Factors affecting soil quality among smallholder macadamia farms in Malawi

Declining soil fertility limits smallholder macadamia productivity in Malawi. To reverse this trend, it is essential to apply organic and inorganic fertilisers in an efficient and effective manner. Yet, fertilizer recommendations for smallholder macadamia (Macadamia integrifolia) production in Malawi are not site-specific. Nutrient imbalances can occur if fertilisers are applied without a clear understanding of whether they are required or not. This may lead to yield losses, unnecessary costs, and other environmental issues associated with excess fertiliser application. To address this research need/ knowledge gap, our study examined the current soil fertility status among smallholder macadamia farms in Malawi. Specifically, the objective was to establish an evidence base for promoting soil fertility restoration interventions for smallholder macadamia production. One hundred and eighty nine soil samples at a depth of 0–15 cm were collected from sixty three smallholder macadamia farms belonging to the Highlands Macadamia Cooperative Union Limited members in central and southern Malawi. We found that the majority of the soils were sandy loams (52%), strongly acidic (mean pH ≤ 5.1), and deficient in essential nutrients required for the healthy growth of macadamia. The soils had an average low cation exchange capacity of 1.67 cmol ( +) kg−1, which is inadequate for macadamia cultivation. More than half of the sampled soils had very low organic matter content (≤ 1%). The low soil organic matter content, coupled with the sandy texture and high acidity, contributed to the observed low concentrations of essential nutrients and cation exchange capacity. Poor agronomic practices and inherent soil characteristics are responsible for this low soil fertility. Altogether, our findings underscore the urgent need to identify and implement more sustainable and effective soil nutrient management practices that help to improve the soil fertility of macadamia farms under smallholder systems

Quantifying Agroforestry Yield Buffering Potential Under Climate Change in the Smallholder Maize Farming Systems of Ethiopia

Agroforestry is a promising adaptation measure for climate change, especially for low external inputs smallholder maize farming systems. However, due to its long-term nature and heterogeneity across farms and landscapes, it is difficult to quantitatively evaluate its contribution in building the resilience of farming systems to climate change over large areas. In this study, we developed an approach to simulate and emulate the shading, micro-climate regulation and biomass effects of multi-purpose trees agroforestry system on maize yields using APSIM, taking Ethiopia as a case study. Applying the model to simulate climate change impacts showed that at national level, maize yield will increase by 7.5 and 3.1 % by 2050 under RCP2.6 and RCP8.5, respectively. This projected increase in national-level maize yield is driven by maize yield increases in six administrative zones whereas yield losses are expected in other five zones (mean of −6.8% for RCP2.6 and −11.7% for RCP8.5), with yields in the other four zones remaining stable overtime. Applying the emulated agroforestry leads to increase in maize yield under current and future climatic conditions compared to maize monocultures, particularly in regions for which yield losses under climate change are expected. A 10% agroforestry shade will reduce maize yield losses by 6.9% (RCP2.6) and 4.2 % (RCP8.5) while 20% shade will reduce maize yield losses by 11.5% (RCP2.6) and 11% (RCP8.5) for projected loss zones. Overall, our results show quantitatively that agroforestry buffers yield losses for areas projected to have yield losses under climate change in Ethiopia, and therefore should be part of building climate-resilient agricultural systems

Activated carbon from baobab fruit shells through domestic processes

Surface and groundwater pollution is rampant due to poor waste management and runoff. Dry regions of the country also writhe from water scarcity which leaves communities to resort to unsafe water supplies for domestic use. It is estimated that about 90% rural households in Zimbabwe consume untreated water (Hoko, 2005) and that more than 75% of Zimbabwe's population lives under water stressed conditions in most rural areas (Manyanhaire et al., 2009). Commercially produced activated carbon is expensive. The aim of the research was to investigate the production of activated carbon from baobab fruit shells (a cheap raw material) using a method that can be employed at rural homesteads in removing organic pollutants. Two methods of producing activated carbon were also compared i.e. activating before carbonization and activating after carbonization. Activating with salt after carbonization proved to be the efficient (adsorption% 93.2). A contact time of 60 minutes was determined as the maximum time required for adsorption and a pollutant concentration equivalent to 0.3M oxalic acid gave the highest adsorption of 98.9%. The activated carbon from baobab fruit shells follows a Langmuir isotherm which explains the existence of a monolayer and the saturation of adsorption sites on the activated carbon. It was concluded that activated carbon from baobab fruit shells have the potential of removing organic pollutants from water.Keywords: activated carbon, percentage adsorption, carbonization, adsorption, organic pollutants and fruit shell

Determination of the age of oil palm from crown projection area detected from WorldView-2 multispectral remote sensing data: the case of Ejisu-Juaben district, Ghana

Information about age of oil palm is important in sustainability assessments, carbon mapping, yield projections and precision agriculture. The aim of this study was to develop and test an approach to determine the age of oil palm plantations (years after planting) by combining high resolution multispectral remote sensing data and regression techniques using a case study of Ejisu-Juaben district of Ghana. Firstly, we determined the relationship between age and crown projection area of oil palms from sample fields. Secondly, we did hierarchical classification using object based image analysis techniques on WorldView-2 multispectral data to determine the crown projection areas of oil palms from remote sensing data. Finally, the crown projection areas obtained from the hierarchical classification were combined with the field-developed regression model to determine the age of oil palms at field level for a wider area. Field collected data showed a strong linear relationship between age and crown area of oil palm up to 13 years beyond which no relationship was observed. A user’s accuracy of 80.6% and a producer’s accuracy of 68.4% were obtained for the delineation of oil palm crowns while for delineation of non-crown objects a user’s accuracy of 65.6% and a producer’s accuracy of 78.6% were obtained, with an overall accuracy of 72.8% for the OBIA delineation. Automatic crown projection area delineation from remote sensing data produced crown projection areas which closely matched the field measured crown areas except for older oil palms (13+ years) where the error was greatest. Combining the remote sensing detected crown projection area and the regression model accurately estimated oil palm ages for 27.9% of the fields and had an estimation error of 1 year or less for 74.6% of the fields and an error of a maximum 2 years for 92.4% of the fields. The results showed that 6 and 11 year old oil palm stands were dominating age categories in the study area. Although the method could be reliably applied for estimating oil palm age at field level, more attention is required in improving crown area delineation to improve the accuracy of the approac

Climate change and specialty coffee potential in Ethiopia

Current climate change impact studies on coffee have not considered impact on coffee typicities that depend on local microclimatic, topographic and soil characteristics. Thus, this study aims to provide a quantitative risk assessment of the impact of climate change on suitability of five premium specialty coffees in Ethiopia. We implement an ensemble model of three machine learning algorithms to predict current and future (2030s, 2050s, 2070s, and 2090s) suitability for each specialty coffee under four Shared Socio-economic Pathways (SSPs). Results show that the importance of variables determining coffee suitability in the combined model is different from those for specialty coffees despite the climatic factors remaining more important in determining suitability than topographic and soil variables. Our model predicts that 27% of the country is generally suitable for coffee, and of this area, only up to 30% is suitable for specialty coffees. The impact modelling showed that the combined model projects a net gain in coffee production suitability under climate change in general but losses in five out of the six modelled specialty coffee growing areas. We conclude that depending on drivers of suitability and projected impacts, climate change will significantly affect the Ethiopian speciality coffee sector and area-specific adaptation measures are required to build resilience