Mapping of Tomatoes Pest Susceptibility in Zaria, Kaduna, Nigeria

Abstract

This study aimed at analyzing the spatial distribution of tomato farm pest susceptibility in Zaria using Geospatial techniques. The actual GPS positions of 153 tomato farms were observed and represented on a Landsat 8 image of Zaria. Their locations were then verified with a SPOT 5 imagery. Supervised classification was used to classify the image into land use/Land cover (LULC) by using Maximum likelihood classifier and subsequently used as a land utilization factor in the study. Other factors influencing tomato crop health and pest susceptibility such as the Normalized Difference Vegetation Index (NDVI), temperature, bioclimatic and solar radiation, soil moisture, proximity to drainage, water vapour, wind speed elevation, slope and aspect of the study area were considered as parameters. These parameters were ranked and assigned weight by pairwise comparison for producing a weighted overlay in the Multi Criteria Elevation (MCE) for susceptibility class of tomato crops to pest. The results were classified into four classes using the fuzzy set classification model. Result of the analysis shows that the majority of tomato farms have low susceptible to pest while few tomato farms have high susceptibility to pest. Finding depicts that out of 153 tomato farms studied susceptibility to pest while 20.26% with 31 tomato farms have high susceptibility to pest. The result of this study implies that the geographic space with favourable conditions for pest breeding suggest a very high tendency of pest development and spread to other farm locations with low or moderate susceptibility tendency overtime when adequate measures are not taken into consideration. It is therefore recommended that regular update of tomato farms location should be carried out and an effective information system should be established for monitoring infestation so as to effectively make informed decisions during pest outbreak.