Biological Assets in Agricultural Accounting: A Systematic Review of the Application of IAS 41
Bibliographic record
Abstract
The valuation of biological assets represents a crucial component for the generation of accounting information, especially in the context of the agricultural sector, where assets subject to continuous transformation processes predominate. This study aims to analyze, through a systematic review of the literature, how the measurement methods established by International Accounting Standard 41 (IAS 41) affect the quality, accuracy, and usefulness of accounting reports. The results show that the correct valuation of biological assets significantly improves strategic and financial decision-making by providing more reliable and representative data on the economic reality of the sector. Finally, the study highlights the main practical challenges in the application of IAS 41, including fair value volatility, the subjectivity of estimates, the limited availability of reliable data, and the need for more flexible accounting frameworks that consider the cultural, climatic, and productive realities of each environment. Based on these findings, the importance of strengthening transparency and accounting disclosure and adapting measurement methods to the particularities of the agricultural sector in order to improve the quality of information and the confidence of external users is highlighted.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".