CFS corn growth forecast and backtest

This is an experimental, held-out 2024 corn thermal-time forecast—not agronomic advice. The dashboard asks a narrow question: if corn development follows temperature accumulation at one CFS grid cell, when would statewide crop stages be expected to cross 50%?

How the forecast is constructed

The dashboard uses NOAA's CFS FLXF dataset, whose six-hour output extends to roughly 215 days, with a modified Growing Degree Day (GDD) model calibrated and tested against USDA crop progress reports. This produces a season-length thermal-time forecast while keeping its assumptions and thresholds inspectable.

The displayed case is frozen at 1 July 2024 00 UTC. It joins exact-six-hour-lead CFS history before that issue time to one archived CFS run afterward. The history is model guidance initialized six hours earlier—not station observations—and the forecast is never stitched from later cycles. The vertical seam in the charts marks where known-in-retrospect history ends and the archived forecast begins.

Because CFS is coarse, each series uses a native Gaussian-grid coordinate rather than pretending to represent a city or a statewide average. The seven points are:

The six-hour thermal-time calculation

For every six-hour CFS interval, the API converts 2 m maximum and minimum temperature from kelvin to Fahrenheit, caps both values to the standard modified-corn range of 50–86 °F, averages them, subtracts the 50 °F base, and multiplies by one quarter of a day:

Tmax_F = (TMAX_K - 273.15) × 9 / 5 + 32
Tmin_F = (TMIN_K - 273.15) × 9 / 5 + 32

GDD50_6h = (
  (clamp(Tmax_F, 50, 86) + clamp(Tmin_F, 50, 86)) / 2 - 50
) × 6 / 24

The 50 °F base and 86 °F ceiling follow the Iowa State University Extension modified corn GDD method. The factor 6 / 24 = 0.25 turns the interval's capped degree-day rate into a six-hour contribution. Applying the cap to every CFS interval is not numerically identical to first finding a calendar day's maximum and minimum, so the stage constants below were calibrated in this same six-hour metric rather than copied from a daily-GDD table.

How the calibration constants were generated

The biological targets come from USDA NASS Crop Progress and Condition. USDA reports the weekly percentage of each state's crop at or beyond a stage. For each state, year, and stage, we linearly interpolate the timestamp at which the reported series crosses 50%. The 50%-planted crossing is the start of thermal accumulation; emerged, silking, dough, dented, and mature crossings are the targets.

For a particular stage and season, its implied threshold is simply the six-hour GDD accumulated from the interpolated 50%-planted time to the interpolated 50%-stage time. To keep the displayed 2024 season out of its own calibration, every dashboard constant is the median of the four implied thresholds from 2020–2023 for that state and stage. This is a small historical calibration, not machine-learning training. The exact constants used in the dashboard are:

The large state-to-state differences are intentional. A single national maturity threshold fit the observations poorly; crop mix, hybrid relative maturity, planting patterns, and the mismatch between one grid point and a whole state are all absorbed into these empirical constants.

Does the archived forecast look plausible?

We repeated the fit as a leave-one-year-out test across 2020–2024: each scored year was excluded from the constants used to predict it. For the same 1 July history-plus-forecast split shown below, the held-out results across seven states and five seasons were:

Coverage is the share of eligible state-seasons in which the CFS run accumulated enough heat to reach the stage before the forecast ended. Bias is predicted minus USDA date, so positive values are late. These results make a roughly one-week July maturity estimate plausible as a demo; they do not establish accuracy for a particular field or guarantee the next forecast.

Important limitations

• USDA observations are weekly statewide aggregates; weather comes from one coarse CFS cell per state.
• The historical weather side is short-lead CFS guidance, not observations or reanalysis.
• Planting time is an explicit input represented here by the interpolated USDA 50%-planted date. A field-level version would need the user's planting time.
• The study spans only five seasons, and the displayed 2024 forecast is calibrated on four of them. Of 27,965 six-hour weather values, 28 were missing and linearly interpolated between adjacent values.
• This is a temperature-only phenology model. It does not model cultivar, soil moisture, nutrient stress, pests, management, yield, or field-scale variability.

Reproducing the weather calculation

The dashboard batches all seven coordinates, but the two minimal Iowa requests below expose the complete weather calculation. The first asks /timeseries for exact-six-hour-lead history available by a fixed retrospective cutoff. The second asks /runs for one exact archived 1 July cycle. Grafana concatenates their six-hour GDD contributions, subtracts the state-specific amount accumulated before planting, sorts by valid time, and computes the cumulative curve.

Exact-six-hour-lead history request

curl -X POST 'https://gribstream.com/api/v2/cfsflxf/timeseries' \
  -H "Content-Type: application/json" \
  -H "Accept: text/csv" \
  -H "Authorization: Bearer [API_TOKEN]" \
  -d '{
    "fromTime": "2024-04-15T06:00:00Z",
    "untilTime": "2024-07-01T06:00:00Z",
    "asOf": "2024-11-02T00:00:00Z",
    "minLeadTime": "6h",
    "maxLeadTime": "6h",
    "coordinates": [
      {"name": "Iowa", "lat": 42.047066, "lon": -93.750371}
    ],
    "variables": [
      {"name": "TMAX", "level": "2 m above ground", "info": "", "alias": "temperature_max_k", "hidden": true},
      {"name": "TMIN", "level": "2 m above ground", "info": "", "alias": "temperature_min_k", "hidden": true}
    ],
    "expressions": [
      {"expression": "(temperature_max_k - 273.15) * 9 / 5 + 32", "alias": "temperature_max_f"},
      {"expression": "(temperature_min_k - 273.15) * 9 / 5 + 32", "alias": "temperature_min_f"},
      {"expression": "((func.Min(86.0, func.Max(50.0, temperature_max_f)) + func.Min(86.0, func.Max(50.0, temperature_min_f))) / 2 - 50.0) * 0.25", "alias": "gdd50_f_degree_days_6h"}
    ]
  }'

Archived 1 July forecast request

curl -X POST 'https://gribstream.com/api/v2/cfsflxf/runs' \
  -H "Content-Type: application/json" \
  -H "Accept: text/csv" \
  -H "Authorization: Bearer [API_TOKEN]" \
  -d '{
    "forecastedFrom": "2024-07-01T00:00:00Z",
    "forecastedUntil": "2024-07-01T00:00:00Z",
    "minLeadTime": "6h",
    "maxLeadTime": "2952h",
    "coordinates": [
      {"name": "Iowa", "lat": 42.047066, "lon": -93.750371}
    ],
    "variables": [
      {"name": "TMAX", "level": "2 m above ground", "info": "", "alias": "temperature_max_k", "hidden": true},
      {"name": "TMIN", "level": "2 m above ground", "info": "", "alias": "temperature_min_k", "hidden": true}
    ],
    "expressions": [
      {"expression": "(temperature_max_k - 273.15) * 9 / 5 + 32", "alias": "temperature_max_f"},
      {"expression": "(temperature_min_k - 273.15) * 9 / 5 + 32", "alias": "temperature_min_f"},
      {"expression": "((func.Min(86.0, func.Max(50.0, temperature_max_f)) + func.Min(86.0, func.Max(50.0, temperature_min_f))) / 2 - 50.0) * 0.25", "alias": "gdd50_f_degree_days_6h"}
    ]
  }'

Example rows at the history/forecast seam:

forecasted_at,forecasted_time,lat,lon,name,member,gdd50_f_degree_days_6h,temperature_max_f,temperature_min_f
2024-06-30T18:00:00Z,2024-07-01T00:00:00Z,42.0471,-93.7504,Iowa,0,4.7475,70.2500,67.7300
2024-07-01T00:00:00Z,2024-07-01T06:00:00Z,42.0471,-93.7504,Iowa,0,3.3300,69.5300,57.1100

Background and primary sources: NOAA CFSv2 documentation · CFS FLXF model and inventory · USDA NASS survey methodology · official Crop Progress report archive · Iowa State modified GDD method.