Biochar as a soil amendment tool: effects on soil properties and yield of maize and cabbage in Brong-Ahafo Region, Ghana.

Ghana's soil is continuously declining in fertility due to continuous cultiva- tion and rapid mineralization of its soil organic matter. Previous studies have touted the potential of biochar to help improve soil properties and increase the yield of crops. This study investigated the effects of the application of bi- ochar on physicochemical properties of soil and the yield of maize and cab- bage in Ghana. The study indicated that application of biochar significantly increased soil organic matter (SOM) from 3.88% (for control) to 5.72% (for biochar application rate 20 ton/ha and 0 ton/ha of NPK). It also increased soil pH from 6.55 in (for control) to 7.30 (for biochar application rate 20 ton/ha) and 0 ton/ha of nitrogen (N), phosphorus (P) and potassium (K) which can help ameliorate the soil acidity problem of Ghanaian soils. This field study, demonstrated that addition of biochar from sawdust increased the yield (be- tween the control (0 ton/ha of biochar, 0% of recommended dose of NPK) and 20 ton/ha, 0% recommended dose of NPK) of maize and cabbage by 6.66% and 7.57% respectively. This study concluded that application of bio- char offers a great potential to improve soil quality and the yield of maize and cabbage in Ghana

Machine learning based groundwater prediction in a data-scarce basin of Ghana

Groundwater (GW) is a key source of drinking water and irrigation to combat growing food insecurity and for improved water access in rural sub-Saharan Africa. However, there are limited studies due to data scarcity in the region. New modeling techniques such as Machine learning (ML) are found robust and promising tools to assess GW recharge with less expensive data. The study utilized ML technique in GW recharge prediction for selected locations to assess sustainability of GW resources in Ghana. Two artificial neural networks (ANN) models namely Feedforward Neural Network with Multilayer Perceptron (FNN-MLP) and Extreme Learning Machine (FNN-ELM) were used for the prediction of GW using 58 years (1960–2018) of GW data. Model evaluation between FNN-MLP and FNN-ELM showed that the former approach was better in predicting GW with R2 ranging from 0.97 to 0.99 while the latter has an R2 between 0.42 to 0.68. The overall performance of both models was acceptable and suggests that ANN is a useful forecasting tool for GW assessment. The outcomes from this study will add value to the current methods of GW assessment and development, which is one of the pillars of the sustainable development goals (SDG 6)