Optimal irrigation scheduling for wheat production in the Canadian prairies: A modelling study
Bibliographic record
Abstract
Food security and reducing malnutrition of the growing world population is a permanent issue in agricultural research. Wheat (Triticum aestivum L.) is an important part of the world food market. The Canadian Prairies comprise the provinces of Alberta, Saskatchewan, and Manitoba which produce very large quantities of wheat, mostly for export. While in many other countries, and in particular in the Middle East and North Africa (MENA) region, wheat is grown with supplemental irrigation, the common practice in Canadian Prairies is to grow wheat as a rain-fed crop. Taking into account the growing pressure on fresh water resources demand and factors of the profitability the possibly optimal use of the irrigation water should be determined according to the its deficit in the region. Thus the set of the optimization problems must be solved: find maximal wheat yield with respect to the limited irrigation water quota and given weather and hydrological data. Systematically solving this problem for different values of the water quota ( W W ) allows to create an irrigation water use efficiency function I W U E = Y ( W ) IWUE=Y(W) which presents the yield as a function of the total irrigation water applied optimally during the season. We demonstrate this approach using the FAO model AquaCrop in conjunction with the TOMLAB optimization library. The results of the model-based optimization show that for this specific case study (Carman,Manitoba 2006) wheat yield could be roughly doubled with limited amount of irrigation water. The average increase of the yield in the range of 0 − 100 0-100 mm of irrigation water was more than 20 kg/ha per mm of water.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".