Al-26 imaging details from COMPTEL Diehl, R; Knoedlseder, J; Bennett, K ...
Advances in space research,
05/1995, Letnik:
15, Številka:
5
Journal Article
Recenzirano
Odprti dostop
The COMPTEL map of 1.809 MeV emission from Galactic Al-26 is now based on 2.5 yr of data. Different imaging analysis approaches are discussed. Likelihood maps from point source scans are utilized for ...searches of emission regions; astrophysical source models are fitted to the measurement with the maximum likelihood method; and deconvolved images are produced with the maximum entropy algorithm. Simulations and statistical analysis with the bootstrap method demonstrate that the intensity irregularity and asymmetry along the Galactic plane is significant, although weaker individual features are insignificant by themselves. The comparison of classical Al-26 source tracer distributions shows that none of these models respresents the COMPTEL data well. The measured 1.809 MeV feature from the Vela region, positionally consistent with the Vela SNR, shows an indication of extended emission. (Author)
26Al imaging details from COMPTEL Diehl, R; Knödlseder, J; Bennett, K ...
Advances in space research,
5/1995, Letnik:
15, Številka:
5
Journal Article
Recenzirano
Odprti dostop
The COMPTEL map of 1.809 MeV emission from Galactic
26Al is now based on 2
1
2
years of data. Different imaging analysis approaches are discussed: Likelihood maps from point source scans are utilized ...for searches of emission regions; astrophysical source models are fitted to the measurement with the maximum likelihood method; deconvolved images are produced with the maximum entropy algorithm. Simulations and statistical analysis with the bootstrap method demonstrate that the intensity irregularity and asymmetry along the Galactic plane is significant, although weaker individual features are insignificant by themselves. The comparison of classical
26Al source tracer distributions shows that none of these models respresents the COMPTEL data well. The measured 1.809 MeV feature from the Vela region, positionally consistent with the Vela supernova remnant, shows an indication for extended emission.
Although existing simulation tools can be used to study the impact of agricultural management on production activities in specific environments, they suffer from several limitations. They are largely ...specialized for specific production activities: arable crops/cropping systems, grassland, orchards, agro-forestry, livestock etc. Also, they often have a restricted ability to simulate system externalities which may have a negative environmental impact. Furthermore, the structure of such systems neither allows an easy plug-in of modules for other agricultural production activities, nor the use of alternative components for simulating processes. Finally, such systems are proprietary systems of either research groups or projects which inhibits further development by third parties.
SEAMLESS aims to provide a tool to integrate analyses of impacts on the key aspects of sustainability and multi-functionality, particularly in Europe. This requires evaluating agricultural production and system externalities for the most important agricultural production systems. It also requires a simulation framework which can be extended and updated by research teams, which allows a manageable transfer of research results to operational tools, and which is transparent with respect to its contents and its functionality.
The Agricultural Production and Externalities Simulator (APES) is a modular simulation system aimed at meeting these requirements, and targeted at estimating the biophysical behavior of agricultural production systems in response to the interaction of weather and agro-technical management. APES is a framework which uses components that offer simulation options for different processes of relevance to agricultural production systems. Models are described in the associated help files of components, and a shared ontology is built on the web. Components like these, which are designed to be inherently re-usable, that is not targeted specifically to a given modelling framework, also represent a way to share modelling knowledge with other projects and the scientific community in general.
This chapter describes the current state of APES development and presents modelling options in the system, and its software architecture.
Between 2003 and 2004, 264 face-to-face interviews were undertaken to determine farmers’ perceptions of silvoarable agroforestry across 14 sample areas in seven European countries. Across the 14 ...sample areas, 40% of respondents had heard the term “agroforestry” and 33% then defined it as an association of trees with crops or livestock. By contrast those farmers, who had not heard of the term, were almost as likely to define “agroforestry” as “silviculture” (24%) as an “ association of trees and crops or trees and livestock” (25%). Farmers were then shown pictures of silvoarable agroforestry, where trees and arable crops were grown on the same land unit. Farmers in Mediterranean areas felt that the principal benefit of silvoarable systems would be increased farm profitability (37%), whereas farmers in Northern Europe placed greatest value on environmental benefits (28%). When asked to identify the greatest negative attribute, Mediterranean farmers tended to identify intercrop yield decline (31%), whereas farmers in Northern Europe tended to highlight the general complexity of work (21%) and difficulties with mechanisation (17%). When asked to design a silvoarable system for their farm, Mediterranean farmers tended to envisage systems with a higher tree density (100 trees per hectare) than those in Northern Europe (55 trees per hectare). Overall half of all farmers interviewed indicated that they would “attempt” silvoarable agroforestry on their farm, ranging from 18% to 90% within the individual sample areas. These results suggest that with appropriate promotion and support, silvoarable agroforestry would become a more common feature of the European landscape.
Although existing simulation tools can be used to study the impact of agricultural management on production activities in specific environments, they suffer from several limitations. They are largely ...specialized for specific production activities: arable crops/cropping systems, grassland, orchards, agro-forestry, livestock etc. Also, they often have a restricted ability to simulate system externalities which may have a negative environmental impact. Furthermore, the structure of such systems neither allows an easy plug-in of modules for other agricultural production activities, nor the use of alternative components for simulating processes. Finally, such systems are proprietary systems of either research groups or projects which inhibits further development by third parties. The EU Sixth Framework Integrated Project SEAMLESS aims to provide a tool to integrate analyses of impacts on the key aspects of sustainability and multi-functionality, particularly in Europe. This requires evaluating agricultural production and system externalities for the most important agricultural production systems. It also requires a simulation framework which can be extended and updated by research teams, which allows a manageable transfer of research results to operational tools, and which is transparent with respect to its contents and its functionality. The Agricultural Production and Externalities Simulator (APES) is a modular simulation system aimed at meeting these requirements, and targeted at estimating the biophysical behavior of agricultural production systems in response to the interaction of weather and agro-technical management. APES is a framework which uses components that offer simulation options for different processes of relevance to agricultural production systems. Models are described in the associated help files of components, and a shared ontology is built on the web. Components like these, which are designed to be inherently re-usable, that is not targeted specifically to a given modelling framework, also represent a way to share modelling knowledge with other projects and the scientific community in general. This chapter describes the current state of APES development and presents modelling options in the system, and its software architecture.
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