Abstract
Climate change makes the future of Arabica coffee (Coffea arabica L.) uncertain, a crop supporting Coffee is produced on approximately 12.5 million farms worldwide, predominantly family-run smallholdings. Identifying varieties that combine high productivity with disease resistance across diverse environments is critical for climate adaptation, yet global empirical evidence remains limited. We leveraged a dataset from an international multi-environment trial evaluating 32 varieties for yield and coffee leaf rust severity across a total of 30 sites in 17 countries. Genotype-by-environment interactions were analyzed using factor analytic mixed-effects models, and latent environmental factors were linked to climatic gradients through partial least squares regression. Yield stability was associated with thermal gradients, distinguishing varietal adaptation between cooler environments with high diurnal temperature variation and warmer, thermally stable conditions. In contrast, rust severity and its interactions were associated with joint gradients of temperature, precipitation, humidity, and thermal variability. Among the evaluated genotypes, the F1 hybrids H1 Centroamericano and EC16, followed by the introgressed line Parainema, showed the strongest combined performance for yield and rust resistance, although with distinct climatic preferences and trade-offs in yield stability. Overall, introgressed varieties consistently outperformed pure Arabica varieties, which exhibited lower yields and greater rust susceptibility. These findings provide a robust empirical basis for climate-informed variety deployment and breeding strategies in Arabica coffee.
Abbreviations
- AIC:
Akaike information criterion (model selection metric)
- BIC:
Bayesian information criterion (model selection metric)
- CBD:
Coffee berry disease
- CDD:
Consecutive (continuous) dry days
- CHIRPS:
Climate Hazards Group InfraRed Precipitation with Station data
- CLR:
Coffee leaf rust
- FA:
Factor analytic (mixed-model) structure
- F1:
First filial generation (hybrid)
- G(times )E:
Genotype (times ) environment interaction
- IMLVT:
International multi-location Variety Trial
- MET:
Multi-environment trial
- PET:
Potential evapotranspiration
- PLS:
Partial least squares (regression)
- REML:
Restricted maximum likelihood (estimation)
- SREG:
Site regression model (statistical model for G(times )E analysis)
- T:
Temperature ((^circ ) C)
- VIP:
Variable importance in projection (PLS metric)
- VPD:
Vapor pressure deficit
Acknowledgements
The authors would like to acknowledge the breeders from public and private coffee research organizations around the world who contributed the 32 coffee varieties used in this study. These entities include CCRI, CENICAFÉ, CRI, ECOM, EPAMIG, ICCRI, IHCAFE, INIFAP, PROCAFE, PROMECAFE, F. Serracin and L. Pacas. We thank Philippe Letourmy and Benoît Bertrand for their feedback regarding the original draft of the article.
Funding
This research was funded by member companies of World Coffee Research.
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World Coffee Research member companies had no role in the study design, data collection, analysis, or reporting of results.
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Gautron, R., Berny Mier y Teran, J.C., Delgado Muñoz, L.F. et al. An international multilocation trial reveals genotype-by-environment interactions in Arabica coffee across climate gradients.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-73765-4
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DOI: https://doi.org/10.1038/s41598-026-73765-4
Keywords
- Arabica coffee
- Genotype-by-environment
- Climate gradients
- Multi-location trial
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