Analysing urban and peri-urban youth employment in agribusiness in Malawi.

The study aims to analyse key determinants of urban and peri-urban youth employment in agribusiness in Malawi to support youth policies. A mixed-methods approach is used, which combines both quantitative and qualitative analyses. The quantitative method involved a Bivariate Logit Model and Multinomial Logit Model to analyse nationally representative survey data from the Fourth Integrated Household Survey in 2016-2017. The qualitative method employed thematic analysis to data generated through Focus Group Discussions and Key Informant Interviews for key stakeholders involved in agri-business in Lilongwe district. The qualitative analysis, which focuses on a case study for urban and peri-urban youth in agribusiness, was used to validate, and provide context for the quantitative analysis. The results revealed that a majority of the urban and peri-urban youth engaged in agribusiness across Malawi work in sole farming (family farms or ganyu); in addition, women outnumber men in terms of engagement in agribusiness, and this stems from tradition. In addition, the determinants that affect youth's engagement in agribusiness consist of demographic factors, institutional support, assets, and shocks. It was also shown that men were more likely than women to be engaged in sole farming, but they were as likely as women to be engaged in other forms of agribusiness. The policy recommendation from this study is that programs aimed at supporting youth engagement in agribusiness should consider a variety of factors; If resources are limited, the programs should ensure that they offer capacity strengthening for the youth in the form of extension services and practical training in agribusiness.

Generic algorithm for multicriteria ranking of crop technological options based on the "Technique for Order of Preference by Similarity to Ideal Solution" using ShinyApps.

Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa. • The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators. • The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations.

Ex-ante evaluation of promising soybean innovations for sub-Saharan Africa.

This study undertakes an ex-ante evaluation of the effects of alternative technology and policy options on soybean supply and demand in sub-Saharan Africa (SSA) to 2050. Current soybean consumption in SSA is dominated by cooking oil followed by soybean cake used as animal feed. Due to weak processing sectors and low soybean yields, the region is currently importing about 70% of its consumption requirements. Based on the results from a geospatial bio-economic modeling framework, soybean consumption in SSA is projected to more than double by 2050 compared to 2010 due in part to a rising population and rising incomes. On the other hand, supply from domestic production is projected to increase by 80% over the same period. Hence, by 2050, net imports into SSA would be nearly 4 times higher than supply from domestic production. Under a future drier climate, some of the production gains achieved through soybean research and extension would be lost and this would further worsen the soybean demand gap in SSA relative to the baseline. This study shows that relying on conventional breeding alone to increase soybean yields in SSA would not be enough to substantially reduce the future demand gap. A combination of promising innovations affecting the soybean value chain across SSA would be needed to close the soybean demand gap in SSA by 2050 under a drier future climate.