Forecasting National Crop Yield Using Weather Indices
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Solution Overview
Problem
Accurate national crop yield forecasting during the growing season is challenging due to the difficulty in obtaining reliable data from widely geographically distributed local and regional measurements, with existing methods being either survey-based or requiring costly and complex simulation models.
Innovation Solution
A computer system that aggregates geo-specific weather indices from agricultural data records to estimate crop yields using linear regression, allowing for national crop yield forecasting by calculating weather stress events and adjusting for geographic variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If survey-based data collection is used to obtain national crop yield data, then data coverage is improved, but data accuracy during the growing season deteriorates because farmers cannot provide reliable estimates until harvest time
Solution Approach 1:
The patent introduces an intermediary system that collects local and regional crop yield measurements from various sources (farmers, agricultural extensions, harvest data) and uses a computer model to aggregate these into national-level forecasts. This intermediary processing layer transforms fragmented local data into reliable national estimates, solving the contradiction between data coverage and accuracy.
2Measurement precision
If process models are used to predict regional crop yields, then forecast accuracy is improved, but cost and complexity increase due to requiring multitude of local inputs and calibration
Solution Approach 1:
The patent segments the complex process model into a simpler computer model that uses aggregated local and regional measurements as inputs. Instead of requiring detailed local inputs for each field, the system divides the country into regions and uses regional aggregation, significantly reducing complexity while maintaining forecast accuracy.
Solution Approach 2:
The patent changes the parameters from detailed local field-level inputs to aggregated regional measurements. By transforming the input parameters from numerous local data points to summarized regional statistics, the system reduces the complexity of data collection and model calibration while preserving the ability to generate accurate national forecasts.
3Reliability
If local and regional measurements are collected from widely geographically distributed areas, then data representativeness is improved, but data collection difficulty increases during the growing season
Solution Approach 1:
The patent creates a universal data collection system that accepts multiple types of local and regional measurements from various sources (farmers' reports, agricultural extension data, harvest measurements). This multi-functional system can process different data types through a single platform, making data collection easier while maintaining representativeness across widely geographically distributed areas.
Data Source
AI summary
A method for determining national crop yields during the growing season may be accomplished using a system that receives agricultural data records that are used to forecast a national crop yield for a particular year. Weather index values are calculated and aggregated from the agricultural data records. Crop yield estimating instructions select representative features from aggregated weather index data and create a covariate matrix for each specific geographic area. Linear regression instructions calculate the crop yield for the specific geographic area for the specific year using the corresponding covariate matrix for that specific year. The crop estimating instructions determine a national crop yield for the specific year using the sum of the crop yields for the specific geographic areas for the specific year nationally adjusted using national yield adjustment instructions. In an embodiment, the crop yield may refer to a specific crop yield such as corn yield.