Geostatistical modeling of soil physicochemical properties and environmental drivers in agro-industrial landuse systems in Bangladesh
Bibliographic record
Abstract
Effective land use practices and sustainable land management require a thorough assessment of the spatial variability of soil properties across distinct landuse zones. The research analyzed the spatial variability of key soil physicochemical parameters across agro-industrial (Diversified farming, specialized farming, and industrial area) zones using geostatistical methods and examined the relationship with environmental variables. A total of 123 soil samples were collected at 0–15 cm depth using systematic sampling techniques, and semivariogram modelling was used to identify soil property distribution patterns, with nugget-to-sill ratios calculated to assess spatial structure. The findings of the one-way ANOVA test revealed significant differences ( p <0.05) in soil parameters, except moisture, across the landuse zones. Pearson correlation analysis revealed strong positive correlations between Soil Organic Matter (SOM), Soil Organic Carbon (SOC), and Soil Total Nitrogen (STN), with Normalize Difference Built-up Index (NDBI) being the most associated environmental factor, while PCA analysis highlighted SOM, SOC, and STN as the most influential soil variables, while Land Surface Temperature (LST), Normalize Difference Built-up Index (NDBI), and Normalize Difference Vegetation Index (NDVI) as the most dominant environmental factors influencing the soil properties. This study revealed varying distribution patterns of environmental-anthropogenic and soil parameters across landuse zones and their impact on this variation. Nugget-to-sill ratios indicated weak to moderate spatial structure for most soil properties, except for STN (12.28%) in industrial area, moisture (3.36%) in the diversified farming land, pH in specialized (0%) and industrial area (0%), and C:N ratio in the industrial area (0%), which showed strong spatial dependence. In industrial areas, soil properties exhibited moderate to strong spatial dependence, except for SOM and SOC. This study highlights the role of land use, environmental, and anthropogenic factors in soil property distribution, supporting precision agriculture and conservation. • Significant differences (p<0.05) in soil properties across land use zones. • Exponential, circular, spherical models effectively mapped soil property variations. • Environmental-anthropogenic variables influenced distribution of soil properties. • Soil properties exhibit weak to strong spatial dependency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".