Long-term soil mulching combined with N fertilizer could maintain soil water balance in semi-arid regions of Northwest China
Bibliographic record
Abstract
Soil mulching combined with nitrogen (N) fertilization has been widely applied to enhance crop yields and crop water productivity in (semi-) arid regions. However, its long-term effects on soil water balance are unclear. Therefore, a 6-year field experiment with five tillage treatments (CT: conventional tillage without mulch, RT: ridge-furrow tillage without mulch, PM: flat cultivation with plastic film mulch, RTPM: ridge-furrow cultivation with plastic film mulch on the ridge, SM: flat cultivation with straw mulch) and two rates of N fertilization (i.e., 0 kg N ha −1 and 225 kg N ha −1 ) was conducted on the Loess Plateau. Results showed that during normal or dry growing seasons, transpiration increases of 78.0 mm (PM) and 91.2 mm (RTPM) substantially exceeded evaporation decreases of 39.7 mm and 57.6 mm. Consequently, the application of PM and RTPM during dry or normal growing seasons resulted in soil water deficits of −38.9 mm and −42.0 mm, respectively. Conversely, during humid growing seasons, the transpiration increase mediated by PM (38.9 mm) and RTPM (30.7 mm) were markedly outweighed by respective evaporation reduction of 67.3 mm and 73.4 mm. Therefore, PM and RTPM enhanced soil water storage in humid growing seasons. Averaged across the humid and dry growing seasons, the evaporation reduction under PM and RTPM (53.5 mm and 65.5 mm) effectively offset the corresponding transpiration enhancement mediated by these treatments (58.5 mm and 61.0 mm). Furthermore, the rainfall during the humid growing seasons and fallow seasons partially offset the depletion of original soil water during the dry or normal seasons. Consequently, the average soil water balance of soil mulching remained positive (15.3 mm,13.1 mm and 16.1 mm under PM, RTPM and SM respectively). Additionally, soil mulching markedly enhanced transpiration/evapotranspiration ratio and water productivity, with increment of 5.7 %-15.3 % and 17.5 %-62.8 % relative to CT, respectively. Collectively, the integration of soil mulching with N application substantially improved water productivity; crucially, sustained soil mulching practices demonstrated non-depletive effects on soil water reserves. These findings validated the strategic recommendation for large-scale adoption of this integrated approach across globally distributed arid/semi-arid zones and other water-scarce agroecosystems.
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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) | 0.000 | 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".