Managing biodiversity in agricultural landscapes: Conservation, restoration and rewilding
Bibliographic record
Abstract
Table of ContentsPart 1 Principles 1.Key concepts for conserving biodiversity in agricultural landscapes: Andrew F. Bennett, Angie Haslem, Frederick W. Rainsford, Alex C. Maisey and James Q. Radford, La Trobe University, Australia 2.Biodiversity for agriculture: the role of integrated farm management in supporting agricultural production through biodiversity: G. R. Squire and C. Hawes, James Hutton Institute, UK 3.Engaging local voices: farmers, rural communities, and ecological restoration: Theodore Alter and Kayla Faith Laddin, Pennsylvania State University, USA; and Lauren Hull, Michael Reid and Heidi Kleinert, Department of Energy, Environment and Climate Action, Victoria, Australia 4.Implementing sustainable land use change programmes: Liz Lewis-Reddy, ADAS Policy and Economics, UK Part 2 Farmland and conservation practices 5.Soil health and ecological restoration: Alice Day, The University of Manchester, UK; Ezekiel M. Njeru, Kenyatta University, Kenya; and David Johnson, The University of Manchester, UK 6.The impact and design of field margins in promoting biodiversity in agricultural landscapes: Jane Morrison, Bishop's University, Canada 7.The impact and management of hedgerows in promoting biodiversity in agricultural landscapes: Ian Montgomery and Neil Reid, Queen’s University of Belfast, UK Part 3 The role of government and the private sector in promoting on-farm conservation practices 8.Stick your wellies on: messy development and co-design processes with England’s new Environmental Land Management (ELM) policy: Jennifer Dodsworth and Rachel Lasko, University of Oxford, UK; and Ruth Little, University of Sheffield, UK 9.Developments in agri-environment schemes (AES): North America: Gordon Rausser and David Zilberman, University of California, Berkeley, USA 10.Developments in agri-environment schemes (AES): Australia: Dean Ansell, Andrew Macintosh, Don Butler and Marie Waschka, Australian National University, Australia Part 4 Habitat and animal rewilding 11.Restoring peatlands in European landscapes: Rudy van Diggelen and Tobias Ceulemans, University of Antwerp, Belgium; Camiel Aggenbach, KWR Watercycle Research Institute, The Netherlands; and Willem-Jan Emsens, Royal Zoological Society of Antwerp and University of Antwerp, Belgium 12.Rewilding grasslands and rangelands: Thomas A. Jones, Forage and Range Research Laboratory – U.S. Department of Agriculture – Agricultural Research Service, USA 13.Lessons from reforestation of agricultural landscapes in South-Eastern Australia: David Lindenmayer, David Smith, Daniel Florance, Clare Crane, Eleanor Lang, Angelina Siegrist, Michelle Young and Ben C. Scheele, The Australian National University, Australia 14.The future of animal rewilding in agricultural landscapes: Kiarrah J. Smith, Iain. J. Gordon, Belinda A. Wilson and Adrian D. Manning, The Australian National University, Australia 15.Animal rewilding in theory and practice: Australia and New Zealand: Christopher R. Dickman, Aaron C. Greenville and Glenda M. Wardle, The University of Sydney, Australia Part 5 Looking ahead 16.Biodiversity and agricultural landscapes: where are the gaps?: Nick C. H. Reid, University of New England, Australia; and David C. Paton, University of Adelaide, Australia About the Editor(s)Dr Nick Reid is Emeritus Professor in Ecosystem Management and former Head of the School of Environmental and Rural Science at the University of New England, Australia. His research interests span the stewardship of social–ecological systems, biodiversity management in production landscapes and protected areas, ecosystem restoration and sustainable agriculture.Dr Rhiannon Smith is a Senior Lecturer in Environmental Management in the School of Environmental and Rural Science at the University of New England. Her research focuses particularly on the measurement and management of biodiversity and ecosystem services in the Australian agricultural sector.What others are saying about this book“Agricultural landscapes have the potential to bring people and nature together in ways that support human wellbeing and sustain valuable biodiversity. When we get it wrong, however, we create landscapes that harm biodiversity and satisfy only the narrowest of human needs. There is a lot at stake in these landscapes and increasing environmental pressures globally increase the risks. This edited volume brings together an outstanding selection of experts to help us to understand how it is possible to get better outcomes for nature and people, considering principles practice and policy.” (Professor Saul Cunningham, Fenner School of Environment and Society – Australian National University, Australia)
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".