The Characteristics of Five Food Production Systems and Their Implications for Sustainable Landscapes
Bibliographic record
Abstract
This paper presents a classification of agricultural production systems that we believe characterises the complex interface between agriculture and the landscapes in which they are managed. Farmers have a choice about how they will manage their land, either to exclude inherent environmental complexity or to engage with it, mindful of risks associated with their approach. Adding to this complexity is the interplay between key natural, social, human, physical and financial resources in agricultural systems, highlighting the importance of extending sustainability principles to aspects of ecology, economics and culture. Decisions about agricultural systems hinge on a balance of productive outcomes, on sensitivity to the issues of environmental complexity, on economic grounds including the access to resources, and the socio-cultural needs of the community in which the farmer participates. Further, farm managers will make a choice that both satisfies and suffices (satisfices) against production, ecological efficiencies and resilience outcomes when choosing which food production system to adopt. In this paper, these complexities are analysed against five different agricultural systems on an ecological continuum; from biologically simple industrial systems that minimise interaction with the natural environment, to ecologically complex systems that are closely engaged with their environment. Production viability is a necessary consideration to maintain farming operations but is not sufficient if operational capacity is to be achieved in the long term. This analysis finds that it is also necessary to work with ecological, economic and social complexities, satisficing against productivity, ecological efficiency and inherit system resilience. No one particular farming systems is appropriate in all cases. The farmer’s choice may apply a mix of the five different agricultural systems described, allowing for the blending of these attributes in order to sustain rural landscapes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".