Corn Production Return Prediction Model Using Factor Extraction
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Solution Overview
Problem
Current methods for predicting crop production and optimizing returns in corn production are limited by the difficulty in accurately determining marginal revenue and marginal cost, and the complexity of factors influencing these variables, leading to suboptimal resource allocation and profitability.
Innovation Solution
A data-driven multivariate non-linear statistical model is developed to identify significant individual and interaction factors contributing to corn production returns, using historical data from the USDA, which transforms and fits the data to select significant factors through backward elimination, resulting in a predictive model that maximizes returns by optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine marginal revenue and marginal cost in corn production, then the analysis is simpler, but the accuracy of predicting production returns is insufficient
Solution Approach 1:
The patent transforms the traditional approach by changing the parameters from simple marginal revenue and cost calculations to a multivariate non-linear statistical model that incorporates multiple factors including weather conditions, market prices, input costs, and interaction effects between variables. This parameter transformation enables 98.22% accuracy in predicting production returns while systematically managing model complexity through structured factor selection.
2Measurement precision
If a comprehensive model including all factors is used to predict crop production returns, then the prediction accuracy improves, but the difficulty of detecting and measuring significant factors increases
Solution Approach 1:
The patent extracts and isolates significant factors from a comprehensive set of potential variables using systematic statistical methods. Through backward elimination and interaction term analysis, the model identifies and extracts the most influential factors (weather conditions, market prices, input costs) while removing redundant variables. This extraction process achieves 98.22% prediction accuracy by focusing only on the critical subset of factors that genuinely drive production returns.
3Productivity
If resource allocation is optimized using accurate predictive models, then profitability increases, but the time and computational resources required for analysis increase
Solution Approach 1:
The patent performs preliminary action by pre-identifying and structuring the significant factors and their interactions before actual prediction and optimization tasks. The model framework is established in advance with predetermined factor categories (weather, market, inputs) and relationship structures, allowing for rapid deployment and execution when making actual production decisions. This preliminary structuring reduces the time required for real-time analysis while maintaining high profitability optimization.
Data Source
AI summary
A data-driven model to predict the returns of the production of corn in the U.S. is described. In one example, the model can account for 25 elements or factors presumed by the U.S. department of agriculture (USDA) to be contributing to the returns from corn production in the US. The model is designed on the basis of a number of parameters, including the selection of a significant set of the 25 factors, the extent or percentage of contribution of each factor, the extent of contribution to unknown factors, the identification of which of the significant factors are interacting, and others. In one example, 7 out of the 25 factors were found to be statistically significant, and 6 interaction terms were identified. The proposed model accurately predicts the returns from corn production in the U.S. with 98.22% accuracy.