CropSyst model for wheat under deficit irrigation using sprinkler and drip irrigation in sandy soil
Bibliographic record
Abstract
Abstract CropSyst (Cropping Systems Simulation) is used as an analytic tool for studying irrigation water management to increase wheat productivity. Therefore, two field experiments were conducted to 1) calibrate CropSyst model for wheat grown under sprinkler and drip irrigation systems, 2) to use the simulation results to analyse the relationship between applied irrigation amount and the resulted yield and 3) to simulate the effect of saving irrigation water on wheat yield. Drip irrigation system in three treatments (100%, 75% and 50% of crop evapotranspiration – ETc) and under sprinkler irrigation system in five treatments (100%, 80%, 60%, 40%, and 20% of ETc) were imposed on these experiments. Results using CropSyst calibration revealed-that results of using CropSyst calibration revealed that the model was able to predict wheat grain and biological yield, with high degree of accuracy. Using 100% ETc under drip system resulted in very low water stress index (WSI = 0.008), whereas using 100% ETc sprinkler system resulted in WSI = 0.1, which proved that application of 100% ETc enough to ensure high yield. The rest of deficit irrigation treatments resulted in high yield losses. Simulation of application of 90% ETc not only reduced yield losses to either irrigation system, but also increased land and water productivity. Thus, it can be recommended to apply irrigation water to wheat equal to 90% ETc to save on the applied water and increase water productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".