RISE, a Tool for Holistic Sustainability Assessment at the Farm Level
Bibliographic record
Abstract
Sustainability must be adopted as a key principle in global markets. Numerous studies have been conducted to evaluate the degree of sustainability on a national and local level. However, only little information for single farm assessment is currently available. The present paper introduces a tool, the "Response-Inducing Sustainability Evaluation" (RISE), which allows an easy assessment at the farm level. It is system-oriented and offers a holistic approach for advice, education and planning. The model covers ecological, economical and social aspects by defining 12 indicators for Energy, Water, Soil, Biodiversity, Emission Potential, Plant Protection, Waste and Residues, Cash Flow, Farm Income, Investments, Local Economy and Social Situation. For each indicator a "State" (S) and a "Driving force" (D) are determined from direct measures of a number of parameters. The "State" indicates the current condition of the specific indicator, higher values are more desirable, and the "Driving force" is a measure of the estimated pressure the farming system places on the specific indicator; in this case lower values are desirable. D and S are standardized on a 0 to 100 scale; a perfect indicator would be identified by S=100 and D=0, whereas significant challenges would be captured by a combination of a low S and a high D. The degree of sustainability (DS) of each indicator is defined as DS= (S-D), bound by construction to the -100 to +100 range. The overall results are summarized and displayed in a sustainability polygon. In addition to this polygon a strength/weakness profile is determined for 1) the stability of the social, economic and ecological framework, 2) farmer's risk awareness and risk management measures, 3) grey energy in machines, buildings and external inputs, 4) animal health and welfare. RISE has been tested and used to evaluate very different farms in Brazil, Canada, China and Switzerland. Results are considered relevant with regard to the objective stated. Further testing, adaptation and fine-tuning is under way. A similar model covering the supply chain to the factory gate is also under development.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".