Application of a Mechanistic Model to Explore Management Strategies for Biological Control of an Agricultural Pest

Madeleine G. Barton, Hazel Parry (), Paul A. Umina, Matthew R. Binns, Thomas Heddle, Ary A. Hoffmann, Joanne Holloway, Dustin Severtson, Maarten Van Helden, Samantha Ward, Rachel Wood and Sarina Macfadyen
Additional contact information
Madeleine G. Barton: Commonwealth Science and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, Australia
Hazel Parry: Commonwealth Science and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, Australia
Paul A. Umina: School of BioSciences, The University of Melbourne, Parkville, VIC 3052, Australia
Matthew R. Binns: Commonwealth Science and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, Australia
Thomas Heddle: South Australian Research and Development Institute, Urrbrae, SA 5064, Australia
Ary A. Hoffmann: School of BioSciences, The University of Melbourne, Parkville, VIC 3052, Australia
Joanne Holloway: Department of Primary Industries, Wagga Wagga, NSW 2650, Australia
Dustin Severtson: Department of Primary Industries and Regional Development, Northam, WA 6401, Australia
Maarten Van Helden: South Australian Research and Development Institute, Urrbrae, SA 5064, Australia
Samantha Ward: School of BioSciences, The University of Melbourne, Parkville, VIC 3052, Australia
Rachel Wood: Department of Primary Industries, Wagga Wagga, NSW 2650, Australia
Sarina Macfadyen: Commonwealth Science and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, Australia

Agriculture, 2024, vol. 14, issue 1, 1-13

Abstract: Despite the known benefits of integrated pest management, adoption in Australian broadacre crops has been slow, in part due to the lack of understanding about how pests and natural enemies interact. We use a previously developed process-based model to predict seasonal patterns in the population dynamics of a canola pest, the green peach aphid ( Myzus persicae ), and an associated common primary parasitic wasp ( Diaeretiella rapae ), found in this cropping landscape. The model predicted aphid population outbreaks in autumn and spring. Diaeretiella rapae was able to suppress these outbreaks, but only in scenarios with a sufficiently high number of female wasps in the field (a simulated aphid:wasp density ratio of at least 5:1 was required). Model simulations of aphid-specific foliar pesticide applications facilitated biological control. However, a broad-spectrum pesticide negated the control provided by D. rapae , in one case leading to a predicted 15% increase in aphid densities compared to simulations in which no pesticide was applied. Biological and chemical control could therefore be used in combination for the successful management of the aphid while conserving the wasp. This modelling framework provides a versatile tool for further exploring how chemical applications can impact pests and candidate species for biological control.

Keywords: integrated pest management (IPM); biological control; Myzus persicae; Diaeretiella rapae; population models; pest control; natural enemy; process-based model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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