Agricultural Land Parcel Valuation via Crop Simulation
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Solution Overview
Problem
Current methods for valuing agricultural land parcels lack a standardized approach to compare profitability across different regions due to varying data availability and lack of common metrics, relying solely on common attributes and historical sales, which does not account for unique factors like weather, topography, and management practices.
Innovation Solution
A system that combines public, commercial, and field trial data with crop simulation to generate agro-economic metrics, allowing for objective comparisons by assessing management practices, historical weather, locations, soil types, and crop types, and simulating crop growth to calculate weighted valuations relative to other parcels within a region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized agro-economic metrics are generated using crop simulation and multiple data sources, then measurement precision of parcel valuation is improved, but device complexity increases
Solution Approach 1:
The system segments the complex valuation process into distinct functional modules: a crop simulation processor that handles biophysical modeling, an agro-economic metrics processor that calculates productivity and sustainability metrics, and a valuation processor that determines final parcel values. Each module operates independently with defined inputs and outputs, managing complexity through functional decomposition while maintaining high measurement precision through specialized processing at each stage.
Solution Approach 2:
The patent introduces agro-economic metrics as intermediary variables that bridge the gap between raw simulation data and final valuation outputs. These metrics (e.g., productivity scores, sustainability indices) serve as standardized mediators that translate complex biophysical simulation results into comparable economic indicators, enabling precise valuation without requiring direct complex interactions between all system components.
2Loss of information
If crop simulation and multiple data sources are integrated, then information completeness is improved, but loss of information increases due to data integration challenges
Solution Approach 1:
The system transforms heterogeneous data from multiple sources into standardized parameters that the crop simulation model can process. Management practices, weather data, soil characteristics, and topography information are converted into uniform input parameters with consistent formats and units. This parameter standardization enables complete utilization of diverse data sources while minimizing information loss during integration, as each data type is preserved in a compatible form.
3Ease of operation
If common attributes and historical sales alone are used for valuation, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary crop growth simulation and agro-economic metric calculation before the actual valuation process. By pre-computing productivity metrics, sustainability indices, and simulated yield data for each parcel, the system prepares standardized information that simplifies the subsequent valuation step. This preliminary action enables accurate valuation without requiring complex real-time calculations, maintaining ease of operation while improving precision.
Data Source
AI summary
A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a weighted valuation corresponding to the each of the parcels, where the agricultural metrics and the weighted valuation for the each of the parcels are expressed relative to all of the parcels within the prescribed region.