---
license: mit
library_name: sklearn
tags:
- tabular-regression
- agriculture
- yield-prediction
- coffee
- vietnam
- scikit-learn
model_format: skops
datasets:
- synthetic
---

# Coffee Yield Forecasting Model — Điện Biên, Vietnam

Predicts **coffee yield (tons/hectare)** for districts in Điện Biên province,
NW Vietnam, from growing-season weather features (rainfall timing by
phenological stage, frost/cold days, growing degree days, elevation).

Part of a broader [yield forecasting & harvest planning pipeline](https://github.com/)
(weather ingestion → feature engineering → model → harvest-window + risk alerts).

## Model details

- **Algorithm:** Random Forest
- **Format:** [skops](https://skops.readthedocs.io/) (`model.skops`) — safe to
  load without pickle's arbitrary-code-execution risk
- **Cross-validated MAE:** 0.115 t/ha (5-fold CV)
- **Cross-validated R²:** 0.471
- **Training samples:** 80 (district-year observations)
- **Trained:** 2026-07-18

⚠️ **Important:** this model was trained on **synthetic, domain-informed
weather+yield data**, not real measured yield records (see the parent repo's
README for why, and how to retrain on real GSO/ICO data). Treat predictions
as illustrative until retrained on real district-level yield history.

## Input features

```
dry_spell_rain_mm
flowering_rain_mm
fruitdev_rain_mm
ripening_rain_mm
fruitdev_rain_cv
frost_days_sensitive
mean_annual_temp_c
growing_degree_days
mean_sunshine_ripening
elevation_m
annual_rain_mm
```

All features are derived from daily weather (`tmax_c, tmin_c, tmean_c,
precip_mm, et0_mm, sunshine_hours`) aggregated over crop-specific
phenological windows — see `features.py` in the parent repo for exact
definitions (e.g. `flowering_rain_mm` = total rainfall during the
flowering month(s), `frost_days_sensitive` = count of nights below the
frost threshold during frost-sensitive months).

## Usage

```python
from huggingface_hub import hf_hub_download
import skops.io as sio
import pandas as pd

model_path = hf_hub_download(repo_id="imaflower/dienbien-coffee-yield", filename="model.skops")
model = sio.load(model_path, trusted=sio.get_untrusted_types(file=model_path))

X = pd.DataFrame([{
    "dry_spell_rain_mm": 30, "flowering_rain_mm": 60, "fruitdev_rain_mm": 450,
    "ripening_rain_mm": 120, "fruitdev_rain_cv": 1.8, "frost_days_sensitive": 1,
    "mean_annual_temp_c": 21.5, "growing_degree_days": 4200, "mean_sunshine_ripening": 6.2,
    "elevation_m": 900, "annual_rain_mm": 1600
}])
predicted_yield_t_ha = model.predict(X)[0]
```

## Intended use & limitations

- Scoped to Điện Biên province district-level, seasonal (annual) yield
  forecasting — not validated for other provinces/climates without
  retraining.
- Does not account for management practices (fertilization, pest control,
  variety-specific agronomy beyond the multiplier applied in the parent
  pipeline's `predict.py`).
- Synthetic training data encodes plausible agronomic relationships but
  is not a substitute for real yield statistics.