🌾 Empowering Local Farmers with Data - KaPaN

Mon, 06 Nov 2023 00:00:00 +0000

Extreme weather is inevitable, but crop failure doesn’t have to be.

🏆 Regional Recognition at the United Nations Datathon 2023

As Team 4SKA1, we were honored to be selected as The Best Team in the Asian Region at the United Nations Datathon 2023. This global competition challenged participants to develop innovative data-driven applications combining geospatial and statistical data to advance the Sustainable Development Goals (SDGs).

🚜 The Problem: Climate Uncertainty Meets Agriculture

Farmers in West Java, one of Indonesia’s major rice-producing regions, are increasingly vulnerable to extreme weather events, such as droughts, floods, and irregular rainfall. These threats can severely disrupt food supply and jeopardize farmers’ livelihoods.

Despite the urgency, many farmers lack access to early and localized information to prepare for such risks.

💡 The Solution: KaPaN

We proposed KaPaN (Kalender Padi Nusantara - Nusantara Rice Calendar), a prototype website that serves as an early warning system for extreme weather, tailored specifically for local farmers.

KaPaN empowers users to:

Track when and how long extreme weather is forecasted in their area
Visualize the spatial extent of weather anomalies
Report and share real-time ground-level information with fellow farmers

The name “KaPaN” (Indonesian for “When?”) reflects our goal to answer farmers’ biggest question when facing uncertainty.

🔬 The Method: Extreme Value Analysis

We used Extreme Value Analysis (EVA) (specifically the Block Maxima method) to analyze rare and high-impact events such as intense rainfall and drought.

With the help of Python and the pyextremes library, we simulated thresholds for extreme conditions and provided early warnings that farmers can act on.

🌐 Accessible, Actionable, and Community-Driven

The KaPaN platform is designed to be:

User-friendly: intuitive dashboard tailored for farmers
Insightful: visualizations help interpret trends over time and across regions
Collaborative: enables farmers to contribute ground-level observations

📂 Learn More

🤝 Meet the Team – 4SKA1

We are a passionate group of data practitioners from Indonesia who believe in the power of data for local impact:

Bagoes Rahmat Widiarso
Bhaskoro Muthohar
Figarri Keisha
Muhammad Nassirudin

🍚 Food Insecurity Early Warning System

Fri, 11 Nov 2022 00:00:00 +0000

Overview

At the 2022 United Nations Big Data Hackathon, Team 4SKA1, formed by young Indonesian data scientists including myself, took on the global challenge of Food Insecurity. Our solution was a scalable Early Warning System prototype built using open data and visualized via Google Data Studio. The project won Best Visualization Award in the Youth Track for its clarity, insight, and practical utility.

The Challenge

Climate change and socio-economic instability are increasingly affecting global food security. Yet, early warning systems are often absent, especially in vulnerable regions. Our task was to answer:

Can we monitor and anticipate food insecurity trends globally?
How does a country’s food insecurity affect others through trade flows?

Our Approach

We created a predictive model using data from:

The UN Hackathon 2022 datasets
World Bank, AtlasBig, and OurWorldInData

We engineered three key scores for each country:

Socio-Economic Score (SES): clustering countries based on unemployment, undernourishment, mortality, etc.
Climate Score: using PCA to combine metrics like CO₂ emissions, energy use, forest area.
Food Score: integrating food production, consumption, waste, and trade patterns.

These were combined to train a linear regression model to estimate national food insecurity scores with ~10% error.

The Dashboard Prototype

We built an interactive Google Data Studio dashboard to visualize:

Global food insecurity status by country
SES, Climate, and Food score comparisons
Country-level food trade relationships
Exploratory visualizations to empower policy and humanitarian decisions

👉 View the Dashboard

📄 Download Full Project PDF

UN Datathon | Figarri Keisha