Quantifying the interactions of land management practices and agricultural productivity using a soil quality index
Bibliographic record
Abstract
Abstract It is desirable to develop an objective Soil Quality Index ( SQI ) to guide sustainable agronomic intensification, thereby promoting socio‐economic well‐being. This study pioneers the use of Ward's cluster and principal component regression methods to evaluate soil homogeneity and construct a SQI (expressed as %). Field data were acquired from five different sites within Ohio, USA , that were under no‐till ( NT ), conventional till ( CT ) management and natural vegetation ( NV ) land use. Soil pH , carbon/nitrogen (C/N) ratio, nitrate and soil organic carbon ( SOC ) concentrations were identified as primary drivers of soil quality. Based on Ward's cluster method, the soil properties of croplands were not significantly different from those under NV land use. However, SQI ranked surface soils under CT management as higher in quality than NV and NT managed soils, respectively. The coefficient of determination ( R 2 ) between SQI and corn ( Zea mays L .) and soya bean [ Glycine max (L.) Merr .] yields was 0.7 and 0.9, respectively, implying this SQI effectively relates soil properties, a function of anthropogenic land management practices, with crop yields. In future, time series analyses will be used to assess SQI versus crop yield dynamics, with key socio‐economic and climate variables.
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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 | 0.001 | 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".