Mining the Gaps: Using Machine Learning To Map A Million Data Points from Agricultural Research From the Global South
Until recently, agricultural research and innovation has been largely focused on improving productivity, focused mainly on a small number of crops (Serraj & Pingali, 2018). While we’ve seen very high returns from this approach, we have also seen the unintended and negative consequences it can have on nutrition and diets, social inclusion, and the environment (Davidson, 2016; Webb & Kennedy, 2014). We are now witnessing a major shift in thinking about agriculture, one which puts agriculture in the larger context of a system with complex interactions between food production, processing, consumption, and climate change (Barrett et al., 2020). This same shift implies a need for rethinking the role of agricultural research and development efforts, and a push for innovations that go beyond productivity. There is a corresponding urgency to identify priority investments (Laborde et al., 2020; Reardon, Lu, et al., 2019). In order to do so, however, we must have an adequate and accessible evidence base on agricultural innovations and their potential in the context of a transformation (Herrero et al., 2020; Reardon, Echeverria, et al., 2019). And it has become increasingly clear that there are several gaps in evidence. This study looks at the summaries of more than 1.2 million past publications and uses these to assess the current landscape of research for the Global South using machine learning (Porciello, 2020).
Global South, Machine Learning, Bibliometrics
Attribution-NonCommercial-NoDerivatives 4.0 International
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