Analysis of Spatiotemporal Features of Cassava Evapotranspiration in Benin Using Integrated FAO-56 Method and Terra/MODIS Data
Bibliographic record
Abstract
This study analyzed the temporal and spatial features of cassava evapotranspiration from 1985 to 2015 in Benin using linear regression, Mann-Kendall trend test, Sen’s slope estimator, and interpolation. The study used basic meteorological data from the Met office of Benin and the Terra/MODIS vegetation index. The estimated crop coefficients (Kc) from FAO and NDVI have shown a strong and positive linear relationship with a correlation coefficient of r = 0.968, while NDVI-Kc presented values slightly lower than FAO-Kc. The rates of crop evapotranspiration (ETc) varied from 1.23 to 7.63 mm/day and 2.92 mm/day on average. At the local level, there were significant upward trends in the seasonal ETc for stations located in the bimodal rainfall pattern area (Cotonou, Bohicon, and Save) and non-significant for stations in the unimodal rainfall pattern area (Kandi, Parakou, and Natitingou). At the country level, both methods revealed a non-significant positive trend in cassava evapotranspiration in the study area while showing a strong and positive linear relationship in variations throughout the growing season, r = 0.956. Cassava’s growth in Benin may encounter in the future the risk of water deficit.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".