Using local knowledge to reconstruct climate‐mediated changes in disease dynamics and yield—A case study on Arabica coffee in its native range

Abstract

Adapting agriculture to climate change requires an understanding of the long‐term relationship between climate, disease dynamics, and yield. While some countries have monitored major crop diseases for decades or centuries, comparable data is scarce or non‐existent for many countries that are most vulnerable to climate change. For this, a novel approach was developed to reconstruct climate‐mediated changes in disease dynamics and yield. Here, a case study on Arabica coffee in its area of origin demonstrates how to combine local knowledge, climate data, and spatial field surveys to reconstruct disease and yield time series and to postulate and test hypotheses for climate–disease–yield relationships. While some countries have monitored crop diseases for several decades or centuries, other countries have very limited historical time series. In such areas, we lack data on long‐term patterns and drivers of disease dynamics, which is important for developing climate‐resilient disease management strategies. We adopted a novel approach, combining local knowledge, climate data, and spatial field surveys to understand long‐term climate‐mediated changes in disease dynamics in coffee agroforestry systems. For this, we worked with 58 smallholder farmers in southwestern Ethiopia, the area of origin of Arabica coffee. The majority of farmers perceived an increase in coffee leaf rust and a decrease in coffee berry disease, whereas perceptions of changes in coffee wilt disease and Armillaria root rot were highly variable among farmers. Climate data supported farmers' understanding of the climatic drivers (increased temperature, less rainy days) of these changes. Temporal disease‐climate relationships were matched by spatial disease‐climate relationships, as expected with space‐for‐time substitution. Understanding long‐term disease dynamics and yield is crucial to adapt disease management to climate change. Our study demonstrates how to combine local knowledge, climate data and spatial field surveys to reconstruct disease time series and postulate hypotheses for disease‐climate relationships in areas where few long‐term time series exist.