Nitrogen losses from soil as affected by water and fertilizer management under drip irrigation: Development, hotspots and future perspectives
Bibliographic record
Abstract
While soil nitrogen (N) losses under drip irrigation water and fertilizer management have become a key issue in global environmental N pollution, no current systematic review of this issue exists in the literature. Drawn from the Web of Science Core Collection database, 290 related articles were identified as research subjects (1991–2022). To reveal the basic characteristics, research power, hotspots and future perspectives of this research field, an in-depth bibliometrics analysis and graphical knowledge display were undertaken by using CiteSpace software. By analyzing the evolution process of keywords, greenhouse gases, water use efficiency and crop yield have been research hotspots of this field in recent years. Irrigation systems, soil moisture, fertigation and N losses have always been the core research topics. The focus on N losses pathways has gradually shifted from nitrate (NO3-) leaching alone to comprehensive consideration of multiple losses pathways including NO3- leaching, and emissions of N2O, NH3 and NO. The corresponding water and fertilizer management strategies have gradually shifted from concentrating on water and fertilizer application amounts to diversified management methods involving combinations of amounts, methods and types. Moreover, the development and widespread application of new water and fertilizer management technologies and exogenous additives have further enriched the research direction of soil N losses under drip irrigation water and fertilizer management. Future research still needs to explore how to balance high crop yields and minimize environmental impacts, which will provide effective strategies for controlling agricultural non-point source pollution and mitigating global warming.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".