Prototype Spatio-temporal Predictive System of pest development of the codling moth, Cydia pomonella, in Kazakhstan

A. Afonin 1 , B. Kopzhassarov 2 , E. Milyutina 1 , E. Kazakov 3 , 4 , A. Sarbassova 2  und A. Seisenova 2
  • 1 St. Petersburg State University, , 199034, St. Petersburg
  • 2 Kazakh Research Institute for Plant Protection and Quarantine named after Zhazken Zhiembayev, , Almaty, Kazakhstan
  • 3 Russian State Hydrological Institute, , 199034, St. Petersburg
  • 4 LLC «NextGIS», , 117312, Moscow

Summary

A prototype for pest development stages forecasting is developed in Kazakhstan exploiting data from the geoinformation technologies and using codling moth as a model pest in apples. The basic methodology involved operational thermal map retrieving based on MODIS land surface temperature products and weather stations data, their recalculation into accumulated degree days maps and then into maps of the phases of the codling moth population dynamics. The validation of the predicted dates of the development stages according to the in-situ data gathered in the apple orchards showed a good predictivity of the forecast maps. Predictivity of the prototype can be improved by using daily satellite sensor datasets and their calibration with data received from a network of weather stations installed in the orchards.

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  • Afonin, A.N., Kazakov, E.E, Milyutina, E.A. unpublished.

  • Afonin, A.N., Sevryukov, S.Yu., Soloviev, P.A., Luneva, N.N. 2016. Web-GIS for the solution of ecological-geographical analysis and modeling tasks: new opportunities. Vestnik of Saint Petersburg University, Geography and geology, 7(4): 97-111.

  • Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N. and Santos, A. 2012. Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment, 124: 108-121.

  • Blum M., Lensky, I.M., Nestel, D. 2013. Estimation of olive grove canopy temperature from MODIS thermal imagery is more accurate than interpolation from meteorological stations. Agricultural and Forest Meteorology, 176: 90-93.

  • Blum, M., Lensky, I.M., Rempoulakis, P., Nestel, D. 2015. Modeling insect population fluctuations with satellite land surface temperature. Ecological Modelling, 311: 39–47.

  • Blum, M., Nestel, D., Cohen, Y., Goldshtein, E., Helman, D., Lensky, I.M. 2018. Predicting Heliothis (Helicoverpa armigera) pest population dynamics with an age-structured insect population model driven by satellite data. Ecological Modelling, 369: 1–12.

  • Boldyrev, M.I. 1981. Short-term forecasting of the development of codling moths. Plant protection, 5: 38-39.

  • Boldyrev, M.I. 1991. Optimal timing and measures to combat the codling moth. Gardening and viticulture, 6: 13-15.

  • Bulygina, O.N., Razuvaev, V.N., Aleksandrova, T.M. 2018. Description of the data of air daily temperatures and precipitation at meteorological stations of Russia and neighboring countries. http://meteo.ru/data/162-temperature-precipitation

  • Drozda, V.F., Sagitov, A.O. 2017. Evaluation of technologies for protection of apple trees from codling moth. Plant Protection and Quarantine, 5: 17-27.

  • Eastman, J.R. 2012. IDRISI Selva Tutorial, Manual Version 17.0, Clark University. http://uhulag.mendelu.cz/files/pagesdata/eng/gis/idrisi_selva_tutorial.pdf

  • Fu, G., Shen, Z., Zhang, X., Shi, P., Zhang, Y. and Wu, J. 2011. Estimating air temperature of an alpine meadow on the Northern Tibetan Plateau using MODIS land surface temperature. Acta Ecologica Sinica, 31(1): 8-13.

  • Hill, T. and Lewicki, P. 2007. STATISTICS: Methods and Applications. StatSoft,Tusla, OK, 719 p.

  • Jones, V.P., Hilton, R., Brunner, J.F., Bentley, W.J., Alston, D.G., Barrett, B. et al. 2013. Predicting the emergence of the codling moth, Cydia pomonella (Lepidoptera: Tortricidae), on a degree-day scale in North America. Pest Management Science, 69: 1393-1398.

  • Knight, A.L. 2007. Adjusting the phenology model of codling moth (Lepidoptera: Tortricidae) in Washington state apple orchards. Environmental Entomology, 36: 1485-1493.

  • Lensky, I.M. and Dayan, U. 2011. Detection of finescale climatic features from satellites and implications for agricultural planning. Bull. Am. Meteorol. Soc., 92: 1131-1136.

  • Marques da Silva, J.R., Damásio, C.V., Sousa, A.M.O., Bugalho, L., Pessanha, L., Quaresma P. 2015. Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature. International Journal of Applied Earth Observation and Geoinformation, 38: 40-50.

  • Meyer, H., Katurji, M., Appelhans, T., Müller, M.U., Nauss, T., Roudier, P. and Zawar-Reza, P. 2016. Mapping daily air temperature for Antarctica based on MODIS LST. Remote Sensing, 8(9), 732.

  • MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V006. 2017. https://lpdaac.usgs.gov/dataset_discovery

  • Pralya, I.I. 2013. Protection of the apple orchard. AMA-Press, Moscow, 91 p.

  • Riedl, H., Croft, B.A. and Howitt., A.J. 1976. Forecasting codling moth phenology based on pheromone trap catches and physiological-time models. Canadian Entomologist, 108: 449-460.

  • Sepulcre-Cantó, G., Zarco-Tejada, P.J., Jiménez-Mun oz, J.C., Sobrino, J.A., Soriano, M.A., Fereres, E., Vega, V., Pastor, M. 2007. Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER. Remote Sensing of Environment, 107: 455–470.

  • Shen, S. and Leptoukh, G.G. 2011. Estimation of surface air temperature over central and eastern Eurasia from MODIS land surface temperature. Environmental Research Letters, 6(4) 045206.

  • Sona, N.T., Chena, C.F., Chenb, C.R., Changa L.Y., Minh V.Q. 2012. Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data. International Journal of Applied Earth Observation and Geoinformation, 18: 417–427.

  • The Ministry of Agriculture of the Republic of Kazakhstan. 2017. http://mgov.kz/en/napravleniya-razvitiya/fitosanitarnaya-bezopasnost/

  • Vancutsem, C., Ceccato, P., Dinku, T. and Connor, S.J. 2010. Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114(2): 449-465.

  • Wan, Z., Hook, S., Hulley, G. 2015. MOD11A2 MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V006 [Data set]. NASA EOSDIS LP DAAC. doi: 10.5067/MODIS/MOD11A2.006

  • Welch, S., Croft, B.A. Brunner, J.F. and Michels, M. 1978. PETE: An extension phenology modeling system for management of multi-species pest complex. Environmental Entomology, 7: 487-494.

  • Williamson, S.N., Hik, D.S., Gamon, J.A., Kavanaugh, J.L. and Flowers, G.E. 2014. Estimating temperature fields from MODIS land surface temperature and air temperature observations in a sub-arctic alpine environment. Remote Sensing, 6(2): 946-963.

  • Yones, M.S., Arafat, S., Hadid, A.A., Elrahman H.A. and Dahi, H.F. 2012. Determination of the best timing for control application against cotton leaf worm using remote sensing and geographical information techniques. The Egyptian Journal of Remote Sensing and Space Sciences, 15: 151-160.

  • Zlatanova, A.A. 1978. Forecast of the emergence of individual phases in codling moth development. Proceedings of the Kazakh RIPPQ, 14: 30-36.

  • Zlatanova, A.A., Pastukhova, N.P. 1975. Influence of the photoperiod and temperature on codling moth and microdus development during reactivation. Ekologiya, 5: 82-83.

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