Cost competitiveness of apple production in British Columbia versus Washington State
Bibliographic record
Abstract
The objective of this study is to determine the cost of producing apples in British Columbia and Washington State and then compare the estimated costs between the two regions. A conventional 'cost of production model, whereby long-run costs (i.e. depreciation costs) have been included, is developed to determine the average per acre and per pound cost of producing apples. The model assumes a representative orchard for British Columbia and Washington State. A set of characteristics, along with a set of management schedules, are defined for each of the representative orchards. In keeping with the assumption that the representative orchards include mature as well as trees in various establishment stages, each management schedule defines a set of operations for trees of a specific age. There are nine schedules representing trees age one through mature. Aside from the type of operations performed, each management schedule also specifies the number of times an operation is executed, the type of machine(s) used, the machine and labour time required, and the material/service cost involved. From the information provided in the management schedules, a corresponding set of production cost schedules is developed. These schedules show the depreciation, opportunity, insurance, repair and maintenance, fuel and lubricant, labour and material/service costs associated with each operation. The theory of Capital Budgeting is used here to provide a consistent and accurate estimation of the per hour or annual cost of machinery, equipment and buildings. For each schedule, the sum of the total cost per operation plus the overhead charges, interest on operating capital, and rent and tan on land yield the per acre cost of producing apples. A comparison based on the per acre cost by tree age is performed to determine cost differences that may exist at this level. On average (average of orchard block) per acre cost is determined for British Columbia and Washington State based on the proportion of trees of a specific age and its total cost. This average per acre cost is compared, as well as the individual categories of costs (i.e. labour) to determine where differentials exist between the two regions. Based on an average per acre yield, per pound cost of producing apples is also calculated. The efficiency ratio, total ouput value/total input value, is calculated and compared to provide an insight into British Columbia's producer’s ability to extract profits from inputs.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".