DEALING WITH RISK IN AGRICULTURE: A CROP LEVEL ANALYSIS AND MANAGEMENT PROPOSAL FOR ITALIAN FARMS
Bibliographic record
Abstract
Risk management plays a critical role in agriculture, which is particularly exposed to multiple and heterogeneous risk factors. In addition to the traditional basic risks that generally characterize any business venture, agriculture faces external factors, generally difficult to control and with a strong impact on farm profitability. These are firstly environmental (pests and diseases) and climatic conditions that affect the quantity and quality of agricultural production, but also the structural constraints of the agricultural market, which is characterised by a high degree of supply rigidity, price volatility and inelasticity of demand. This leads to the need to implement risk management tools, some of which aimed at income stabilization (already in place by many years in other countries, i.e. the USA and Canada) and requiring the active participation of the farmer on the one hand and of the institutional system on the other. In order to suggest risk management solutions to Italian farmers, this thesis makes efforts in simulating the feasibility of a risk management tool introduced in the EU with Regulation (EU) No 2017/2393 but not yet implemented: the sector-specific Income Stabilization Tool. This is based on a public-private partnership and is managed by a mutual fund steered by associated farmers. These latter pay an annual contribution to become eligible for receiving indemnities when experiencing a severe income drop. Unlike others that are limited to covering specific types of risk, this tool makes it possible to look at the farmer's entire income risk considering the correlation among several sources of risk (particularly between production level and prices). This thesis provides first a theoretical background on risk analysis and risk management in agriculture (concepts, classification, literature and methodology). Second, the role of policies within the European Union framework and, Italy, in particular, has been viewed by analysing the normative framework and the reference context of insurance instruments in agriculture. Subsequently, since assessing farm profitability and economic risk is important to support farmers’ decisions about investments and whether or not to join the insurance instruments, an explorative analysis on profitability and riskiness of a perennial crop in Italy, such as hazelnut, has been done. Finally, the implementation of a sector-specific 3 Income Stabilization Tool for the crop investigated has been suggested by following this structure: - assessment of the profitability and risk of hazelnut production, in the four main production areas in Italy; - assessment of the most important parameters generating risk; - simulation of the feasibility of using an income risk management tool to make supply and demand able to interact and its impact on the level and riskiness of farm income; - assessment of the geographical scale at which the Income Stabilization Tool scheme could be implemented. Using data from the Italian Farm Accountancy Data Network on hazelnut producing farms, a downside risk analysis showed that riskiness is distributed in different ways on the entire country with sensitivity on yield risk affecting farmers' income level and economic risk. The simulation implemented in this study demonstrates the tool could reduce substantially the risk faced by hazelnut farmers in Italy. The additional public support is essential in case of joining the tool. In addition, in view of the differences within the Italian territory, the farmers’ payments should be differentiated based on the requisites and the specific climatic and environmental characteristics of each region. Concurrently, recourse to a national mutual fund would make it possible to benefit from the principle of risk pooling.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.031 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".