Land Suitability Assessment for Soybean (Glycine max (L.) Merr.) Production in Kabwe District, Central Zambia
Bibliographic record
Abstract
Soybean (Glycine max (L.) Merr.), is a high value crop that can generate income for households. As a legume, soybean is incorporated in cropping systems to improve soil fertility. Soybean productivity is however limited by factors including declined soil fertility, climate change and partly due to inadequate land suitability information. This study aimed at identifying suitable land for soybean production in Kabwe district. Data layers of selected attributes relevant to soybean production were generated with slope and wetness data layers extracted from the digital elevation model (DEM). Elevation was used as a proxy for climate (rainfall and temperature) and was generated by reclassifying the elevation grid into elevation classes. Data layers for soil reaction (pH), soil organic carbon, phosphorus and texture were generated by inverse distance weighting interpolation method based on soil point data. A distance to roads layer was created using the euclidean distance tool. A spatial process model based on multi-criteria evaluation was used to integrate data layers in a weighted sum overlay to generate a soybean suitability map, whose quality was assessed using an error matrix. Results showed that 15.07% of the investigated area was highly suitable for soybean production, whereas 26.53% was suitable and 25.18% was moderately suitable. The other 20.57% was marginally suitable, 10.74% was currently not suitable and 1.92% was permanently not suitable. Based on ground truth data, the overall classification accuracy of the suitability map was 65%. The map was therefore good enough for use as a guide in selecting suitable sites for soybean production.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".