Contributions of biodiversity to the sustainable intensification of food production – Thematic study for The State of the World’s Biodiversity for Food and Agriculture
Bibliographic record
Abstract
Biodiversity supports sustainable food production, although recognition of its roles has been relatively neglected in the sustainable intensification literature. In the current study, the roles of biodiversity in sustainable food production are considered, assessing how these roles can be measured, the current state of knowledge and opportunities for intervention. The trajectory of global food production, and the challenges and opportunities this presents for the roles of biodiversity in production, are also considered, as well as how biodiversitybased interventions fit within wider considerations for sustainable food systems. The positive interactions between a diverse array of organisms, including annual crops, animal pollinators, trees, micro-organisms, livestock and aquatic animals, support food production globally. To support these interactions, a range of interventions related to access to materials and practices are required. For annual crops, major interventions include breeding crops for more positive crop–crop interactions, and the integration of a wider range of crops into production systems. For animal pollinators, major interventions include the introduction of pollinator populations into production landscapes and the protection and improvement of pollinator habitat. For trees, a major required intervention is the greater integration of perennial legumes into farmland. For micro-organisms, the implementation of agronomic practices that support beneficial crop-microbe interactions is crucial. For livestock production, breed and crop feedstock diversification are essential, and the implementation of improved methods for manure incorporation into cropland. Finally, in the case of aquatic production, it is essential to support the wider adoption of multi-trophic production systems and to diversify crop- and animal-based feed resources. These and other interventions, and the research needs around them, are discussed. Looking to the future, understanding the drivers behind trends in food systems is essential for determining the options for biodiversity in supporting sustainable food production. The increased dominance of a narrow selection of foods globally indicates that efforts to more sustainably produce these foods are crucial. From a biodiversity perspective, this means placing a strong emphasis on breeding for resource use efficiency and adaptation to climate change. It also means challenging the dominance of these foods through focusing on productivity improvements for other crop, livestock and aquaculture species, so that they can compete successfully and find space within production systems. New biodiversity-based models that support food production need not only to be productive but to be profitable. Thus, as well as describing appropriate production system management practices that enhance production and support the environment, the labour, knowledge, time required to operationalize, and other costs of new production approaches, must be considered and minimized. To support the future roles of biodiversity in sustainable food production, we recommend that particular attention be given to the longitudinal analysis of food sectors to determine how the diversity of foods consumed from these sectors has changed over time. Analysis is already available for crops, but related research is needed for livestock and aquaculture sectors. This analysis will then support more optimal cross-sectoral interactions, in terms of the contributions each sector provides to supplying the different components of human diets. Additional meta-analyses and synthetic reviews of case studies are required as an evidence base for biodiversity-based food production system interventions, but future studies should pay more attention to articulating the potential biases in case study compilation (the problem of ‘cherry picking’ positive examples) and the measures that have been taken to minimize such effects.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".