Cadastral and Market Value of Agricultural Lands and Their Use in Establishing Land Payments and Allocating State and Municipal Lands to Agricultural Producers under Various Rights
Bibliographic record
Abstract
Land valuation—both market and cadastral—is an important tool for land resource management and local budgeting. Insufficient attention is paid to enhancing the efficiency of agricultural land use resulting from the practical application of land prices in the land resource management process. The market for agricultural land cannot be considered fully active; it is still in its formative stages. A fair system for valuing agricultural land and its taxation is ensured by the method of calculating land rent and its capitalization, based on impartial market data concerning the difference between total sales revenue and all costs, including normal profit attributable to entrepreneurial ability. The land price is the discounted land rent—the sum of money which, if placed in a bank, would yield the landowner a similar return on the invested capital. The capitalization rate for land rent is set equal to the real interest rate, varies from time to time and across different regions, or is established by the state and periodically adjusted. In Australia, Ireland, Sweden, the Netherlands, and the United Kingdom, agricultural land is exempt from tax. In Denmark, the USA, Canada, Finland, and Switzerland, a preferential assessment for agricultural land, significantly lower than the market value, is applied for taxation purposes [3]. Land in agriculture yields a lower rate of profit and is cheaper than land used for industrial, residential development, and other types of land use. This article addresses the imperfect market for agricultural land, ineffective land payments, the low share of private land ownership, the widespread practice of short-term leasehold land use, the excessive retention (65%) of agricultural lands in state and municipal ownership, and other problems of agricultural land use.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".