Models for spring migration of two aphid species Sitobion avenae (F.) and Rhopalosiphum padi (L.) infesting cereals in areas where they are entirely holocyclic

Abstract

1 Cereals can be attacked severely by the grain aphid, Sitobion avenae (F.), and the bird cherry-oat aphid, Rhopalosiphum padi (L.). The time of migration from winter hosts in spring is important regarding input to decision support systems concerning insecticide treatment of aphids. 2 The present study aimed to construct two separate migration models, which could be used immediately for advisors and farmers but also be part of a decision support system for the chemical control of aphids in winter wheat and spring barley. 3 Winter wheat ( Triticum sativum Lam.) and spring barley ( Hordeum vulgare L.) fields were monitored from 1991 -2005 for the occurrence of grain aphids and bird cherry-oat aphids, and the data were used to construct migration models. 4 The models were constructed based on all 9 years data and subsequently validated by using all 9 years data, excluding 1 year at a time. 5 The migration model for the grain aphid producing the best forecast was obtained with migration date M (number of days from 1 June), degree-days ( DD ) of April ( T a ) and DD of May ( T m ), with the model being M = 265.0 -10.2 log e ( T a ) -35.1 log e ( T m ). 6 The migration model for the bird cherry-oat aphid producing the best forecast was obtained with migration date M (number of days from 15 May), DD of April ( T a ) and DD from 1 -15 May ( T m1 ), with the model being M = 294.4 -34.7 log e ( T a ) -22.5 log e ( T m1 ). 7 The models only worked well in areas where the grain aphid and the bird cherryoat aphid are entirely holocyclic.

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

“…It is delayed by low winter temperatures, i.e. the cooler the weather in the winter the later the arrival of cereal aphids [57], [61]. Similar to our results, some studies indicated that maximum aphid abundance increases with length of aphid presence in crop stands [62].…”

Predicting the occurrence of alate aphids in Brassicaceae

“…Although degree-day models are used to predict the occurrence of aphids and other insect pests (Zou et al, 2004;Hansen, 2006), the time lags observed between the prediction and actual occurrence can range from zero to nine days (Hickel et al, 2003;Jiao et al, 2006). Differences between prediction and occurrence dates of an insect population peak in the field can be related to the difficulty of determining the biofix date, which is considered important to get an accurate prediction by the model (Knight & Light, 2005).…”

Improving phenological forecasting models for rape stem weevil,Ceutorhynchus napiGyll., based on long‐term multisite datasets

“…). Various forecasting models have been developed for pest insects attacking major crops in Europe, for example cereal aphids in wheat (Hansen ; Klueken et al. ); western corn root worm, Diabrotica virgifera virgifera (Toepfer and Kuhlmann ); and frit fly, Oscinella frit (Lindblad and Solbreck ), in maize.…”

“…Migration of pests into field crops is strongly influenced by meteorological variables (Johnen et al 2010), and forecasting systems usually use long-term field surveys and weather datasets to incorporate those parameters into their models (Williams 2010;Estay et al 2012). Various forecasting models have been developed for pest insects attacking major crops in Europe, for example cereal aphids in wheat (Hansen 2006;Klueken et al 2009); western corn root worm, Diabrotica virgifera virgifera (Toepfer and Kuhlmann 2006); and frit fly, Oscinella frit (Lindblad and Solbreck 1998), in maize. Winter oilseed rape (WOSR, Brassica napus L.) is a major oilseed crop in Northern and Central Europe with annual seed production of 19.8 million tonnes (Eurostat 2011).…”

“…They rely on several meteorological variables, for example air temperature (Fritzsche 1956;R€ oder 1977), soil temperature (Schmutterer 1956;Oppermann 1990), precipitation (Debouzie and Wimmer 1992), wind speed (Schmutterer 1956) and global radiation (Schmutterer 1956;Bergermeier 1992). Identification of the key meteorological variables influencing the phenology of a pest using long-term datasets on weather and pest phenology enables the development of numerical algorithms linking meteorological variables to pest immigration (Hansen 2006;Johnen et al 2010;Estay et al 2012). Air temperature is commonly the predominant factor in models forecasting insect development and activity (Klueken et al 2009).…”