Decision-making system for the management of date inflorescence rot disease caused by Mauginiella scaettae

Volume 10, Issue 3
September 2021
Pages 433-445

Document Type : Original Research

Authors

Date palm and Tropical Fruits Research Center, Horticulture Science Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Ahwaz, Iran.

Abstract
The inflorescence rot is an essentially high impact (or damaging) disease of date palm. The current research was carried out to help develop a decision-making system in Abadan, Khorramshahr, Shadegan, Ahwaz, Mahshar, and Behbehan regions of Khuzestan province Iran based on climatic and geostatistical models using five-year data from 2011 to 2015. Samples were taken randomly from 10 date palm trees within one orchard in each of 33 villages. The disease started in March, and the damage reached its peak values in April. The forecasting model of damage factors has been significant at levels 1 and 5%. The model nuggets for disease in Abadan-Khorramshahr, Shadegan, Ahwaz, Mahshar, and Behbehan regions were 2.1, 1.1, 0.09, 2.60, and 0.27 km, respectively. These results show that the disease damage estimation errors were low at distances less than within sampling space. The effective ranges of variograms were 4.9. 8.3, 9.1, 5.1, and 4.2, respectively, indicating the disease distribution in the region. The sill of models were 0.41, 0.46, 0.46, 0.29, and 0.58, respectively, indicating that correlations between the damage data were at the lowest level and could be monitored at distances more than these thresholds. Findings are fundamental steps in creating a decision-making system in the date palm protection network. Therefore, it could be concluded that the date inflorescence rot disease can be monitored, forecasted, and controlled correctly before the maximum damage occurs.

Keywords

Subjects
Abdullah, S., Asensio, L., Monfort, E., Gómez-Vidal, S., Palma-Guerrero, J., Salinas, J., Lopez-Llorca, L,, Jansson, H. B. and Guarro, J. 2005. Occurrence in Elx, SE Spain of inflorescence rot disease of date palms caused by Mauginiella scaettae. Journal of Phytopathology, 153: 417-422.
Al-Sharidi, A.M. and Al-Shahwan, I.M. 2003. Fungi associated with rot diseases of inflorescence and fruit of date palm in Riyadh region Saudi Arabia. Arab Journal of Plant Protection, 21: 84-89.
Al-Shawaf, A.M.A., S. Al-Abdan, A.H. Al-Abbad, A.B. Abdallah and Faleiro, J.R. 2012. Validating area-wide management of Rhynchophorus ferrugineus (Coleoptera: Curculionidae) in date plantations of Al-Hassa, Saudi Arabia. Indian Journal of Plant Protection, 40: 255-259.
Andrewartha, H. G. and Brirch, L. C. 1953. The distribution and abundance of animals. Univ. Chicago Press, Chicago.
Caffarra, A. Donnelly, A.and Chuine, I. 2011. Modeling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models. Climate Research, 46:159–170.
Dent, D. 1995. Integrated Pest Management. Chapmanand Hall. London.
Devadas, R., Lamb, D. W., Blackhouse, D. and Simpfendorfer, S. 2015. Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat. Precision Agriculture, 16: 477–491.
Ellsbury, M. M., woodson,W. D., Clay, S. A., Malo, D., Schumacher, J., Clay, D .E. and Carlson, C. G. 1998 . Geostatistical characterization of spatial distribution, Environmental Entomology, 27(4): 910-917.
FAOSTAT.2018. Food and agricultural commodities production. http://faostat.fao.org/site/339/default.aspx.
Gaston, K. J. 2003. The Structure and Dynamics of Geographic Ranges.Oxford University Press. Oxford.
Gendi, S. M. 1998. Population fluctuation of Thrips tabaci on onion plants under environmental condition. Arab Universities Journal of Agriculture Science, 69(11): 267-276.
Ghaedi, H., Kocheili, F., Latifian, M. andFarrokhi Nejad, R. 2020. Spatial and temporal distribution of rhinoceros beetles Oryctes Hellwig (Col.: Scarabaeidae) in date palm plantations of Khuzestan province. Plant Pests Research, 10(2): 59-73.
Goovaets,p. 1997. Geostatictics for Natural Resources Evaluation. OxfordUniversityPress.
Hameed, M.A. 2012. Inflorescence rot disease of date palm caused by Fusarium proliferatum in Southern Iraq. African Journal of Biotechnology, 11(35): 8616-8621.
Journel,A. G. and Huijbregts, C. J. 1978. Mining Geostatistics. Academic Press, London.
Karimzadeh, R., Hegazi, M. J., Helali, H., Iranpour, S. and Mohammadi, S. A. 2011. Analysis of the spatio-temporal distribution of Eurygaster integriceps (Hemiptera: Scutelleridae) by using spatial analysis by distance indices and geostatistics. Environmental Entomology, 40(5): 1253-1265.
Katherine, A. R. 2001. Geostatistic using SAS software. Own analytic inc. Deep. River, CT. 6pp.
Latifian, M. 2001. Management factors roles in pest and disease control. Date Palm and Tropical Fruit Research Institute, Iran, 32 p.
Latifian, M. and Solymannejadian, E. 2002. Study of the lesser moth Batrachedra amydraula (Lep: Batrachedridae) distribution based on geostatistical models in Khuzestan province. Journal of Entomological Research, 1(1): 43-55.
Latifian, M. and Zare, M. 2003. The forecasting model of The Date Lesser moth (Batrachedra amydraula) based on climatic factors. Journal of Agriculture Science, 2(26): 27-36.
Latifian, M. 2014. Date palm spider mite (Oligonychus afrasiaticus McGregor) forecasting and monitoring system. WALIA Journal, 30: 79-85.
Latifian, M. 2017. Integrated pest management of date palm fruit pests: A review. Journal of Entomology, 14: 112–121.
Latifian, M. and Rahkhodaei,E. 2020. Forecasting and monitoring system of date palm bunch fading in Khuzestan province. Plant PathologyScience, 9: 40-56.
Liebhold, A. Zhang, M. X., Hohn, M. E., Elkinton, J. S.,Ticehurst, M., Benzon, C. L. and Campbell, R. W. 1991. Geostatistical analysis of Gypsy moth (Lepidoptera: Lymantriidae) egg mass population. Environmental Entomology, 20(5): 1407- 1417.
Madden, L.V. and Ellis, M. A. 1988. How to develop plant disease forecasters. In: Rotem,J. (Ed.),Experimental Techniques in Plant Disease Epidemiology. Springer-Verlag., New York,PP: 191-208.
Mahlein, A. K.2016. Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Diseases, 100: 241–251
Manion, P.D. 2003. Evolution of concepts in forest pathology. Phytopathology, 93:1052-1055.
Mawby, W. D. and Gold, H.J. 1984. A stochastic simulation model for large-scale southern pine beetle (Dendroctonus frontalis Zimmerman) infestation dynamics in the southeastern United States. Researches in Population Ecology, 26: 275-283.
Newberry, F., Qi, A. and Fitt, B. D. L. 2016. Modeling impacts of climate change on arable crop diseases: progress, challenges and applications. Current Opinion in Plant Biology, 32: 101–109.
Ojiambo, P. S., Yuen, J., van den Bosch, F. and Madden, L. V. 2017. Epidemiology: past, present and future impacts on understanding disease dynamics and improving plant disease management – A summary of focus issue articles. Phytopathology, 107: 1092–1094.
Park, Y. L., Krell, R. K. and Carroll, M. 2007. Theory, technology, and practice of site-specific insect pest management. Journal of Asia Pacific Entomology, 10:89-101.
Rabab, M. A. Gassan, R. I., Roqia, A. A. and Nagham, T. S.2019. Isolation and Identification of fungi caused data palms inflorescence rot disease in some area of diyala- iraq and control of disease by sodium chloride salt. Journal of Pure and Applied Microbiology, 13(1):459-463.
Russo, J. M., Liebhold, A. M. and Kelley, JG.W. 1993. Mesoscale weather data as input to a gypsy moth (Lepitopttera:Lymantriidae) phenology model. Journal of EconomicEntomology, 86:838-844.
Russo, J.M. 2000. Weather forecasting for IPM. In: Kennedy, G. G.and Sutton,T. B. (Eds.),Emerging Technologies For Integrated Pest Management: Concepts, Research, and Implementation. APS Press, St. Paul, MN,pp. 453-473.
Schaub, L. P., Raulin, F. W., Gray, D. R. and Logan, J. A. 1995. Landscapeframework to predict phonological events for gypsymoth (Lep: Lymantriidae) management programs. Environmental Entomology, 24: 10-18.
Sciarretta, A. and Trematerra, P. 2014. Geostatistical tools for the study of insect spatial distribution: practical implications in the integrated management of orchard and vineyard pests. Plant Protection Science, 50(2): 97-110.
Sharov, A.A. 1996. Modeling insect dynamics. In: Korpilahti, E.,Mukkela, H. and Salonen,T. (Eds.), Caring for the Forest: Research in A Changing World. Congress Report, Vol. II., IUFRO XX World Congress, 6-12 August 1995, Tampere, Finland. Gummerus Printing, Jyvaskyla, Finland. pp. 293-303.
Steel, R.G.D.and Torrie, J.H. 1980. Principles of Statistics: A Biometrical Approach, 2nded., McGraw-Hill Book Co., New York.
Story, M. and Congalton, R.G. 1994. Accuracy assessment: A user’s perspective.In: Fenestermaleer, L. K. (Ed.), Remote Sensing Thematic Assessment. American Society for Photogrammetry and Remote Sensing, pp: 257-259.
West, J. and Kimber, R. 2015. Innovations in air sampling to detect plant pathogens. Annal Applied. Biology, 166: 4–17.
Wright, R.J.Devries, T.A., Young, L. J.,Jarvi, K.J. and Seymout, R. C.2002. Geostatistical analysis of small-scale distribution of European corn borer Coleop: Carabidae larvae and injury in whorl stage corn. Environmental Entomology, 31(11): 160-167.
Young, J. K. andKwang-Hyung, K.2019. An Integrated Modeling Approach for Predicting Potential Epidemics of Bacterial Blossom Blight in Kiwifruit under Climate Change. Plant Pathology Journal, 35(5): 459–472.
Zaid, A, de Wet, P. F., Djerbi, M. and Oihabi, A. 2002. Disease and pests of date palm. In: Zaid, A. and Arias-Jimenez, E. J. (Eds.), Date Palm Cultivation, Rome, FAO. pp. 1–47.