Farm Level Supply of Short Rotation Woody Crops: Economic Assessment in the Long‐Term for Household Farming Systems
Bibliographic record
Abstract
In this paper, we propose an analysis and modeling of farmers’ decision to convert a part of their annual crops area into woody crops: short rotation coppices (SRCs). Different criteria—technical, economic, and financial—are highlighted in the farmers’ decision to adopt this production. A farm‐level model is proposed and incorporates these decision criteria. The objective is to test several incentive scenarios to encourage risk‐averse cereal farmers to plant trees. A multiperiod model of investment is built and tests possible adoption by farmers based on financial and structural parameters and according to the outlook of agricultural markets. The simulations show that if the cash crop prices fluctuate in the future and if farmers are risk averse the strategy of on‐farm diversification toward woody crops under contractual arrangements could be relevant for farmers to mitigate the risks in the long run. Cet article présente une analyse et une modélisation de la décision d’un agriculteur céréalier qui souhaite convertir une partie de sa surface en grande culture en Taillis à Courte Rotation (TCR). Un modèle multi‐périodique d’investissement est construit et incorpore différents critères techniques, économiques et financiers qui entrent dans la décision d’adopter ces nouvelles cultures pérennes. Le modèle permet de tester le rôle de différents types de soutiens, proposés aujourd’hui dans le cadre de la Politique Agricole Commune européenne, permettant d’encourager les agriculteurs averses au risque à planter des arbres. Les simulations montrent que si les prix des grandes cultures annuelles fluctuent à l’avenir, si la plantation d’arbres est soutenue et si les agriculteurs sont averses au risque, la stratégie de se diversifier vers la plantation d’arbres à croissance rapide, sous contrat, peut permettre d’atténuer le risque à long terme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".