AgriQuant AI bridges peer-reviewed agricultural economics research with cutting-edge AI to systematically capture weather-driven volatility in commodity futures. Our approach transforms decades of academic findings — anchored in the University of Florida's Institute of Food and Agricultural Sciences — into actionable trading signals, combining proven methodologies with real-time NOAA, INMET, and satellite data processing across global growing regions.
Academic Foundation: UF/IFAS Citrus Research
Our predictive models leverage methodologies from the University of Florida's Institute of Food and Agricultural Sciences (UF/IFAS), the world's premier citrus research institution with over 60 years of continuous field data from Florida's growing regions. Claude Sonnet 4.6 has been trained to systematically apply these academic frameworks to live weather streams — creating a bridge between proven agricultural economics and modern algorithmic execution.
60+
Years of UF/IFAS continuous field data
70%
Confidence threshold for signal generation
5%
Minimum expected price move for entry
Research Paper #1
Economic Impact Modeling
"Singerman, A., Burani-Arouca, M., and Futch, S.H. (2018). 'The Profitability of New Citrus Plantings in Florida in the Era of HLB.' HortScience 53(11):1655-1663."
Application
Our AI uses regression models from this research to calculate expected yield impact when NOAA forecasts indicate freeze, drought, or disease-favorable conditions across Florida's citrus belt — estimating both the magnitude and direction of price movement.
Research Paper #2
Weather-Yield & Disease Correlation
"Li, S., Wu, F., Duan, Y., Singerman, A., and Guan, Z. (2020). 'Citrus Greening: Management Strategies and their Economic Impact.' HortScience 55(5):604-612."
Application
When NOAA data shows warm, wet conditions persisting 10+ days, the system flags favorable conditions for Asian citrus psyllid growth — typically 7-10 days before USDA confirms increased disease pressure.
10+ days
Warm/wet NOAA conditions trigger flag
7–10 days
Lead time before USDA confirmation
HLB pressure
Disease risk quantified and priced in
Research Paper #3
Spatial Risk Analysis
"Singerman, A., Lence, S.H., and Useche, P. (2017). 'Is Area-Wide Pest Management Useful? The Case of Citrus Greening.' Applied Economic Perspectives and Policy 39(4):609-634."
Application
Spatial propagation models estimate total supply impact when NOAA forecasts show freeze risk for specific counties within Florida's concentrated 100-mile citrus production radius.
100-mile
Florida's entire citrus production zone, making county-level freeze forecasts highly predictive of total supply impact.
Expanding the Research Base: Coffee & Cocoa
Beyond citrus, our models incorporate published work on tropical soft commodities — including studies on Brazilian frost events and Arabica coffee yield loss, and research on West African rainfall variability and cocoa pod development. The same weather-to-yield-to-price logic proven in Florida citrus extends to Minas Gerais coffee and Ghanaian cocoa, where production is equally concentrated and weather-sensitive.
Brazilian Coffee (Arabica)
Frost event modeling in Minas Gerais using INMET data and published Arabica yield-loss curves. Cold air mass trajectories from GFS/ECMWF trigger signals 48–72 hours before market reaction.
West African Cocoa (Ghana & Côte d'Ivoire)
Rainfall variability and dry season anomalies mapped against cocoa pod development windows. Ghana Met data integrated with ICCO supply forecasts for systematic signal generation.
Comprehensive Data Sources
All inputs are publicly available and verifiable; our edge is processing them faster and more systematically than human analysts.
Weather & Climate
NOAA 15-minute updates, GOES-16/17 imagery, GFS/NAM/HRRR/ECMWF models, plus INMET (Brazil) and Ghana Met.
Agricultural
USDA NASS, Florida Department of Citrus, CONAB, ICCO — weekly crop reports, monthly production forecasts, and grove health surveys.
Market
CME and ICE futures with tick-by-tick pricing and 40+ years of historical settlement data, options implied volatility, and volume analysis.
Satellite
Planet Labs 3m daily imagery and Sentinel-2 multispectral NDVI for canopy health analysis, irrigation stress detection, and grove monitoring.
From Research to Signals: Methodology
Claude Sonnet 4.6 monitors all data sources continuously, cross-referencing current conditions against four decades of historical patterns. When probabilistic forecasts exceed 70% confidence and expected price moves exceed 5%, the system generates a trade signal with position sizing calibrated to confidence — no human discretion.
Data Ingestion
Continuous monitoring of NOAA, INMET, USDA, CME, and satellite feeds
Pattern Recognition
Cross-referencing live conditions against 40 years of historical weather-price data
Academic Model Application
UF/IFAS regression models quantify yield impact and price direction
Position sizing calibrated to confidence level; fully systematic, no discretion
Validation & Quality Control
Peer-Review Standard
Only research from journals such as HortScience, Applied Economic Perspectives and Policy, and Journal of Financial Economics — peer-reviewed by agricultural and financial economics experts.
Data Verification
Historical data cross-referenced across NOAA archives, university weather stations, and multiple market-data vendors. USDA reports validated against Florida Department of Citrus records.
Backtesting Rigor
Out-of-sample testing, walk-forward analysis through time, 10,000 Monte Carlo simulations, and parameter sensitivity analysis ensure model robustness.
Real-Time Monitoring
Predicted vs. actual outcome tracking with rolling accuracy metrics, automated model-degradation alerts, and quarterly retraining with new data.
Transparency & Continuous Research
Open Science Commitment
Unlike proprietary funds operating in secrecy, AgriQuant AI is committed to open science: publicly accessible data, cited academic foundations, and a backtesting framework documented in full.
Publicly accessible data sources only
All academic foundations cited
Backtesting framework fully documented
Continuous Research
Quarterly literature reviews keep models current across agricultural economics, meteorology, and quantitative finance, with annual retraining that incorporates the latest UF/IFAS findings and climate-adjusted historical correlations.
Coffee futures — Brazilian frost patterns
Cocoa futures — West African rainfall
Wheat futures — U.S. Plains & Black Sea
Weather derivatives — climate risk transfer
AgriQuant AI — Where Academic Rigor Meets Algorithmic Execution