Detection of stress areas in cocoa farms using GIS and remote sensing: A case study of Offinso Municipal & Offinso North district, Ghana

Abstract

Abstract Cocoa (Theobroma cacao) is a major backbone of Ghana s economy, but it is largely affected by numerous environmental stresses provided by abiotic causes (e.g., humidity, rainfall and temperature) and biotic causes (e.g. pest and illness). Traditionally, farm managers identify these stresses through ground survey, however, this method is limited in the scale of monitoring and cannot give historical field information on cocoa health over a period to help in its effective management. Remote sensing and GIS have the potential to identify on a larger scale the spatial distribution of environmentally-stressed areas on cocoa farms to aid in their early management and mitigation which can improve cocoa production and provide protection of the environment. This study used monthly average NDVI pattern of selected cocoa farms in the study area extracted from sentinel 2 images to create a model. The model was then used as the bases for classifying cocoa areas into stressed and healthy areas for the months of November, December 2018 and January, 2019. Additionally, suitability map was developed to show the areas suitable for cocoa production and display the spatial representation of these areas. The result indicated that very healthy and healthy farms mostly out numbers the stressed farms. However, Offinso North district had relatively higher proportion of the stressed cocoa farms than Offinso municipal both recording 90.7% against 15.6% in November 2018, 13.5% against 0.8% in December 2018 and 1.9% against 0.1% in January 2019 respectively. The statistical analysis for the land suitability analysis generally showed that the study area was suitable for cocoa production. The accurate identification of cocoa stressed areas would give the producers much information in cocoa management, which would maximize yields and protect their investments.