Arabica coffee (Coffea arabica) is economically important for many smallholder farmers in the Mount Elgon region of East Uganda, but its production is increasingly threatened by climate change. However, ecosystem services (ES) provided by companion trees in coffee agroforestry systems (AFS) can help farmers adapt to climate change. The objectives of this research were to develop agroforestry species recommendations and tailor these to the farmers' needs and local context, taking into consideration gender. Local knowledge of agroforestry species and ES preferences was collected through farmer interviews and rankings. Using the Bradley-Terry approach, analysis was done along an altitudinal gradient in order to study different climate change scenarios for coffee suitability. Farmers had different needs in terms of ES and tree species at different altitudes, e.g. at low altitude they need a relatively larger set of ES to sustain their coffee production and livelihood. Local knowledge is found to be gender blind as no differences were observed in the rankings of species and ES by men and women. Ranking species by ES and ranking ES by preference is a useful method to help scientists and extension agents to use local knowledge for the development of recommendations on companion trees in AFS for smallholder farmers.

SUMMARYThis paper presents the main features of a unique decision-support tool developed for selecting tree species in coffee and cocoa agroforestry systems. This tool aims at assisting in the selection of appropriate shade trees taking into account local conditions as well as needs and preferences of smallholder farmers while maximizing ecosystem services from plot to landscape level. This user-friendly and practical tool provides site-specific recommendations on tree species selection via simple graphical displays and is targeted towards extension services and stakeholders directly involved in sustainable agroforestry and adaptation to climate change. The tool is based on a simple protocol to collect local agroforestry knowledge through farmers' interviews and rankings of tree species with respect to locally perceived key ecosystem services. The data collected are first analysed using the BradleyTerry2 package in R, yielding the ranking scores that are used in the decision-support tool. Originally developed for coffee and cocoa systems of Uganda and Ghana, this tool can be extended to other producing regions of the world as well as to other cropping systems. The tool will be tested to see if repeated assessments show consistent ranking scores, and to see if the use of the tool by extension workers improves their shade tree advice to local farmers.

Low productivity and climate change require climate-smart agriculture (CSA) for sub-Saharan Africa (SSA), through (i) sustainably increasing crop productivity, (ii) enhancing the resilience of agricultural systems, and (iii) offsetting greenhouse gas emissions. We conducted a meta-analysis on experimental data to evaluate the contributions of combining organic and mineral nitrogen (N) applications to the three pillars of CSA for maize (Zea mays). Linear mixed effect modeling was carried out for; (i) grain productivity and agronomic efficiency of N (AE) inputs, (ii) inter-seasonal yield variability, and (iii) changes in soil organic carbon (SOC) content, while accounting for the quality of organic amendments and total N rates. Results showed that combined application of mineral and organic fertilizers leads to greater responses in productivity and AE as compared to sole applications when more than 100 kg N ha-1 is used with high-quality organic matter. For yield variability and SOC, no significant interactions were found when combining mineral and organic fertilizers. The variability of maize yields in soils amended with high-quality organic matter, except manure, was equal or smaller than for sole mineral fertilizer. Increases of SOC were only significant for organic inputs, and more pronounced for high-quality resources. For example, at a total N rate of 150 kg N ha-1 season-1 , combining mineral fertilizer with the highest quality organic resources (50:50) increased AE by 20% and reduced SOC losses by 18% over 7 growing seasons as compared to sole mineral fertilizer. We conclude that combining organic and mineral N fertilizers can have significant positive effects on productivity and AE, but only improves the other two CSA pillars yield variability and SOC depending on organic resource input and quality. The findings of our meta-analysis help to tailor a climate smart integrated soil fertility management in SSA.

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