SOIL QUALITY INDICATORS AND CROP YIELD UNDER LONG-TERM TILLAGE SYSTEMS
Bibliographic record
Abstract
SUMMARY Soil quality indicators (SQI) can be used as a synthetic tool for the assessment of the sustainability of agricultural systems. In this study, we developed SQI using minimum data set (MDS) and determined the response of SQI to long-term tillage systems. Field pea (Pisum sativumL.) and spring wheat (Triticum aestivumL.) were grown in alternate years at northwestern China, and soil attributes and crop productivity were measured 6 years after the initiation of the experiment. The MDS used to develop the SQI included soil physical (aggregate, bulk density, capillary porosity, field capacity), chemical (soil organic matter, total nitrogen, available phosphorus, available potassium) and biological (microbial count, microbial biomass, and the activities of catalase, urease, alkaline phosphatase, and invertase) properties. All the property variables were measured in each of the 0–5, 5–10 and 10–30 cm depths and those variables that contributed significantly to the SQI were selected to be included in the MDS. Amongst the measured variables, bulk density and microbial counts occurred in the MDS of all the three depths, suggesting that these two properties are highly affected by the tillage treatments. In the long-term field experiment, the no-till with stubble covering the soil surface treatment received the greatest SQI score and achieved the highest crop yield. Soil quality under tillage systems can be assessed adequately using MDS measured at the top soil (0–5 cm) layer in rainfed agro-ecosystems.
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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 | 0.001 | 0.001 |
| 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 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".