Predicting the time of emergence of frit fly (<i>Oscinella frit</i> L.) (Dipt., Chloropidae) in northern England

Umoru, P. A.; Bale, J. S.; Shorrocks, Bryan

doi:10.1111/j.1439-0418.1990.tb00066.x

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…casting models, predicting the date of frit fly occurrence (Ohnesorge 1973, Tolley and Niemzcyk 1988, Umoru et al 1990, Lindblad and Sigvald 1996 or relating damage to the temperature sum attained at a critical growth stage (Nilsson 1983). Another important factor was the average temperature 1 wk after the date when the crop had reached growth stage DC 11, evidently because there is a positive relation between temperature and the rate of oviposition (Lindblad and Sigvald 1999).…”

Section: Discussionmentioning

confidence: 99%

“…The time of frit fly emergence in early summer varies considerably among years and several degree-day models for prediction of frit fly occurrence have been developed (Ohnesorge 1973, Tolley and Niemzcyk 1988, Umoru et al 1990, Lindblad and Sigvald 1996. Weather data from one weather station within a region can be used to predict median date of first occurrence with an error of Ͻ5 d (Lindblad and Sigvald 1996).…”

Section: Frit Fly Biology and Forecastingmentioning

confidence: 99%

“…The average costs of a true and a false positive decision (C TP and C FP ) are both equal to the cost of chemical control, which was estimated to be 200 kg/ha including pesticide, labor, and wheeling damage. The average cost of a false negative decision (C FN ) is equal to the average yield loss in fields with infestations above the economic threshold, which was estimated to be 700 kg/ha based on the results from 73 field trials performed in central Sweden during 1981-1998(Anonymous 1981-1990, Anonymous 1995-1999. The average cost of a true negative decision (C TN ) amounts to the average yield loss in fields with infestations below the economic threshold and was estimated to be 50 kg/ha.…”

Section: Model Calibration and Evaluationmentioning

confidence: 99%

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Development and Evaluation of a Logistic Risk Model: Predicting Frit Fly Infestation in Oats

Lindblad¹

2001

Ecological Applications

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Forecasting insect pest damage allows for an adjustment of pesticide use to the actual need, and it is important to establish the potential benefits of pest forecasts in terms of profitability and reduction of pesticide use. Here I calibrate and evaluate a risk algorithm for frit fly infestation in spring-sown oats, aiming to improve damage forecasts and assess the profitability of different control strategies with regard to regional infestation prevalence. Oat fields in two regions in Sweden with high and low frequencies of infestation were surveyed for 11 and 13 yr, respectively. The surveyed fields were classified either as infested or noninfested, depending on whether or not the infestation level exceeded the economic threshold. The relation between various risk factors and frit fly damage was examined by logistic regression, and risk points were assigned to significant factors. Regional action thresholds (risk point sums) resulting in the lowest average costs were calculated.In regions where pest outbreaks are common, a control strategy based on the proposed risk algorithm was more profitable than prophylactic or no sprays. The percentage of sprayed fields was reduced to Ͻ50%, compared with routine use of pesticides. In low-risk regions, the profitability of the forecasting strategy and the no-control alternative were similar. The predictive accuracy of the risk algorithm was higher than that of a commonly used degreeday model, especially in regions where the frit fly is a minor pest. Based on these results, I suggest that use of the proposed risk algorithm can enhance the farmer's understanding of the biology of this insect pest, increase profitability, and reduce unnecessary use of pesticides. Long-term monitoring of pest and environmental data can facilitate not only on-farm pest control decisions, but can also provide essential information for developing regional pest control strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Frit Fly Biology and Forecastingmentioning

confidence: 99%

Section: Model Calibration and Evaluationmentioning

confidence: 99%

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Development and Evaluation of a Logistic Risk Model: Predicting Frit Fly Infestation in Oats

Lindblad¹

2001

Ecological Applications

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“…Temperature during spring and early summer affects the timing of insect and host-crop phenologies and, thus, the females' chance to lay their eggs before the crop becomes resistant to infestation (Ohnesorge 1973;Umoru, Bale & Shorrocks 1990;Lindblad & Sigvald 1996). Temperature during spring and early summer affects the timing of insect and host-crop phenologies and, thus, the females' chance to lay their eggs before the crop becomes resistant to infestation (Ohnesorge 1973;Umoru, Bale & Shorrocks 1990;Lindblad & Sigvald 1996).…”

Section: -881mentioning

confidence: 99%

“…Weather conditions during the growing season affect population growth of 0. jrit in several ways. Temperature during spring and early summer affects the timing of insect and host-crop phenologies and, thus, the females' chance to lay their eggs before the crop becomes resistant to infestation (Ohnesorge 1973;Umoru, Bale & Shorrocks 1990;Lindblad & Sigvald 1996). Furthermore, fewer eggs are laid in years with cold and cloudy weather (Southwood & Jepson 1962b) and egg mortality is higher when there is heavy rainfall (Shapiro & Vilkova 1963;Allen & Pienkowski 1975).…”

Section: -881mentioning

confidence: 99%

Predicting Oscinella frit population densities from suction trap catches and weather data

Lindblad

Solbreck

1998

Journal of Applied Ecology

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1.With the aim of developing a model for prediction of OscineIZn frit (L.) population levels, migrating adults were sampled by suction traps at two locations in Sweden for I0 and 14 years, respectively. Catches were separated into two periods: spring migration, representing the size of the overwintering generation; and summer migration, representing the total size of one or two progeny generations. 2. The temperature threshold for sustained flight of 0. frit was estimated to be 14 "C, measured as daily maximum temperature. Current weather conditions during the migration periods had weak effects on the seasonal progress of migration. There was, however, a lagged weather effect: spring migration ceased earlier in years following a warm autumn. 3. Although the number of spring migrants could not be predicted from suction trap catches in the previous year, the number was correlated with weather factors in the previous summer. Sunshine conditions early in summer, i.e. from May to July, seemed to be most important. The long time lag between this period and migration in the following spring implies that weather affects 0. frit population dynamics indirectly, possibly by influencing the amount of food resources available in the autumn. 4. The number of summer migrants was correlated with the number of spring migrants, except for three observations with very small summer catches due to extremely cold and cloudy weather in early summer. 5. The number of migrants of 0. frit varied up to 100-fold among years, a fact that needs to be accounted for in damage forecasts. The number of spring migrants can be predicted fairly well from weather conditions in the previous and current year, whereas the number of summer migrants can, in most years, be predicted from spring catches in suction traps.

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A degree-day model for regional prediction of first occurrence of frit flies in oats in Sweden

Lindblad

Sigvald

1996

Crop Protection

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Predicting the time of emergence of frit fly (Oscinella frit L.) (Dipt., Chloropidae) in northern England

Cited by 8 publications

References 10 publications

Development and Evaluation of a Logistic Risk Model: Predicting Frit Fly Infestation in Oats

Development and Evaluation of a Logistic Risk Model: Predicting Frit Fly Infestation in Oats

Predicting Oscinella frit population densities from suction trap catches and weather data

A degree-day model for regional prediction of first occurrence of frit flies in oats in Sweden

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