An economic analysis of afforestation on agricultural land in east central Saskatchewan
Bibliographic record
Abstract
The economic viability of farming in Saskatchewan is eroding and the future of the industry is becoming uncertain given the current economic state. The combination of low commodity prices, increasing input and transportation costs, ongoing drought conditions, inadequate safety net programs, and environmental concerns resulting from agricultural greenhouse gas emissions has led to this uncertainty. One possible solution for producers to help overcome or at least minimize the negative trends occurring in agriculture, which is proposed, is afforestation of agricultural land. Afforestation not only provides net private benefits of timber income but external benefits, including carbon sequestration, and preservation of native forests, which provides areas for hunting, wildlife viewing, and conservation of land. The economic efficiency of afforestation was examined using a transitional benefit cost framework for both crop and pasture systems. This allowed for both private and social perspectives, along with the opportunity costs, to be included and the economic efficiency of afforestation from each perspective was determined. The potential conversion of agricultural land to afforestation was based solely on economic efficiency and assumed producers demonstrated an economically rational decision making process. The results from the benefit cost analysis indicated that the net private benefits from afforestation were never significant enough to warrant the conversion of either crop or pasture systems to afforestation. The results did however show that the net social benefits from afforestation would warrant the conversion of crop systems to afforestation for a limited number of situations. Crop systems on physically marginal land with a carbon payment of either $22.58 or $33.55 tonne of C would warrant conversion to afforestation, using either a single or infinite rotation. The infinite rotation resulted in a larger allocation of land to afforestation. The role afforestation can play in helping producers diversify and increase income levels is limited. The low price paid for timber and the high costs of establishment for afforestation are the main constraints. In order for afforestation to become economically efficient on a large scale the constraints facing producer’s needs to be addressed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".