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DOI10.1002/ppp3.10510
Using local knowledge to reconstruct climate-mediated changes in disease dynamics and yield-A case study on Arabica coffee in its native range
发表日期2024
EISSN2572-2611
英文摘要Societal Impact StatementAdapting 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.Summary 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. 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. image
英文关键词climate change; coffee berry disease; coffee leaf rust; coffee wilt disease; disease dynamics; local knowledge; perception; yield
语种英语
WOS研究方向Biodiversity & Conservation ; Plant Sciences ; Environmental Sciences & Ecology
WOS类目Biodiversity Conservation ; Plant Sciences ; Ecology
WOS记录号WOS:001195542000001
来源期刊PLANTS PEOPLE PLANET
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/308736
作者单位Stockholm University; Stockholm University; Jimma University; Addis Ababa University; Swedish University of Agricultural Sciences
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GB/T 7714
. Using local knowledge to reconstruct climate-mediated changes in disease dynamics and yield-A case study on Arabica coffee in its native range[J],2024.
APA (2024).Using local knowledge to reconstruct climate-mediated changes in disease dynamics and yield-A case study on Arabica coffee in its native range.PLANTS PEOPLE PLANET.
MLA "Using local knowledge to reconstruct climate-mediated changes in disease dynamics and yield-A case study on Arabica coffee in its native range".PLANTS PEOPLE PLANET (2024).
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