How to design multifunctional landscapes?
Bibliographic record
Abstract
Abstract The expansion of homogeneous landscapes has been a major driver of biodiversity loss, climate change and land degradation. There is an urgent need for a transition to multifunctional landscapes that provide abundant and nutritious food while also delivering several other contributions essential for a good quality of life. However, implementing this process, especially in large‐scale agriculture without economic subsidies, remains unclear. We discuss guidelines for a transition to multifunctional landscapes based on science and our experience as practitioners. In this transition, practitioners manage crop fields, natural habitats and field edges. We propose an iterative process for designing multifunctional landscapes. Initially, at a fine‐scale resolution, we identify and classify areas with low opportunity costs (e.g. low crop productivity) or a high appreciation for nature (e.g. near housing areas). These areas are categorized into either ‘wide’ patches or ‘narrow’ corridors (i.e. edges <100 m wide). Subsequently, wide patches (including those with remnants of native species regardless of size) are allocated for natural habitat restoration (covering at least 20% of the farmland), while narrow zones are designated as biological corridors (making up at least 10% of the farmland and designed to be 50–100 m wide). Also, field size and configuration are redesigned to enhance the efficiency of agricultural practices and edge density. This entails creating smaller fields with strip cropping that follows environmental heterogeneity, instead of relying on large, squared monocultures. Ultimately, this design is continually refined through engagement with stakeholders, incorporating cost–benefit analyses, as well as a process of ongoing monitoring, evaluation and mutual learning. Synthesis and applications . We describe an iterative process by which large‐scale agriculture can support biodiversity and leverage nature's contributions to people while providing more nutritious food and stabilizing crop yields and profits. Multifunctional landscapes will be critical in achieving the targets of the Kunming‐Montreal Global Biodiversity Framework by 2030 and moving the world towards net‐zero emissions by 2050.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".