A Framework for Investment and Risk Assessment of Agricultural Projects
Bibliographic record
Abstract
Investment appraisal of agricultural projects (APs) is particularly demanding due to several factors. Namely, APs may have longer time horizons, higher external and internal volatility, and uncertainty caused by less control of production and external conditions (e.g., climatic conditions and market demand). Indeed, these APs may face high and different risks that should be managed properly. Nevertheless, both the literature and practice do not address such complexity and uncertainty conveniently. Thus, this research aimed to develop an integrative and easy-to-use framework to support the investment appraisal of APs, which goes beyond the traditional approach based on simple and deterministic models. This framework is based on an approach that includes several capital budgeting techniques integrating extended multi-index methodology (EMIM), Monte Carlo simulation (MCS), and real options analysis (ROA). This framework allows dealing with different risk and uncertainty scenarios and managerial flexibilities, which allow alternative and additional investment options. A simpler and easier approach can be particularly important for family agribusinesses, which usually do not use sophisticated decision-making tools. An AP in an agrosilvopastoral system (i.e., agriculture, livestock, forestry) was used to present and discuss the proposed methodology considering the relevance of such systems for sustainable agriculture and their higher risk and complexity when compared to traditional approaches. The main contribution of the framework is structuring a set of steps based on several tools to carry out investment appraisal in APs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".