Annual losses of cocoa in Ghana from mirids are significant; therefore, accurate timing of insecticide application is critical to enhance yields. However, cocoa farmers often lack information on the expected mirid population for each season to enable them to optimize pesticide use. This study assessed farmers' knowledge and perceptions of mirid control and their willingness to use forecasting systems informing them of the expected mirid peaks and the time of pesticide application. A total of 280 farmers were interviewed in the Eastern and Ashanti regions of Ghana with a structured open- and closed-ended questionnaire. Most farmers (87%) considered mirids the most important insect pest on cocoa, with 47% of them attributing 30–40% of annual crop losses to mirid damage. There was a wide variation in the timing of insecticide application as a result of farmers using different sources of information to guide the start of application. The majority of farmers (56%) did not have access to information about the type, frequency and timing of insecticide use. However, respondents who were members of farmer groups had better access to such information. Extension officers were the preferred channel for information transfer to farmers, with 72% of farmers preferring them to other available methods of communication. Almost all the respondents (99%) saw the need for a comprehensive forecasting system to help farmers manage cocoa mirids. The importance of the accurate timing of mirid control based on forecasted information to farmer groups and extension officers is discussed.

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Electricity has become one of the inelastic goods in our world today. The proper functioning of most equipment today relies on electricity. Taking Tarkwa which is a mining community into consideration, the various mines, schools, shops, banks and other companies in the municipality massively rely on electricity for their day to day running. Therefore, knowing the exact amount of electricity to produce and distribute for the smooth running of businesses and basic living is of great necessity. This study compared and formulated a model to forecast and predict the daily electrical energy consumption in Tarkwa for the year 2019. The data used was a monthly dataset for the year 2018 and it comprised of the temperature, wind speed, population and electricity consumption for Tarkwa. The methods used were Artificial Neuro-Fuzzy Inference System (ANFIS) and Autoregressive Integrated Moving Average (ARIMA). The ANFIS was used as a predictor to predict the electricity consumption based on the training and testing of the dependent and independent variables. The ARIMA was used to forecast the dependent and independent variables for 2019. These simulations were done using MATLand Minitab. The results of the analysis revealed that the training and testing dataset allowed ANFIS to learn and understand the system but the ANFIS could only forecast the 2019 electricity consumption after the input data to the system was changed to the ARIMA forecasted 2019 independent variables. It was observed that the amount of electricity consumed in 2019 increased by 14%.

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