faostat

Food and Agriculture Biomass Input-Output Tables

Food and Agriculture Biomass Input-Output Tables

AI skills for analyzing UN FAOSTAT food & agriculture data — country profiles, trade analysis, climate assessments, commodity briefings. Plugin for Claude Code & Codex.

Crop yield prediction using ML on real-world weather, pesticide, and agricultural data.

Global food & agriculture statistics from the UN FAOSTAT bulk-download corpus, served from a local SQLite mirror with a DataCanvas SQL surface, over MCP. STDIO & Streamable HTTP.

This repository contains the work done as a part of a Data Analytics Program at neuefische.

FAOSTAT food-systems analytics: Python ETL to SQLite, 35 SQL analyses with Plotly, predictive models and an interactive dashboard

Crop production and arable-land pipeline in Streamlit

Sistema de predicción del rendimiento económico de reventa de cosechas — Proyecto Integrador de Analítica de Negocios 7A (ETL FAOSTAT + Banco Mundial, esquema estrella, Random Forest, dashboard)

FoodLife MRIO Pipeline - Analysing the biodiversity impacts of food consumption

This script is designed to be flexible and adaptable to any FAOSTAT data. Making it a useful tool for exploring and visualizing FAOSTAT datasets.

FAOSTAT Beta MCP (SDMX) - Internal Only

🌶️ Interactive Streamlit data story on where the world's spices are grown vs. where they're eaten — 28 years of UN FAOSTAT data, 9 spice categories, 198 countries.

41 recipes for pulling public statistics — with the gotchas written down

Stata and R scripts to harmonize countries' name among different database sources. In collaboration with Federica Cappelli, Ph.D.

Land-Use Efficiency and Crop Yield Trends in the European Union: a data-driven assessment using FAOSTAT data (1995 - 2024)

Python analysis of FAOSTAT forestry data and geospatial mapping of plantations and ports in Vietnam, 1961–2023

High-risk diet prediction from FAOSTAT food-balance data (12 countries, 2016-2023): feature engineering, Random Forest / Logistic Regression / XGBoost with stratified CV, SHAP

R scripts building ggplot2 pie/polar charts (stacked-bar-to-coord_polar transform, cumulative-sum label positioning, ggrepel labels, viridis and ColorBrewer palettes, gridExtra composition). FAOSTAT crop-production and MODIS land-cover shares; FAOSTAT script used in Lemenkova (2026), J. Anatolian Geography 5(1):1-14.

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