Drought Impacts on Soil Nitrogen Dynamics and Agricultural Management in the Canadian Prairies: Monitoring, Simulation, and Fertilizer Optimization
Bibliographic record
Abstract
This research aims to enhance agricultural drought monitoring and optimize nitrogen (N) fertilizer application in the Canadian Prairies, addressing two key research questions: (1) how effective are soil moisture–based indices compared to traditional meteorological indices for monitoring agricultural drought, and (2) how does drought-induced residual soil nitrogen (RSN) affect nitrogen fertilizer requirements in subsequent cropping seasons? To answer these, the study first evaluated the Empirical Standardized Soil Moisture Index (ESSMI) against the Standardized Precipitation Evapotranspiration Index (SPEI) from 1980 to 2022, using remote sensing soil moisture data and crop yield records. Results showed that ESSMI captured localized soil moisture extremes more effectively than SPEI and explained slightly more variability in wheat and canola yields, highlighting its value for agricultural drought monitoring. The second component applied the Alberta Farm Fertilizer Information and Recommendation Manager (AFFIRM) to simulate how drought-induced RSN alters optimal fertilizer rates across wheat–canola and canola–wheat rotations under varying soil zones and economic scenarios. Simulations revealed that drought generally increases RSN, reducing fertilizer requirements in following seasons, though crop and fertilizer prices strongly moderated recommendations. Together, these findings advance understanding of drought-soil nitrogen interactions and provide practical guidance for precision agriculture, supporting resilient management strategies in drought-prone regions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".