Water, Energy and Nutrient Losses from Food Wastage of Selected Crops in Three Agro-Climatic Zones in British Columbia, Canada
Bibliographic record
Abstract
Food waste is a growing global concern. Data on the factors and magnitude are largely global estimates, thus local studies aid in providing information on the impacts of food waste. Three important agro-climatic zones in British Columbia and nine common crops, both annual and perennial, were selected to evaluate the environmental and nutritional implications of local food waste. Using Canadian estimates of total food waste, the constituent water, caloric content, protein, vitamin C, phosphorus and potassium wasted by each crop were estimated. Regionally, the total production and losses were the highest in the Lower Fraser Valley which had high production of potatoes and blueberries, followed by the Okanagan, with grapes and apples, and Vancouver Island, with potatoes. Virtual water was estimated by the BC Agriculture Water Calculator and used to assess the soil and climatic factors impacting the local water demand. Although soil texture seemed to influence water demand, the agro-climatic zone was the main factor controlling the water demand and the corresponding amount of water wasted. Dry agro-climatic zones had annual virtual water up to two times higher for the same crop and soil texture. Lower water demand crops, finer soils and more efficient irrigation systems were more congruent with water stress scenarios. Total losses for each region were based on conservative estimates and would have supplied the caloric energy and protein for over 40,000 adults, and vitamin C for over 300,000 adults for one year. Additionally, the total N, P and K wasted accounted for up to 32, 2 and 13 kg/ha respectively for common fertilizers used in British Columbia. This study confirmed the significance of food waste impacts on local water demand, human nutrition and soil management based on regional data for representative crops.
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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 | 0.001 |
| 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".