Fungal diseases represent a widely spread natural phenomenon affecting many of wild and domesticated plants. In nature, all plant species forms plant communities of a mixed character, and the spatial pattern of dominant species is usually irregular and spotted. Some species are impregnable to a certain infection, which provides a kind of natural barrier to the infection spread within the natural community. Under the agricultural environment, when the single plant species may occupy a huge area, the species-specific parasite takes a great advantage to develop focal outbreaks and fast spreading of the infection within the area. The concentration of vulnerable plants and the absence of natural barriers within the agricultural areas provokes outbreaks of fungal diseases, that may have highly harmful consequences and result in significant yield losses. One of the purposes of the satellite optical data is an operative, cost-effective diagnostic and, in combination with climatic datasets and crop rotation information, a prognosis of fungal disease appearance and severity. In this paper, we describe the system of prognostic and monitoring measures to control the fungal diseases of wheat in Central Kazakhstan with special attention to septoria leaf blotch. The prognostic procedure provides a map of the probability of septoria leaf blotch appearance. The prognosis takes into consideration the combination of three main variables: the model of ecological niche for Septoria, the presence of wheat residue, and Vegetation Condition Index counted for the late spring (May) of the current year. The new spectral index, introduced in this paper, is the core component of monitoring activity. The index is sensitive to septoria leaf blotch severity at middle to late (stages 8-11, accordingly Feekes growth stages) periods of wheat development. Several other indices (RETA, VSDI, vegetation indices) may be of help in providing information on the spatial unevenness of wheat crops that may indicate the presence of fungal infection.
fungal wheat diseases, remote sensing, monitoring, prognosis
- Ashourloo, D., Mobasheri, M. R., Huete, A. (2014a). Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. Remote Sens. 6, 5107-5123. doi:10.3390/rs6065107.
- Ashourloo, D., Mobasheri, M. R., Huete, A. (2014b). Developing two spectral disease indices for detection of wheat leaf rust (Puccinia triticina). Remote Sens. 6, 4723-4740. doi:10.3390/rs6064723.
- Babkenova, S. A., Babkenov, A. T., Pakholkova, E. V., Kanafin, B. K. (2020). Pathogenic complexity of septoria spot disease of wheat in northern Kazakhstan. Plant Science Today. 7(4), 601–606. https://doi.org/10.14719/pst.2020.7.4.798.
- Beest, D. E., Shaw, M. W., Pietravalle, S., van den Bosch, F. (2009). A predictive model for early-warning of Septoria leaf blotch on winter wheat. Eur J Plant Pathol. 124, 413–425 doi:10.1007/s10658-009-9428-0.
- Chaloner, T.M., Fones, H.N., Varma, V., Bebber, D.P., Gurr, S.J. (2019). A new mechanistic model of weather-dependent Septoria tritici blotch disease risk. Phil. Trans. R. Soc. B, 374, 20180266. http://dx.doi.org/10.1098/rstb.2018.0266.
- Cooke, B.M., Jones, G.D., Kaye, B. (2006). The epidemiology of plant diseases. 2nd Edition, 43-80.
- Daughtry, C. S.T., Serbin, G., Reeves, J. B., Doraiswamy, P. C., Hunt, E. R. (2010). Spectral reflectance of wheat residue during decomposition and remotely sensed estimates of residue cover. Remote Sens. 2, 416-431; doi:10.3390/rs2020416.
- Diseases of crops. (V.F.Peresypkin, Ed.). (1989). Vol.1: Diseases of grain and leguminous crops. Kiev. 231 p.
- El Wazziki, H., El Yousfi, B., Serghat, S. (2015). Grain yield prediction from brown rust (Puccinia triticina) and leaf blotch (Septoria tritici) severity on wheat flag leaves. Revue Marocaine de Protection des Plantes, 7, 51-65.
- Gao, B. (1995). Normalized difference water index for remote sensing of vegetation liquid water from space. Proceedings of SPIE 2480., 225-236.
- Gitelson, A. A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., Derry, D. (2002). Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction. International Journal of Remote Sensing, 23, 25372562.
- Hess, D. E., Shaner, G. (1987). Effect of moisture on Septoria tritici blotch development on wheat in the field. Phytopathology, 77(2), 220-226
- Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.
- Jackson, T. J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P., Hunt, E. R. (2004). Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, 92, 475–482.
- El Jarroudi, M., Kouadio, A. L., Mackels, C., Tychon, B., Delfosse, P., Bock, C. H. (2015). A comparison between visual estimates and image analysis measurements to determine septoria leaf blotch severity in winter wheat. Plant Pathology, 64, 355–364. doi: 10.1111/ppa.12252
- Karjalainen, R., Karjalainen, S. (1990). Yield reduction of spring wheat in relation to disease development caused by Septoria nodorum. Journal Of Agricultural Science In Finland, 62, 255—263.
- Kaufman, Y., Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 2, 261-270.
- Kogan, F.N. (2001). Operational space technology for global vegetation assessment. Bull. Amer. Meteor. Soc., 82(9), 1949-1964.
- Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259-263. https://doi.org/10.1127/0941-2948/2006/0130
- Koyshibayev, M. (2002). Diseases of grain crops. Almaty. 367 p.
- Minchinton, E., Galea, V., Auer, D., Harapas, D., Thomson, F., Vassiliadis, S., Trapnell, L. N., Vujovic, S. (2008). Validation of a disease forecasting model to manage late blight (Septoria) in celery. HAL Final report VG06047. State of Victoria, Department of Primary Industries. 68 p.
- McNairn, H.; Protz, R. (1993). Mapping corn residue cover on agricultural fields in Oxford County, Ontario, using Thematic Mapper. Can. J. Remote Sens., 19, 152-159.
- Melkumov, G.M., Brazhnikova, I.A. (2018). Taxonomy and ecological peculiarities of Septoria Sacc. species in Voronezh Region. Proceedings of Volga State University. Series: Chemistry, Biology, Pharmacy. 2, 185-190.
- Malakhov, D.V., Tsychuyeva, N.Yu., Vitkovskaya, I.S. (2017). Modelling the ecological niche of wheat septoriosis using remote sensing data. Current Problems In Remote Sensing Of The Earth From Space. 14(1), 113-124. doi:10.21046/2070-7401-2017-14-1-113-124
- Parker, S.R., Shaw, M.W., Royle, D.J. (1997). Measurements of spatial patterns of disease in winter crops and the implications for sampling. Plant Pathology, 46,470-480.
- Peñuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J., Field, C.B. (1994). Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment, 48, 135-146.
- Peresypkin, V.F. 1(969). Agricultural phytopathology. “Kolos”, 479 p.
- Qi, J.; Marsett, R.; Heilman, P.; Biedenbender, S.; Biedenbender, S.; Moran, M.S.; Goodrich, D.C.; Weltz, M. (2002). RANGES improves satellite-based information and land cover assessments in Southwest United States. EOS Trans. Am. Geophys. Union, 83, 601-606.
- Roujean, J.L., Breon, F.M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ., 51, 375–384.
- Rouse, J. W. Jr., Haas, R. H., Schell, J. A. and Deering, D. W. (1973). Monitoring vegetation systems in s with ERTS. In Third ERTS Symposium, NASA SP-351, U.S. Government Printing Office, Washington, DC, 1, 309-317.
- Savary, S., Stetkiewicz, S., Brun, F., Willocquet, L. (2015). Modelling and mapping potential epidemics of wheat diseases-examples on leaf rust and Septoria tritici blotch using EPIWHEAT. Eur J Plant Pathol., doi 10.1007/s10658-015-0650-7
- Selyaninov, G.T. (1937). Methodics of agricultural characteristics of climate. World Agroclimatic Hand Book. Leningrad-Moscow. 412 p.
- Shaw, M.W., Royle, D.J. (1989). Estimation and validation of a function describing the rate of yield loss in winter wheat due to infection by Mycosphaerella graminicola. Annals of Applied Biology, 115, 425-442.
- Shaw, M.W., Royle, D.J. (1993). Factors determining the severity of epidemics of Mycosphaerella graminicola (Septoria tritici) on winter wheat in the UK. Plant Pathology, 42, 882-899.
- Suffert, F., Galet, N., Sache, I. (2011). Effect if wheat debris as source of primary inoculum on the early stages of Septoria leaf blotch epidemics. 8. International symposium on Mycosphaerella and Stagonospora diseases of cereals, Sep 2011, Mexico, Mexico, 80. hal-01000709
- Tadesse, Y., Chala, A., Kassa, B. (2020). Yield loss due to Septoria tritici Blotch (Septoria Tritici) of bread wheat (Triticum aestivum L.) in the Central Highlands of Ethiopia. Journal of Biology, Agriculture and Healthcare, doi 10(10):1-7. DOI: 10.7176/JBAH/10-10-01
- Toropova, E.Yu., Kazakova, O.A., Selyuk, M.P. (2016). Monitoring of Septoria blight on spring wheat in the forest-steppe of Western Siberia. Dostizheniya nauki i tekhniki APK., 30(12), 33-35.
- Verreet, J.A., Klink, H., Hoffmann, G.M. (2000). Regional monitoring for disease prediction and optimization of plant protection measures: the IPM Wheat Model. Plant Disease, 84(8), 816-826.
- Yang, Z., Willis, P., Mueller, R. (2008). Impact of band-ratio enhanced AWIFS image to crop classification accuracy. Proceedings of the Pecora 17 Remote Sensing Symposium. Denver, CO.
- Wang, Y., Zia, S., Owusu-Adu, S., Gerhards, R., Müller, J. (2014). Early detection of fungal diseases in winter wheat by multi-optical sensors. APCBEE Procedia, 8, 199 – 203.
- Wang, F.-M., Huang, J.-F., Tang, Y.-L., Wang, X.-Z. (2007). New vegetation index and its application in estimating leaf area index of rice. Rice Science, 14(3), 195-203.
- Zhang, N., Hong, Y., Qina, Q., Liu, L. (2013). VSDI: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing. International Journal of Remote Sensing, 34(13), 4585–4609.