{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Generates climatic impact drivers from Earth observation data and builds machine-learning models to forecast crop yields and monitor agricultural conditions.","skillfed_tags":["geospatial-ml","agriculture-forecasting","earth-observation"],"use_cases":["Build regional crop yield forecasts by training ML models on historical yields and EO-derived climate indices.","Monitor agricultural conditions and stress across growing seasons using agmet plots and climate metrics.","Optimize cropland masks for a region by tuning threshold or per-cell inclusion rules to improve model accuracy.","Generate annual yield outlooks and production reports with maps and accuracy scorecards for policy or market analysis.","Experiment with different ML architectures (CatBoost, TabPFN, etc.) and feature sets to find the best forecasting model for a crop and region."],"what_it_does":"Geocif is a machine-learning toolkit for agricultural forecasting that ingests Earth observation (EO) data\u2014satellite imagery, climate records, soil data\u2014and derives climatic impact drivers (CIDs) to predict crop yields and monitor field conditions. It wraps a large ecosystem of geospatial (gdal, rasterio, fiona, shapely, pyproj, rtree) and ML libraries (catboost, xgboost, tabpfn, tabicl, ngboost) to automate the pipeline from raw EO extraction through model training to visualization and reporting.\n\nThe package is organized around configuration files (geobase.txt, countries.txt, crops.txt, geocif.txt) that define regions, crop calendars, extraction methods, and model settings. It provides runners for data preparation (via geoprepare), index calculation, ML training, agricultural meteorology monitoring, and yield outlook generation. It also includes cropmask optimizers (threshold-based and per-cell) to refine which agricultural areas are used in modeling, and supports multiple model architectures including gradient boosting and prior-fitted networks.","worth_installing":"Yes, with conditions. Geocif is actively maintained and permissively licensed, making it suitable for agricultural forecasting research and operational monitoring. However, the 57 runtime dependencies\u2014especially heavy geospatial and ML libraries\u2014create substantial install complexity and disk footprint. Install only if you have a specific crop-yield or agricultural-monitoring use case and can manage the geospatial dependency chain (particularly on Linux/macOS). Not suitable for lightweight or embedded deployments."},"id":"geocif","links":{"html":"https://skillfed.io/packages/geocif","md":"https://skillfed.io/packages/geocif.md","pypi":"https://pypi.org/project/geocif/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":null,"license_treatment":"permissive","name":"geocif","python_support":"supports_current","summary":"Models to visualize and forecast crop conditions and yields"},"popularity":{"monthly_downloads":103893,"position":12778,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.908"}