Dynamic Modeling of Soil Water Dynamics and Nitrogen Species Transport with Multi-Crop Rotations Under Variable-Saturated Conditions
Bibliographic record
Abstract
Excessive application of nitrogen (N) fertilizers in agriculture poses significant environmental risks, notably nitrate leaching into groundwater. This study evaluates soil water dynamics and the transport of urea, ammonium, and nitrate under variable-saturated conditions in a long-term experimental field in Croatia, Europe. Utilizing HYDRUS-1D and HYDRUS-2D models, we simulated water flow and nitrogen transformation and transport across six lysimeter-monitored locations over four years (2019–2023), incorporating diverse crop rotations and N addition. Key modeled processes included nitrification, urea hydrolysis, and nitrate leaching, integrating field-measured parameters and climatic conditions. The models achieved high reliability, with R2 values for water flow ranging from 0.58 to 0.97 and for nitrate transport from 0.13 to 0.97; however, some cases reported lower reliability. Results revealed that nitrate leaching was influenced by precipitation patterns, soil moisture, crop growth stages, and fertilization timing. Peak nitrate losses were observed during early crop growth and post-harvest periods when elevated soil moisture and reduced plant uptake coincided. The findings highlight the importance of optimizing nitrogen application strategies to balance crop productivity and environmental protection. This research demonstrates the effectiveness of numerical modeling as a tool for sustainable nitrogen management and groundwater quality preservation in agricultural systems. It also indicates the need for further development by capturing some of the processes such as identification in the N cycle.
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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".