Assessment of the efficiency of different epidemiological models in predicting the temporal progress of barley leaf stripe disease caused by Pyrenophora graminea in Khuzestan province, Iran

Document Type : Research Paper

Authors

1 1- Plant protection department, Khuzestan Agriculture and Natural resources research and Education center, AREEO. Ahvaz, Iran.

2 Department of Horticulture and Plant Protection, Faculty of Agriculture, Shahrood University of Technology, Shahrood, Iran.

Abstract

To provide suitable models for predicting the temporal progress of barley leaf stripe disease caused by Pyrenophora graminea, epidemiological studies were conducted in 2019-2021. The temporal progress of disease was examined in five cultivars (Zehak, Nimroz, Auxin, Nowruz and Izeh) of barley in a randomized complete block design with four replications at Golestan Research Station in Khuzestan Province. Well-known epidemiological models such as Monomolecular, Logistics, Log-logistics, Gampertz and Weibel were used to assess the disease progress. The results showed that, in all cultivars, the log-logistic model is the most proper model in describing disease progress. In order to determine crop loss caused by barley stripe disease, 1000-kernel weight, yield and area under disease progress curve (AUDPC) during the growing season were measured. The results showed that the integral model using AUDPC as an independent variable and the loss percentage as a dependent variable in the Nowruz cultivar (L=33.241+0.0002AUDPC) can justify 50% of AUDPC changes against crop loss. In the multivariate model, disease severity at different stages was considered as an independent variable and yield was considered as a dependent variable. This model had high efficiency in estimating the amount of yield. The results also showed that disease severity in the early stages of disease symptoms and after heading is more important than other phenological stages in yield estimation. This multivariate model (Y = 4836.17 – 38.51X1- 33.78X7+ 16.83X28- 26.56X35) could explain more than 90% of changes in disease severity versus crop yield.

Keywords

References

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Journal of Applied Research in Plant Protection
Volume 12, Issue 3
September 2023
Pages 287-300

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History

  • Receive Date: 30 October 2022
  • Revise Date: 30 December 2022
  • Accept Date: 04 January 2023

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