DEVELOPING ON-FARM IRRIGATION SCHEMES FOR CURRENT AND FUTURE CLIMATE CONDITIONS ON THE WESTERN BANK OF LAKE NASSER
Bibliographic record
Abstract
The agricultural region in the western bank of Lake Nasser is one of the new promising agricultural settlements, which has the advantage of early-season production of several high quality cash crops. The agriculture production in this region is fully-irrigated, and constrained by sever environmental, biophysical and socio- economical conditions. Moreover, farmers have been facing several additional challenges in irrigation management due to lack of knowledge of the best practices. The aim of this study is to evaluate the current farmers’ irrigation schemes in terms of water productivity. The study examined some possible irrigation schemes in order to improve water productivity with minimum irrigation requirement and to cope with projected water shortage and temperature increase induced by climate change. The study is based on modeling approach utilizing the FAO crop model “AquaCrop” on drip irrigated tomato. It was performed (i) to calibrate AquaCrop via two field trials at two seasons, (ii) to observe and evaluate the current farmers’ irrigation schemes based on fixed application frequency, and (iii) to evaluate six irrigation schemes under current and future climate conditions. The evaluated schemes included different combinations between net irrigation application and deficit practices at two levels of 80% and 60% of net water requirements, under current and future climate conditions, based on the change in irrigation application and water productivity. The results demonstrated that the combination of deficit irrigation levels and irrigation scheduling could improve tomato water productivity to the optimal level determined under the current study conditions (2.4 kg/m3), especially when deficit levels of 80 and 60% applied at both early and late stages of crop growth stages. These schemes could present acceptable adaptation options with the projected temperature increase due to climate change.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".