Context
Problem & Responsibility
Operational Problem
Manual interpolation and prescription preparation could consume half a day or a full working day.
My Responsibility
Automated soil-data analysis, spatial interpolation, prescription generation and operational export.
System Design
Architecture & Workflow
- 01
Soil-data validation and analysis
- 02
Spatial interpolation
- 03
Prescription generation
- 04
Operational export workflows
- 05
Current-year soil tests compared with the nutrient requirements for a target yield to estimate the phosphorus and potassium gap by zone
- 06
Regression of fertility against historical yield, validated on trial plots and prior-year performance
Deep Dive
Inside the Build
Approach
Automated soil-data analysis, spatial interpolation, prescription generation and operational export. Developed in-house Python/geospatial workflows combining soil-test data, historical yield performance and management-zone context to estimate the nutrient gap against a target-yield goal, using linear-regression-based analysis of fertility against historical yield, validated on trial plots and prior-year performance.
- 01
Moved critical calculation logic out of difficult-to-customize desktop software into a controlled, repeatable internal workflow.
- 02
Estimated zone-specific nutrient needs rather than applying a uniform rate across a field.
- 03
Generated spatial outputs suitable for use by field equipment.
- 04
Made complex recommendation processing accessible to analysts without manually repeating every GIS step.
- 05
Current working scale is approximately 200,000 acres.
Evidence
Scale & Measurable Impact
- Reduced processing to approximately 15–30 minutes
- Estimated savings of roughly $2–$5 per acre, approximately $250,000 in total for 2020
- Applied to approximately 85,000 acres in 2020