Water Use Dynamics in Double Cropping of Rainfed Upland Rice and Irrigated Melons Produced Under Drought‐Prone Tropical Conditions
Bibliographic record
Abstract
Abstract Agricultural expansion and intensification is occurring in seasonally dry regions of Central America, while droughts are intensifying due to increasing water demand and climatic change. Empirical measurements of water consumption of major crops in this region are scarce but crucial to assess agricultural water use dynamics in the light of increasing regional water conflicts. We empirically quantify total crop water use (CWU) and water footprints (WFs) of rainfed upland rice (wet season) and groundwater‐irrigated melons (dry season) grown sequentially as a double cropping system, one of the major cropping systems in the seasonally dry province of Guanacaste in northwestern Costa Rica. Data for this study cover 2 years and were measured with a state‐of‐the‐art eddy covariance water and carbon flux station. Upland rice only consumed green water (CWUgreen = 383 L/m2), while melons only consumed blue water (CWUblue = 177 L/m2). Irrigation was found to be 1.5 times larger than the actual melon water consumption, with better irrigation efficiencies than reported for melon farms in Brazil but slightly inferior to farms in Spain. Melon WFblue was 79 m3/t, a much lower value than global and regional estimates reported but similar to values reported for melons produced in Brazil or Spain. Upland rice WFgreen (681 m3/t) was reported for the first time and was proven to be much lower than flood irrigated‐rice WFblue‐green. Our results demonstrated lower overall water demand for upland rice‐melon double crop compared to the two other major monocultures of the region (flood‐irrigated rice and irrigated sugar cane).
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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) | 0.000 | 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 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".