Mapping seasonal flood-recession cropland extent in the Senegal River Valley
Why this work is in the frame
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Bibliographic record
Abstract
Flood-recession agriculture (FRA) represents a crucial source of livelihood for numerous communities across Africa who reside near expansive floodplains and wetlands. However, it is currently insufficiently monitored. In this study, we present a methodology for mapping FRA harvested areas in the Senegal River Valley that is both reproducible and scalable. Our methodology entails the integration of optical and radar data from Sentinel platforms, conducted through a multitemporal analysis with a seasonal focus, and the application of the Random Forest algorithm. The results, supported by a kappa coefficient of 91.9%, demonstrate the first comprehensive mapping of FRA in the Senegal River valley, conducted between 2019 and 2023. This mapping facilitates the identification of the hydrological factors that influence FRA harvesting. The results of the analyses have demonstrated the importance of interannual variability in the cultivated areas of FRA, which range from 14,000 to 75,000 ha depending on the intensity of the annual flood. The duration and flooded extension are the primary factors that regulate the cropping pattern of FRA over the floodplain. The flood duration must be around 35 days to permit the cultivation, with growth generally starting between 10 and 30 November. In consideration of these findings, we recommend that future water management strategies and rural development initiatives give due consideration to FRA, to enhance the visibility of farmers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it