Assessment of soil quality for guided fertilization in 7 barley agro-ecological areas of China
Bibliographic record
Abstract
Soil quality is the basis for the development of sustainable agriculture and may be used for evaluating the sustainability of soil management practices. Soil quality status and integrated soil quality index (SQI) in sampled 97 farmlands distributed in 7 barley agro-ecological areas of China were analyzed by using 13 soil chemical parameters. The results showed six principal components totally explained 72% variability for the 13 parameters and identified 9 parameters (includes pH, NH4+-N, NO3--N, available P, available K, exchangeable Mg, DTPA-Fe, DTPA-Cu and Cl-) with high factor loading values as the minimum data set (MDS) for assessing soil quality. Average soil quality of all farmlands is moderate (SQI = 0.62). The SQI of barley farmlands in 7 agro-ecological areas showed the following order: Inner Mongolia Plateau (0.75 ± 0.02) > Yunnan-Kweichow Plateau (0.72 ± 0.06) > Qinghai-Tibet Plateau (0.63 ± 0.08) > Yangtze Plain (0.62 ± 0.10) > Huanghuai Region (0.58 ± 0.09) > Northeast China Plain (0.56 ± 0.07) > Xinjiang Province (0.54 ± 0.07). Total 29 out of 97 farmlands in 7 areas have low SQI level (< 0.55). Hence, these farmlands require urgent attention for soil quality improvement through modification of the soil parameters in the MDS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 source (direct Gemma or distilled Codex), 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".