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Improving Rice Modeling Success Rate with Ternary Non-structural Fertilizer Response Model
Juan Li1, Mingqing Zhang1, Fang Chen2,3
1Soil and Fertilizer Institute, Fujian Academy of Agricultural Science, Fuzhou, 350013, China.
A new ternary non-structural fertilizer response model (TNFM) improves rice fertilization accuracy over the traditional ternary quadratic polynomial model (TPFM). This enhanced model offers better applicability and addresses issues like bias and multicollinearity in fertilizer response modeling.
Area of Science:
- Agricultural Science
- Agronomy
- Soil Science
Background:
- Fertilizer response modeling is crucial for precise rice fertilization.
- Traditional models like the ternary quadratic polynomial model (TPFM) have limitations, including low success rates and applicability issues.
Purpose of the Study:
- To develop an improved fertilizer response model for rice.
- To address the limitations of the TPFM, specifically bias and multicollinearity.
- To enhance the accuracy and applicability of fertilizer response modeling.
Main Methods:
- Established a ternary non-structural fertilizer response model (TNFM).
- Utilized experimental data from N, P, and K fertilized rice fields.
- Compared TNFM performance against TPFM using 88 rice field trials in China.
Main Results:
- TNFM significantly improved modeling success rates compared to TPFM.
- A higher proportion of TNFMs (40.9%) satisfied plant nutrition laws versus TPFMs (26.1%).
- Parameters N0, P0, and K0 from TNFM serve as effective indicators of yield potential.
Conclusions:
- The TNFM offers superior fitting accuracy and a wider application range than TPFM.
- TNFM effectively resolves bias and multicollinearity issues present in TPFM.
- The developed TNFM advances metrological fertilization techniques for rice cultivation.
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