GEO_BUG: a geographical modelling environment for assessing the likelihood of pest development
Identifieur interne : 000463 ( Istex/Corpus ); précédent : 000462; suivant : 000464GEO_BUG: a geographical modelling environment for assessing the likelihood of pest development
Auteurs : Claire H. JarvisSource :
- Environmental Modelling and Software [ 1364-8152 ] ; 2001.
Abstract
This paper describes software designed to explore pest phenology (development) over space and time. The framework presented links sequences of interpolated daily maximum and minimum temperatures with a variety of process-based phenology and accumulated temperature models. The flexibility offered by this approach is demonstrated using examples of gridded maps of pest phenology on target dates, graphs of the sequences of pest development at individual locations and assessments of error in the predicted dates over the course of a model run. Finally, the potential application of the software in support of agricultural management systems, policy development and integrated research is discussed.
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DOI: 10.1016/S1364-8152(01)00040-8
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<front><div type="abstract" xml:lang="en">This paper describes software designed to explore pest phenology (development) over space and time. The framework presented links sequences of interpolated daily maximum and minimum temperatures with a variety of process-based phenology and accumulated temperature models. The flexibility offered by this approach is demonstrated using examples of gridded maps of pest phenology on target dates, graphs of the sequences of pest development at individual locations and assessments of error in the predicted dates over the course of a model run. Finally, the potential application of the software in support of agricultural management systems, policy development and integrated research is discussed.</div>
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<notesStmt><note type="content">Fig. 1: Relationship of GIS and dedicated modelling software.</note>
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<note type="content">Fig. 3: UKMO algorithm for computing accumulated temperature sum (ACC_SUM) using recorded daily maxima (maxn) and minima (minn) over temperature base (After Anonymous, 1969).</note>
<note type="content">Fig. 4: The three main structures of phenology models in common use: (a) linear (e.g. Anonymous, 1969), (b) multi-linear by stage, (e.g. Baker and Cohen, 1985; Welch et al., 1978) and (c) non-linear (e.g. logistic) by stage.</note>
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<note type="content">Fig. 8: Potential development of Colorado beetle in Yorkshire. (a) Distribution of potato crop (1994); (b) elevation, Vale of York; (c) estimated life-cycle development of young adults, larvae and mature adults throughout the year 1976 for Colorado beetle at upland (2) and lowland (1) sites marked within (a).</note>
<note type="content">Fig. 9: (a) Estimated Julian dates for 50% emergence of Colorado beetle for mature adults; (b)–(d) estimated percentage emergence of larval stage, Colorado beetle, Vale of York, 28–30 June 1976.</note>
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<abstract lang="en">This paper describes software designed to explore pest phenology (development) over space and time. The framework presented links sequences of interpolated daily maximum and minimum temperatures with a variety of process-based phenology and accumulated temperature models. The flexibility offered by this approach is demonstrated using examples of gridded maps of pest phenology on target dates, graphs of the sequences of pest development at individual locations and assessments of error in the predicted dates over the course of a model run. Finally, the potential application of the software in support of agricultural management systems, policy development and integrated research is discussed.</abstract>
<note type="content">Fig. 1: Relationship of GIS and dedicated modelling software.</note>
<note type="content">Fig. 2: Software components and attribution.</note>
<note type="content">Fig. 3: UKMO algorithm for computing accumulated temperature sum (ACC_SUM) using recorded daily maxima (maxn) and minima (minn) over temperature base (After Anonymous, 1969).</note>
<note type="content">Fig. 4: The three main structures of phenology models in common use: (a) linear (e.g. Anonymous, 1969), (b) multi-linear by stage, (e.g. Baker and Cohen, 1985; Welch et al., 1978) and (c) non-linear (e.g. logistic) by stage.</note>
<note type="content">Fig. 5: Flow of spatial data.</note>
<note type="content">Fig. 6: Program components and flow of data and control in the spatial phenological modelling system (GEO_BUG).</note>
<note type="content">Fig. 7: Accumulated temperatures (base 8.5°C) over England and Wales, (a) 1976 and (b) 1986.</note>
<note type="content">Fig. 8: Potential development of Colorado beetle in Yorkshire. (a) Distribution of potato crop (1994); (b) elevation, Vale of York; (c) estimated life-cycle development of young adults, larvae and mature adults throughout the year 1976 for Colorado beetle at upland (2) and lowland (1) sites marked within (a).</note>
<note type="content">Fig. 9: (a) Estimated Julian dates for 50% emergence of Colorado beetle for mature adults; (b)–(d) estimated percentage emergence of larval stage, Colorado beetle, Vale of York, 28–30 June 1976.</note>
<note type="content">Fig. 10: National estimate of cross validated error, Colorado beetle 1976: (a) 50% emergence adult stage; (b) 50% emergence egg stage.</note>
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