Modeling of phosphorus dynamics in contrasting agroecosystems using long-term field experiments
Bibliographic record
Abstract
Morel, C., Ziadi, N., Messiga, A., Bélanger, G., Denoroy, P., Jeangros, B., Jouany, C., Fardeau, J. C., Mollier, A., Parent, L. E., Proix, N., Rabeharisoa, L. and Sinaj, S. 2014. Modeling of phosphorus dynamics in contrasting agroecosystems using long-term field experiments. Can. J. Soil Sci. 94: 377–387. Long-term field experiments on phosphorus (P) fertilization were originally designed to study crop needs in different soil types by analyzing the effects of several rates of P fertilization on yields, their P concentrations and dynamics of plant-available soil P. The objective of this study was to test a computer-based model to simulate the P dynamics at the field scale using plant database and analyzing for plant-available P by a hierarchical process-based approach. It predicts both the concentration (CP) of phosphate ions (Pi) in soil solution and the associated Pi amounts that in time equilibrate with Pi in solution. Five experiments, representative of contrasting soil types, land-use, and climates were selected. Our model equilibrates the change in plant-available P in the upper soil layer to the P budget between annual P inputs and outputs. Rates of P fertilization affected simulations following the same expected pattern across sites. Field-observed and simulated values are in good agreements in all sites. The field-observed variations of CP per unit of P budget ranged from 0.007 to 2.49 (µg P L−1) (kg P ha−1)−1. The predictions are of the same order of magnitude. Predictions were compared with empirical long-term data and mismatches were discussed. This investigation highlights the scientific interest of long-term field P experiments to test and validate models describing P dynamics at the scale of the agricultural fields under different agricultural management practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".