Use of atypical plant resources for cattle farming in Western Europe to drive agroecological transition
Bibliographic record
Abstract
In the efforts to optimize their production processes and yields, livestock systems overlook less productive on-farm areas and resources. Given the challenges of feed self-sufficiency, climate change mitigation and adaptation, and biodiversity conservation, it makes sense to revalue these resources to support agroecological transition in livestock systems. This paper introduces the concept of ‘atypical resources’, defined as plant resources that are part of the farm environment but are not conventionally used. The aim is to explore their nature, access, use and potential contribution to the performance of livestock systems. The study examines four unique and under-researched cattle farming systems (dairy or beef) using atypical resources in Western France through comprehensive analysis, and assessment of nitrogen metabolism. Our results show a rich diversity of atypical resources such as abandoned land, hedgerow slopes, woody leaves, ditch bottoms and marsh reeds. These resources, coming from the farms themselves or their surroundings, are used for animal feed and/or bedding. The contribution of atypical resources in the nitrogen metabolism of the system ranges from almost 0 % to 12 %, while their contribution to animal feed varies for almost 0 % to 29 %. In addition, the management practices and grassland-based farming systems associated with these resources may limit N waste, preserve habitats and enhance biodiversity. This article examines an under-explored but critical issue that is essential to address the current challenges of livestock systems in Western Europe. We advocate for further research to generate knowledge and methods that harness the multiple services provided by atypical resources, thereby facilitating agroecological transition and addressing spatial management challenges. • We analyse the use of atypical resources in 4 low-input cattle farming systems in Western Europe. • There is a wide range of atypical resources that can be used in livestock farming for animal feed and/or bedding. • These atypical resources can make a significant contribution to the N metabolism and self-sufficiency of cattle systems. • These resources contribute to biodiversity preservation and can help livestock farming systems adapt to global changes.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".