Soil property and cotton productivity changes with nutrient input intensity in the Taklimakan desert of China
Bibliographic record
Abstract
Conversion of native desert into irrigated cropland is common in southern Taklimakan desert of China though the effect on soil fertility are not well understood. This study examined the effect of cultivation intensity on soil properties and crop productivity using 9-year data from cotton grown at three intensities of no fertilizer, low, and high fertilizer inputs. A native desert with sparse vegetation of Alhagi sparsifolia Shap. was used as a check. Treatment effects on soil properties fluctuated during 2005–2009 due to mineralization of applied manure or weather variation, and kept stable during 2010–2013. Soil organic carbon (SOC) during 2010–2013 increased with intensity, being 1.0–2.4 times greater in the fertilized than unfertilized cotton and desert. Soil total N, plant available N, and P content increased with fertilizer inputs. Available N content in the desert was 28.8 mg kg−1, being comparable to the low fertilizer treatment but approximately double that of unfertilized cotton, suggesting N fixing capacity of the leguminosae A. sparsifolia. Available K content was highest in desert and decreased with cultivation intensity. There were positive relationships between SOC and available N and P contents, suggesting SOC as a nutrient reservoir. Soil C:N ratio during 2010–2013 increased with intensity, being 8.7, 9.7, 10.8, and 12.0, for desert, no fertilizer, low, and high fertilizer input, respectively. Fertilizer application increased the 2010–2013 average cotton seed yield by 4.3–6.4 times compared to the unfertilized cotton field. In conclusion, our results suggest the intensive inputs of fertilizers are crucial to accelerate soil fertility and maintain the productivity of the recently converted lands from the desert.
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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 | 0.001 | 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".