A century of nitrogen dynamics in agricultural watersheds of Denmark
Bibliographic record
Abstract
Abstract Intensive agriculture has been linked to increased nitrogen loads and adverse effects on downstream aquatic ecosystems. Sustained large net nitrogen surpluses have been shown in several contexts to form legacies in soil or waters, which delay the effects of reduction measures. In this study, detailed land use and agricultural statistics were used to reconstruct the annual nitrogen surpluses in three agriculture-dominated watersheds of Denmark (600–2700 km 2 ) with well-drained loamy soils. These surpluses and long-term hydrological records were used as inputs to the process model ELEMeNT to quantify the nitrogen stores and fluxes for 1920–2020. A multi-objective calibration using timeseries of river nitrate loads, as well as other non-conventional data sources, allowed to explore the potential of these different data to constrain the nitrogen cycling model. We found the flux-weighted nitrate concentrations in the root zone percolate below croplands, a dataset not commonly used in calibrating watershed models, to be critical in reducing parameter uncertainty. Groundwater nitrate legacies built up in all three studied watersheds during 1950–1990 corresponding to ∼2% of the surplus (or ∼1 kg N ha yr −1 ) before they went down at a similar rate during 1990–2015. Over the same periods active soil nitrogen legacies first accumulated by approximately 10% of the surplus (∼5 kg N ha yr −1 ), before undergoing a commensurate reduction. Both legacies appear to have been the drivers of hysteresis in the diffuse load at the catchments’ outlet and hindrances to reaching water quality goals. Results indicate that the low cropland surpluses enforced during 2008–2015 had a larger impact on the diffuse river loads than the European Union’s untargeted grass set-aside policy of 1993–2008. Collectively, the measures of 1990–2015 are estimated to have reset the diffuse load regimes of the watersheds back to the situation prevailing in the 1960s.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".