Farmers’ Incremental and Transformational Climate Change Adaptation in Different Regions: A Natural Language Processing Comparative Literature Review

Climate change is expected to affect agriculture worldwide adversely. This is especially true if farmers fail to adapt, at least incrementally, early in the twenty-first century and fail to pursue transformational adaptation necessary to withstand changes later this century. Many publications discuss the underlying mechanisms of autonomous private adaptation to climate change in quantitative, qualitative, and mixed methods terms. However, the review of empirical evidence on adaptation is normally performed on articles’ quantitative data using metanalysis, ignoring much of the vast literature evidence coming from qualitative work. We address this gap by performing a comparative analysis of factors associated with farmers’ climate change adaptation in both quantitative and qualitative literature using Natural Language Processing. By retrieving relevant peer review publications from Scopus, we derive a database with metadata from both quantitative and qualitative articles’ findings. We then use this as an input for generalized linear models to analyze whether farmers’ climate change adaptation factors differ by type of adaptation (incremental vs transformational) or across different regions of the world. Results show that access to information, access to technology, age, economic factors, farming experience, and income are more likely to be associated with transformational adaptation than with incremental adaptation. Regarding world regions, results highlight uneven access to infrastructure, with farmers in the Global North having an advantage, while farmers in the Global South requiring it the most for effective adaptation to changing climate.