Deep vertical rotary tillage optimizes soil water-temperature-salinity conditions and enhances cotton growth in salinized arid farmland
Bibliographic record
Abstract
Soil salinization hinders cotton ( Gossypium hirsutum L. ) production and sustainable agricultural in arid regions. In this study, the effects of deep vertical rotary tillage (DVRT) on soil moisture, temperature, salinity, root system, cotton photosynthetic characteristics, and yield were evaluated over a two-year experiment. Two tillage methods were implemented: conventional tillage (CT, 0.20 m depth) and DVRT at 0.20, 0.40, and 0.60 m depths. Three treatments were evaluated: (i) continuous CT, (ii) alternating DVRT (DT20, DT40, DT60) with CT, and (iii) continuous DVRT (CDT20, CDT40, CDT60). Deep vertical rotary tillage (DT and CDT) increased soil moisture, reduced electrical conductivity, regulated soil temperature, enhanced cotton photosynthesis, root boll capacity, and ionic balance (Cl − , Ca 2+ , and Na + uptake). Among all treatments, DT60 showed the best yield response, increasing yield by 47 % and 44 %, improving fiber quality index by 35 % and 32 %, and enhanced water use efficiency (WUE) by 45 % and 50 %. Although CDT treatments enhanced salt leaching, they reduced soil moisture in bare ground more significantly than in mulched areas, indicating that excessive tillage increases evaporation, reduces water retention, and inhibits root development, thereby lowering WUE. The partial least squares path model revealed that DVRT optimized soil conditions, promoting root development and photosynthetic efficiency, thereby supporting biomass accumulation and yield formation The random forest model showed that soil moisture was the primary factor for yield formation, emphasizing its role in saline agriculture. These results highlight the importance of regulating soil moisture to improve crop yield, resource efficiency, and address climate change.
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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".