Improving water use efficiency of surface irrigated sugarcane
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sugarcane ( Saccharum officinarum ) is a traditional major crop and export of Guyana. This study aims to assess the current irrigation scenario and propose scenarios to maximize the yield and water use efficiency of sugarcane ( S. officinarum ) in Guyana, using the AquaCrop model. Field-measured climate and soil data, and local crop parameters were used in the simulations. The crop simulations were calibrated with actual yields from 2005 to 2008. The calibrated parameters were then validated using the 2009 to 2012 yield dataset. The good agreement (RMSE of 7.15%) with the recorded yield during validation and the low sensitivity of calibrated parameters indicate the acceptability of AquaCrop and the parameters used for simulations. During calibration, the yield was weakly sensitive (0.6–2% ΔRMSEn) to changes in crop parameter values with the highest sensitivity observed for the maximum canopy cover (CCx) and the crop coefficient (kc max ). Several irrigation scenarios were then simulated, of which no significant reduction or increase in yield was observed between the scenarios 50% to 100% of the total available water (TAW). A threshold of 50%TAW is advised during dry periods to avoid significant yield loss. It is recommended that this scenario be validated with field experiments. The results of this study will assist in maintaining high sugarcane yields even during dry conditions.
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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