Spatial analysis of oil palm growth and soil properties in a plantation in Nigeria
Bibliographic record
Abstract
Relationships between spatially correlated soil properties and crop performance at field scale are vital when planning specific site management for sustainable crop production. This study investigated the correlations and relationships between some soil properties and oil palm trunk diameter at breast height (DBH) at the Oil Palm Plantation, Teaching and Research Farm, Ekiti State University, Ado Ekiti, southwest Nigeria. Soil samples were collected from 0 to 10 cm surface layer at 81 georeferenced points within the plantation to determine soil properties and oil palm trunk DBH. The soil properties and trunk DBH varied widely with particle density, soil pH, bulk density, and field capacity showing least variability (coefficient of variation (CV) < 12%), oil palm trunk DBH, soil organic matter, air capacity, soil texture, soil water content, permanent wilting point, total porosity, and available water showed moderate variability (12.0% < CV < 60.0%), while saturated hydraulic conductivity was highly variable (CV > 60%). Classical linear multiple regression showed that the sand, soil pH, and bulk density could only explain 16% of the variability in DBH, whereas the principal component regression analysis had explained about 72% of the variability in DBH. The minimum suitable sampling interval for spatially independent variables was 10 m (1 lag), while it was the sampling point (0 lag) for spatially dependent variables. The results could be used as baseline data for delineating soil and nutrient management zones for the oil palm plantation and other crops in the study area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".