Volume 9, Issue 1 (3-2020)
Plant Pathol. Sci. 2020, 9(1): 40-56 |
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Latifian M, Rahkhodaei E. (2020). Frocasting and monitoring system of date palm bunch feding in Khozestan province. Plant Pathol. Sci.. 9(1), 40-56. doi:10.29252/pps.9.1.40
URL: http://yujs.yu.ac.ir/pps/article-1-302-en.html
URL: http://yujs.yu.ac.ir/pps/article-1-302-en.html
Agricultural and Natural Resources Research and Education Center, AREEO, Horticultural Science Research Institute, Karaj, Iran , masoud_latifian@yahoo.com
Abstract: (4924 Views)
Latifian M , Rahkhodaei E (2020) Frocasting and monitoring system of date palm bunch feding in Khozestan province. Plant Pathology Science 9(1):40-56. DOI: 10.2982/PPS.9.1.40.
Introduction: Bunch feding is an important injurious disease of date palm. Materials and Methods: This research was carried out to its descsion making system in Abadan-Khoramshhar, Shadegan, Ahwaz, Mahshar and Behbehan regions of Khozestan province by climatic and geoststistical models from 2012 to 2016. Samples were taken randomly from 10 trees located in one date palm orchards of any villages Results:Results showed that the disease damage reached to the peak values in September. Forecasting model of damage factors have been significant at level 1 and 5 percent. Variography of distributions on different sites were calculated that the model nuggets for date palm bunch feding in Abadan - Khoramshhar, Shadegan, Ahwaz, Mahshar and Behbehan regions were 1.6, 1.7, 0.15, 0.51 and 2.5 kilometers respectively. These results show that errors of the damage estimation were low at the distances less than whithin sampling sapace. Effective ranges of variograms were 4.1. 12.9, 4.7, 1.9 and 11.06 respectively which indicated the date palm bunch feding distribution in region. Sill of models were 0.49, 0.76, 0.37, 0.31, and 0.51 respectively that at the distances more than these thresholds, correlations between the injury data were at the lowest level and could be monitored. Conclusion: The results of this study
were the basic steps in creating a decision making system in date palm protection network. According to the results of this research, the bunch feding damge can be properly monitored, forecasted and controlled before the maximum damage occurs.
References
1. Caffarra A, Donnelly A, Chuine B (2011) Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models. Climate Research 46:159-170. [DOI:10.3354/cr00983]
2. Dent D (1995) Integrated Pest Management. Chapmans and Hall, London, 356p.
3. Devadas R Lamb DW Blackhouse D, Simpfendorfer S (2015) Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat. Precision Agriculture 16:477-491. [DOI:10.1007/s11119-015-9390-0]
4. Loiselle, BA, Howell CA, Graham CH, Goerck JM, Brooks T, Smith KG, Williams PH (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology, 17:1591-1600. [DOI:10.1111/j.1523-1739.2003.00233.x]
5. Gendi SM (1998) Population fluctuation of Thrips tabaci on onion plants under environmental condition. Arab Universities Journal of Agriculture Science (Egypt). 69:267-276.
6. Goovaets P (1997) Geostatictics for Natural Resources Evaluation. Oxford University Press, 512p.
7. Johnston KJ, Ver Hoef K, Krivoruchko M, Lucas N (2003). Using ArcGIS Geostatistical Analyst. ESRI Press, Redlands, CA.
8. Journel AG, Huijbregts CJ (1978) Mining Geostatistics. Academic Press. Inc. 599p.
9. Karimzadeh R, Hegazi MJ, Helali H, Iranpour S, Mohammadi SA (2011). Analysis of the spatio-temporal distribution of Eurygaster integriceps (Hemiptera: Scutelleridae) by using spatial analysis by distance indices and geostatistics. Environmental Entomology 40:1253-1265. [DOI:10.1603/EN10188]
10. Karampour F, Davoodian A (2010) Study on effects of bunch covering on date palm bunch fading in Iran. In IV International Date Palm Conference 882:1219-1227. [DOI:10.17660/ActaHortic.2010.882.140]
11. Katherine AR (2001) Geostatistic using SAS software. Own analytic inc. Deep. River, CT, 6p.
12. Latifian M, Solymannejadian E (2002) Study of the lesser moth Batrachedra amydraula (Lep: Batrachedridae) distribution based on geostatistical models in Khuzestan province. Journal of Entomological Research 1:43-55.
13. Latifian M, Zare M (2003) The forecasting model of The Date Lesser moth (Batrachedra amydraula) based on climatic factors. Journal of Agriculture Science 2:27-36.
14. Latifian M, Zare M (2009) The effects of climatic factors on Date palm scale (Parlatoria blanchardi Targ.) (Hem.: Dispididae) in Khuzestan province. Plant Protection Journal 1:277-287
15. Latifian M (2014) Date palm spider mite (Oligonychus afrasiaticus McGregor) forecasting and monitoring system. WALIA Journal 30:79-85.
16. Liebhold AM, Rossi RE, Kemp WP (1993) Geostatistics and geographic information systems in applied insect ecology. Annual Review of Entomology 38:303-327. [DOI:10.1146/annurev.en.38.010193.001511]
17. Latifian M, Rad B (2017) Efficacy of cultural control for date palm borer management. Indian Journal of Plant Protection 45:7-11.
18. Madden LV, Ellis MA (1988) How to develop plant disease forecasters. Pp: 191-208. In: Experimental Techniques in Plant Disease Epidemiology. Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-95534-1_14]
19. Mahlein AK (2016) Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping. Plant Diseases 100:241-251 [DOI:10.1094/PDIS-03-15-0340-FE]
20. Manion PD (2003) Evolution of concepts in forest pathology. Phytopathology 93:1052-1055. [DOI:10.1094/PHYTO.2003.93.8.1052]
21. Mawby WD, Gold HJ (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. [DOI:10.1007/BF02515494]
22. Newberry F, Qi A, Fitt BDL (2016) Modelling impacts of climate change on arable crop diseases: progress, challenges and applications. Current opinion in plant biology 32:101-109. [DOI:10.1016/j.pbi.2016.07.002]
23. Ojiambo PS, Yuen J, Van den Bosch F, Madden LV (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. [DOI:10.1094/PHYTO-07-17-0248-FI]
24. Park YL, Krell RK, Carroll M (2007) Theory, technology, and practice of site-specific insect pest management. Journal of Asia Pacific Entomology 10:89-101. [DOI:10.1016/S1226-8615(08)60337-4]
25. Russo JM, Liebhold AM, Kelley JGW (1993) Mesoscale weather data as input to a gypsy moth (Lepitopttera: Lymantriidae) phenology model. Journal of Economic Entomology 86:838-844. [DOI:10.1093/jee/86.3.838]
26. Russo JM (2000) Weather forecasting for IPM. pp. 453-473. In: Emerging Technologies For Integrated Pest Management: Concepts, Research, and Implementation. American Phytopathological Society (APS) Press.
27. Sciarretta A, 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, 50:97-110. [DOI:10.17221/40/2013-PPS]
28. Schaub LP, Raulin FW, Gray DR, Logan JA (1995) Lanscape frameworke to predict phonological events for gypsymoth (Lep: Lymantriidae) management programs. Environmental Entomology 24:10-18. [DOI:10.1093/ee/24.1.10]
29. Sharov AA (1996) Sharov, A. A. (1996). Modelling forest insect dynamics. PP.293-303. In: E Ko-rpilahti EM, Mukkela T, Salonen (eds.). Caring for the Forest: Research in a Changing World. Congress Report.
30. Qadir A, Shakeel, F, Ali A (2020) Phytotherapeutic potential and pharmaceutical impact of Phoenix dactylifera (date palm): current research and future prospects. Journal of Food Science and Technology 57:119-1204. [DOI:10.1007/s13197-019-04096-8]
31. Story M, Congalton RG (1994) Accuracy assessment: A user's perspective: LK Fenestermaleer. Remote sensing thematic assessment. American society for Photo-Grammetric and Remote Sensing 257-259.
32. West J, Kimber R (2015) nnovations in air sampling to detect plant pathogens. Annals of Applied Biology 166:4-17. [DOI:10.1111/aab.12191]
33. Wright RJ, Devries TA, Young LJ, Jarvi KJ, Seymout RC (2002) Geostatistical analysis of small-scale distribution of european corn borer Coleop: Carabidae larvae and damage in whorl stage corn. Environmental Entomology 31:160-167. [DOI:10.1603/0046-225X-31.1.160]
34. Young JK, Kwang-Hyung K (2019) An Integrated Modeling Approach for Predicting Potential Epidemics of Bacterial Blossom Blight in Kiwifruit under Climate Change. Plant Pathology Journal 35:459-472.
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