Determining and Mapping Soil Nutrient Content Using Geostatistical Technique in a Durian Orchard in Malaysia
Bibliographic record
Abstract
Soil nutrient are essential for crop growth. Spatial variability of nutrient can be occurred in various scales, betweenregion, field or within field especially in variation in soil properties. Precision farming is a technology currentlyavailable for sustainable agriculture. This technology enables farm management is based on small-scale spatialvariability of soil and crop parameters in the field. This study was carried out in a Durian Orchard at Bendang ManAgrotourism Project, Sik, Kedah, Malaysia. The objectives of this study are to determine and map soil nutrient contentespecially Nitrogen, Phosphorus and Potassium (NPK) variability in a durian orchard using geostatistical technique.The NPK was analyzed and mapped by Geostatistic Plus (GS++) to quantify the level of spatial nutrient available andpredict nutrient values at unsampled location. Results indicated that NPK ranged from < 0.1 to 1.0 % (N), < 3 to > 45ppm (P) and 0.8 to >1.4 cmol(+)/kg (K), respectively. Nutrient map showed that the area has less sufficient of N, whileP and K were sufficient. This study revealed the potential and ability of geostatistical-variogram in determining andmapping soil nutrient content in a durian orchard. Furthermore NPK map can be used to apply fertilizer to an area,where less NPK content for efficient fertilizer management.
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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 | 0.001 |
| 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".