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Related Experiment Videos

Evaluating an interpolation approach for modelling spatial variability in pest development.

C H Jarvis1, R H Collier

  • 1Department of Geography, University of Edinburgh, Drummond Street, Edinburgh, EH8 9XP, UK. chj@geo.ed.ac.uk

Bulletin of Entomological Research
|June 29, 2002
PubMed
Summary
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Accurate temperature data is crucial for modeling horticultural pest phenology. Interpolated temperature data improved pest modeling accuracy compared to nearest station data, aiding agricultural decision support systems.

Area of Science:

  • Agricultural Entomology
  • Environmental Modeling
  • Horticultural Science

Background:

  • Accurate air temperature data is essential for predicting the phenology of horticultural pests.
  • Previous phenological models relied on 'nearest station' temperature data, which may lack spatial accuracy.
  • Understanding pest phenology is vital for effective crop management and reducing pesticide use.

Purpose of the Study:

  • To compare the accuracy of two air temperature estimation methods (spline interpolation vs. nearest station) for modeling pest phenology.
  • To assess the impact of temperature data resolution on phenological models for three key horticultural pests.
  • To evaluate the potential of high-resolution interpolated temperature data for agricultural decision support systems.

Main Methods:

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  • Air temperatures were estimated using partial thin plate spline interpolation and the 'nearest station' (Voronoi polygon) method.
  • Temperature data for hot (1976) and cold (1986) years were interpolated to a 1 km grid resolution.
  • Phenology models were developed for spring emergence (Cecidophyopsis ribis), generations (Plutella xylostella), and mating (Merodon equestris), with accuracy assessed via jack-knife cross-validation.

Main Results:

  • Interpolated temperature data resulted in more accurate phenological models for C. ribis and P. xylostella compared to nearest station data.
  • The Merodon equestris phenology model, based on an activity threshold, was most sensitive to both temperature input methods.
  • Crude spatial modeling (up to 25x25 km) masked local pest activity variations, highlighting the importance of high-resolution data.

Conclusions:

  • Partial thin plate spline interpolation provides a more accurate method for estimating air temperatures for horticultural pest phenology modeling.
  • 1 km resolution interpolated temperature data, derived from the UK synoptic network, show significant potential for enhancing agricultural decision support systems.
  • High-resolution temperature data are crucial for capturing local variations in pest activity and improving the efficacy of pest management strategies.