Application of grazing land models in ecosystem management: Current status and next frontiers

Authors: Ma, Liwang; Derner, Justin; Harmel, Daren; Tatarko, John; MOORE, ANDREW - Csiro, Black Mountain Laboratories; Rotz, Clarence - Al; Augustine, David; BOONE, RANDALL - Colorado State University; COUGHENOUR, MICHAEL - Colorado State University; BEUKES, PIERRE - Dairy Nz, Ltd; VAN WIJK, MARK - International Livestock Research Institute (ILRI) - Kenya; BELLOCCHI, GIANNI - Inra, Génétique Animale Et Biologie Intégrative , Jouy-En-josas, France; CULLEN, BRENDAN - University Of Melbourne; Wilmer, Hailey

Submitted to: Advances in Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/29/2019
Publication Date: 9/17/2019
Citation: Ma, L., Derner, J.D., Harmel, R.D., Tatarko, J., Moore, A., Rotz, C.A., Augustine, D.J., Boone, R., Coughenour, M. 2019. Application of grazing land models in ecosystem management: Current status and next frontiers. Advances in Agronomy. 158:173-216. https://doi.org/10.1016/bs.agron.2019.07.003.
DOI: https://doi.org/10.1016/bs.agron.2019.07.003

Interpretive Summary: Grazing land models are useful in studying the effects of animal grazing management on ecosystems. We reviewed 12 grazing land models used for evaluating forage and animal (meat and milk) production, soil carbon sequestration, greenhouse gas emission, and nitrogen leaching, under both current and projected climate conditions. Given the effects of variability in space and time on grazing land and animal-plant-soil interactions, none of the models evaluated has the capability of simulating all the effects on ecosystems from grazing management at different scales and across multiple locations. A large number of models have focused on topics such as the environmental impacts of grazing land management and sustainability of ecosystems. Additional study is needed into the space and time changes in grazing lands and their impacts on animal performance. In addition to identifying the knowledge gaps in simulating the biological and physical processes of grazing lands, this review suggests further improvements, which will increase adoption of these models for making decisions about grazing land management. Grazing land models need to be user-friendly by utilizing available large data sets to reduce simulation uncertainty. Efforts need to reduce differences among grazing land models in simulated ecosystems and grazing management effects by carefully examining the biological and physical interactions in each model. Models also need to account for experiences of modelers, experimentalists, and land managers.

Technical Abstract: 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 iin 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.