Pest Control Living Database (PCLD)

The Pest Control Living Database (PCLD) is a USDA-funded initiative (project #1023888) that integrates agricultural pest observation data, biological traits of insects, and satellite-based Earth observation resources into a comprehensive, interactive data resource. Designed to streamline data-driven analysis for agricultural pest management, PCLD enables scientists, researchers, and growers to leverage data science and remote sensing technologies to predict pest dynamics, ultimately guiding agricultural decision-making and stewardship worldwide.

Key Resources and Features

The Pest Control Living Database provides:

• Over 100,000 pest-related observations capturing insect activity, abundance, and impacts on crop yields.
• Integrated remote sensing datasets tailored for agricultural sampling locations.
• Detailed insect traits data to facilitate ecological and agricultural research.
• Visualizations of remote sensing datasets and agricultural data.
• Standardized templates for organizing and contributing data.

Explore the detailed slideshow for the PCLD here.

Directory Structure Overview

├── app
│ ├── dataset_defns # Definitions for datasets used by the database
│ ├── live_database # Primary database files and configuration
│ ├── secrets # Sensitive configuration and authentication details
│ ├── templates # HTML templates for web interface
│ └── pycache # Python compiled files cache
├── data # Miscellaneous data for database initialization or reference
├── gee_apps # JavaScript apps deployed on Google Earth Engine
└── llm_trait_pipeline # Standalone LLM pipeline for automated pest trait discovery

• docker-compose.yml: Main Docker configuration, orchestrating all core services.

Contributing Data

We invite contributions of datasets containing information on pest abundance, natural enemies, parasitism/predation rates, or pest-related crop damage. Ideal datasets include over 100 farm-years of observations. Minimum data requirements are:

• Crop sampled
• Sampling date
• Metric type (e.g., pest abundance, predation rate)
• Measurement (per sampling unit)
• Management unit or unique farm ID
• Insect identification (if known)
• Geographical coordinates (if shareable)
• Sampling methodology (metadata)

Submit completed datasets using provided templates to Richard Sharp.

Project Team

This project is managed by an interdisciplinary team including:

• Becky Chaplin-Kramer (rchaplin@umn.edu)
• Colleen Miller (Colleen Miller)
• Danny Karp (dkarp@ucdavis.edu)
• Richard Sharp (rich@springinnovate.org)

For general inquiries or further details, please contact the project leads above.