Agricultural Land Suitability Assessment towards Promoting Community Crop Production in Tolon-Ghana
Bibliographic record
Abstract
Abstract Continuous use of agricultural land without periodic assessment of its suitability or performance for the cultivation of a specific crop could degrade soil fertility and compromise the long-term sustainability of the land to support production. Our aim is to use an integrated approach to assess agricultural land suitability in a small-scale farming system in the semi-arid region of northern Ghana, identify limiting factors for optimum crop production, and recommend intervention options towards sustainable farm management. We developed a data-driven model for land suitability analysis based on the Generalized Additive Models (GAM) approach. We validated the model with the actual yield data for six food crops (maize, pepper, yam, rice, peanut, and cowpea) under various biological, physical and chemical soil conditions across six communities. The result showed that the farmlands across the communities were highly suitable for maize and pepper but not suitable for cowpea. A qualitative validation method based on the contingency table showed the accuracy percentage of 84–100% for POD (Probability of Detection) and 6–21% for FAR (False alarm ratio) for all crops type. Hence, our model could be considered excellent to predict land suitability for different crop types. We recommend that stakeholders in the agricultural value chain should collaborate to develop low-cost and effective means of helping farmers determine the suitability of soils for specific crops to ensure that farmlands are not of depleted nutrients. In addition, periodic farmer training on appropriate farm management practices, including the right use of fertilizers, is needed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".