Environment
Environmental Intelligence (GDD, Heat & Drought)
Pull real historical weather and compute crop-relevant agroclimatic indicators.
How it works
We pull historical daily weather from the Open-Meteo archive for any field location and compute Growing Degree Days (GDD) using the standard base temperature for your crop, plus heat-stress days, dry days, and precipitation totals. These indicators turn a year-long weather record into a small set of decision-grade numbers you can correlate with yield.
Formula
GDD = max(0, (T_max + T_min)/2 − T_base). Heat-stress day: T_max > threshold. Dry day: precipitation < threshold.
What you get
- ▸Cumulative GDD time series for the growing season
- ▸Heat-stress and dry-day counts per month
- ▸Total precipitation and extreme-event days
When to use it
- ▸You're zoning trial locations by climate match
- ▸You're correlating yield outcomes with weather
- ▸You're stress-testing varieties against historical extremes
Inputs
| Site list | CSV with `id`, `lat`, `lon`, optional `sow_date`, `harvest_date` |
| Crop | Preset key (`wheat`, `maize`, `rice`, `soy`, …) — sets base temperature and thresholds |
Parameters
| Name | Default | Description |
|---|---|---|
| Base temperature | crop preset | 10°C for maize, 0°C for wheat, etc. Override per site if needed. |
| Heat threshold | 32°C | T_max above this counts as a heat-stress day. |
| Dry-day threshold | 1 mm | Daily precipitation below this counts as dry. |
| Window | sow → harvest | Falls back to a crop-typical window if dates aren't provided. |
Workflow
- 1. Fetch archiveOpen-Meteo historical daily min/max/precip for each site and year.
- 2. Season sliceTrim to sow → harvest window per site.
- 3. Compute indicatorsGDD cumulative curve, heat-stress days, dry days, total precipitation.
- 4. Join to trialsSite-level indicators merged with trial IDs for downstream correlation.
Interpreting results
- ▸GDD tracks phenological progress; check that cumulative GDD at flowering matches the crop's known requirement.
- ▸Heat-stress-day count during flowering is a stronger yield-drop predictor than season-total heat.
- ▸Correlate dry days in the grain-fill window with yield — often the single strongest weather-yield link.
Common pitfalls
- ✕Wrong base temperature silently shifts every GDD curve by hundreds of degree-days.
- ✕Ignoring sow/harvest windows compares seasons of different lengths.
Worked example
Maize belt, 2020–2024
Six locations; 2022 shows 24 heat-stress days at flowering vs a 12-day average — matches a documented 18% yield drop that season.
References
Run Environment on your data
Open the module and upload a CSV.