*Result*: Unravelling the role of simulation modelling techniques (SMTs) in insect pest management.

Unravelling the role of simulation modelling techniques (SMTs) in insect pest management.

Rakesh V; Division of Entomology, ICAR-Indian Agricultural Research Institute, New Delhi, India., Karthik R; Department of Entomology, Assam Agricultural University, Jorhat, India., Chandana CR; Department of Entomology, University of Agricultural Sciences, Raichur, India., Jayanth BV; Division of Entomology, ICAR-Indian Agricultural Research Institute, New Delhi, India., Ajith S; Department of Agricultural Statistics, Uttar Banga Krishi Viswavidyalaya, Cooch Behar, India.

Pest management science [Pest Manag Sci] 2026 Feb; Vol. 82 (2), pp. 1213-1227. Date of Electronic Publication: 2025 Nov 19.

Journal Article; Review

English

Publisher: Published for SCI by Wiley Country of Publication: England NLM ID: 100898744 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1526-4998 (Electronic) Linking ISSN: 1526498X NLM ISO Abbreviation: Pest Manag Sci Subsets: MEDLINE

Original Publication: West Sussex, UK : Published for SCI by Wiley, c2000-

Insect Control*/methods , Computer Simulation* , Models, Theoretical*, Animals ; Insecta ; Population Dynamics ; Climate Change

Deutsch CA, Tewksbury JJ, Thogmartin WE et al., Increase in crop losses to insect pests in a warming climate. Science 361:916–919 (2018). https://www.science.org/doi/full/10.1126/science.aat3466.
van Huis A, Potential of insects as food and feed in assuring food security. Annu Rev Entomol 58:563–583 (2013). https://doi.org/10.1146/annurev-ento-120811-153704.
Curk M and Trdan S, Benefiting, from complexity: Exploring enhanced biological control effectiveness via the simultaneous use of various methods for combating pest pressure in agriculture. Agronomy 14:199 (2024). https://doi.org/10.3390/agronomy14010199.
Chander S, Ahuja LR, Peairs FB et al., Modelling the effect of Russian wheat aphid, Diuraphis noxia (Mordvilko) and weeds in winter wheat as a guide to management. Agric Syst 88:494–513 (2006). https://doi.org/10.1016/j.agsy.2005.07.009.
Pritsker A, Alan B and O'Reilly JJ, Simulation with Visual SLAM and AweSim. John Wiley & Sons, New York, USA (1999).
Rossi V, Salinari F, Poni S, Caffi T and Bettati T, Addressing the implementation problem in agricultural decision support systems: the example of vite.Net (R). Comput Electron Agric 100:88–99 (2014). https://doi.org/10.1016/j.compag.2013.10.011.
Liu C, Zhai Z, Zhang R, Bai J and Zhang M, Field pest monitoring and forecasting system for pest control. Front Plant Sci 13:990965 (2022). https://doi.org/10.3389/fpls.2022.990965.
Plant RE and Mangel M, Modelling and simulation in agricultural pest management. SIAM Rev 29:235–261 (1987). https://doi.org/10.1137/1029043.
Dai M, Shen Y, Li X, Liu J, Zhang S and Miao H, Digital twin system of Pest management driven by data and model fusion. Agri 14:1099 (2024). https://doi.org/10.3390/agriculture14071099.
Cressman K, Desert locust, in Biological and Environmental Hazards, Risks, and Disasters, ed. by Sivanpillai R and Shroder JH. Elsevier, Amsterdam, Netherlands, pp. 87–105 (2016). https://doi.org/10.1016/B978-0-12-394847-2.00006-1.
Walters JP, Archer DW, Sassenrath GF, Hendrickson JR, Hanson JD, Halloran JM et al., Exploring agricultural production systems and their fundamental components with system dynamics modelling. Ecol Model 333:51–65 (2016). https://doi.org/10.1016/j.ecolmodel.2016.04.015.
Manetsch TJ and Park GL, System Analysis and Simulation with Applications to Economic and Social Systems. Department of Electrical Engineering and Systems Science, Michigan State University, Michigan, USA, (1972).
Miller GG and Miller JL, The earth as a system, in Models of Reality Shaping Thought and Action, ed. by Richardson J. Lomond Press, Mt Airy, Maryland, pp. 19–49 (1984).
Kropff MJ, Teng PS and Rabbinge R, The challenge of linking pest and crop models. Agric Syst 49:413–434 (1995). https://doi.org/10.1016/0308-521X(95)00034-3.
Rossi V, Sperandio G, Caffi T, Simonetto A and Gilioli G, Critical success factors for the adoption of decision tools in IPM. Agronomy 9:710 (2019). https://doi.org/10.3390/agronomy9110710.
Abramowitz M, Handbook of mathematical functions with formulas, graphs, and mathematical tables. Applied Math Series 55:232 (1964).
Peck SL, A tutorial for understanding ecological modelling papers for the non‐modeller. Am Entomol 46:40–49 (2000). https://doi.org/10.1093/ae/46.1.40.
Paez Chavez J, Jungmann D and Siegmund S, Modelling and analysis of integrated pest control strategies via impulsive differential equations. Int J Differ Equ 1:1–18 (2017). https://doi.org/10.1155/2017/1820607.
Rabbinge R and Carter N, Application of simulation models in the epidemiology of pests and diseases: an introductory review. IOBC/WPRS Bulletin 6:18–30 (1983).
Dalal PK and Arora R, Model‐based phenology prediction of Helicoverpa armigera (Hübner) (Noctuidae: lepidoptera) on tomato crop. J Plant Dis Prot 126:281–291 (2019). https://doi.org/10.1007/s41348-019-00223-x.
Beverton RJH and Holt SJ, On the Dynamics of Exploited Fish Populations. Her Majesty's Stationery Office, Springer Science & Business Media, London, p. 19 (1957).
Weiss P and Kavanau JL, A model of growth and growth control in mathematical terms. J Gen Physiol 41:1–47 (1957). https://doi.org/10.1085/jgp.41.1.1.
Moser H, The Dynamics of Bacterial Populations Maintained in the Chemostat. Carnegie Institution of Washington, USA, p. 136 (1958).
Bailey NT, Some stochastic models for small epidemics in large populations. Appl Stat 13:9 (1964). https://doi.org/10.2307/2985218.
Wright S, The distribution of gene frequencies in populations. Proc Natl Acad Sci USA 23:307–320 (1937). https://doi.org/10.1073/pnas.23.6.307.
Johnson FH, Eyring H and Polissar JM, The Kinetic Basis of Molecular Biology. Wiley, New York (1954).
Watt KE, Use of a computer to evaluate alternative insecticidal programs. Science 133:706–707 (1961b). https://doi.org/10.1126/science.133.3454.70.
Watt KE, The use of mathematics and computers to determine optimal strategy and tactics for a given insect pest control problem. Can Entomol 96:202–220 (1964). https://doi.org/10.4039/Ent96202-1.
Keen RE and Spain JD, Computer Simulation in Biology: A BASIC Introduction. John Wiley & Sons, Inc., United States, p. 516 (1994).
Watt KE, Mathematical models for use in insect pest control. Mem Entomol Soc Can 93:5–62 (1961a). https://doi.org/10.4039/entm9319fv.
Kerry B, Decision tools for pest management. J Agric Sci 123:416–417 (1993).
Ruesink WG, Status of the systems approach to pest management. Annu Rev Entomol 21:27–44 (1976). https://doi.org/10.1146/annurev.en.21.010176.000331.
Morris RF, The development of sampling techniques for forest insect defoliators, with particular reference to the spruce budworm. Can J Zool 33:225–294 (1955). https://doi.org/10.1139/z55-015.
Morris RF, The dynamics of epidemic spruce budworm populations. Mem Entomol Soc Can 95:1–12 (1963). https://doi.org/10.4039/entm9531fv.
Berryman AA and Pienaar LV, Simulation: A powerful method of investigating the dynamics and management of insect populations. Environ Entomol 3:199–207 (1974). https://doi.org/10.1093/ee/3.2.199.
Teng PS, Blackie MJ and Close RC, Simulation of the barley leaf rust epidemic: structure and validation of BARSIM—I. Agric Syst 5:85–103 (1980). https://doi.org/10.1016/0308-521X(80)90001-3.
Overton W, A strategy of model construction, in Ecosystem Modelling in Theory and Practice: An Introduction with Case Histories, ed. by Hall CAS and Day JW. John Wiley & Sons, New York, NY, New York, NY, pp. 49–73 (1977).
Coop L and Barker BS, Advances in understanding species ecology: phenological and life cycle modelling of insect pests, in Integrated Management of Insect Pests: Current and Future Developments, ed. by Kogan M and Higley L. Burleigh Dodds Science Publishing, Sawston, England, pp. 43–96 (2020). https://doi.org/10.19103/AS.2019.0047.02.
Logan JA, Toward an expert system for development of pest simulation models. Environ Entomol 17:359–376 (1988). https://doi.org/10.1093/ee/17.2.359.
Chuine I and Régnière J, Process‐based models of phenology for plants and animals. Annu Rev Ecol Evol Syst 48:159–182 (2017). https://doi.org/10.1146/annurev-ecolsys-110316-022706.
Rasche L and Taylor RA, EPIC‐GILSYM: modelling crop‐pest insect interactions and management with a novel coupled crop‐insect model. J Appl Ecol 56:2045–2056 (2019). https://doi.org/10.1111/1365-2664.13426.
Park B and Qi H, Development and evaluation of a procedure for the calibration of simulation models. Transp Res Rec 1934:208–217 (2005). https://doi.org/10.1177/036119810519340012.
Malleson N, Calibration of simulation models, in Encyclopedia of Criminology and Criminal Justice, ed. by Bruinsma G and Weisburd D. Springer, New York, pp. 115–118 (2014). https://doi.org/10.1007/978-1-4614-5690-2_688.
Ruiz GR, Bandera CF, Temes TGA, Ramos Ruiz G, Fernández Bandera C, Gómez‐Acebo Temes T et al., Genetic algorithm for building envelope calibration. Appl Energy 168:691–705 (2016). https://doi.org/10.1016/j.apenergy.2016.01.075.
Rossini L, Bruzzone OA, Speranza S and Delfino I, Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods. Eco Inform 77:102232 (2023). https://doi.org/10.1016/j.ecoinf.2023.102232.
Zhang R, Lin CD and Ranjan P, A sequential design approach for calibrating a dynamic population growth model arXiv preprint arXiv:1811.00153 (2018). https://doi.org/10.48550/arXiv.1811.00153.
Palaic D, Stajduhar I, Ljubic S and Wolf I, Development, calibration, and validation of a simulation model for indoor temperature prediction and HVAC system fault detection. Buildings 13:1388 (2023). https://doi.org/10.3390/buildings13061388.
Dupin M, Reynaud P, Jarošík V, Baker R, Brunel S, Eyre D et al., Effects of the training dataset characteristics on the performance of nine species distribution models: application to Diabrotica virgifera virgifera. PLoS One 6:e20957 (2011). https://doi.org/10.1371/journal.pone.0020957.
Baddeley A, Rubak E, Rakshit S and Nair G, ROC curves for spatial point patterns and presence‐absence data arXiv preprint arXiv:250603414 (2025). https://doi.org/10.48550/arXiv.2506.03414.
Johnson BJ, Gomez MM and Munch SB, Empirical dynamic modelling for prediction and control of pest populations. Ecol Model 504:111081 (2025). https://doi.org/10.1016/j.ecolmodel.2025.111081.
van Vuuren BJ, Potgieter L and Van Vuuren JH, An agent‐based simulation model of Eldana saccharina Walker. Nat Resour Model 31:e12153 (2018). https://doi.org/10.1111/nrm.12153.
Lux SA, Wnuk A, Vogt H, Belien T, Spornberger A and Studnicki M, Validation of individual‐based Markov‐like stochastic process model of insect behaviour and a “virtual farm” concept for enhancement of site‐specific IPM. Front Physiol 7:363 (2016). https://doi.org/10.3389/fphys.2016.00363.
Schaub L, Breitenmoser S, Derron J and Graf B, Development and validation of a phenological model for the univoltine European corn borer. J Appl Entomol 141:421–430 (2017). https://doi.org/10.1111/jen.12364.
Lee H, Wintermantel WM, Trumble JT, Fowles TM and Nansen C, Modelling and validation of oviposition by a polyphagous insect pest as a function of temperature and host plant species. PLoS One 17:e0274003 (2022).
Svobodova E, Trnka M, Dubrovský M et al., Pests occurrence model in current climate‐validation study for the European domain. Acta Univ Agric Silvic Mendel Brun 61:205–214 (2013). https://doi.org/10.11118/actaun201361010205.
Rossini L, Bruzzone OA, Contarini M, Bufacchi L and Speranza S, A physiologically based ODE model for an old pest: modelling life cycle and population dynamics of Bactrocera oleae (Rossi). Agronomy 12:2298 (2022). https://doi.org/10.3390/agronomy12102298.
Mendez‐Vazquez LJ, Lasa‐Covarrubias R, Cerdeira‐Estrada S and Lira‐Noriega A, Using simulated pest models and biological clustering validation to improve zoning methods in site‐specific pest management. Appl Sci 12:1900 (2022).
Barker BS, Coop L, Duan JJ and Petrice TR, An integrative phenology and climatic suitability model for emerald ash borer. Front Insect Sci 3:1239173 (2023).
Gebauer K, Hemerik L and Meyhöfer R, Effects of climate change on pest‐parasitoid dynamics: development of a simulation model and first results. J Plant Dis Prot 122:28–35 (2015).
Garcia AG, Ferreira CP, Godoy WA, Godoy WAC and Meagher RL, A computational model to predict the population dynamics of Spodoptera frugiperda. J Pest Sci 92:429–441 (2019). https://link.springer.com/article/10.1007/s10340-018-1051-4.
Satish D, Chander S and Reji G, Simulation of economic injury levels for leaf folder (Cnaphalocrocis medinalis Guenee) on rice (Oryza sativa L.). J Sci Ind Res 66:905–911 (2007). http://nopr.niscpr.res.in/handle/123456789/1333.
Daryaei MG, Assessment of yield loss in rice due to yellow stem borer, Scirpophaga incertulas, using simulation models. Casp J Environ Sci 3:59–62 (2005).
Knudsen GR and Schotzko DJ, Simulation of Russian wheat aphid movement and population dynamics on preferred and non‐preferred host plants. Ecol Model 57:117–131 (1991). https://doi.org/10.1016/0304-3800(91)90057-8.
Castex V, de Cortázar‐Atauri IG, Beniston M, Moreau J, Semenov M, Stoffel M et al., Exploring future changes in synchrony between grapevine (Vitis vinifera) and its major insect pest, Lobesia botrana. OENO One 57:161–174 (2023). https://doi.org/10.20870/oeno-one.2023.57.1.7250.
Lee H, Stephanus AP, Fowles TM, Wintermantel WM, Trumble JT, Gilbertson RL et al., Insect vector manipulation by a plant virus and simulation modelling of its potential impact on crop infection. Sci Rep 12:8429 (2022).
Stratonovitch P, Elias J, Denholm I, Slater R and Semenov MA, An individual‐based model of the evolution of pesticide resistance in heterogeneous environments: control of Meligethes aeneus population in oilseed rape crops. PLoS One 9:e115631 (2014). https://doi.org/10.1371/journal.pone.0115631.
Storer NP, Peck SL, Gould F, Van Duyn JW and Kennedy GG, Spatial processes in the evolution of resistance in Helicoverpa zea (lepidoptera: Noctuidae) to Bt transgenic corn and cotton in a mixed agroecosystem: a biology‐rich stochastic simulation model. J Econ Entomol 96:156–172 (2003). https://doi.org/10.1093/jee/96.1.156.
Cabello T, Gámez M and Varga Z, Selection of Trichogramma spp. (Hym.: Trichogrammatidae) for the biological control of Tuta absoluta (Lep.: Gelechiidae) in greenhouses by an entomo‐ecological simulation model. IOBC/WPRS Bull 80:171–176 (2012).
Potgieter L, Van Vuuren JH and Conlong DE, Simulation modelling as a decision support in developing a sterile insect‐inherited sterility release strategy for Eldana saccharina (lepidoptera: Pyralidae). Fla Entomol 99:13–22 (2016). https://doi.org/10.1653/024.099.sp103.
Amarathunga DC, Parry H, Grundy J and Dorin A, A predator‐prey population dynamics simulation for biological control of Frankliniella occidentalis (Western flower thrips) by Orius laevigatus in strawberry plants. Biol Control 188:105409 (2024). https://doi.org/10.1016/j.biocontrol.2023.105409.
Liljesthröm GG and Rabinovich JE, Biological control of the stink bug Nezara viridula (Heteroptera: Pentatomidae) by two parasitoids and their interaction in non‐crop habitats: a simulation model. Bull Entomol Res 113:315–325 (2023). https://doi.org/10.1017/S0007485322000591.
Rincon DF, Gutierrez‐Illan J and Crowder DW, Assessing pest control treatments from phenology models and field data. Pest Manag Sci 81:1851–1859 (2025).
Ferreira TB, Pavan W, Fernandes JMC, Berton Ferreira T, Cunha Fernandes JM, Asseng S et al., Coupling a pest and disease damage module with CSM‐NWheat: a wheat crop simulation model. Trans ASABE 64:2061–2071 (2021).
Su NY, Stith BM, Puche H et al., Characterization of tunnelling geometry of subterranean termites (lsoptera: Rhinotermitidae) by computer. Sociobiology 44:471–483 (2004).
Lee SH and Su NY, Simulation study on the tunnel networks of subterranean termites and the foraging behavior. J Asia Pac Entomol 13:83–90 (2010). https://doi.org/10.1016/j.aspen.2010.01.003.
Parry HR, Cereal aphid movement: general principles and simulation modelling. Mov Ecol 1:1–15 (2013). https://link.springer.com/article/10.1186/2051-3933-1-14.
Yaciuk G, Muir WE and Sinha RN, A simulation model of temperatures in stored grain. J Agric Eng Res 20:245–258 (1975). https://doi.org/10.1016/0021-8634(75)90060-8.
Ascerno ME, Insect phenology and integrated pest management. Arboric Urban For 17:13–15 (1991).
Chuine I, Why does phenology drive species distribution? Philos Trans R Soc Lond Ser B Biol Sci 365:3149–3160 (2010).
Nietschke BS, Magarey RD, Borchert DM, Calvin DD and Jones E, A developmental database to support insect phenology models. Crop Prot 26:1444–1448 (2007). https://doi.org/10.1016/j.cropro.2006.12.006.
Damos PT, Kouloussis NA and Koveos DS, A degree‐day phenological model for Cydia pomonella and its validation in a Mediterranean climate. Bull Insectol 71:1–12 (2018).
Knight AL, Adjusting the phenology model of codling moth (lepidoptera: Tortricidae) in Washington state apple orchards. Environ Entomol 36:1485–1493 (2014).
Song Y, Coop LB, Omeg M and Riedl H, Development of a phenology model for predicting western cherry fruit fly, Rhagoletis indifferens curran (Diptera: Tephritidae), emergence in the mid Columbia area of the western United States. J Asia Pac Entomol 6:187–192 (2003).
El messoussi S, Hafid H, Lahrouni A, El messoussi S and Afif M, Simulation of temperature effect on the population dynamic of the Mediterranean fruit fly Ceratitis capitata (Diptera; Tephritidae). Agron J 6:374–377 (2007).
Barker BS and Coop L, Phenological mapping of invasive insects: decision support for surveillance and management. Insects 15:6 (2023).
Barker BS, Coop L, Wepprich T, Grevstad F and Cook G, DDRP: real‐time phenology and climatic suitability modelling of invasive insects. PLoS One 15:e0244005 (2020).
Takeuchi Y, Tripodi A and Montgomery K, AFARIS: a spatial analytic framework for pest forecast systems. Front Insect Sci 3:1198355 (2023).
Kamiyama MT, Bradford BZ, Groves RL and Guédot C, Degree day models to forecast the seasonal phenology of Drosophila suzukii in tart cherry orchards in the Midwest US. PLoS One 15:e0227726 (2020).
Sporleder M, Carhuapoma P, Tonnang EZH et al., ILCYM ‐ Insect Life Cycle Modelling. A software package for developing temperature‐based insect phenology models with applications for local, regional and global analysis of insect population and mapping. International Potato Centre, Lima, Peru, p. 175 (2017).
Sporleder M, Chavez D, Gonzales JC et al., ILCYM‐Insect life cycle modelling: software for developing temperature‐based insect phenology models with applications for regional and global pest risk assessments and mapping. 15th Triennial ISTRC Symposium (2009).
Sporleder M, Tonnang HEZ, Carhuapoma P et al., Insect life cycle modelling (ILCYM) software: a new tool for regional and global insect pest risk assessments under current and future climate, in Potential Invasive Pests of Agricultural Crops, ed. by Peña JE. CABI Publishing, Wallingford, UK, pp. 412–427 (2013).
Fand BB, Tonnang HE, Kumar M, Tonnang HEZ, Bal SK, Singh NP et al., Predicting the impact of climate change on regional and seasonal abundance of the mealybug Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) using a temperature‐driven phenology model linked to GIS. Ecol Model 288:62–78 (2014a). https://doi.org/10.1016/j.ecolmodel.2014.05.018.
Khadioli N, Tonnang ZEH, Muchugu E, Ong'amo G, Achia T, Kipchirchir I et al., Effect of temperature on the phenology of Chilo partellus (Swinhoe) (lepidoptera, Crambidae); simulation and visualisation of the potential future distribution of C. Partellus in Africa under warmer temperatures through the development of life‐table parameters. Bull Entomol Res 104:809–822 (2014). https://doi.org/10.1017/S0007485314000601.
Fand BB, Tonnang HE, Kumar M, Tonnang HEZ, Kamble AL and Bal SK, A temperature‐based phenology model for predicting development, survival and population growth potential of the mealybug, Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae). Crop Prot 55:98–108 (2014b). https://doi.org/10.1016/j.cropro.2013.10.020.
Curry GL, Feldman RM and Smith KC, A stochastic model of a temperature‐dependent population. Theor Popul Biol 13:197–213 (1978). https://doi.org/10.1016/0040-5809(78)90042-4.
Fand BB, Sul NT, Bal SK and Minhas PS, Temperature impacts the development and survival of common cutworm (Spodoptera litura): simulation and visualization of potential population growth in India under warmer temperatures through life cycle modelling and spatial mapping. PLoS One 10:e0124682 (2015).
Ndlela S, Azrag AG and Mohamed SA, Determination of temperature thresholds for the parasitoid Diachasmimorpha longicaudata (Hymenoptera: Braconidae), using life cycle simulation modelling: implications for effective field releases in classical biological control of fruit flies. PLoS One 16:e0255582 (2021). https://doi.org/10.1371/journal.pone.0255582.
Sutherst RW, Maywald GF, Yonow T and Stevens PM, CLIMEX: Predicting the Effects of Climate on Plants and Animals, CSIRO Publishing, Collingwood, Australia, p. 88 (1999).
Kumar S, Neven LG, Zhu H and Zhang R, Assessing the global risk of establishment of Cydia pomonella (lepidoptera: Tortricidae) using CLIMEX and MaxEnt niche models. J Econ Entomol 108:1708–1719 (2015). https://doi.org/10.1093/jee/tov166.
Jung JM, Jung S, Byeon DH et al., Model‐based prediction of the potential distribution of the invasive insect pest, spotted lanternfly Lycorma delicatula (Hemiptera: Fulgoridae), by using CLIMEX. J Asia Pac Biodivers 10:532–538 (2017). https://doi.org/10.1016/j.japb.2017.07.001.
Sridhar V, Verghese A, Vinesh LS et al., CLIMEX simulated predictions of oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) geographical distribution under climate change situations in India. Curr Sci 106:1702–1710 (2014).
EFSA PLH Panel (EFSA Panel on Plant Health), Scientific opinion on the pest risk assessment of Elasmopalpus lignosellus for the European Union. EFSA J 21:8004 (2023). https://doi.org/10.2903/j.efsa.2023.8004.
Naranjo SE, Castle SJ, De Barro PJ et al., Population dynamics, demography, dispersal and spread of Bemisia tabaci, in Bemisia: Bionomics and Management of a Global Pest, ed. by Stansly P and Naranjo S. Springer, Dordrecht, pp. 185–226 (2010). https://doi.org/10.1007/978-90-481-2460-2_6.
Maywald GF, Kriticos DJ, Sutherst RW and Bottomley W, DYMEX model builder version 3: user's guide. Hearne Scientific Software Pty Ltd, Melbourne, Australia, p. 172 (2007).
Nurhayati E and Koesmaryono Y, Predictive modelling of rice yellow stem borer population dynamics under climate change scenarios in Indramayu, in IOP Conference Series: Earth and Environmental Science. IOP Publishing, Bristol, England, 012054 (2017). https://doi.org/10.1088/1755-1315/58/1/012054.
Rasche L and Taylor RA, A generic pest sub model for use in integrated assessment models. Trans ASABE 60:147–158 (2017).
Williams JR, Dagitz S and Magre M, Environmental policy integrated climate model. Model User Manual Version, 810 (2013).
Feusthuber E, Mitter H, Schonhart M et al., Integrated modelling of efficient crop management strategies in response to economic damage potentials of the Western corn rootworm in Austria. Agric Syst 157:93–106 (2017). https://doi.org/10.1016/j.agsy.2017.07.011.
Willocquet L, Savary S, Fernandez L, Elazegui FA, Castilla N, Zhu D et al., Structure and validation of RICEPEST, a production situation‐driven, crop growth model simulating rice yield response to multiple pest injuries for tropical Asia. Ecol Model 153:247–268 (2002). https://doi.org/10.1016/S0304-3800(02)00014-5.
Willocquet L, Elazegui FA, Castilla N, Fernandez L, Fischer KS, Peng SB et al., Research priorities for rice pest management in tropical Asia: a simulation analysis of yield losses and management efficiencies. Phytopathology 94:672–682 (2004).
Willocquet L, Aubertot JN, Lebard S, Robert C, Lannou C and Savary S, Simulating multiple pest damage in varying winter wheat production situations. Field Crop Res 107:12–28 (2008). https://doi.org/10.1016/j.fcr.2007.12.013.
Aggarwal PK, Kalra N, Chander S and Pathak H, InfoCrop: a dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro‐ecosystems in tropical environments. I. Model description. Agric Syst 89:1–25 (2006). https://doi.org/10.1016/j.agsy.2005.08.001.
Stern VM, Smith RF, van den Bosch R et al., The integrated control concept. Hilgardia 29:81–101 (1959).
Overton K, Maino JL, Day R, Umina PA, Bett B, Carnovale D et al., Global crop impacts, yield losses and action thresholds for fall armyworm (Spodoptera frugiperda): A review. Crop Prot 145:105641 (2021). https://doi.org/10.1016/j.cropro.2021.105641.
Pinnschmidt HO, Batchelor WD and Teng PS, Simulation of multiple species pest damage in rice using CERES‐rice. Agric Syst 48:193–222 (1995). https://doi.org/10.1016/0308-521X(94)00012-G.
Sujithra M, Chander S and Selvaraj K, Simulation of rice brown planthopper [Nilaparvata lugens (Stal)] damage for determining economic injury levels. J Sci Ind Res 70:338–345 (2011). http://nopr.niscpr.res.in/handle/123456789/11641.
Choi YH, Bohan DA, Potting RPJ, Semenov MA and Glen DM, Individual‐based model of slug population and spatial dynamics. Ecol Model 190:336–350 (2006).
Rebaudo F and Dangles O, Adaptive management in crop pest control in the face of climate variability: an agent‐based modelling approach. Ecol Soc 20:1–13 (2015).
Parry HR, Evans AJ and Morgan D, Aphid population dynamics in agricultural landscapes: an agent‐based simulation model In the 2nd International Congress on environmental modelling and software, Osnabruck, Germany (2004).
Shelton AM and Badenes‐Perez FR, Concepts and applications of trap cropping in pest management. Annu Rev Entomol 51:285–308 (2006). https://doi.org/10.1146/annurev.ento.51.110104.150959.
Gripenberg S, Mayhew PJ, Parnell M and Roslin T, A meta‐analysis of preference‐performance relationships in phytophagous insects. Ecol Lett 13:383–439 (2010). https://doi.org/10.1111/j.1461-0248.2009.01433.x.
Fenoglio MS, Videla M and Morales JM, Evaluating trap cropping strategies for insect pest control through simulation models. J Pest Sci 90:601–610 (2017). https://link.springer.com/article/10.1007/s10340-016-0807-y.
Hannunen S, Modelling the interplay between pest movement and the physical design of trap crop systems. Agric For Entomol 7:11–20 (2005). https://doi.org/10.1111/j.1461-9555.2005.00237.x.
Potting RPJ, Perry JN and Powell WJEM, Insect behavioural ecology and other factors affecting the control efficacy of agro‐ecosystem diversification strategies. Ecol Model 182:199–216 (2005). https://doi.org/10.1016/j.ecolmodel.2004.07.017.
Miao J, Wu YQ, Gong ZJ, He Y‐Z, Duan Y and Jiang Y‐L, Long‐distance wind‐borne dispersal of Sitodiplosis mosellana Géhin (Diptera: Cecidomyiidae) in northern China. J Insect Behav 26:120–129 (2013).
Wu QL, Hu G, Westbrook JK, Sword G and Zhai BP, An advanced numerical trajectory model tracks a corn earworm moth migration event in Texas, USA. Insects 9:115 (2018).
Wu QL, He LM, Shen XJ, Jiang YY, Liu J, Hu G et al., Estimation of the potential infestation area of newly‐invaded fall armyworm Spodoptera frugiperda in the Yangtze River valley of China. Insects 10:298 (2019). https://doi.org/10.3390/insects10090298.
Rossini L, Severini M, Contarini M and Speranza S, EntoSim, a ROOT‐based simulator to forecast insects' life cycle: description and application in the case of Lobesia botrana. Crop Prot 129:105024 (2020).
Diouf EG, Brevault T, Ndiaye S et al., An agent‐based model to simulate the boosted sterile insect technique for fruit fly management. Ecol Model 468:109951 (2022). https://doi.org/10.1016/j.ecolmodel.2022.109951.
Tuan SJ, Lee CC, Tang LC and Saska P, Economic injury level and demography‐based control timing projection of Spodoptera litura (lepidoptera: Noctuidae) at different growth stages of Arachis hypogaea. J Econ Entomol 110:755–762 (2017). https://doi.org/10.1093/jee/tox033.
Vanninen I, Advances in Insect Pest and Disease Monitoring and Forecasting in Horticulture. Burleigh Dodds Science Publishing, Sawston, England, pp. 1–67 (2022). https://doi.org/10.19103/AS.2021.0095.05.
Magarey RD, Sutton TB and Thayer CL, A simple generic infection model for foliar fungal plant pathogens. Phytopathology 95:92–100 (2005). https://doi.org/10.1094/PHYTO-95-0092.
Rebaudo F and Rabhi VB, Modelling temperature‐dependent development rate and phenology in insects: review of major developments, challenges, and future directions. Entomol Exp Appl 166:607–617 (2018).
Voulgaris S, Stefanidakis M, Floros A et al., Stochastic modelling and simulation of olive fruit fly outbreaks. Procedia Technol 8:580–586 (2013).
Carrasco LR, Mumford JD, MacLeod A, Harwood T, Grabenweger G, Leach AW et al., Unveiling human‐assisted dispersal mechanisms in invasive alien insects: integration of spatial stochastic simulation and phenology models. Ecol Model 221:2068–2075 (2010). https://doi.org/10.1016/j.ecolmodel.2010.05.012.
Adeva JG, Botha JH and Reynolds M, A simulation modelling approach to forecast establishment and spread of Bactrocera fruit flies. Ecol Model 227:93–108 (2012). https://doi.org/10.1016/j.ecolmodel.2011.11.026.
Peterson RK and Hunt TE, The probabilistic economic injury level: incorporating uncertainty into pest management decision‐making. J Econ Entomol 96:536–542 (2003).
Phelps K, Collier RH, Reader RJ and Finch S, Monte Carlo simulation method for forecasting the timing of pest insect attacks. Crop Prot 12:335–342 (1993).
Qin Y, Ullah F, Fang Y, Singh S, Zhao Z, Zhao Z et al., Prediction of potential economic impact of Bactrocera zonata (Diptera: Tephritidae) in China: peaches as the example hosts. J Asia Pac Entomol 24:1101–1106 (2021). https://doi.org/10.1016/j.aspen.2021.10.004.
Garcia AG, Wajnberg E and Parra JRP, Optimizing the releasing strategy used for the biological control of the sugarcane borer Diatraea saccharalis by Trichogramma galloi with computer modelling and simulation. Sci Rep 14:9535 (2024). https://doi.org/10.1038/s41598-024-60146-y.
Wajnberg E, Monte Carlo simulations to model the behaviour of agricultural pests and their natural enemies, in Modelling Insect Populations in Agricultural Landscapes, ed. by A Moral R and Godoy WA. Springer International Publishing, Cham, pp. 29–47 (2024). https://doi.org/10.1007/978-3-031-43098-5_3.
Makowski D, Uncertainty and sensitivity analysis in quantitative pest risk assessments; practical rules for risk assessors. NeoBiota 18:157–171 (2013). https://doi.org/10.3897/neobiota.18.3993.
Rossi V, Giosuè S and Caffi T, Modelling Plant diseases for decision making in crop protection, in Precision Crop Protection ‐ the Challenge and Use of Heterogeneity, ed. by Oerke EC, Gerhards R, Menz G and Sikora R. Springer, Dordrecht (2010). https://doi.org/10.1007/978-90-481-9277-9_15.
van Klinken RD, Gladish DW, Manoukis NC, Caley P and Hill MP, Simulation to investigate site‐based monitoring of pest insect species for trade. J Econ Entomol 116:1296–1306 (2023). https://doi.org/10.1093/jee/toad112.
Park YS, Rabinovich J and Lek S, Sensitivity analysis and stability patterns of two‐species pest models using artificial neural networks. Ecol Model 204:427–438 (2007). https://doi.org/10.1016/j.ecolmodel.2007.01.021.
Anderson T and Dragićević S, Deconstructing geospatial agent‐based model: sensitivity analysis of forest insect infestation model, in Agent‐Based Models and Complexity Science in the Age of Geospatial Big Data: Selected Papers from a Workshop on Agent‐Based Models and Complexity Science (GI Science). Springer International Publishing, Cham, pp. 31–44 (2017).
Qiao H, Soberón J and Peterson AT, No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation. Methods Ecol Evol 6:1126–1136 (2015).

Keywords: computational modelling; decision‐making; pest management; pest risk analysis; simulation; sustainability

Date Created: 20251119 Date Completed: 20260111 Latest Revision: 20260111

20260130

10.1002/ps.70378

41255302

MEDLINE