Estimating Impacts of Land Use Change on Evapotranspiration for Three Agricultural Crops in Malta—A Preliminary Assessment
Bibliographic record
Abstract
Estimating evapotranspiration is crucial for better management of catchment water resources. In this study, the FAO CropWat model was used to estimate reference evapotranspiration (ETo), crop evapotranspiration (ETc), and total gross water requirements for three economically important agricultural crops grown in Malta: potatoes, wheat, and vineyards for three years representative of a typically wet (2003), average (2009), and dry (2013) year. In addition, changes in ETc due to changes in land use were estimated for 2009 and 2013 relative to a 2003 baseline. Across all three years and crops, the average ETo rates were estimated to range between 3.7 mm day -1 (2003) and 4.0 mm day-1 (2013) while average ETc rates were estimated to range between 1.6 mm day-1 and 5.3 mm day-1, respectively. For all three years, the highest total gross water requirement was estimated for wheat, reaching a maximum of 1450 mm in 2013. The results suggest that changes in land use between 2003 and 2013 were the main driver for changes in crop water demand. Differences in water demand compared to 2003 were estimated to range between -38% and 60%. This could have a substantial impact on the future sustainability of Malta’s increasingly constrained water resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".