A Coffee Yield Next-Generation Forecast System for Rain-Fed Plantations: The Case of the Samalá Watershed in Guatemala.

The provision of climate services has the potential to generate adaptive capacity and help coffee farmers become or remain profitable by integrating climate information in a risk-management framework. Yet, to achieve this goal, it is necessary to identify the local demand for climate information, the relationships between coffee yield and climate variables, and farmers' perceptions and to examine the potential actions that can be realistically put in place by farmers at the local level. In this study, we assessed the climate information demands from coffee farmers and their perception on the climate impacts to coffee yield in the Samalá watershed in Guatemala. After co-identifying the related candidate climate predictors, we propose an objective, flexible forecast system for coffee yield that is based on precipitation. The system, known as NextGen, analyzes multiple historical climate drivers to identify candidate predictors and provides both deterministic and probabilistic forecasts for the target season. To illustrate the approach, a NextGen implementation is conducted in the Samalá watershed in southwestern Guatemala. The results suggest that accumulated June–August precipitation provides the highest predictive skill associated with coffee yield for this region. In addition to a formal cross-validated skill assessment, retrospective forecasts for the period 1989–2009 were compared with agriculturalists' perception on the climate impacts to coffee yield at the farm level. We conclude with examples of how demand-based climate service provision in this location can inform adaptation strategies like optimum shade, pest control, and fertilization schemes months in advance. These potential adaptation strategies were validated by local agricultural technicians at the study site. Significance Statement: In this study we wanted to provide climate services to coffee farmers in Guatemala who currently face challenges associated with climate variability and change. To do this, we first assessed what climate information they currently have at the farm level, how they use it in decision-making processes, and what improvements would benefit their risk-management framework (e.g., shade management) In addition, we evaluated farmers' perceptions related to the impact of climate to their coffee productivity. We verified the historical impact of several climate variables on coffee yield at the study site location and found that total rainfall for June–August is associated with coffee yield as originally referred by farmers. After identifying rainfall as one of the critical factors associated with coffee yield in this particular watershed of Guatemala, we moved to create a model that could help coffee farmers to forecast precipitation and the associated coffee yield months in advance. We then validated each of the management activities that could be put in place at the farm level with local technical and extension agricultural services in the region, including managing shade, adapting fertilization schemes, and managing weed removal at the farm level. [ABSTRACT FROM AUTHOR]