2017
DOI: 10.1093/jee/tox067
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A Temperature-Dependent Phenology Model for Liriomyza huidobrensis (Diptera: Agromyzidae)

Abstract: Liriomyza huidobrensis (Blanchard) is an economically important and highly polyphagous worldwide pest. To establish a temperature-dependent phenology model, essential for understanding the development and growth of the pest population under a variety of climates and as part of a pest risk analysis, L. huidobrensis life-table data were collected under laboratory conditions at seven constant temperatures on its host faba bean (Vicia faba L.). Several nonlinear equations were fitted to each life stage to model th… Show more

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Cited by 28 publications
(23 citation statements)
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“…The initial model developed based on life tables determined at constant temperatures, however, poorly predicted those determined in studies at naturally fluctuating temperatures, principally due to an overestimation of immature mortality rates and an underestimation of adult survival and reproduction rate. We have not observed such substantial differences between laboratory and field collected data in previous studies ( Aregbesola et al, 2020 ; Sporleder et al, 2004 , 2016 ; Mujica et al, 2017 ). This gives the impression that the species T. vaporariorum is a special case showing high variability in adult survival and reproduction in response to variable temperature.…”
Section: Discussioncontrasting
confidence: 77%
See 1 more Smart Citation
“…The initial model developed based on life tables determined at constant temperatures, however, poorly predicted those determined in studies at naturally fluctuating temperatures, principally due to an overestimation of immature mortality rates and an underestimation of adult survival and reproduction rate. We have not observed such substantial differences between laboratory and field collected data in previous studies ( Aregbesola et al, 2020 ; Sporleder et al, 2004 , 2016 ; Mujica et al, 2017 ). This gives the impression that the species T. vaporariorum is a special case showing high variability in adult survival and reproduction in response to variable temperature.…”
Section: Discussioncontrasting
confidence: 77%
“…Temperature is a critical abiotic factor affecting the development, survival, and reproduction of insect species. The ability of an insect to develop at different temperatures is an important adaptation to survive in various climatic conditions, and its understanding is important for predicting pest outbreaks ( Gilbert and Raworth, 1996 ; Mujica et al, 2017 ). One of the main factors that influence the development of larger populations of whiteflies in agricultural regions of Latin America is the diversification of crops which provide increased availability of hosts for whiteflies and has also contributed to a significant increase in the use of agrochemicals ( Anderson and Morales, 2005 ).…”
Section: Introductionmentioning
confidence: 99%
“…Additionally, Abdel‐Rahman, Awad, Omar, and Mahmoud () reported that 28°C was the optimal developmental time of B. brassicae . The functions Sharpe & DeMichele and Janisch used in our study also successfully predicted temperature range of development in Phenacoccus solenopsis (Fand et al., ) and Liriomyza huidobrensis (Mujica, Sporleder, Carhuapoma, & Kroschel, ), two insect species with a wide range of thermal tolerance and cosmopolitan distribution as B. brassicae .…”
Section: Discussionmentioning
confidence: 51%
“…This was performed using a friendly-user software called Insect Life Cycle Modelling (ICLYM), in which the aim is to assist researchers in developing insect temperature-based models . (Fand et al, 2014) and Liriomyza huidobrensis (Mujica, Sporleder, Carhuapoma, & Kroschel, 2017), two insect species with a wide range of thermal tolerance and cosmopolitan distribution as…”
Section: Discussionmentioning
confidence: 99%
“…Potato disease modelling, foresight, further model development Kroschel et al, 2013 [167]; Sporleder et al, 2013 [168]; Condori et al, 2014 [169]; Carli et al, 2014 [170]; Kleinwechter et al, 2016 [171]; Kroschel et al, 2017 [172]; Fleisher et al, 2017 [173]; Raymundo et al, 2017 [174]; Raymundo et al, 2017 [175]; Quiroz et al, 2017 [176]; Ramirez et al, 2017 [177]; Mujica et al, 2017 [178]; Scott and Kleinwechter, 2017 [179]; Petsakos et al, 2018 [180] AfricaRice: Model improvement, yield gap analysis, genotype × environment interactions, impact of climate change van Oort et al, 2014 [181]; van Oort et al, 2015 [182]; van Oort et al, 2015 [183]; Dingkuhn et al, 2015 [184]; van Oort et al, 2016 [185]; El-Namaky and van Oort, 2017 [186]; van Oort et al, 2017 [187]; Dingkuhn et al, 2017 [104,105]; van Oort and Zwart, 2018 [188]; van Oort, 2018 [189], Duku et al, 2018 [190] ICRAF: Agroforestry and intercropping modelling Africa Luedeling et al, 2014 [191]; Araya et al, 2015 [192]; Luedeling et al, 2016 [193]; Smethurst et al, 2017 [194], Masikati et al, 2017 [195] ILRI: crop-livestock-farm interactions Van Wijk et al, 2014 [196]; Herrero et al, 2014 [197] IITA: Modelling on Yams in West Africa Marcos et al, 2011 [198]; Cornet et al, 2015 [199]; Cornet et al, 2016 [200] ICARDA: Climate variability and change impact studies, foresight, conservation agriculture impact, genotype × environment interactions Sommer et al, 2013 [201]; Bobojonov and Aw-Hassan, 2014…”
Section: Cipmentioning
confidence: 99%