A GIS-based tool for the analysis of the distribution and abundance of Chilo sacchariphagus indicus under the influence of biotic and abiotic factors

Abstract There is evidence showing that factors like biotic, abiotic, anthropogenic activities, and biological situations affect the profuse spread of insect pests. In our study, high-resolution data are created using Geographic Information System (GIS) to analyze the impacts of the temperature, precipitation, humidity, soil nutrients, soil microbes, and predators on the profuse spread of Chilo sacchariphagus indicus, a sugarcane borer along with its egg parasitoid Trichogramma chilonis. This borer is a significant pest of sugarcane studied by block survey at nine random blocks per site during few years with the highest records in 2015. Ecological factors were shown to hugely impact fertility and distribution and abundance within varied parts of the study area. Abundance recorded throughout the study period showed a significant variation from season to season. Furthermore, parasitoids were recorded higher in 2016 than in 2015. A negative correlation was observed monthly between the pest and parasitoid populations. The parasitoids like T. chilonis, Cotesia flavipes, Sturmiopsis inferens, and Isotima javensis were recorded in our study area with a considerable ability to influence pest. The current study used Raster Interpolation, predictive analysis tools, and backward stepwise regression to analyze spatiotemporal distribution of pest along with the influence of biotic and abiotic factors on them. The obtained results revealed that among rainfall, temperature, humidity, and predators, rainfall has the highest impact on the distribution of pests. Our results also showed the considerable influence of soil nutrients on pests at four sites, elucidating the minimal role of soil microbes on pests. On the other hand, excessive application of bio manure leading to the development of biowaste can also significantly affect the soil microbes and nutrients. In conclusion, the GIS model data analysis acts as a key to determining the factors effective on pests.