Baseline high-resolution maps of soil nutrients in Morocco to support sustainable agriculture
Bibliographic record
Abstract
Phosphorus (P) and potassium (K) are essential fertilizer elements whose recommendations require soil analysis tailored to crop-specific needs. However, in developing countries like Morocco, access to open databases on nutrient bioavailability remains limited, hindering data-driven agricultural strategies. This paper presents the first national reference maps of available P and exchangeable K at 250 m resolution over Morocco’s croplands using digital soil mapping with machine learning algorithms and environmental covariates. Unlike previous efforts employing traditional interpolation methods, these maps were developed using Random Forest by integrating 5,276 soil samples for P and 6,978 for K with 76 environmental covariates representing climate, topography, vegetation, and parent material. Model validation using independent test datasets demonstrated strong performance, with R 2 values of 0.78 for P and 0.80 for K. Uncertainty assessment through bootstrapping confirmed prediction stability across diverse agricultural landscapes. These baseline maps enhance fertilizer recommendations, promote precision farming, and support agricultural sustainability. The maps are freely available through open-access repository, enabling evidence-based decision-making for researchers, practitioners, and policymakers to improve nutrient management efficiency.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".