Keywords
grassland yield, root dynamics, crop model
Start Date
16-9-2020 11:40 AM
End Date
16-9-2020 12:00 PM
Abstract
For simulating spatio-temporal dynamics of biotic and abiotic processes at the landscape scale and for quantifying water and nutrient consumption used for agricultural production, the simulation of grassland production is crucial. Most process-based crop models have been developed to simulate growth of major food crops, thus, the integration of other vegetation types such as grassland, is challenging. During the last decades several grassland models have been successfully developed. However, modeling approaches and model results differ, thus, integrating them in existing crop modeling frameworks may introduce non-systematic errors and restricts the comparability of simulated yields with those of other crops. We here present a simple approach combining an existing crop model (LINTUL5) with more detailed model routines within the modelling framework SIMPLACE for simulating grassland yields for North Rhine Westphalia (Germany). The model solution was validated by using biomass data reported between 2000 and 2008 at the state scale. The model solution is based on a light use efficiency approach to simulate vegetation growth and was coupled to dynamic soil water and nutrient models (SLIM, SoilCN) within the SIMPLACE modeling framework. Simple modifications of existing parameters and modeling routines and the integration of an additional routine for simulating grassland cuts and vegetation regrowth allowed for a realistic assessment of spatio-temporal biomass dynamics and annual grass yields. At the daily or seasonal scale, however, the use of simple rules to trigger cutting events restricts a reasonable simulation of the effects of cutting events on above- and belowground vegetation dynamics.
A simple approach to simulate regional grassland dynamics with a process-based crop model
For simulating spatio-temporal dynamics of biotic and abiotic processes at the landscape scale and for quantifying water and nutrient consumption used for agricultural production, the simulation of grassland production is crucial. Most process-based crop models have been developed to simulate growth of major food crops, thus, the integration of other vegetation types such as grassland, is challenging. During the last decades several grassland models have been successfully developed. However, modeling approaches and model results differ, thus, integrating them in existing crop modeling frameworks may introduce non-systematic errors and restricts the comparability of simulated yields with those of other crops. We here present a simple approach combining an existing crop model (LINTUL5) with more detailed model routines within the modelling framework SIMPLACE for simulating grassland yields for North Rhine Westphalia (Germany). The model solution was validated by using biomass data reported between 2000 and 2008 at the state scale. The model solution is based on a light use efficiency approach to simulate vegetation growth and was coupled to dynamic soil water and nutrient models (SLIM, SoilCN) within the SIMPLACE modeling framework. Simple modifications of existing parameters and modeling routines and the integration of an additional routine for simulating grassland cuts and vegetation regrowth allowed for a realistic assessment of spatio-temporal biomass dynamics and annual grass yields. At the daily or seasonal scale, however, the use of simple rules to trigger cutting events restricts a reasonable simulation of the effects of cutting events on above- and belowground vegetation dynamics.
Stream and Session
false