Diversification for sustainable and resilient agricultural landscape systems
Bibliographic record
Abstract
Abstract This virtual issue comprises papers that address diversification for providing sustainable solutions at different scales from cropping and grassland to food systems. The authors investigated processes in case studies at the landscape scale where synergies and trade-offs between social and environmental objectives become the most tangible. Contributions from all continents highlighted regional specificities related to diversification and include research from natural and social sciences, with inter- and transdisciplinary approaches including synthesis of knowledge (reviews), empirical studies with experiments as well as assessments with interviews in case studies: Model-based design of crop diversification, the role of digitalization for achieving sustainability in the European context, ecological engineering for rice pest suppression in China, the role of cereal species mixtures in Ethiopian smallholder farmers, diversified planting in arid irrigation areas in northwestern China, integration of legumes in European and Canadian cropping systems, screening of native forage legumes for northern Swedish grassland systems, cropping system diversification of smallholder farmers in south-central Bangladesh, identification of how farmers imagine diversified landscapes in southern Idaho in the US, farm diversification affecting impacts from COVID-19 across Europe, the role of diversified farming in Mato Grosso Brazil, diversification and soil management measures in Germany, value chain formation for the scaling of crop diversification, and the design process with farmers and scientists for the transition toward legume-supported farming in Europe. A key finding from these examples is that agricultural intensification has led to the simplification of cropping systems and landscapes in terms of species diversity and ecosystem function. To instead move towards sustainable transformation, all system levels (i.e. from the plot, farm, landscape, governance and overall food systems) need to interact and reinforce each other for diversification to deliver the desired outcomes.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Editorial introducing a virtual issue on agricultural diversification; a domain editorial about agronomy findings, not about research practice.
It introduces an agricultural virtual issue about diversification, not research itself.
Virtual-issue intro on agricultural diversification science; domain content, not research methods.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".