Regional specialization and market integration: agroecosystem energy transitions in Upper Austria
Bibliographic record
Abstract
We investigate agroecosystem energy flows in two Upper Austrian regions, the lowland region Sankt Florian and the prealpine region Grünburg, at five time points between 1830 and 2000. Energetic agroecosystem productivity (energy contents of crops, livestock products, and wood per unit area) is compared to different types of energy inputs, i.e., external inputs from society (labor, industrial inputs, and external biomass inputs) and biomass reused from the local agroecosystem (feed, litter, and seeds). Energy transfers between different compartments of the agroecosystem (agricultural land, forest, and livestock) are also quantified. This allows for delineating an agroecosystem energy transition: In the first stage of this transition, i.e., in the nineteenth century, agroecosystem productivity was low (final produce ranged between 14 and 27 GJ/ha/yr), and local biomass reused made up 97% of total energy inputs in both regions (25-61 GJ/ha/yr). In this period, agroecosystem productivity increase was achieved primarily through more recycling of energy flows within the agroecosystems. In the second stage of the agroecosystem energy transition, i.e., after World War II, external energy inputs increased by factors 2.5 (Sankt Florian) and 5.0 (Grünburg), partly replacing local energy transfers. Final produce per area increased by factors 6.1 (Sankt Florian) and 2.9 (Grünburg). The difference in the resulting energy returns on investment (EROI) owes to regional specialization on cropping versus livestock rearing and to increasing market integration. Our results suggest that sustainable land-use intensification may benefit from some regional specialization harnessing local production potentials based on a mix of local and external inputs.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".