Irrigation water strategies to intensify vegetable production on smallholder farms in Guyana
Bibliographic record
Abstract
Abstract As part of its development program, Guyana is diversifying and expanding its agricultural sector to increase the production of higher‐value vegetable crops. Apart from ensuring food security, this also reduces the country's food import bill. Abandoned sugarcane lands are targeted for the intensification and expansion of vegetable production. This study seeks to determine the supplemental irrigation requirements of vegetable farms located along coastal lands, recommend scenarios to manage water during the two annual dry seasons, and understand the effects of irrigation thresholds on the yields of six commonly planted vegetables. The AquaCrop model was used for this purpose, together with inputs of field‐measured soil and climate data obtained from 2005 to 2012. Yield simulations of seven irrigation thresholds (40, 50, 60, 70, 80, 90, and 100% total available water [TAW]) were evaluated. At 40, 50, and 60% TAW, a decreasing irrigation requirement did not significantly reduce yield (pairwise t‐test, p > 0.05). The use of 40, 50, or 60% TAW irrigation thresholds during the two annual dry seasons is recommended. The low irrigation requirements for vegetables do not compete with the water requirements of rice and sugarcane production.
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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 | 0.000 | 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) | 0.002 | 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".