Exploring the Impact of Climatic Variables on Arecanut Fruit Rot Epidemic by Understanding the Disease Dynamics in Relation to Space and Time

Balanagouda Patil; Vinayaka Hegde; Shankarappa Sridhara; Hanumappa Narayanaswamy; Manjunatha K Naik; Kiran Kumar R Patil; Hosahatti Rajashekara; Ajay Kumar Mishra

doi:10.3390/jof8070745

Exploring the Impact of Climatic Variables on Arecanut Fruit Rot Epidemic by Understanding the Disease Dynamics in Relation to Space and Time

J Fungi (Basel). 2022 Jul 19;8(7):745. doi: 10.3390/jof8070745.

Authors

Balanagouda Patil^{1

2}, Vinayaka Hegde², Shankarappa Sridhara³, Hanumappa Narayanaswamy¹, Manjunatha K Naik¹, Kiran Kumar R Patil⁴, Hosahatti Rajashekara⁵, Ajay Kumar Mishra⁶

Affiliations

¹ Department of Plant Pathology, University of Agricultural and Horticultural Sciences, Shivamogga 577255, Karnataka, India.
² Division of Crop Protection, ICAR-Central Plantation Crops Research Institute, Kasaragod 671124, Kerala, India.
³ Center for Climate Resilient Agriculture, University of Agricultural and Horticultural Sciences, Shivamogga 577255, Karnataka, India.
⁴ Department of Agricultural Economics, University of Agricultural and Horticultural Sciences, Shivamogga 577255, Karnataka, India.
⁵ Division of Crop Protection, ICAR-Directorate of Cashew Research, Puttur 574202, Karnataka, India.
⁶ Khalifa Centre for Genetic Engineering and Biotechnology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates.

Abstract

To understand the spatio-temporal dynamics and the effect of climate on fruit rot occurrence in arecanut plantations, we evaluated the intensity of fruit rot in three major growing regions of Karnataka, India for two consecutive years (2018 and 2019). A total of 27 sampling sites from the selected regions were monitored and the percentage disease intensity (PDI) was assessed on 50 randomly selected palms. Spatial interpolation technique, ordinary kriging (OK) was employed to predict the disease occurrence at unsampled locations. OK resulted in aggregated spatial maps, where the disease intensity was substantial (40.25-72.45%) at sampling sites of the Malnad and coastal regions. Further, Moran's I spatial autocorrelation test confirmed the presence of significant spatial clusters (p ≤ 0.01) across the regions studied. Temporal analysis indicated the initiation of disease on different weeks dependent on the sampling sites and evaluated years with significant variation in PDI, which ranged from 9.25% to 72.45%. The occurrence of disease over time revealed that the epidemic was initiated early in the season (July) at the Malnad and coastal regions in contrary to the Maidan region where the occurrence was delayed up to the end of the season (September). Correlations between environmental variables and PDI revealed that, the estimated temperature (T), relative humidity (RH) and total rainfall (TRF) significantly positively associated (p = 0.01) with disease occurrence. Regression model analysis revealed that the association between T_max, RH1 and TRF with PDI statistically significant and the coefficients for the predictors T_max, RH1 and TRF are 1.731, 1.330 and 0.541, respectively. The information generated in the present study will provide a scientific decision support system, to generate forecasting models and a better surveillance system to develop adequate strategies to curtail the fruit rot of arecanut.

Keywords: GLM; arecanut; climatic indicators; epidemiology; fruit rot disease; ordinary kriging; spatial dependency; spatio-temporal pattern.

Grants and funding

This research received no external funding.