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  The ‘Nigerian Diet’ and its evolution: review of the existing literature and household survey data 

  Petrikova, I.; Bhattacharjee, R.; Fraser, P.D. (2023-01)

  Natural and social science studies have commonly referenced a ‘typical’ or ‘habitual’ Nigerian diet, without defining what such a diet entails. Our study, based on a systematic review of the existing literature and an analysis of household-level survey data, describes the general outline of a common Nigerian diet and how it varies based on spatial, demographic, and socio-economic characteristics. We further try to establish whether Nigeria has embarked on a dietary transition common in most modern ...
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  Integrating APSIM model with machine learning to predict wheat yield spatial distribution 

  Kheir, A.M.S.; Mkuhlani, S.; Mugo, J.W.; Elnashar, A.; Nangia, V.; Devare, M.; Govind, A. (2023-09)

  Traditional simulation models are often point based; thus, more research is needed to emphasize spatial simulation, providing decision-makers with fast recommendations. Combining machine learning algorithms with spatial process-based models could be considered an appropriate solution. We created a spatial model in R (APSIMx_R) to generate fine-resolution data from coarse-resolution data, which is typically available at the regional level. The APSIM crop model outputs were then deployed to train ...
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  Large language models and agricultural extension services 

  Tzachor, A.; Devare, M.; Richards, C.; Pypers, P.; Ghosh, A.; Koo, J.; Johal, S.; King, B. (2023-11)

  Several factors have traditionally hampered the effectiveness of agricultural extension services, including limited institutional capacity and reach. Here we assess the potential of large language models (LLMs), specifically Generative Pre-trained Transformer (GPT), to transform agricultural extension. We focus on the ability of LLMs to simplify scientific knowledge and provide personalized, location-specific and data-driven agricultural recommendations. We emphasize shortcomings of this technology, ...
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  Training report: developing gender- and youth-responsive agronomic solutions Course 

  GREAT; IITA (Makerere University, 2023-06)
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  Impact of agribusiness empowerment interventions on youth livelihoods: insight from Africa 

  Adeyanju, D.; Mburu, J.; Gituro, W.; Chumo, C.; Mignouna, D.; Mulinganya, N. (2023-11)

  This study generates evidence to understand the impact of agribusiness empowerment programmes on youth livelihoods in developing countries based on the ENABLE-TAAT programme implemented in Kenya, Nigeria, and Uganda. A multistage sampling technique was used in obtaining primary agribusiness-level data from a sample of 1435 young agripreneurs from the study countries. An Endogenous Treatment Effect Regression (ETER) model was used to assess the impact of programme participation on youth livelihoods ...

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