Spatiotemporal variability of soil physical properties and water, salt, nitrogen, and phosphorus contents for farm level
Bibliographic record
Abstract
Soil water, salt, and nutrient variability are essential factors that impact crop productivity in agriculture systems. However, effective management of small farms requires access to fine-scale data on soil water, salt, and nutrients. Large-scale assessments of spatial variability using classical statistics and geostatistical methods can help identify nutrient-deficient zones. In Xinjiang, China, inadequate water and nutrient management has resulted in low crop productivity in agriculture systems. To address this issue, this study evaluated the mechanical composition, bulk density, and contents of water, salt, ammonium nitrogen ([Formula: see text]), nitrate nitrogen ([Formula: see text]), and available phosphorus (A-P) in soil at the farm level in the Xinjiang region. Results showed low variability in soil bulk density, medium variability in soil water content, mechanical composition, [Formula: see text], and A-P, and high variability in soil salt content and [Formula: see text]. Mechanical composition and A-P showed a small range of variation across different soil depths, while soil water content and [Formula: see text] in the surface layer varied significantly more than in other soil layers. [Formula: see text] variability increased with soil depth. Soil properties showed minimal differences over time. Multi-factor deficiencies, particularly in nitrogen, were observed throughout the study area. The generated maps offer a useful tool for farm managers and policymakers. In summary, this study highlights the significance of evaluating the spatial variability of soil properties for identifying zones deficient in water and nutrients, as well as those with salt accumulation. This information can be utilized to develop effective strategies for site-specific nutrient management.
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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 | 0.001 |
| 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".