Assessment of the Spatial Distribution and Risk Associated with Fruit Rot Disease in Areca catechu L.

Abstract

Phytophthora meadii (McRae) is a hemibiotrophic oomycete fungus that infects tender nuts, growing buds, and crown regions, resulting in fruit, bud, and crown rot diseases in arecanut (Areca catechu L.), respectively. Among them, fruit rot disease (FRD) causes serious economic losses that are borne by the growers, making it the greatest yield-limiting factor in arecanut crops. FRD has been known to occur in traditional growing areas since 1910, particularly in Malnad and coastal tracts of Karnataka. Systemic surveys were conducted on the disease several decades ago. The design of appropriate management approaches to curtail the impacts of the disease requires information on the spatial distribution of the risks posed by the disease. In this study, we used exploratory survey data to determine areas that are most at risk. Point pattern (spatial autocorrelation and Ripley’s K function) analyses confirmed the existence of moderate clustering across sampling points and optimized hotspots of FRD were determined. Geospatial techniques such as inverse distance weighting (IDW), ordinary kriging (OK), and indicator kriging (IK) were performed to predict the percent severity rates at unsampled sites. IDW and OK generated identical maps, whereby the FRD severity rates were higher in areas adjacent to the Western Ghats and the seashore. Additionally, IK was used to identify both disease-prone and disease-free areas in Karnataka. After fitting the semivariograms with different models, the exponential model showed the best fit with the semivariogram. Using this model information, OK and IK maps were generated. The identified FRD risk areas in our study, which showed higher disease probability rates (>20%) exceeding the threshold level, need to be monitored with the utmost care to contain and reduce the further spread of the disease in Karnataka.

Xylosandrus crassiusculus (Motschulsky) (Coleoptera: Curculionidae) and Its Fungal Symbiont Ambrosiella roeperi Associated with Arecanut Kernel Decay in Karnataka, India

“…The data collected here in preliminary form seem to indicate a relatively low damage density in this phase, with only young plantations infested. However, since areca palms are already subject to innumerable pests and diseases that compromise their productivity and survival [38][39][40][41], a further loss of product, albeit modest, could have important effects on the farming communities that depend from it. To date, very little is known about possible effects of climate change on the biology and behavior of X. crassiusculus; however, the important changes taking place could have relevant repercussions on the impact of this species both on a small and large scale [42,43].…”

Geostatistical Analysis of Apricot Shot Hole Disease and Influence Factors in District Nagar, Gilgit-Baltistan, Pakistan

“…Where, 𝑍(𝑥 0 ) is the interpolated value, 𝑛 represents the total number of sample data values, 𝑥 𝑖 is the 𝑖th data value, ℎ 𝑖𝑗 is the separation distance between interpolated value and the sample data value, and 𝛽 denotes the weighting power. A semivariogram function can characterize the disease variable's spatial distribution, and its function's calculation can be expressed based on the following equation (Davis et al, 2022;Balanagouda et al, 2021;Hussain et al, 2021).…”

Spatial distribution and identification of potential risk regions to rice blast disease in different rice ecosystems of Karnataka

“…Spatial autocorrelation can be performed to determine the correlation between spatial data at varying intervals to find the spatial dependence 21 , 22 . With the use of an array of observations in space, models of spatial dependence can be expressed as semivariograms, through which the occurrence of disease with least variance and without bias can be estimated by kriging interpolation techniques 8 , 23 , 24 .…”