Mathematical probit and logistic mortality models of the Khapra beetle fumigated with plant essential oils

  • Received: 01 May 2014 Accepted: 29 June 2018 Published: 01 April 2015
  • MSC : Primary: 62P10, 92B05; Secondary: 62P12, 92B10.

  • In the current study, probit and logistic models were employed to fit experimental mortality data of the Khapra beetle, Trogoderma granarium (Everts) (Coleoptera: Dermestidae), when fumigated with three plant oils of the gens Achillea. A generalized inverse matrix technique was used to estimate the mortality model parameters instead of the usual statistical iterative maximum likelihood estimation. As this technique needs to perturb the observed mortality proportions if the proportions include 0 or 1, the optimal perturbation in terms of minimum least squares ($L_2$) error was also determined. According to our results, it was better to log-transform concentration and time as explanatory variables in modeling mortality of the test insect. Estimated data using the probit model were more accurate in terms of $L_2$ errors, than the logistic one. Results of the predicted mortality revealed also that extending the fumigation period could be an effective control strategy, even, at lower concentrations. Results could help in using a relatively safe and effective strategy for the control of this serious pest using alternative control strategy to reduce the health and environmental drawbacks resulted from the excessive reliance on the broadly toxic chemical pesticides and in order to contribute safeguard world-wide grain supplies.

    Citation: Alhadi E. Alamir, Gomah E. Nenaah, Mohamed A. Hafiz. Mathematical probit and logistic mortality models of the Khapra beetle fumigated with plant essential oils[J]. Mathematical Biosciences and Engineering, 2015, 12(4): 687-697. doi: 10.3934/mbe.2015.12.687

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  • In the current study, probit and logistic models were employed to fit experimental mortality data of the Khapra beetle, Trogoderma granarium (Everts) (Coleoptera: Dermestidae), when fumigated with three plant oils of the gens Achillea. A generalized inverse matrix technique was used to estimate the mortality model parameters instead of the usual statistical iterative maximum likelihood estimation. As this technique needs to perturb the observed mortality proportions if the proportions include 0 or 1, the optimal perturbation in terms of minimum least squares ($L_2$) error was also determined. According to our results, it was better to log-transform concentration and time as explanatory variables in modeling mortality of the test insect. Estimated data using the probit model were more accurate in terms of $L_2$ errors, than the logistic one. Results of the predicted mortality revealed also that extending the fumigation period could be an effective control strategy, even, at lower concentrations. Results could help in using a relatively safe and effective strategy for the control of this serious pest using alternative control strategy to reduce the health and environmental drawbacks resulted from the excessive reliance on the broadly toxic chemical pesticides and in order to contribute safeguard world-wide grain supplies.


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    [1] $2^{nd}$ edition, Wiley, Hoboken,New Jersey, 2007.
    [2] J. Stored Prod. Res., 31 (1995), 199-205.
    [3] Springer press, New York, 2003.
    [4] Ann. Entomol. Soc. Am., 33 (1940), 721-766.
    [5] in: FAO Plant Production and Protection Paper, 54, FAO, Rome, 1984.
    [6] B. Entomol. Res., 102 (2012), 213-229.
    [7] Pestic Sci., (Now Pest Manag. Sci.), 49 (1997), 213-228.
    [8] J. Stored Prod. Res., 41 (2005), 373-385.
    [9] CAB Rev., 8 (2013), 1-13.
    [10] $3^{nd}$ edition, Cambridge University Press, UK, 1971.
    [11] Appl. Entomol. Zool, 32 (1997), 551-559.
    [12] Annu. Rev. Entomol., 51 (2006), 45-66.
    [13] in Pesticide Chemistry (Wiley-VCH, Weinheim, Germany (Ohkawa H, Miyagawa H, Lee P (Ed)), Academic Press, (2007), 201-209.
    [14] in The 18th World IMACS Congress and MODSIM09, International Congress on Modelling and Simulation, Cairns, Australia, 2009, http://mssanz.org.au/modsim09.
    [15] in: World Conservation Union, 2000, http://www.issg.org/database/species/reference_files/100English.pdf.
    [16] J. Stored Prod. Res., 47 (2011), 185-190.
    [17] J. Pest Sci., 87 (2014), 273-283.
    [18] Ind. Crop Prod., 53 (2014), 252-260.
    [19] J. Pest Sci., 84 (2011), 393-402.
    [20] mechanism and management strategies, Lap. Lambert Acad. Pub., UK, 2010.
    [21] in D. (Ed), Encyclopedia of Pest Management , Marcel Dekker, Inc., 2002.
    [22] J. Stored Prod. Res., 44 (2008), 126-135.
    [23] Annu. Rev. Entomol., 57 (2012), 405-424.
    [24] J. Stored Prod. Res., 51 (2012), 23-32.
    [25] Math. Biosci., 243 (2013), 137-146.
    [26] Math. Biosci., 233 (2011), 77-89.
    [27] J. Pest Sci., 85 (2012), 451-468.
    [28] J. Stored Prod. Res., 21 (1985), 25-29.
    [29] Extracting the Most Information From Experiments, Springer press, New York, 2005.
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