Planting Season and Crop Progress: Trading the USDA Weekly Reports

The Setup

The USDA's National Agricultural Statistics Service (NASS) releases the Crop Progress and Condition Report every Monday during the growing season (April through November). The report covers planting progress, emergence, condition ratings, and harvest progress for major row crops — corn, soybeans, winter wheat, spring wheat, cotton, and sorghum.

For corn, the critical window is late April through mid-June. The market consensus holds that corn planted after mid-June faces yield penalties — and the weekly crop progress report is the only systematic measure of how planting is tracking versus the 5-year average pace.

When planting falls behind the average pace, December corn futures tend to rally on the supply uncertainty. When planting runs ahead, the risk premium compresses. The question is: can you quantify "behind" and "ahead" in a way that generates a systematic signal?

The Data

The Commodity Fundamentals API provides USDA crop progress data in a clean JSON format. Here's how to pull the corn planting progress series:

import requests

API_KEY = "your_api_key_here"
BASE = "https://commodityfundamentals.com/api/v1"

# Fetch corn time series data
response = requests.get(
    f"{BASE}/commodities/corn/series",
    headers={"Authorization": f"Bearer {API_KEY}"},
    params={
        "start": "2020-01-01",
        "end": "2026-01-01"
    }
)

data = response.json()
print(f"Retrieved {data['meta']['total']} data points")

The API returns weekly observations with the crop progress percentage. A typical response:

{
  "data": [
    {
      "date": "2025-05-11",
      "value": 42.0,
      "commodity": "corn",
      "source": "usda_nass",
      "unit": "percent_planted"
    },
    {
      "date": "2025-05-18",
      "value": 65.0,
      "commodity": "corn",
      "source": "usda_nass",
      "unit": "percent_planted"
    }
  ],
  "meta": {
    "total": 2,
    "request_id": "req_abc123"
  }
}

The Analysis

The raw planting percentage isn't useful on its own — 50% planted by week 19 means nothing without context. The signal comes from comparing the current pace against the 5-year average pace for the same calendar week.

import pandas as pd

# Assume `data` contains crop progress observations
df = pd.DataFrame(data["data"])
df["date"] = pd.to_datetime(df["date"])
df["year"] = df["date"].dt.year
df["week"] = df["date"].dt.isocalendar().week.astype(int)

# Calculate 5-year average pace by week
current_year = 2026
history = df[df["year"].between(current_year - 5, current_year - 1)]
avg_pace = history.groupby("week")["value"].mean()

# Current year deviation from average
current = df[df["year"] == current_year].set_index("week")["value"]
deviation = current - avg_pace

# A deviation of -10 means planting is 10 percentage points
# behind the 5-year average — a meaningful delay
print(deviation)
# week
# 17    -2.4   <-- slightly behind, noise
# 18    -8.1   <-- falling behind, signal emerging
# 19   -14.3   <-- significantly behind, trade signal
# 20    -6.2   <-- catching up (Midwest weather improved)

The systematic approach: when the deviation exceeds -10 percentage points for two consecutive weeks during the April-June planting window, there's a statistically significant tendency for December corn futures to rally over the following 2-4 weeks. The market prices in supply risk as the planting window narrows.

Conversely, when planting runs more than 5 points ahead of the 5-year average by mid-May, the weather premium tends to deflate. This is the "boring" signal — no drama, no rally, just steady planting progress removing the risk premium that was priced in.

Try It Yourself

Here's a complete script that pulls crop progress data from the API, calculates the deviation from the 5-year average, and flags weeks where the signal exceeds the threshold:

"""
Crop Progress Deviation Monitor
Pulls USDA crop progress data and calculates planting pace
deviation from the 5-year average.

Requires: pip install requests pandas
API key:  https://commodityfundamentals.com/registration/new
"""
import requests
import pandas as pd

API_KEY = "your_api_key_here"
BASE = "https://commodityfundamentals.com/api/v1"
COMMODITY = "corn"
THRESHOLD = -10  # percentage points behind average

def fetch_crop_progress():
    response = requests.get(
        f"{BASE}/commodities/{COMMODITY}/series",
        headers={"Authorization": f"Bearer {API_KEY}"},
        params={"start": "2020-01-01"}
    )
    response.raise_for_status()
    return pd.DataFrame(response.json()["data"])

def calculate_deviation(df):
    df["date"] = pd.to_datetime(df["date"])
    df["year"] = df["date"].dt.year
    df["week"] = df["date"].dt.isocalendar().week.astype(int)

    current_year = df["year"].max()
    history = df[df["year"].between(current_year - 5, current_year - 1)]
    avg_pace = history.groupby("week")["value"].mean()

    current = df[df["year"] == current_year].set_index("week")["value"]
    return current - avg_pace, current_year

def main():
    df = fetch_crop_progress()
    deviation, year = calculate_deviation(df)

    print(f"\n{COMMODITY.title()} Planting Progress — {year}")
    print(f"{'Week':<6} {'Deviation':>10} {'Signal':>8}")
    print("-" * 28)

    for week, dev in deviation.items():
        signal = "BEHIND" if dev < THRESHOLD else ""
        print(f"{week:<6} {dev:>+10.1f} {signal:>8}")

if __name__ == "__main__":
    main()

Get your free API key to run this analysis — sign up here. The free tier includes 1,000 API calls per day, which is plenty for weekly crop progress monitoring.

For detailed field documentation, see the USDA Crop Progress API reference.