Between Forestry and Farming: Policy and Environmental Implications of the Barriers to Agroforestry Adoption
Bibliographic record
Abstract
Farming and forestry are practices with clearly defined institutions, markets, and policies. These are not as clearly defined for agroforestry, a practice experiencing increased interest in the USA. This study examined the barriers preventing the adoption of agroforestry within a household level theoretical framework informed by transaction costs and multifunctionality, using survey data from 353 Missouri (USA) landowners. Costs of establishing or managing trees, the time required to manage, and the lack of tree management experience are perceived as the most influential barriers limiting implementation of agroforestry on the farm. A principal component factor analysis of the perceived barriers identified two factors: the first, labeled Transaction Costs, related to information access and perceived establishment costs; the second factor, labeled Profitability Concerns, was associated with perceptions of the effects of agroforestry on farm profitability and agricultural production. Overall, Transaction Costs appears to be a greater barrier to implementation of agroforestry. Cluster analysis yielded three types of landowners: environmentalists, agriculturalists, and disengaged, who differ in their perceptions of these barriers. Statistical tests revealed differences among clusters on their farmland attributes, multifunctionality indicators, and their resources for adopting agroforestry. Environmentalists appear as more likely to adopt agroforestry, followed by the agriculturalists. Policy implications are also discussed. Les secteurs de l’agriculture et de la foresterie possèdent des institutions, des marchés et des politiques clairement définis. Ces éléments ne sont pas aussi clairement définis dans le cas du secteur de l’agroforesterie, qui recueille un intérêt croissant aux États‐Unis. Dans la présente étude, nous avons examiné les obstacles à l’adoption de l’agroforesterie à l’aide d’un cadre théorique au niveau des ménages comprenant des données sur les coûts de transaction et la multifonctionnalité issues d’un sondage réalisé auprès de 353 propriétaires fonciers dans l’État du Missouri, aux États‐Unis. Les coûts de plantation, le temps nécessaire à la gestion et le manque d’expérience en gestion arboricole sont perçus comme étant les principaux obstacles à l’implantation de l’agroforesterie sur la ferme. Une analyse en composantes principales a permis de déterminer deux facteurs : le premier, appelé Coûts de transaction (Transaction Costs), était liéà l’accès à l’information et aux coûts de plantation perçus; le deuxième, appelé Inquiétudes sur la rentabilité (Profitability Concerns), était liéà la perception des conséquences de l’agroforesterie sur la rentabilité et la production de la ferme. De manière générale, les Coûts de transaction semblent constituer le plus important obstacle à l’implantation de l’agroforesterie. Une analyse de grappes a fait ressortir trois types de propriétaires fonciers : les environnementalistes, les agriculturalistes et les désengagés, qui ont une perception bien différente de ces obstacles. Des tests statistiques ont révélé des différences parmi les grappes quant aux attributs de leurs terres agricoles, aux indicateurs de multifonctionnalité et à leurs ressources pour adopter l’agroforesterie. Les Environnementalistes semblent plus enclins à adopter l’agroforesterie, suivis des Agriculturalistes. Nous avons aussi abordé l’incidence sur les politiques.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".