Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-08T02:50:42.914Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluating an interpolation approach for modelling spatial variability in pest development

Published online by Cambridge University Press:  09 March 2007

C.H. Jarvis  and
R.H. Collier
C.H. Jarvis*
Affiliation:
Department of Geography, University of Edinburgh, Drummond Street, Edinburgh, EH8 9XP, UK
R.H. Collier
Affiliation:
Horticulture Research International, Wellesbourne, Warwick, CV35 9EF, UK
*
*Fax: +44 131 650 2524 E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Air temperatures estimated by partial thin plate spline interpolation, or from the ‘nearest station’ (Voronoi polygon method), were used to model the phenology of three pests of horticultural crops throughout England and Wales. Temperatures for a particularly hot (1976) and a particularly cold (1986) year were interpolated to a grid resolution of 1 km. Estimates were made of the timing of spring emergence (Cecidophyopsis ribis (Westwood)), the maximum number of generations completed during the summer (Plutella xylostella (Linnaeus)) and the numbers of days when mating was possible (Merodon equestris (Fabricius)). The relative accuracy of the two temperature estimation methods was compared using jack-knife cross-validation. For C. ribis and P. xylostella, modelling with interpolated temperature input data was more accurate than using data from the ‘nearest station’. Of the three phenology models used, the one that relied on an activity threshold (M. equestris) was the most sensitive to both types of input data. Spatial variability in the activity of M. equestris adults was investigated in the two main areas (south-west peninsula and Lincolnshire) where its host crop (Narcissus) is grown. Modelling at cruder scales (up to 25*25 km) masked local variation, but the degree to which this was important varied from region to region and over time, as did the geography of the variability itself. The results indicate that interpolated data, computed to a resolution of 1 km using the UK synoptic network, have the potential for wider use within agricultural decision support systems for horticultural crops.

Type
Research Article
Information
Bulletin of Entomological Research , Volume 92 , Issue 3 , June 2002 , pp. 219 - 231
Copyright
Copyright © Cambridge University Press 2002

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anony. (1969) Tables for the evaluation of daily values of accumulated temperature above and below 42F from daily values of maximum and minimum temperature. 10, Meteorological Office: Bracknell.Google Scholar
Baker, C.R.B. & Cohen, L.I. (1985) Further development of a computer model for simulating pest life cycles. Bulletin OEPP/EPPO Bulletin 15, 317–324.CrossRefGoogle Scholar
Beard, M.K. & Buttenfield, B.P. (1999) Detecting and evaluating errors by graphical methods. pp 219–233. in Longley, P.A., Goodchild, M.F., Maguire, D.J. & Rhind, D.W. (ed.) Geographical information systems. New York, Wiley.Google Scholar
Butts, R.A. & McEwen, F.L. (1981) Seasonal populations of the diamondback moth, Plutella xylostella (Lepidoptera: Plutellidae) in relation to day-degree accumulation. Canadian Entomologist 113, 127–131.CrossRefGoogle Scholar
Collier, R.H. & Finch, S. (1992) The effects of temperature on development of the large narcissus fly (Merodon equestris). Annals of Applied Biology 120, 383–390.CrossRefGoogle Scholar
Collier, R.H., Tatchell, G.M., Ellis, P.R. & Parker, W.E. (1999) Strategies for the control of aphid pests of lettuce, integrated control in field vegetable crops. IOBC Bulletin 22, 25–35.Google Scholar
Cressie, N. (1991) Statistics for spatial data. 900 pp. New York, Wiley.Google Scholar
Cross, J. & Ridout, M.S. (2001) Emergence of blackcurrant gall mite (Cecidophyopsis ribis) from galls in spring. Journal of Horticultural Science and Biotechnology 76, 311–319.CrossRefGoogle Scholar
Downing, K. & Bartos, D.L. (1991) AI methods in support of forest science: modeling endemic level mountain pine beetle population dynamics. AI Applications 5, 105–115.Google Scholar
Efron, B. (1982) The jacknife, the bootstrap and other resampling plans. Philadelphia: S.I.A.M.CrossRefGoogle Scholar
Harcourt, D.G. (1954) The biology and ecology of the diamondback moth, Plutella maculipennis. In (Curtis) in eastern Ontario. 107 pp. PhD thesis, Cornell University, Ithaca, New York.Google Scholar
Heuvelink, G.B.M. (1998) Error propagation in environmental modelling. 127 pp. London, Taylor and Francis.CrossRefGoogle Scholar
Higley, L.G., Larry, P.P. & Ostlie, K.R. (1986) DEGDAY: a program for calculating degree-days, and assumptions behind the degree-day approach. Environmental Entomology 15, 999–1016.CrossRefGoogle Scholar
Hutchinson, M.F. (1991) The application of thin plate smoothing splines to continent-wide data assimilation. pp 104–113. in Jasper, J.D. (ed.) Data assimilation systems, BMRC Research Report No. 27. Melbourne: Bureau of Meteorology.Google Scholar
Jarvis, C.H. (1999) Insect phenology: a geographical perspective. 302 pp. PhD thesis, University of Edinburgh.Google Scholar
Jarvis, C.H. (2001) GEO_BUG: a geographical modelling environment for assessing the likelihood of pest development. Environmental Modelling and Software 8, 739–751.Google Scholar
Jarvis, C.H. & Stuart, N. (2001a) A comparison between strategies for interpolating maximum and minimum daily air temperatures: a. The selection of ‘guiding’ topographic and land cover variables. Journal of Applied Meteorology 40, 1060–1074.2.0.CO;2>CrossRefGoogle Scholar
Jarvis, C.H. & Stuart, N. (2001b) A comparison between strategies for interpolating maximum and minimum daily air temperatures: b. The interaction between number of guiding variables and the type of interpolation method. Journal of Applied Meteorology 40, 1075–1084.2.0.CO;2>CrossRefGoogle Scholar
Jarvis, C.H. & Stuart, N. (2001c) Uncertainties in modelling with time series data: estimating the risk of crop pests throughout the year. Transactions in GIS 5, 327–343.CrossRefGoogle Scholar
Jarvis, C.H., Stuart, N., Morgan, D. & Baker, R.H.A. (1999) To interpolate and thence to model, or vice versa?. pp 229–242. in Gittings, B. (ed.) Integrating information infrastructures with geographical information technology. London, Taylor and Francis.Google Scholar
Jarvis, C.H., Stuart, N. & Hims, M. (in prees) Towards a British framework for enhanced value agrometeorological data. Applied Geography.Google Scholar
Liebhold, A., Luzader, E., Reardon, R., Roberts, A., Ravlin, F.W., Sharov, A. & Zhou, G. (1998) Forecasting gypsy moth (Lepidoptera: Lymantriidae) defoliation with a geographical information system. Journal of Economic Entomology 91, 464–472.CrossRefGoogle Scholar
Loh, D.K., Connor, M.D. & Janiga, P. (1991) Jack pine budworm decision support system: a prototype. AI Applications 5, 29–45.Google Scholar
Mineter, M.J., Dowers, S. & Gittings, B.M. (2000) Towards a HPC framework for integrated processing of geographical data: encapsulating the complexity of parallel algorithms. Transactions in GIS 4, 245–262.CrossRefGoogle Scholar
Morgan, D. (1992) Predicting the phenology of lepidopteran pests in orchards of S.E. England. Acta Phytopathologica et Entomologica Hungarica 27, 473–477.Google Scholar
Phelps, K., Collier, R.H., Reader, R.J. & Finch, S. (1993) Monte Carlo simulation method for forecasting the timing of insect pest attacks. Crop Protection 12, 335–342.CrossRefGoogle Scholar
Régnière, J. (1996) A generalized approach to landscape-wide seasonality forecasting with temperature driven simulation models. Environmental Entomology 25, 869–881.CrossRefGoogle Scholar
Régnière, J. & Bolstad, P. (1994) Statistical simulation of daily air temperature patterns in eastern North America to forecast seasonal events in insect pest management. Environmental Entomology 23, 1368–1380.CrossRefGoogle Scholar
Russo, J.M., Liebhold, A.M. & Kelley, J.G.W. (1993) Mesoscale weather data as input to a gypsy moth (Lepidoptera: Lymantriidae) phenology model. Journal of Economic Entomology 86, 838–844.CrossRefGoogle Scholar
Schaub, L.P., Ravlin, F.W., Gray, D.R. & Logan, J.A. (1995) Landscape framework to predict phenological events for gypsy moth (Lepidoptera: Lymantriidae) management problems. Environmental Entomology 24, 10–18.CrossRefGoogle Scholar
Sharpe, P.J.H. & deMichele, D.W. (1977) Reaction kinetics of poikilotherm development. Journal of Theoretical Biology 64, 649–670.CrossRefGoogle ScholarPubMed
Tiilikkala, K. & Ojanen, H. (1999) Use of a geographical information system (GIS) for forecasting the activities of carrot fly and cabbage root fly. IOBC Bulletin 22, 15–24.Google Scholar
Vanclay, J.K. & Skovsgaard, J.P. (1997) Evaluating forest growth models. Ecological Modelling 98, 1–12.CrossRefGoogle Scholar
Weisz, R., Fleischer, S. & Smilowitz, Z. (1995) Map generation in high-value horticultural integrated pest-management & ndash; appropriate interpolation methods for site-specific pest management of Colorado potato beetle (Coleoptera, Chrysomelidae). Journal of Economic Entomology 88, 1650–1657.CrossRefGoogle Scholar
Weisz, R., Fleischer, S. & Smilowitz, Z. (1995) Site specific integrated pest management for high value crops: sample units for map generation using the Colorado potato, Coleoptera: Chrysomelidae, as a model system beetle. Journal of Economic Entomology 89, 501–509.CrossRefGoogle Scholar
Welch, S.M., Croft, B.A., Brunner, J.F. & Michels, M.F. (1978) PETE: an extension phenology modeling system for management of multi-species pest complex. Environmental Entomology 7, 482–494.CrossRefGoogle Scholar
Weseloh, R.M. (1996) Developing and validating a model for predicting gypsy moth (Lepidoptera: Lymantriidae) defoliation in Connecticut. Journal of Economic Entomology 89, 1546–1555.CrossRefGoogle Scholar
Yang, D., Pijanowski, B.C. & Gage, S.H. (1998) Analysis of gypsy moth (Lepidoptera: Lymantriidae) population dynamics in Michigan using geographical information systems. Environmental Entomology 27, 842–852.CrossRefGoogle Scholar