Water Footprint for Pulse, Cereal, and Oilseed Crops in Saskatchewan, Canada
Bibliographic record
Abstract
The water footprint (WF) of crop production is a friendly approach for the analysis of water resource consumption in agricultural production systems. This study assessed the inter-annual variability of the total WF of three types of main crops, namely, cereal (i.e., spring wheat and barley), oilseed (i.e., canola and sunflower) and pulse (i.e., lentils and chickpea), from the perspective of yield and protein. It also determined the major factors that influence the WFs in Saskatchewan province of Canada. Over the period of 1965–2014, the annual precipitation in Saskatchewan fluctuated considerably but increased slightly with time. The grain yield-based WF ranged between 1.08 and 1.80, 0.90 and 1.38, 1.71 and 2.58, 1.94 and 4.28, 1.47 and 2.37, and 1.39 and 1.79 m3 kg−1; whereas the protein yield-based WF ranged between 7.69 and 10.44, 8.27 and 16.47, 3.79 and 7.75, 4.86 and 11.17, 5.09 and 7.42, and 5.51 and 10.69 m3 kg−1 for spring wheat, barley, canola, sunflower, lentils, and chickpea, respectively. All the WFs of crops generally decreased with time, which could be attributed to precipitation factors. In addition, the scientific and technological progress and agricultural inputs also evidently influenced the grain yield-based WFs of all crops. Pulse crops had a higher grain yield-based WF (an average of 1.59 m3 kg−1 for pulse crops and 1.18 m3 kg−1 for cereal crops) but a lower protein yield-based WF (an average of 6.58 m3 kg−1 for pulse crops and 9.25 m3 kg−1 for cereal crops) than cereal crops. Under conditions of improved protein consumption and healthy living in the future, pulse crops may be a preferred crop.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".