Comparison and validation of population models for cereal aphids

Klueken, A. M.; Hau, B.; Freier, Bernd; Friesland, H.; Kleinhenz, B.; Poehling, Hans‐Michael

doi:10.1007/bf03356299

Cited by 15 publications

(14 citation statements)

References 31 publications

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“…Therefore, there is a great interest in controlling aphids in winter wheat, and a number of simulation models have been developed to predict the population development of these pests (e.g. Ciss, Parisey, Moreau, Dedryver, & Pierre, 2014;Duffy, Fealy, & Fealy, 2017;Holst & Ruggle, 1997;Klueken et al, 2009). Furthermore, some population dynamic models have been combined with modules on economy and pesticide choices to produce decision support systems with the aim of helping farmers optimizing the timing of pesticide applications.…”

Section: Introductionmentioning

confidence: 99%

The effect of spatial variation for predicting aphid outbreaks

Damgaard

Bruus

Axelsen

2019

J Applied Entomology

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In order to improve forecasting of pest epidemics, it is important to know the spatial scale at which specific forecasts are reliable. To investigate the spatial scale of aphid outbreaks, we have developed a spatio-temporal stochastic aphid population growth model and fitted the model to empirical spatial time series of aphid population data using a Bayesian hierarchical fitting procedure. Furthermore, detailed spatial data of the initial phases of population growth were investigated in semivariograms. Our results suggest that spatial variation is low in the initial occurrence probability at a spatial scale of 10 km. Consequently, the results support the hypothesis that initial aphid population sizes and outbreaks may be predicted in fields within a 10 km radius. For farmers, this may imply that they can rely their decision of whether to spray against aphids on observations made by other nearby farmers or by the consultancy service. K E Y W O R D S aphid population model, hierarchical models, spatial and temporal model S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Damgaard C, Bruus M, Axelsen JA.The effect of spatial variation for predicting aphid outbreaks. J

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Section: Introductionmentioning

confidence: 99%

The effect of spatial variation for predicting aphid outbreaks

Damgaard

Bruus

Axelsen

2019

J Applied Entomology

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“…The models of aphid population growth in cereal stands naturally contain temperature as a proxy of biological time and further parameters including aphid reproduction rate, migration, host plant phenology and effect of natural enemies [58], [59]. The models well predict aphid population development after rectification using field data established at the beginning of the season [60]. One factor limiting the length of aphid presence in wheat stands is spring immigration.…”

Section: Discussionmentioning

confidence: 99%

Population Dynamics of Aphids on Cereals: Digging in the Time-Series Data to Reveal Population Regulation Caused by Temperature

et al. 2014

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Aphid populations show periodic fluctuations and many causes are attributed to their dynamic. We investigated the regulation by temperature of the aphid populations composed of Metopolophium dirhodum, Sitobion avenae, and Rhopalosiphum padi on winter wheat using a 24 years long time series data. We computed the sum of daily temperatures above 5°C, the threshold temperature for aphid development, and the sum of daily temperatures within the [0(threshold for wheat development),5] °C interval. Applying Generalised Additive Model framework we tested influences of temperature history expressed via degree days before the start of the aphid immigration on the length of their occurrence. We aimed to estimate the magnitude and direction of this influence, and how far to the past before the start of the aphid season the temperature effect goes and then identify processes responsible for the effect. We fitted four models that differed in the way of correcting for abundance in the previous year and in specification of temperature effects. Abundance in the previous year did not affect the length of period of aphid population growth on wheat. The temperature effect on the period length increased up to 123 days before the start of the current season, i.e. when wheat completed vernalization. Increased sum of daily temperatures above 5°C and the sum of daily temperatures within the [0,5] °C interval both shortened the length of period of aphid population growth. Stronger effect of the latter suggests that wheat can escape from aphid attacks if during winter temperatures range from 0 to 5°C. The temperature influence was not homogeneous in time. The strongest effect of past temperature was about 50 to 80 and 90 to 110 days before the beginning of the current aphid season indicating important role of termination of aphid egg dormancy and egg hatching.

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“…27 Therefore, there is a great interest in controlling aphids in winter wheat, and a number of simulation 28 models have been developed to predict the population development of these pests (e.g. Ciss Klueken et al 2009). Furthermore, some population dynamic 30 models have been combined with modules on economy and pesticide choices to produce decision support 31 systems with the aim of helping farmers optimizing the timing of pesticide applications.…”

Section: Introduction 22mentioning

confidence: 99%

The effect of spatial variation for predicting aphid epidemics

Damgaard

Bruus

Axelsen

2018

Preprint

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9In order to improve forecasting of aphid epidemics, it is important to know the spatial scale at which 10 specific forecasts are reliable. To investigate the spatial scale of aphid epidemics, we have developed a 11 spatio-temporal stochastic aphid population growth model, and fitted the model to empirical spatial time-12 series aphid population data using a Bayesian hierarchical fitting procedure. Furthermore, detailed spatial 13 data of the initial phases of epidemic development was investigated in a semivariogram. Our results 14 suggest that there is limited spatial variation in the initial occurrence probability at a spatial scale of 10 km. 15Consequently, the results support the hypothesis that initial aphid population sizes and epidemics may be 16 predicted in fields within a 10 km radius. For farmers, this may imply that they can rely their decision of 17 whether to spray against aphids on observations made by other nearby farmers or by the consultancy 18 service. 19

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Comparison and validation of population models for cereal aphids

Cited by 15 publications

References 31 publications

The effect of spatial variation for predicting aphid outbreaks

The effect of spatial variation for predicting aphid outbreaks

Population Dynamics of Aphids on Cereals: Digging in the Time-Series Data to Reveal Population Regulation Caused by Temperature

The effect of spatial variation for predicting aphid epidemics

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