Global Land Use Change Impacts on Soil Nitrogen Availability and Environmental Losses
Bibliographic record
Abstract
Anthropogenic activities, particularly land use change and management practices, alter the global nitrogen (N) cycle. As a central part of the global N cycle, soil N supply from net N mineralization (NNM) and net nitrification (NN) contributes to over 50% of crop N uptake. However, how global land use changes impact soil N supply and potential N loss remains elusive. By compiling a global data set of 1,782 paired observations from 185 publications, we show that land use conversion from natural to managed ecosystems significantly reduced NNM by 7.5% (-11.5, -2.8%) and increased NN by 150% (86, 194%), indicating decreasing N availability while increasing potential N loss through denitrification and nitrate leaching. In contrast, reversing managed to natural ecosystems significantly increased NNM by 20% (9.7, 25.4%) and decreased NN by 89% (-125, -46%), indicating increasing N availability while decreasing potential N loss. Structural equation modeling revealed that land use change induced changes in soil properties, including organic matter content, bulk density, microbial biomass and pH, and anthropogenic activities, including application of ammonium-based fertilizers and manure, were the most important factors regulating NNM and NN. The land use change effect was the strongest in tropical and subtropical regions, where NNM was negatively affected and NN positively affected by land use change. Our findings indicate that increasing soil organic matter content and enhancing soil structural development post land use change can boost soil N supply and reduce the risk for N loss.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".