Volume 13, Issue 1, January 2020 ISSN 1791-3691 Hellenic Plant Protection Journal A semiannual scientifi c publication of the BENAKIBEE PHYTOPATHOLOGICAL INSTITUTE EDITORIAL POLICY The Hellenic Plant Protection Journal (HPPJ) (ISSN 1791-3691) is the scientifi c publication of the Benaki Phytopathological Institute (BPI) replacing the Annals of the Benaki Phytopathological Insti- tute (ISSN 1790-1480) which had been published since 1935. Starting from January 2008, the Hel- lenic Plant Protection Journal is published semiannually, in January and July each year. HPPJ publishes scientifi c work on all aspects of plant health and plant protection referring to plant pathogens, pests, weeds, pesticides and relevant environmental and safety issues. In addition, the topics of the journal extend to aspects related to pests of public health in agricultural and urban areas. Papers submitted for publication can be either in the form of a complete research article or in the form of a suffi ciently documented short communication (including new records). Only origi- nal articles which have not been published or submitted for publication elsewhere are considered for publication in the journal. Review articles in related topics, either submitted or invited by the Editorial Board, are also published, normally one article per issue. Upon publication all articles are copyrighted by the BPI. Manuscripts should be prepared according to instructions available to authors and submitted in electronic form on line at http://www.hppj.gr. All submitted manuscripts are considered and pub- Hellenic Plant Protection Journal Hellenic Plant lished after successful completion of a review procedure by two competent referees. The content of the articles published in HPPJ refl ects the view and the offi cial position of the au- thors. The information and opinions contained herein have not been adopted or approved by the HPPJ Editorial Board. The HPPJ Editorial Board neither guarantees the accuracy of the information included in the published articles nor may be held responsible for the use to which information contained herein may be put. For all parties involved in the act of publishing (the author(s), the journal editor(s), the peer review- ers, and the publisher) it is necessary to agree upon standards of expected ethical behavior. HPPJ follows the ethics statements of De Gruyter journals, which are based on the Committee on Publi- cation Ethics (COPE) Code of Conduct guidelines available at www.publicationethics.org. EDITORIAL BOARD Editor: Dr F. Karamaouna (Pesticides Control & Phytopharmacy Department, BPI) Associate Editors: Dr A.N. Michaelakis (Entomology & Agric. Zoology Department, BPI) Dr K.M. Kasiotis (Pesticides Control & Phytopharmacy Department, BPI) Dr I. Vloutoglou (Phytopathology Department, BPI) Editorial Offi ce: M. Kitsiou (Library Department, BPI) A. Karadima (Information Technology Service, BPI) For back issues, exchange agreements and other publications of the Institute contact the Li- brary, Benaki Phytopathological Institute, 8 St. Delta Str., GR-145 61 Kifi ssia, Attica, Greece, e-mail: [email protected]. This Journal is indexed by: AGRICOLA, CAB Abstracts-Plant Protection Database, INIST (Institute for Scientifi c and Technical Information) and SCOPUS. The olive tree of Plato in Athens is the emblem of the Benaki Phytopathological Institute Hellenic Plant Protection Journal also available at www.hppj.gr © Benaki Phytopathological Institute Hellenic Plant Protection Journal 13: 1-12, 2020 DOI 10.2478/hppj-2020-0001 Prototype Spatio-temporal Predictive System of pest development of the codling moth, Cydia pomonella, in Kazakhstan A. Afonin1,*, B. Kopzhassarov2, E. Milyutina1, E. Kazakov3,4, A. Sarbassova2 and A. Seisenova2 Summary A prototype for pest development stages forecasting is developed in Kazakhstan exploit- ing 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 tem- perature 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 pre- dicted 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 us- ing daily satellite sensor datasets and their calibration with data received from a network of weather stations installed in the orchards. Additional keywords: Codling Moth, day degrees, meteorological stations, land surface temperature, plant protection, remote sensing Introduction oped over the past century (Zlatanova and Pastukhova, 1975; Riedl et al., 1976; Zlatano- According to the data of the Ministry of Agri- va, 1978; Welch et al., 1978; Boldyrev, 1981; culture of the Republic of Kazakhstan (MoA Boldyrev, 1991; Knight, 2007; Jones et al., RK), inadequate implementation of plant 2013; Drozda and Sagitov, 2017). The usual protection measures leads to an increase pest development forecast is based on the in infestation of agricultural lands by pests, data of the closest meteorological stations diseases and weeds, resulting in gross har- or according to the interpolated data of me- vest losses of 2.2 million tons or 191 million teorological stations which are extremely U.S. dollars annually in 80% of the acreage sparse (the average distance between the areas (The Ministry of Agriculture, 2017). Ac- nearest meteorological stations in the Re- curate forecasts allow to conduct the most public of Kazakhstan (RK) is more than 100 eff ective actions during the phases of the km). In this regard, farms that are remote greatest vulnerability of pests. from meteorological stations often receive Pest forecasting models linking the dy- distorted weather information and errone- namics of the pest development stages with ous forecasts since at a complex terrain the agro-climatic factors (e.g. accumulated tem- meteorological conditions in the area be- perature, precipitation) have been devel- tween weather stations can diff er largely - for example, the average daily temperatures can vary up to tens of degrees. Today, remote sensing data (land surface 1 St. Petersburg State University, St. Petersburg, 7/9 Universitetskaya nab., St. Petersburg, 199034 Russia. temperature – LST – from satellite sensors) 2 Kazakh Research Institute for Plant Protection and can supplement meteorological data in the Quarantine named after Zhazken Zhiembayev, Kaza- intervals between weather stations and khstan, Almaty, Kazbek-bi, 1. 3 Russian State Hydrological Institute, V.O. Line 2, 23, St. make the forecast more precise in space. Petersburg, 199034 Russia. However, due to the fact that previous- 4 LLC «NextGIS», Vavilova, 41, offi ce 2, Moscow, 117312 Russia. ly developed methods of agro-climatic fore- * Corresponding author: [email protected] casts for pest development are based on © Benaki Phytopathological Institute 2 Afonin et al. meteorological data, it is necessary to con- in the East Mediterranean and claimed LST vert the LST data to match the data from data to be more accurate than data gathered weather stations (2 m above the ground). from meteorological stations and improve Conversion of such data is widely discussed the monitoring of the olive fruit fl y (Bactro- in the scientifi c literature, but a uniform ap- cera oleae) (Diptera: Tephritidae). proach has not been developed. The ma- Blum et al. (2018) compared MODIS LST jority of studies discuss regional problems data and weather stations data in terms of of modelling temperatures of surface layer computing thermal thresholds for cotton of the atmosphere from space thermal im- bollworm (Helicoverpa armigera), taking also agery data, focusing on the features of local into account parameters such as migration landscapes (Fu et al., 2011; Benali et al., 2012; patterns and pesticide use. Yones et al. (2012) Williamson et al, 2014), while in other works used the thermograph and NOAA satellite algorithms, usually less successful, for recal- imagery data in calculating the expected culations in continental scales (Vancutsem stages of the cotton leafworm Spodoptera et al., 2010; Shen and Leptoukh, 2011; Meyer littoralis (Boisd.), which were further com- et al., 2016) are proposed. pared with in-situ data results and produced Land surface temperature (LST) is used correction factors to improve the predict- in studies on argoclimatology and Integrat- ability of their model. Blum et al. (2013) built ed Pest management (IPM). Sepulcre-Canto a correction function for LST based on mean et al. (2007) demonstrated the applicability diff erences between LST and in-situ temper- of LST, derived from Airborne Hyperspectral atures, included in Fourier series. For areas Scanner (AHS) and ASTER satellite, in olive where in-situ measurements were not avail- and peach orchards parameters indicating able, these parameters were estimated with quality. Sona et al. (2012) applied MODIS LST the use of NDVI data. data in order to calculate temperature vege- Thus, despite the fact that the use of raw tation dryness index (TVDI) in the Lower Me- LST data shows good forecasting results, kong Basin. Raw LST data in this study was conversion of LST to meteorological data is used along with Normalized Diff erence Veg- still relevant to the present discussion, be- etation Index (NDVI). As already noted, LST is cause the majority of