The sustainable agriculture imperative: A perspective on the need for an agrosystem approach to meet the United Nations Sustainable Development Goals by 2030
Bibliographic record
Abstract
Abstract The development of modern, industrial agriculture and its high input–high output carbon energy model is rendering agricultural landscapes less resilient. The expected continued increase in the frequency and intensity of extreme weather events, in conjunction with declining soil health and biodiversity losses, could make food more expensive to produce. The United Nations has called for global action by establishing 17 sustainable development goals (SDGs), four of which are linked to food production and security: declining biodiversity (SDG 15), loss of ecosystem services and agroecosystem stability caused by increasing stress from food production intensification and climate change (SDG 13), declining soil health caused by agricultural practices (SDGs 2 and 6), and dependence on synthetic fertilizers and pesticides to maintain high productivity (SDG 2). To achieve these SDGs, the agriculture sector must take a leading role in reversing the many negative environmental trends apparent in today's agricultural landscapes to ensure that they will adapt and be resilient to climate change in 2030 and beyond. This will demand fundamental changes in how we practice agriculture from an environmental standpoint. Here, we present a perspective focused on the implementation of an agrosystem approach, which we define to promote regenerative agriculture, an integrative approach that provides greater resilience to a changing climate, reverses biodiversity loss, and improves soil health; honors Indigenous ways of knowing and a holistic approach to living off and learning from the land; and supports the establishment of emerging circular economies and community well-being. Integr Environ Assess Manag 2022;18:1199–1205. © 2021 SETAC KEY POINTS The agrosystem approach promotes regenerative agriculture, an integrative approach that can provide greater resilience to a changing climate, reverse biodiversity loss, and improve soil health. The agrosystem approach honors Indigenous ways of knowing and a holistic approach to living off and learning from the land The agrosystem approach supports the establishment of emerging circular economies and community well-being
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".