Keywords
bioenergy, biogas, biomass potentials, crop modeling
Start Date
1-7-2012 12:00 AM
Abstract
With the trend of a growing production and use of agricultural substrates in bio-gas facilities in Lower Saxony (Germany), the competition between the production of food crops, environmental conservation issues and, sustainability goals in general, has seen an increase in the last decade. To mitigate the conflict, accurate knowledge of agricultural potentials can be of help. When questions of medium or long range regional planning are concerned, policy makers and other stakeholders often lack reliable yield data. From statistical data sources, usually only limited information with low spatial resolutions on agricultural biomass potentials is available in Lower Saxony (administration district level).To overcome this hindrance in the assessment of biomass potentials, a software tool for the computation of such potentials has been developed (BIS, 2012). The tool BioSTAR (Biomass Simulation Tool for Agricultural Resources) can be classified as a generic crop model and it is currently tested and validated for several agricultural biomass crops grown in Lower Saxony. The model uses climate and soil input data and calculates carbon accumulation rates on a daily or monthly basis. The model belongs to the family of carbon based models (Azam-Ali, et al., 1994). Climate input data are precipitation, solar radiation, temperature, humidity and wind speed. Soil data can be either of the FAO soil texture classification type or of the more differentiated German classification of the KA5 (DIN, 4220, 2008). Special features of the model BioSTAR are the ability to process either single or multiple sites in one calculation procedure, and a simple, and user friendly graphic interface. The program uses MS-Access data base tables to read in and write out data. The model has been kept simple enough to avoid some of the difficulties (e.g. unavailable input parameters and input data) often associated with more complex models which are often overburdened for simple biomass analysis.
Optimizing Land use and the Yields of Bio-Energy Crops by using site specific Biomass Calculations: Introduction of the Crop Modelling Software BioSTAR
With the trend of a growing production and use of agricultural substrates in bio-gas facilities in Lower Saxony (Germany), the competition between the production of food crops, environmental conservation issues and, sustainability goals in general, has seen an increase in the last decade. To mitigate the conflict, accurate knowledge of agricultural potentials can be of help. When questions of medium or long range regional planning are concerned, policy makers and other stakeholders often lack reliable yield data. From statistical data sources, usually only limited information with low spatial resolutions on agricultural biomass potentials is available in Lower Saxony (administration district level).To overcome this hindrance in the assessment of biomass potentials, a software tool for the computation of such potentials has been developed (BIS, 2012). The tool BioSTAR (Biomass Simulation Tool for Agricultural Resources) can be classified as a generic crop model and it is currently tested and validated for several agricultural biomass crops grown in Lower Saxony. The model uses climate and soil input data and calculates carbon accumulation rates on a daily or monthly basis. The model belongs to the family of carbon based models (Azam-Ali, et al., 1994). Climate input data are precipitation, solar radiation, temperature, humidity and wind speed. Soil data can be either of the FAO soil texture classification type or of the more differentiated German classification of the KA5 (DIN, 4220, 2008). Special features of the model BioSTAR are the ability to process either single or multiple sites in one calculation procedure, and a simple, and user friendly graphic interface. The program uses MS-Access data base tables to read in and write out data. The model has been kept simple enough to avoid some of the difficulties (e.g. unavailable input parameters and input data) often associated with more complex models which are often overburdened for simple biomass analysis.
