Virtual Control Plots for Fertilizer Efficacy Assessment
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Solution Overview
Problem
On-farm agronomic experimentation for integrated fertilizer management (IFM) is imprecise due to sampling errors and traditional methods failing to accurately assess the efficacy of novel fertilizers like biofertilizers and biostimulants, leading to confusion and uncertainty in their use.
Innovation Solution
Replace traditional control paired plots with pedoclimatic and plot-specific references derived from meta-modelling georeferenced pedoclimatic data, using machine learning algorithms to adjust fertilizer recommendations based on soil productivity indices and geolocation, enabling precise assessment of fertilizer efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional control paired plots or side-by-side control and treatment strips are used for on-farm agronomic experimentation, then the experimental setup is simple to implement, but the measurement precision and statistical significance are insufficient to accurately assess fertilizer efficacy
Solution Approach 1:
The patent creates virtual control plots by copying and replicating pedoclimatic data from actual control plots to generate multiple synthetic control scenarios. This allows statistical analysis with higher precision without requiring physically complex experimental designs. The virtual control plots are generated through data replication and randomization, enabling robust efficacy assessment while keeping the physical experimental setup simple.
2Measurement precision
If multiple variables are measured in traditional N-balance methods, then comprehensive fertilizer recommendations can be generated, but sampling and measurement errors accumulate reducing precision
Solution Approach 1:
The patent extracts and isolates the specific variables that contribute most to N-balance accuracy, separating them from less critical measurements. By focusing on key pedoclimatic parameters and using virtual control plots for statistical analysis, the method reduces the accumulation of measurement errors while maintaining comprehensive fertilizer recommendations. The approach selectively measures and weights variables based on their contribution to overall precision.
3Measurement precision
If conventional paired plot experiments are used to assess biofertilizers and biostimulants, then the experimental design is straightforward, but the marginal effects of these fertilizers cannot be properly detected leading to confusion and uncertainty
Solution Approach 1:
The patent introduces virtual control plots as an intermediary element that mediates between the simple paired plot design and the need for high precision in detecting marginal effects. These virtual plots serve as a statistical bridge, allowing the use of straightforward experimental operations while achieving enhanced detection capability through sophisticated data analysis and comparison with synthetic control scenarios.
Data Source
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AI summary
Method of agronomic experimentation and integrated fertilizer management (ifm) comprised of the harvest of the entire field plot treated with some sort of crop production input including herein and in particular crop onputs (as defined) and this without the usual establishment of one or more control non-treated subplots - or strips.