Web Scraping for Agriculture & AgTech: How AI Agents Track Crop Prices, Weather Data, Supply Chain & Market Intelligence in 2026
Global agriculture generates over $4 trillion annually, feeding 8 billion people across complex supply chains spanning every continent. The U.S. alone produces $480+ billion in agricultural output, with commodity futures on the CME Group exceeding $15 trillion in notional value traded annually. Meanwhile, the AgTech market is projected to surpass $55 billion by 2027, driven by precision agriculture, drone monitoring, and AI-powered crop management.
Yet agricultural intelligence remains surprisingly fragmented: crop prices live on USDA market news portals, commodity futures scroll across CME/CBOT screens, weather data sits in NOAA archives and private forecasters, farm equipment pricing hides behind dealer networks, and global trade flows require cross-referencing customs databases across dozens of countries. Traditional agricultural data platforms charge $3,000–$30,000+/month for comprehensive feeds.
AI agents with web scraping capabilities can build comprehensive agricultural intelligence systems at a fraction of the cost — monitoring crop prices across global markets, tracking weather patterns that affect yields, analyzing equipment and input costs, and generating market intelligence briefs automatically.
What you'll learn in this guide: How to build AI-powered agricultural intelligence systems that scrape USDA market data, track commodity futures on CME/CBOT, monitor weather patterns and satellite indices, compare farm equipment pricing, analyze crop insurance rates, and generate automated agricultural intelligence briefs — all using the WebPerception API.
Why Agriculture Needs Web Scraping
Agriculture is fundamentally a data-driven industry where margins depend on timing — planting, harvesting, selling, and hedging all require real-time market intelligence. Here's what the modern agricultural intelligence stack needs to track:
- Crop prices & cash markets: USDA Market News reports, local elevator bids, regional basis levels, and export terminal pricing for corn, soybeans, wheat, cotton, cattle, hogs, and specialty crops
- Commodity futures: CME/CBOT real-time and settlement prices, options chains, commitment of traders data, technical indicators, and spread relationships (corn-soybean ratio, crush margins)
- Weather & climate: NOAA forecasts, drought monitor updates, soil moisture data, growing degree days (GDD), precipitation anomalies, and El Niño/La Niña indicators
- Crop conditions & yields: USDA Crop Progress reports, WASDE supply/demand estimates, satellite-derived NDVI vegetation indices, and global production estimates (AMIS, FAO)
- Farm inputs & equipment: Seed prices, fertilizer costs (urea, DAP, potash), chemical pricing, new and used equipment listings, and parts availability
- Global trade: Export inspections, sales commitments, USDA FAS data, vessel line-ups at export terminals, and trade policy changes (tariffs, bans, quotas)
- Crop insurance & risk: RMA rates, prevented planting data, indemnity payments, and county-level yield histories
Step 1: USDA Market Data & Crop Price Scraping
The USDA publishes thousands of market reports daily — from livestock auction results to grain elevator bids. Scraping and structuring this data gives you real-time visibility into cash markets that most traders still check manually.
import asyncio
from pydantic import BaseModel
from datetime import datetime, date
from typing import Optional, List
import httpx
class CropPrice(BaseModel):
"""Structured crop price from USDA or local markets."""
commodity: str # corn, soybeans, wheat, cotton, etc.
variety: Optional[str] = None # e.g., "Hard Red Winter", "Yellow #2"
market_location: str # e.g., "Chicago", "Gulf", "PNW"
price_type: str # cash, basis, futures, fob, delivered
price_usd: float # $/bushel, $/cwt, $/lb depending on commodity
unit: str # bushel, cwt, lb, ton
basis_to_futures: Optional[float] = None # Cash - Futures spread
futures_month: Optional[str] = None # e.g., "Jul 2026"
bid_ask: Optional[str] = None # "bid", "ask", "last"
source: str # USDA, DTN, elevator name, etc.
report_date: date
timestamp: datetime
class CropCondition(BaseModel):
"""USDA Crop Progress report data."""
commodity: str
state: str
week_ending: date
planted_pct: Optional[float] = None
emerged_pct: Optional[float] = None
condition_excellent_pct: Optional[float] = None
condition_good_pct: Optional[float] = None
condition_fair_pct: Optional[float] = None
condition_poor_pct: Optional[float] = None
condition_very_poor_pct: Optional[float] = None
good_excellent_pct: Optional[float] = None # Key metric traders watch
prev_week_ge_pct: Optional[float] = None
prev_year_ge_pct: Optional[float] = None
harvested_pct: Optional[float] = None
source: str = "USDA NASS"
# USDA data sources for scraping
USDA_SOURCES = {
"market_news": "https://marketnews.usda.gov",
"nass_quickstats": "https://quickstats.nass.usda.gov",
"crop_progress": "https://usda.library.cornell.edu/concern/publications/8336h188j",
"wasde": "https://usda.gov/oce/commodity/wasde",
"export_sales": "https://apps.fas.usda.gov/export-sales",
"grain_inspections": "https://fgis.usda.gov/inspectionresults"
}
async def scrape_usda_cash_prices(api_key: str) -> List[CropPrice]:
"""Scrape USDA grain cash prices and basis levels."""
async with httpx.AsyncClient() as client:
# Scrape USDA Market News grain prices
response = await client.post(
"https://api.mantisapi.com/scrape",
headers={"x-api-key": api_key},
json={
"url": "https://marketnews.usda.gov/mnp/ls-report?category=Grain",
"extract": {
"type": "list",
"instruction": "Extract all grain price entries including commodity name, location/market, price per bushel, basis to futures if shown, date, and any quality/grade specifications"
},
"wait_for": "table"
}
)
data = response.json()
prices = []
for item in data.get("extracted", []):
prices.append(CropPrice(
commodity=item["commodity"],
variety=item.get("variety"),
market_location=item["location"],
price_type="cash",
price_usd=item["price"],
unit="bushel",
basis_to_futures=item.get("basis"),
futures_month=item.get("futures_reference"),
source="USDA Market News",
report_date=date.today(),
timestamp=datetime.now()
))
return prices
async def scrape_elevator_bids(api_key: str, state: str = "IA") -> List[CropPrice]:
"""Scrape local grain elevator bids for basis tracking."""
elevator_sources = {
"IA": [
"https://www.dtn.com/agriculture/grains/local-grain-bids",
"https://www.agweb.com/markets/local-grain-bids",
],
"IL": [
"https://www.dtn.com/agriculture/grains/local-grain-bids",
]
}
async with httpx.AsyncClient() as client:
all_bids = []
for url in elevator_sources.get(state, []):
response = await client.post(
"https://api.mantisapi.com/scrape",
headers={"x-api-key": api_key},
json={
"url": url,
"extract": {
"type": "list",
"instruction": f"Extract all grain bids for {state}: elevator name, commodity, delivery period, cash price, basis to CME futures, futures month reference"
}
}
)
data = response.json()
for bid in data.get("extracted", []):
all_bids.append(CropPrice(
commodity=bid["commodity"],
market_location=f"{bid['elevator']}, {state}",
price_type="basis",
price_usd=bid["cash_price"],
unit="bushel",
basis_to_futures=bid.get("basis"),
futures_month=bid.get("delivery_period"),
source=bid["elevator"],
report_date=date.today(),
timestamp=datetime.now()
))
return all_bidsStep 2: Commodity Futures & Market Analysis
Commodity futures drive agricultural pricing globally. Tracking CME/CBOT corn, soybeans, wheat, and livestock futures — plus their spreads and options — is essential for any ag intelligence system.
class CommodityFuture(BaseModel):
"""CME/CBOT commodity futures data."""
symbol: str # ZC (corn), ZS (soybeans), ZW (wheat), etc.
commodity: str
contract_month: str # e.g., "Jul 2026", "Dec 2026"
exchange: str # CME, CBOT, KCBT, MGE
last_price: float
change: float
change_pct: float
open_price: float
high_price: float
low_price: float
settlement: Optional[float] = None
volume: int
open_interest: int
prev_oi: Optional[int] = None
oi_change: Optional[int] = None
unit: str # cents/bushel, $/cwt, etc.
timestamp: datetime
class SpreadAnalysis(BaseModel):
"""Intermarket and calendar spread analysis."""
spread_name: str # e.g., "Corn Jul-Dec", "Soybean Crush"
spread_type: str # calendar, intermarket, crush, crack
leg_a: str
leg_b: str
leg_a_price: float
leg_b_price: float
spread_value: float
historical_avg_30d: Optional[float] = None
historical_avg_90d: Optional[float] = None
percentile_rank: Optional[float] = None # Where current spread falls in history
signal: Optional[str] = None # "wide", "narrow", "inverted", "normal"
timestamp: datetime
async def scrape_cme_futures(api_key: str) -> List[CommodityFuture]:
"""Scrape CME Group agricultural futures."""
cme_pages = {
"corn": "https://www.cmegroup.com/markets/agriculture/grains/corn.quotes.html",
"soybeans": "https://www.cmegroup.com/markets/agriculture/oilseeds/soybean.quotes.html",
"wheat_chi": "https://www.cmegroup.com/markets/agriculture/grains/wheat.quotes.html",
"wheat_kc": "https://www.cmegroup.com/markets/agriculture/grains/kc-wheat.quotes.html",
"soybean_meal": "https://www.cmegroup.com/markets/agriculture/oilseeds/soybean-meal.quotes.html",
"soybean_oil": "https://www.cmegroup.com/markets/agriculture/oilseeds/soybean-oil.quotes.html",
"live_cattle": "https://www.cmegroup.com/markets/agriculture/livestock/live-cattle.quotes.html",
"lean_hogs": "https://www.cmegroup.com/markets/agriculture/livestock/lean-hogs.quotes.html",
"cotton": "https://www.theice.com/products/254/Cotton-No-2-Futures",
}
async with httpx.AsyncClient() as client:
futures = []
for commodity, url in cme_pages.items():
response = await client.post(
"https://api.mantisapi.com/scrape",
headers={"x-api-key": api_key},
json={
"url": url,
"extract": {
"type": "list",
"instruction": "Extract all futures contract months showing: contract month, last/settlement price, change, open, high, low, volume, open interest. Include all listed months."
},
"wait_for": ".quote-table, table"
}
)
data = response.json()
for contract in data.get("extracted", []):
futures.append(CommodityFuture(
symbol=contract.get("symbol", commodity.upper()[:2]),
commodity=commodity.replace("_", " ").title(),
contract_month=contract["month"],
exchange="CME" if "cattle" in commodity or "hogs" in commodity else "CBOT",
last_price=contract["last"],
change=contract.get("change", 0),
change_pct=contract.get("change_pct", 0),
open_price=contract.get("open", 0),
high_price=contract.get("high", 0),
low_price=contract.get("low", 0),
settlement=contract.get("settlement"),
volume=contract.get("volume", 0),
open_interest=contract.get("open_interest", 0),
unit="cents/bushel" if commodity in ["corn","soybeans","wheat_chi","wheat_kc"] else "$/cwt",
timestamp=datetime.now()
))
return futures
def calculate_crush_margin(soybean_price: float, meal_price: float, oil_price: float) -> dict:
"""Calculate soybean crush margin — key ag spread.
Standard conversion: 1 bushel soybeans = 44 lbs meal + 11 lbs oil
Board crush = (meal_price * 0.022) + (oil_price * 11) - soybean_price
"""
# Meal: $/short ton, convert to $/bushel equivalent (44 lbs / 2000 lbs per ton)
meal_value_per_bu = meal_price * (44 / 2000)
# Oil: cents/lb, convert to $/bushel equivalent (11 lbs)
oil_value_per_bu = (oil_price / 100) * 11
# Crush margin in cents/bushel
gross_crush = (meal_value_per_bu + oil_value_per_bu) - (soybean_price / 100)
return {
"soybean_cost_per_bu": soybean_price / 100,
"meal_value_per_bu": round(meal_value_per_bu, 4),
"oil_value_per_bu": round(oil_value_per_bu, 4),
"gross_processing_value": round(meal_value_per_bu + oil_value_per_bu, 4),
"crush_margin": round(gross_crush, 4),
"margin_signal": "profitable" if gross_crush > 0 else "unprofitable"
}Step 3: Weather & Climate Intelligence
Weather is the single biggest variable in agriculture. A two-week dry spell during corn pollination can slash yields 30%+. Monitoring weather patterns, drought conditions, and seasonal outlooks is critical for pricing and risk management.
class WeatherData(BaseModel):
"""Agricultural weather data for crop regions."""
region: str # e.g., "Iowa", "Corn Belt", "Delta"
station_id: Optional[str] = None
latitude: float
longitude: float
date: date
temp_high_f: float
temp_low_f: float
precipitation_inches: float
gdd_base50: float # Growing degree days (base 50°F for corn)
gdd_cumulative: Optional[float] = None
soil_moisture_pct: Optional[float] = None
drought_category: Optional[str] = None # D0-D4 or "None"
evapotranspiration: Optional[float] = None
wind_speed_mph: Optional[float] = None
source: str
class CropWeatherOutlook(BaseModel):
"""7-14 day weather outlook for crop production regions."""
region: str
forecast_period: str # "7-day", "8-14 day"
temp_outlook: str # "above_normal", "near_normal", "below_normal"
precip_outlook: str # "above_normal", "near_normal", "below_normal"
temp_departure_f: Optional[float] = None
precip_departure_pct: Optional[float] = None
key_risk: Optional[str] = None # "heat_stress", "drought", "flooding", "frost"
crop_impact: Optional[str] = None
confidence: str # "high", "moderate", "low"
source: str
issued_date: date
WEATHER_SOURCES = {
"drought_monitor": "https://droughtmonitor.unl.edu",
"cpc_outlook": "https://www.cpc.ncep.noaa.gov/products/predictions/610day",
"noaa_forecasts": "https://forecast.weather.gov",
"soil_moisture": "https://nasagrace.unl.edu",
"gdd_tracker": "https://mrcc.purdue.edu/gddtool",
"crop_weather": "https://www.usda.gov/oce/weather-crop-conditions"
}
async def scrape_drought_monitor(api_key: str) -> dict:
"""Scrape U.S. Drought Monitor for crop region conditions."""
async with httpx.AsyncClient() as client:
response = await client.post(
"https://api.mantisapi.com/scrape",
headers={"x-api-key": api_key},
json={
"url": "https://droughtmonitor.unl.edu/CurrentMap/StateDroughtMonitor.aspx",
"extract": {
"type": "object",
"instruction": "Extract drought conditions for major ag states (IA, IL, IN, NE, KS, MN, SD, ND, OH, MO, TX, CA): percentage of state in each drought category (None, D0 Abnormally Dry, D1 Moderate, D2 Severe, D3 Extreme, D4 Exceptional), week-over-week change, and comparison to last year"
},
"screenshot": True
}
)
return response.json()
async def scrape_weather_forecast_ag(api_key: str, region: str = "corn_belt") -> List[CropWeatherOutlook]:
"""Scrape CPC 6-10 and 8-14 day outlooks for ag regions."""
regions_map = {
"corn_belt": ["Iowa", "Illinois", "Indiana", "Nebraska", "Minnesota", "Ohio"],
"wheat_belt": ["Kansas", "Oklahoma", "Texas", "Montana", "North Dakota", "South Dakota"],
"delta": ["Mississippi", "Arkansas", "Louisiana", "Missouri"],
"southeast": ["Georgia", "Alabama", "North Carolina", "South Carolina"]
}
async with httpx.AsyncClient() as client:
# CPC 6-10 day outlook
response = await client.post(
"https://api.mantisapi.com/scrape",
headers={"x-api-key": api_key},
json={
"url": "https://www.cpc.ncep.noaa.gov/products/predictions/610day/",
"extract": {
"type": "object",
"instruction": f"Extract the 6-10 day temperature and precipitation outlook for the {region.replace('_',' ')} region states: {', '.join(regions_map.get(region, ['Iowa']))}. Include above/below/near normal categories and confidence level."
},
"screenshot": True
}
)
# Parse and return structured outlooks
data = response.json()
outlooks = []
for state in regions_map.get(region, []):
outlooks.append(CropWeatherOutlook(
region=state,
forecast_period="6-10 day",
temp_outlook=data.get("extracted", {}).get("temp_outlook", "near_normal"),
precip_outlook=data.get("extracted", {}).get("precip_outlook", "near_normal"),
confidence="moderate",
source="NOAA CPC",
issued_date=date.today()
))
return outlooksStep 4: Farm Equipment & Input Cost Tracking
Farm profitability depends heavily on input costs — equipment, seed, fertilizer, chemicals, and fuel. Tracking these markets helps farmers time purchases and helps equipment dealers optimize inventory and pricing.
class FarmEquipmentListing(BaseModel):
"""Farm equipment listing from dealer or auction sites."""
equipment_type: str # tractor, combine, planter, sprayer, etc.
make: str # John Deere, Case IH, AGCO, CNH, Kubota
model: str
year: int
hours: Optional[int] = None # Engine hours
price_usd: float
condition: str # new, used, certified pre-owned
listing_type: str # dealer, auction, private
dealer_name: Optional[str] = None
location: str # City, State
key_specs: Optional[dict] = None # HP, row count, tank size, etc.
listing_url: str
days_on_market: Optional[int] = None
source: str # MachineFinder, TractorHouse, Equipment Trader, etc.
scraped_date: date
class FertilizerPrice(BaseModel):
"""Fertilizer and crop input pricing."""
product: str # Urea, DAP, MAP, Potash (MOP), Anhydrous Ammonia, UAN-28/32
grade: Optional[str] = None # e.g., "46-0-0" for Urea, "18-46-0" for DAP
price_per_ton: float
region: str # NOLA barge, Corn Belt retail, Pacific NW, etc.
price_type: str # fob, delivered, retail, wholesale
change_week: Optional[float] = None
change_year: Optional[float] = None
source: str
report_date: date
EQUIPMENT_SOURCES = {
"machinefinder": "https://www.machinefinder.com", # John Deere dealer network
"tractorhouse": "https://www.tractorhouse.com", # Sandhills Global
"equipment_trader": "https://www.equipmenttrader.com",
"fastline": "https://www.fastline.com",
"machinery_pete": "https://www.machinerypete.com", # Auction results
"purple_wave": "https://www.purplewave.com", # Online auctions
"bigiron": "https://www.bigiron.com", # Weekly online auctions
}
FERTILIZER_SOURCES = {
"dtn_retail": "https://www.dtn.com/agriculture/input/fertilizer-prices",
"green_markets": "https://fertilizerpricing.com",
"usda_ams": "https://marketnews.usda.gov",
}
async def scrape_equipment_listings(
api_key: str,
equipment_type: str = "combine",
make: str = "John Deere"
) -> List[FarmEquipmentListing]:
"""Scrape farm equipment listings for market intelligence."""
async with httpx.AsyncClient() as client:
all_listings = []
search_url = f"https://www.tractorhouse.com/listings/{equipment_type}s/for-sale?make={make.replace(' ', '+')}"
response = await client.post(
"https://api.mantisapi.com/scrape",
headers={"x-api-key": api_key},
json={
"url": search_url,
"extract": {
"type": "list",
"instruction": f"Extract all {make} {equipment_type} listings: year, model, engine hours, price, condition (new/used), dealer name, city and state location, key specs (HP, separator hours for combines, row count for planters)"
},
"pagination": {"max_pages": 3}
}
)
data = response.json()
for item in data.get("extracted", []):
all_listings.append(FarmEquipmentListing(
equipment_type=equipment_type,
make=make,
model=item["model"],
year=item["year"],
hours=item.get("hours"),
price_usd=item["price"],
condition=item.get("condition", "used"),
listing_type="dealer",
dealer_name=item.get("dealer"),
location=item["location"],
key_specs=item.get("specs"),
listing_url=item.get("url", search_url),
source="TractorHouse",
scraped_date=date.today()
))
return all_listings
async def scrape_fertilizer_prices(api_key: str) -> List[FertilizerPrice]:
"""Scrape retail fertilizer prices across key regions."""
async with httpx.AsyncClient() as client:
response = await client.post(
"https://api.mantisapi.com/scrape",
headers={"x-api-key": api_key},
json={
"url": "https://www.dtn.com/agriculture/input/fertilizer-prices",
"extract": {
"type": "list",
"instruction": "Extract fertilizer retail prices: product name (Urea, DAP, MAP, Potash/MOP, Anhydrous Ammonia, UAN-28, UAN-32), price per ton, week-over-week change, year-over-year change, region"
}
}
)
data = response.json()
prices = []
for item in data.get("extracted", []):
prices.append(FertilizerPrice(
product=item["product"],
grade=item.get("grade"),
price_per_ton=item["price_per_ton"],
region=item.get("region", "Corn Belt"),
price_type="retail",
change_week=item.get("change_week"),
change_year=item.get("change_year"),
source="DTN",
report_date=date.today()
))
return pricesStep 5: Crop Insurance & Risk Management Data
Crop insurance — administered by USDA's Risk Management Agency (RMA) — is a $20+ billion annual market. Tracking insurance rates, prevented planting data, and indemnity payments provides critical signals about crop conditions and farmer profitability.
class CropInsuranceRate(BaseModel):
"""USDA RMA crop insurance rate and coverage data."""
crop: str
county: str
state: str
plan_type: str # RP (Revenue Protection), YP (Yield Protection), ARP, etc.
coverage_level_pct: float # 50%, 55%, ... 85%
premium_per_acre: float
subsidy_pct: float # Federal premium subsidy percentage
farmer_premium_per_acre: float
projected_price: float # CME-derived price used for revenue guarantee
yield_guarantee_bu: Optional[float] = None
revenue_guarantee_per_acre: Optional[float] = None
crop_year: int
source: str = "USDA RMA"
class PreventedPlanting(BaseModel):
"""Prevented planting claims — signals extreme weather events."""
crop: str
state: str
county: Optional[str] = None
acres_prevented: float
claims_count: int
indemnity_usd: float
cause_of_loss: str # "Excess Moisture", "Drought", "Flood", etc.
crop_year: int
report_date: date
RMA_SOURCES = {
"actuarial": "https://webapp.rma.usda.gov/apps/actuarialinformationbrowser",
"sob": "https://webapp.rma.usda.gov/apps/SummaryOfBusiness",
"col": "https://webapp.rma.usda.gov/apps/CauseOfLoss",
"prices": "https://www.rma.usda.gov/Topics/Projected-Prices",
}
async def scrape_rma_prevented_planting(api_key: str, crop_year: int = 2026) -> List[PreventedPlanting]:
"""Scrape prevented planting data — early signal of supply disruption."""
async with httpx.AsyncClient() as client:
response = await client.post(
"https://api.mantisapi.com/scrape",
headers={"x-api-key": api_key},
json={
"url": f"https://webapp.rma.usda.gov/apps/CauseOfLoss?cropYear={crop_year}&causeId=PP",
"extract": {
"type": "list",
"instruction": "Extract prevented planting data by state: crop, state, acres prevented, number of claims, total indemnity paid, primary cause of loss"
},
"wait_for": "table"
}
)
data = response.json()
records = []
for item in data.get("extracted", []):
records.append(PreventedPlanting(
crop=item["crop"],
state=item["state"],
acres_prevented=item["acres"],
claims_count=item["claims"],
indemnity_usd=item["indemnity"],
cause_of_loss=item.get("cause", "Excess Moisture"),
crop_year=crop_year,
report_date=date.today()
))
return recordsStep 6: Global Supply Chain & Trade Intelligence
Agriculture is inherently global. U.S. corn competes with Brazilian safrinha; Australian wheat competes with Black Sea origins. Tracking export sales, vessel line-ups, and trade policy gives you the complete supply-demand picture.
class SupplyChainData(BaseModel):
"""Agricultural trade and supply chain data."""
commodity: str
origin_country: str
destination_country: Optional[str] = None
data_type: str # "export_sales", "inspections", "vessel_lineup", "trade_policy"
quantity_mt: Optional[float] = None # Metric tons
quantity_bu: Optional[float] = None # Bushels
marketing_year: str # e.g., "2025/26"
cumulative_sales_mt: Optional[float] = None
pace_vs_usda_pct: Optional[float] = None # Current pace vs USDA forecast
fob_price_usd_mt: Optional[float] = None
freight_rate_usd_mt: Optional[float] = None
vessel_count: Optional[int] = None
policy_change: Optional[str] = None # Tariff/ban/quota description
source: str
report_date: date
class AgricultureIntelligenceBrief(BaseModel):
"""AI-generated daily agricultural intelligence summary."""
date: date
market_summary: str
key_price_moves: List[dict] # [{commodity, change_pct, driver}]
weather_risk: str # Current weather risk assessment
drought_status: str # U.S. drought monitor summary
crop_condition_change: Optional[str] = None
export_pace: List[dict] # [{commodity, pace_vs_usda_pct, status}]
supply_disruptions: List[str] # Any global supply events
equipment_market: Optional[str] = None
fertilizer_trend: Optional[str] = None
actionable_signals: List[str] # Trading/positioning signals
risk_alerts: List[str] # Items requiring attention
TRADE_SOURCES = {
"export_sales": "https://apps.fas.usda.gov/export-sales/esrd1.html",
"export_inspections": "https://apps.fas.usda.gov/gats/default.aspx",
"vessel_lineups": "https://www.ams.usda.gov/mnreports/sj_gr850.txt",
"brazil_exports": "https://comexstat.mdic.gov.br/en/geral",
"argentina_exports": "https://www.indec.gob.ar/indec/web/Nivel4-Tema-3-2-40",
}
async def generate_ag_intelligence_brief(
prices: List[CropPrice],
futures: List[CommodityFuture],
weather: List[CropWeatherOutlook],
trade: List[SupplyChainData],
insurance: List[PreventedPlanting],
api_key: str
) -> AgricultureIntelligenceBrief:
"""Generate daily agricultural intelligence brief using GPT-4o."""
# Prepare context from all data sources
context = f"""
CROP PRICES (today):
{[(p.commodity, p.market_location, p.price_usd, p.basis_to_futures) for p in prices[:20]]}
FUTURES SETTLEMENTS:
{[(f.commodity, f.contract_month, f.last_price, f.change, f.volume) for f in futures[:15]]}
WEATHER OUTLOOK:
{[(w.region, w.temp_outlook, w.precip_outlook, w.key_risk) for w in weather]}
EXPORT/TRADE:
{[(t.commodity, t.origin_country, t.quantity_mt, t.pace_vs_usda_pct) for t in trade[:10]]}
PREVENTED PLANTING/INSURANCE:
{[(pp.crop, pp.state, pp.acres_prevented, pp.cause_of_loss) for pp in insurance[:10]]}
"""
async with httpx.AsyncClient() as client:
response = await client.post(
"https://api.openai.com/v1/chat/completions",
headers={"Authorization": f"Bearer {api_key}"},
json={
"model": "gpt-4o",
"messages": [
{"role": "system", "content": """You are an expert agricultural market analyst.
Generate a concise daily intelligence brief covering:
1. Market summary (2-3 sentences)
2. Key price moves with drivers
3. Weather risk assessment for major crop regions
4. Export pace analysis
5. Supply disruptions or policy changes
6. Actionable signals for traders and ag businesses
7. Risk alerts requiring immediate attention"""},
{"role": "user", "content": f"Generate today's agricultural intelligence brief:\n{context}"}
],
"response_format": {"type": "json_object"}
}
)
brief_data = response.json()["choices"][0]["message"]["content"]
# Parse and return structured brief
import json
parsed = json.loads(brief_data)
return AgricultureIntelligenceBrief(
date=date.today(),
**parsed
)Build Your Agricultural Intelligence System
Start scraping USDA data, commodity futures, weather patterns, and farm equipment listings today with the WebPerception API.
Get Started — Free 100 Requests/MonthCost Comparison: Enterprise Ag Data vs. AI Agent
Traditional agricultural data providers serve the industry well — but at enterprise pricing that excludes most farmers, small traders, and AgTech startups:
| Provider | Monthly Cost | Coverage | Best For |
|---|---|---|---|
| Gro Intelligence | $5,000–$30,000 | Global supply/demand, climate, satellite | Commodity traders, hedge funds |
| DTN (Progressive Farmer) | $3,000–$15,000 | Cash prices, weather, market news | Farmers, elevators, ag retailers |
| Farmers Business Network (FBN) | $2,000–$10,000 | Input pricing, benchmarking | Farmers, input suppliers |
| Maxar / WSI | $5,000–$25,000 | Agricultural weather, satellite imagery | Commodity traders, insurers |
| S&P Global Commodity Insights | $5,000–$40,000 | Global ag pricing, analytics | Large agribusiness, trading houses |
| AI Agent + WebPerception | $29–$299 | USDA data, futures, weather, equipment, trade | Anyone in agriculture |
An honest caveat: Enterprise agricultural platforms like Gro Intelligence have ingested decades of proprietary satellite imagery with 10m resolution crop classification, exclusive IoT sensor network data from millions of connected farm devices, proprietary yield models calibrated against actual harvest data, and direct relationships with grain originators providing real-time basis data. AI agents won't replace these for institutional commodity trading. But for real-time USDA data monitoring, weather tracking, equipment market intelligence, fertilizer pricing, and export pace analysis, an AI agent delivers 80-90% of the actionable intelligence at 95% lower cost.
Use Cases by Role
1. Commodity Traders & Hedge Funds
- Monitor USDA report releases (WASDE, Crop Progress, Export Sales) and parse data within seconds
- Track weather pattern changes across global growing regions — U.S. Corn Belt, Brazilian Cerrado, Black Sea, Australia
- Calculate and monitor spread relationships: corn-soybean ratio, crush margins, wheat spreads (Chicago vs KC vs Minneapolis)
- Detect export pace anomalies: if weekly export sales deviate >20% from the pace needed to meet USDA projections
- Alert on geopolitical events affecting trade: export bans, tariff changes, port closures, vessel diversions
2. Agribusiness Companies & Grain Elevators
- Track local basis levels across competitors' elevators in real-time
- Monitor farmer selling patterns: compare current year marketings pace to historical averages
- Optimize origination bids based on futures curve, local supply/demand, and transportation differentials
- Track fertilizer and input prices to advise farmer customers on purchase timing
- Monitor prevented planting and crop condition data for supply-side planning
3. Farm Equipment Dealers & Manufacturers
- Monitor used equipment pricing across auction sites and competing dealers nationwide
- Track days-on-market for specific models (e.g., John Deere S790 combines with 500-1000 hours)
- Predict demand based on crop price and farmer income trends — high corn prices → equipment upgrades
- Analyze auction results from Machinery Pete, Purple Wave, and BigIron for fair market values
- Monitor competitor inventory and pricing strategies across TractorHouse and MachineFinder
4. Agricultural Investors & Analysts
- Track publicly traded ag companies (ADM, Bunge, Corteva, Deere, AGCO) earnings and guidance
- Monitor farmland values and auction results across the Corn Belt
- Analyze global supply/demand balances using USDA WASDE data and foreign production estimates
- Track AgTech startup funding, partnerships, and product launches
- Build custom indices: input cost index, farm profitability index, crop-revenue-per-acre tracker
Start Building Your Ag Intelligence Agent
100 free requests/month. No credit card required. Scrape USDA, CME, weather data, equipment listings, and more.
Try WebPerception API Free