An Integration of GIS and Simulation Models for a Cost Benefit Analysis of Irrigation Development
Bibliographic record
Abstract
This study incorporates spatially explicit geographic information system and simulation models to develop an optimal irrigation system. The purpose of the optimized irrigation system was to save depleted ground water supplies. ArcGIS was used to calculate the area of potential irrigable soils, and EPANET (a hydrological simulation program) was used to calculate energy costs. Crop yield response functions were used to estimate the yield of cotton to the amount of irrigation and the accumulation of soil salinity over a 50-year period. Four irrigation designs (A, B, C, and D) were analyzed with different irrigation schedules. Design A allowed all producers to irrigate simultaneously at 600 gallons per minute (gpm) or 2,271 liters per minute (lpm) while designs B and C divided the irrigable areas into two parts. Design D divided the areas into four parts to allow producers to irrigate one part at a time at 800 gpm (3,028 lpm). Irrigation scheduling not only lessened the water use and cost, but also amplified the profitability of the irrigation system. In design A, if all producers adopted 600 gpm (2,271 lpm) pivots and operated simultaneously, the cost of the 360,000 gpm (1363,000 lpm) pipeline would be prohibitive. In contrast, designs B, C, and D increased net benefits and lowered the breakeven price of cotton. The 50-year net present value for designs A, B, C, and D was profitable over 75, 70, 70, and 65 cents of cotton price per pound (454 g), respectively. Thus, this study endorses irrigation scheduling as a tool for efficient irrigation development and management, and increases water conservation.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".