Theme 32: Use of Information and Analytical Systems
Description
In this contribution a view of the promise and difficulties of modelling grassland is given. This is largely centred around work with a grassland ecosystem simulator known as the Hurley Pasture Model.
A brief introduction sets forth possible reasons for building a large ecosystem model, and stresses the importance of modelling objectives. It is suggested that a model is de rigeur for any research programme which aims to take a firm grasp of the complex responses of grassland. Mechanistic models are required to provide the understanding needed for intelligent and flexible management of grassland, whatever the prevailing environmental or economic objectives. The models are necessarily large, reflecting the complexity of the ‘real’ system, and, in a sense, are ‘big’ science. The challenge is to develop models of ‘engineering strength’. This requires an appropriate research environment, which should be reasonably stable, multidisciplinary, well-connected to experimental programmes, and permit adequate support for the three essential legs of an ecosystem model: development, documentation, and application. Some modelling researchers are dismayed by the wasteful fragmentation of many plant ecosystem modelling research programmes.
Next an outline account of the Hurley Pasture Model (HPM) is given. Most plant ecosystem models are now quite similar at the qualitative level, and few would dispute the statement that a reasonable level of consensus is emerging. The HPM is a standard model of the genre. It comprises plant, animal, soil and water submodels. To-date there is no phenology submodel. There are environmental and management drivers, the former accepting monthly, daily or diurnal data, the latter permitting the simulation of fertilizer, grazing, and cutting scenarios, more or less ad libitum.
Recent developments of the HPM include a submodel to take account of acclimation of photosynthesis to light, nitrogen, carbon dioxide (‘down-regulation’) and temperature; and a simple method of using the HPM to simulate legume dynamics in a grass-legume pasture.
Finally, some applications of the model are presented, relating to fertilizer application, grazing, harvesting, and climate change. These are to illustrate the scope of the model, for both application and understanding. The last application shows that in grassland ecosystems climate change responses can be greatly affected by (i) a variable legume content; (ii) management, e.g. how the crop is grazed or cut; and (iii) water stress, as occurs in southern Britain. The impact of climate change on grassland ecosystems is of particular interest. It is known that, in a constant climate, a grassland ecosystem can take hundreds of years to come to equilibrium. Experiments cannot address the problem directly. Short-term experiments can give very variable responses, depending on conditions, which are often misleading, even opposite in sign from long-term responses. Mechanistic models provide a clear framework for unifying these variable results, understanding why they arise, and making predictions about the future time course of plant ecosystems. There seems to be no other way of doing this work.
Citation
Thornley, J. H. M., "Modelling Grassland Ecosystems" (2022). IGC Proceedings (1993-2023). 12.
https://uknowledge.uky.edu/igc/19/32/12
Included in
Modelling Grassland Ecosystems
In this contribution a view of the promise and difficulties of modelling grassland is given. This is largely centred around work with a grassland ecosystem simulator known as the Hurley Pasture Model.
A brief introduction sets forth possible reasons for building a large ecosystem model, and stresses the importance of modelling objectives. It is suggested that a model is de rigeur for any research programme which aims to take a firm grasp of the complex responses of grassland. Mechanistic models are required to provide the understanding needed for intelligent and flexible management of grassland, whatever the prevailing environmental or economic objectives. The models are necessarily large, reflecting the complexity of the ‘real’ system, and, in a sense, are ‘big’ science. The challenge is to develop models of ‘engineering strength’. This requires an appropriate research environment, which should be reasonably stable, multidisciplinary, well-connected to experimental programmes, and permit adequate support for the three essential legs of an ecosystem model: development, documentation, and application. Some modelling researchers are dismayed by the wasteful fragmentation of many plant ecosystem modelling research programmes.
Next an outline account of the Hurley Pasture Model (HPM) is given. Most plant ecosystem models are now quite similar at the qualitative level, and few would dispute the statement that a reasonable level of consensus is emerging. The HPM is a standard model of the genre. It comprises plant, animal, soil and water submodels. To-date there is no phenology submodel. There are environmental and management drivers, the former accepting monthly, daily or diurnal data, the latter permitting the simulation of fertilizer, grazing, and cutting scenarios, more or less ad libitum.
Recent developments of the HPM include a submodel to take account of acclimation of photosynthesis to light, nitrogen, carbon dioxide (‘down-regulation’) and temperature; and a simple method of using the HPM to simulate legume dynamics in a grass-legume pasture.
Finally, some applications of the model are presented, relating to fertilizer application, grazing, harvesting, and climate change. These are to illustrate the scope of the model, for both application and understanding. The last application shows that in grassland ecosystems climate change responses can be greatly affected by (i) a variable legume content; (ii) management, e.g. how the crop is grazed or cut; and (iii) water stress, as occurs in southern Britain. The impact of climate change on grassland ecosystems is of particular interest. It is known that, in a constant climate, a grassland ecosystem can take hundreds of years to come to equilibrium. Experiments cannot address the problem directly. Short-term experiments can give very variable responses, depending on conditions, which are often misleading, even opposite in sign from long-term responses. Mechanistic models provide a clear framework for unifying these variable results, understanding why they arise, and making predictions about the future time course of plant ecosystems. There seems to be no other way of doing this work.
