A network-based approach for semi-quantitative knowledge mining and its application to yield variability
Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes andmechanisms. Cropmodels are the primary tool to project future changes in crop yields under climate change. A systematic overview of drivers andmechanisms of crop yield variability (YV) can thus inform cropmodel development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology andYV,whichmakes a prioritization ofmechanisms for implementation inmodels challenging. Therefore this paper takes on a novel approach to systematicallymine and organize existing knowledge from the literature. The aim is to identify importantmechanisms lacking inmodels, which can help to set priorities inmodel improvement.We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield.We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, ourfindings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, ourmethod allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield.We can identify explicit targets for the improvement of cropmodels. The network can additionally guidemodel development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions.We show the validity of our networkmethod as a structured, consistent and scalable dictionary of literature. Themethod can easily be applied tomany other researchfields.