Effects of the Recognition, Measurement, and Disclosure of Biological Assets Under IAS 41 on Value Creation in Colombian Agribusinesses
Bibliographic record
Abstract
This article examines how the recognition, measurement, and disclosure of biological assets (BAs) under IAS 41 affect value creation in Colombian agribusinesses following IFRS adoption. Using EMIS Benchmark data for Colombia, we construct a panel of 157 agro-industrial firms that are neither subsidiaries of multinationals nor listed on the stock exchange; the panel covers 2012–2022, spanning the period before and after IFRS adoption. The database combines accounting and financial indicators with categorical variables capturing the scope of activities, valuation methods (historical cost, realisable value, present value, fair value), and disclosure policies for BAs. Value creation is proxied by EBITDA, return on equity (ROE), and return on assets (ROA). We estimate fixed-effects panel models for three IFRS groups. Results show that, in Group 1, defining the accounting scope and using fair value and present value as measurement bases are associated with higher firm value, while Groups 2 and 3 display positive but statistically weaker effects. Explicit disclosure is also associated with higher profitability, particularly for SMEs. These findings are consistent with agency and firm theories: when entrepreneurial activities are recognised, measured, and disclosed consistently and transparently, information asymmetry and agency costs fall, and accounting policies become a driver of organisational performance in agribusinesses in emerging markets. The results also support the assumptions of institutional theory, as external regulatory pressures from IFRS and internal pressures arising from relationships among firms in the agro-industrial sector shape and reinforce information disclosure practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".