Modification of the RZWQM2-P model to simulate labile and total phosphorus in an irrigated and manure-amended cropland soil
Bibliographic record
Abstract
With the expansion of the dairy industry, phosphorus (P)-enriched dairy manure has increasingly been used to replace chemical fertilizer to meet crop nutrient demand. This practice could lead to excessive total P accumulation in the soil and increase the risk of P pollution in the environment. The newly-developed RZWQM2-P model uses the soil P pool structure from the EPIC model, which is not sensitive to total soil P. Therefore, we modified the P module in RZWQM2-P to improve its capability in simulating total soil P. We subsequently assessed the ability of the modified model to simulate labile soil P, total soil P, plant P uptake, and crop yield using a dataset collected from an irrigated field treated with dairy manure and inorganic fertilizer at eight rates under a repeating wheat-potato-barley-sugar beet rotation. The results suggested that the modified RZWQM2-P model satisfactorily simulated field-measured annual total soil P, plant P uptake, and crop yield. Labile soil P was simulated less accurately, but the results were acceptable as the model responded well to P treatments. We simulated the long-term soil P dynamics under three P-application scenarios. Long-term simulation results showed that it took 14 years for the labile soil P level to return to the initial level after eight years of manure-P applications at a rate of 65.5 kg P/ha year −1 . The modified RZWQM2-P model can be used to simulate total soil P and labile soil P contents and to assess P management practices in irrigated cropland amended with manure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".