The estimation of economic depreciation for Canadian farm machinery
Bibliographic record
Abstract
The objective of this thesis was to estimate the rate at which four types of farm machinery lose value in Canada. Specifically, Canadian data on used machinery prices was utilized to produce estimates of economic depreciation for two-wheel-drive tractors, combines, square balers and large round balers. The data used in this thesis to make these estimates are special for two reasons. First of all, they represent the only extensive record of Canadian used farm equipment prices available. Most previous studies have based their estimates on American data, assuming that they apply equally well to the Canadian situation. Secondly, these data record actual transactions in the used market and these transactions have been reported in an unaveraged format. This is valuable because information on options, horsepower, condition, and, most importantly, hours of use was retained for each machine. The availability of this information permitted richer and more specific estimates of depreciation. In particular, the inclusion of hours of use in the models enabled a distinction to be made in this thesis between the component of depreciation which is directly attributable to age and that component which is directly attributable to accumulated hours of use. It is felt that this distinction provides a beginning point for the study of depreciation due to simple "wear and tear", and that depreciation which is due to obsolescence and technological change. As well, this thesis extensively reviewed the literature on depreciation in an effort to determine the best approach to follow. The method finally adopted was the "remaining value approach"; however, the thesis went further than the typical remaining value approach because an attempt was made to estimate the pattern as well as the rate of depreciation. This was done by initially adopting a functional form which was flexible enough to let the data "choose for themselves" between the commonly used depreciation rules of thumb (declining balance, straight-line and one-hoss-shay patterns). This was possible by using the Box-Tidwell procedure. This Box-Tidwell procedure when applied to the extensive tractor data indicated that tractors in Canada follow a declining balance (geometric) pattern of depreciation. This was taken as support for the adoption of semi-log models to estimate depreciation. The main findings of this thesis are, first, that depreciation rates vary among assets (from approximately 9% for tractors to nearly 16% for large round balers), and, secondly, that these rates are less than those allowed by the government for tax purposes. It was concluded that this generosity on the part of the government would be acceptable if it applied equally to all depreciable assets, but the divergence in depreciation rates found in this thesis indicate that generous blanket depreciation charges give more advantage to some than to others.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".