A GIS Interface Method Based on Reference Evapotranspiration and Crop Coefficients for the Determination of Irrigation Requirements
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
Abstract A GIS approach was developed to utilize spatially distributed and temporally averaged meteorological data and crop distributions and their coefficients in order to estimate irrigation requirements. The irrigation requirements were estimated as the difference between crop evapotranspiration and effective rainfall. Crop evapotranspiration was evaluated as the product of reference evapotranspiration and the crop coefficient. Reference evapotranspiration was calculated using the FAO Penman-Monteith method. Monthly effective rainfall was estimated from total monthly rainfall according to the method developed by the USDA Soil Conservation Service. In order to illustrate the applicability of this approach, a case study for the country of Greece was used. Based on the share of water use in agriculture for irrigation purposes, results obtained from this study indicate an irrigation efficiency of approximately 66 percent for the year 1991. Taking that number into account, the irrigation requirements for the year 2020 are estimated at 8,350 Mm3.
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.
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