Leaching loss of dissolved organic nitrogen from cropland ecosystems
Bibliographic record
Abstract
Although widely studied in grassland and forest ecosystems, dissolved organic nitrogen (DON), an important form of nitrogen (N) lost through leaching, has received little attention in cropland ecosystem research. A global literature review and partial least squares path modeling (PLS–PM) were adopted to assess the quantity and composition of N leaching loss under different cropping systems, soil types, and management practices, as well as to identify the major factors controlling DON leaching. Annual total dissolved nitrogen (TDN, organic + inorganic N) leaching under different cropping systems ranged from 4.0 to 383.2 kg N ha −1 . Vegetable and rice production systems showed the greatest and least TDN leaching, respectively. Across different cropping systems, DON accounted for 4.7%–34.9% of TDN in leachate. The NH 4 + –N form of N leaching is negligible in most upland cropping systems (<2.3%), but not so in lowland rice systems. The largest ratio of DON leaching to TDN leaching was found in a rice–wheat rotation (34.9%). Catch crop slightly increased DON leaching; however, it greatly decreased TDN leaching. Cropping systems in which animal manure or plant compost was applied or lowland rice was included showed a relatively high DON leaching ratio. Accordingly, in investigating DON leaching, more attention should be focused on cropping systems with manure application or lowland rice systems. Compared with climatic conditions and soil properties, field management practices (irrigation, chemical N input, and manure amendments) proved to be the factors most strongly influencing DON leaching. This suggests that optimizing water and N management practices is the most effective way of reducing the risk of DON leaching and increasing N use efficiency, particularly for vegetable and rice production systems.
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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) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".