agricultural research and technology partnerships:
IRRI
International Rice Research Institute
Lead Organization
DOST-ASTI
Advanced Science and Technology Institute
Philippines
Amazon Web Services
Cloud Infrastructure
Partner
UPLB
University of the Philippines
Los Baños
SEARCA
Southeast Asian Regional Center
for Agriculture
SENTI AI
Data Infrastructure
Optimization Partner
Challenges & Future Directions
⚠️ Implementation Challenges
- COVID-19 Impact: Pandemic disrupted planned MSc and PhD recruitments, requiring consultancy adaptations
- Development Timeline: ETL development extended due to restructuring and SENTI AI partnership onboarding
- Engagement Limitations: In-person conferences and events shifted to online formats affecting outreach
- Resource Constraints: Academic recruitment challenges required alternative staffing strategies
🚀 Expansion & Innovation Plans
Multi-Crop Platform: Expand beyond rice to support other crops and broader agricultural research applications.
Real-Time Integration: Incorporate IoT sensors and CAPI survey inputs for continuous data updating and live analytics.
NREN Deployment: Promote platform usage in TEIN-connected universities through PREGINET training and regional deployment.
API Development: Future public API and download capabilities for offline indicator dataset extraction and research use.
Sustainable Development Goals
This agricultural data platform directly supports global food security and sustainable development objectives: