Application of grazing land models in ecosystem management: Current status and next frontiers
Autor: | Justin D. Derner, Brendan Cullen, Pierre C. Beukes, R. Daren Harmel, Andrew D. Moore, Liwang Ma, Mark T. van Wijk, John Tatarko, David J. Augustine, Randall B. Boone, Michael B. Coughenour, Gianni Bellocchi, C. Alan Rotz, Hailey Wilmer |
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Přispěvatelé: | Rangeland Resources and Systems Research Unit, USDA-ARS : Agricultural Research Service, Center for Agricultural Resources Research, Chinese Academy of Sciences [Changchun Branch] (CAS), Agriculture & Food, Black Mountain Science and Innovation Precinct, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Pasture Systems and Watershed Management Research Unit, Natural Resource Ecology Laboratory, Colorado State University [Fort Collins] (CSU), DairyNZ, International Livestock Research Institute [CGIAR, Nairobi] (ILRI), International Livestock Research Institute [CGIAR, Ethiopie] (ILRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Faculty of Veterinary and Agricultural Sciences, University of Melbourne, United States Department of Agriculture - Agricultural Research Service, Sustainable Livestock Systems, International Livestock Research Institute, CGIAR (ILRI) |
Rok vydání: | 2019 |
Předmět: |
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences
pâturage Land management simulation models modèle de simulation Ecosystem services Grazing gestion gestion de pâturage Ecosystem modélisation business.industry Environmental resource management 04 agricultural and veterinary sciences 15. Life on land écosystème prairial Climate change mitigation 13. Climate action Sustainability 040103 agronomy & agriculture Ecosystem management 0401 agriculture forestry and fisheries Environmental science Spatial variability business outil d'aide à la décision |
Zdroj: | Advances in Agronomy Advances in Agronomy, 158, Academic Press, 326 p., 2019, Advances in Agronomy, 978-0-12-817412-8. ⟨10.1016/bs.agron.2019.07.003⟩ |
DOI: | 10.1016/bs.agron.2019.07.003 |
Popis: | Grazing land models can assess the provisioning and trade-offs among ecosystem services attributable to grazing management strategies. We reviewed 12 grazing land models used for evaluating forage and animal (meat and milk) production, soil C sequestration, greenhouse gas emission, and nitrogen leaching, under both current and projected climate conditions. Given the spatial and temporal variability that characterizes most rangelands and pastures in which animal, plant, and soil interact, none of the models currently have the capability to simulate a full suite of ecosystem services provided by grazing lands at different spatial scales and across multiple locations. A large number of model applications have focused on topics such as environmental impacts of grazing land management and sustainability of ecosystems. Additional model components are needed to address the spatial and temporal dynamics of animal foraging behavior and interactions with biophysical and ecological processes on grazing lands and their impacts on animal performance. In addition to identified knowledge gaps in simulating biophysical processes in grazing land ecosystems, our review suggests further improvements that could increase adoption of these models as decision support tools. Grazing land models need to increase user-friendliness by utilizing available big data to minimize model parameterization so that multiple models can be used to reduce simulation uncertainty. Efforts need to reduce inconsistencies among grazing land models in simulated ecosystem services and grazing management effects by carefully examining the underlying biophysical and ecological processes and their interactions in each model. Learning experiences among modelers, experimentalists, and stakeholders need to be strengthened by co-developing modeling objectives, approaches, and interpretation of simulation results. |
Databáze: | OpenAIRE |
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