Reliability of Degree-Day Models to Predict the Development Time of Plutella xylostella (L.) under Field Conditions

Marchioro, César Augusto; Krechemer, Flávia S.; Moraes, Carla Pedroso de; Foerster, Luís A.

doi:10.1007/s13744-015-0331-4

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…This research points to the caution needed when adopting the use of predictive models in areas in which they were not developed and tested, and highlights the many factors and their interrelationships that can affect model accuracy. The accuracy of growing degree-day accumulation models can vary considerably (Zahiri et al 2010;Marchioro et al 2015), as exemplified by our wide range of accuracy in predicting peak alfalfa weevil growth stage in this study, with the probability of overlap varying from 0.13 to 0.84. Despite their similarities, the three models tested here interacted uniquely with our temperature data and field parameters, and producers need to consider factors other than degree-day values alone when planning to implement alfalfa weevil control measures.…”

Section: Discussionmentioning

confidence: 79%

An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera: Curculionidae) on the Canadian Prairies

et al. 2019

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This study examined the use of degree-day models to predict alfalfa weevil Hypera postica (Gyllenhal) (Coleoptera: Curculionidae) population development on the Canadian prairies. Air temperatures, alfalfa weevil abundance, and instar data were collected in 2013 and 2014 from 13 alfalfa (Medicago sativa Linnaeus; Fabaceae) fields across Alberta, Saskatchewan, and Manitoba. We coupled three alfalfa weevil population prediction models with three temperature data sources to determine which combination most closely aligned with results observed. Our objective was to find the best prediction of peak occurrence of second instar alfalfa weevils, the optimum time for management decisions. Of the parameters analysed, prediction model had the greatest effect on the accuracy of peak instar prediction, with Harcourt and North Dakota models better at predicting population peaks than the Guppy–Mukerji model. Interactions between temperature source and prediction model significantly affected prediction accuracy. The probability of accurate prediction of population peaks to within 3.5 days of actual occurrence using in-field and multiple-site temperature data sets, combined with Harcourt and North Dakota development models, was 0.45–0.70. Lower predictability was found from fields in the Mixed Grass Ecoregion than in other ecoregions. The use of the recommended models can assist growers in timing their monitoring activities and deciding if pest management action is warranted.

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Section: Discussionmentioning

confidence: 79%

An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera: Curculionidae) on the Canadian Prairies

et al. 2019

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“…Therefore, this approach gives fairly accurate results within an intermediate temperature range, but lacks precision in describing temperatures close to the lower and upper development thresholds (Bonhomme, 2000; Moore and Remais, 2014; Molnár et al ., 2017). This can lead to unsatisfying projections on the development time under field conditions (Moore et al ., 2012; Marchioro et al ., 2015). Especially in light of global warming, with rising average temperatures and more and longer heat waves (ICCP, 2014), non-linear TCPs are considered more reliable for predictions of peak population levels (Damos and Savopoulou-Soultani, 2012; Molnár et al ., 2017; Rebaudo and Rabhi, 2018), which are essential for an optimal timing of direct control measures.…”

Section: Discussionmentioning

confidence: 99%

“…on the development time under field conditions (Moore et al, 2012;Marchioro et al, 2015). Especially in light of global warming, with rising average temperatures and more and longer heat waves (ICCP, 2014), non-linear TCPs are considered more reliable for predictions of peak population levels (Damos and Savopoulou-Soultani, 2012;Molnár et al, 2017;Rebaudo and Rabhi, 2018), which are essential for an optimal timing of direct control measures.…”

Section: Bulletin Of Entomological Researchmentioning

confidence: 99%

Modelling of post-diapause development and spring emergence of Cydia nigricana (Lepidoptera: Tortricidae)

Riemer

Schieler²,

Racca³

et al. 2021

Bull. Entomol. Res.

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The prediction of the post-diapause emergence is the first step towards a comprehensive decision support system that can contribute to a considerable reduction of pesticide use by forecasting a precise spraying date. The cumulative field emergence can be described as a function of the cumulative development rate. We investigated the impact of seven constant temperatures and five light regimes on post-diapause development in laboratory experiments. Development rate depended significantly on temperature but not on photoperiod. We therefore fit non-linear thermal performance curves, a better and more modern approach over past linear models, to describe the development rate as a function of temperature. The four parameter Brière function was the most suitable and was subsequently applied to temperature data from 36 previous pea fields, where pea moth emergence was measured with pheromone traps in Northern Hesse (Germany). In order to describe the variation in development times between individuals, we fit five nonlinear distribution models to the cumulative development rate as a function of cumulative field emergence. The three parameter Gompertz model was selected as the best fitted model. We validated the model performance with an independent field data set. The model correctly predicted the first moth in the trap and the peak emergence in 81.82% of cases, with an average deviation of only 2.00 and 2.09 days respectively.

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Temperature Effect on the Development of Tropical Dragonfly Eggs

et al. 2017

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Physiological constraints in insects are related to several large-scale processes such as species distribution and thermal adaptation. Here, we fill an important gap in ecophysiology knowledge by accessing the relationship between temperature and embrionary development time in four dragonfly species. We evaluated two questions (1) what is the effect of temperature on the development time of Odonata eggs, and (2) considering a degree-day relationship, could a simple linear model describe the dependence of embrionary development time on temperature or it is better described by a more complex non-linear relation. Egg development time of Erythrodiplax fusca (Rambur), Micrathyria hesperis Ris, Perithemis mooma Kirby, and Miathyria simplex (Rambur) (Odonata: Libellulidae) were evaluated. We put the eggs at different temperatures (15, 20, 25, and 30°C) and counted the number of hatched larvae daily. A nonlinear response of the development to the temperature was found, differing from the expected pattern for standard degree-day analysis. Furthermore, we observed that there is a similar process in the development time and hatching synchronization between species, with all species presenting faster egg development at high temperatures. Species-specific differences are more evident at lower temperatures (15°C), with no egg development in M. simplex. Only E. fusca was relatively insensitive to temperature changes with similar hatching rates in all treatments.

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Reliability of Degree-Day Models to Predict the Development Time of Plutella xylostella (L.) under Field Conditions

Cited by 4 publications

References 30 publications

An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera: Curculionidae) on the Canadian Prairies

An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera: Curculionidae) on the Canadian Prairies

Modelling of post-diapause development and spring emergence of Cydia nigricana (Lepidoptera: Tortricidae)

Temperature Effect on the Development of Tropical Dragonfly Eggs

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