Applicability of Ground-based Remote Sensors for Crop N Management in Sub Saharan Africa
Bibliographic record
Abstract
Remote sensors have a growing legacy for improving crop N use efficiency (NUE) in several parts of the world. The technology employs crop spectral properties to determine fertilizer rates by matching crop N requirement based on midseason yield potential. Conclusions that the technology is inappropriate for Sub Saharan Africa (SSA) because the farmers use little or no fertilizer, or cannot afford it, are reviewed. Opportunities and applicability using a model concept from the GreenSeeker® sensor ($4000) are presented. Because farmers in SSA inefficiently apply fertilizer through blanket recommendations, they must improve crop NUE to minimize cost. Application of this technology would enable refinement or development of N recommendation protocols for target groups of farmers based on site and delineated management field zones. With new developments of a prototype GreenSeeker®, the Optical Pocket Sensor (<$250), this technology will definitely be affordable and applicable, at least for institutional research purposes in SSA.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".