Intensive monoculture coffee farms quickly expanded in Yunnan Province in the 1990’s and 2000’s. In 2012, local authorities in Pu’er and Xishuangbanna Prefectures, the main coffee producing centre in the province, initiated a large-scale conversion program of these farms towards coffee-agroforestry systems to promote “ecologically-friendly coffee”. Shade tree inventories and household interviews were conducted in these two prefectures to characterize coffee farms and the Local Ecological Knowledge (LEK) of farmers on the provision of ecosystem services by associated tree species. This study on newly emerging coffee farming systems revealed a high level of tree species diversity at both farm and landscape levels despite the previous dominance of intensive coffee monoculture and the large-scale distribution of a limited number of shade tree species by the government. 162 tree species were encountered during farm inventories, out of which the community of coffee farmers was able to rank 30 against 9 ecosystem services and disservices. This study reveals that this LEK is a type of hybrid knowledge that still relies mostly on traditional knowledge of tree species combined with experience acquired from newly-implemented coffee-agroforestry practices. This study also pointed out knowledge gaps regarding the impact of mature trees on coffee yield, coffee quality and pest control. The participatory approach resulted in the identification of non-promoted species with a high potential to provide locally relevant ecosystem services in coffee-agroforestry systems. These results lead to the upgrade of an online tool (www.shadetreeadvice.org) which allows extension services generating lists of recommended shade tree species tailored to the local ecological context and individual farmers’ needs. This tool will benefit farmers’ livelihood, support landscape health and contribute to the sustainability of the emerging Yunnan coffee agriculture sector.

Research Highlights: Global coffee production, especially in smallholder farming systems, is vulnerable and must adapt in the face of climate change. To this end, shaded agroforestry systems are a promising strategy. Background and Objectives: Understanding local contexts is a prerequisite for designing locally tailored systems; this can be achieved by utilizing farmers’ knowledge. Our objective is to explore ecosystem services (ESs) provided by different shade tree species as perceived by farmers and possible factors (elevation, gender, and membership in local farmers groups) influencing these perceptions. We related these factors, as well as farmers’ ESs preferences, to planting densities of tree species. Materials and Methods: During interviews with 263 small-scale coffee farmers on the southern slope of Mt. Kilimanjaro, they ranked the most common shade tree species according to perceived provision of the locally most important ESs for coffee farmers. We asked them to estimate the population of each tree species on their coffee fields and to identify the three ESs most important for their household. Results: Food, fodder, and fuelwood emerged as the most important ESs, with 37.8% of the respondents mentioning all three as priorities. Density of tree species perceived to provide these three ESs were significantly higher for farmers prioritizing these services compared to farmers that did not consider all three ESs in their top three. Albizia schimperiana scored the highest for all rankings of regulatory ESs such as coffee yield improvement, quality shade provision, and soil fertility improvement. Influence of elevation, gender, and farmer group affiliation was negligible for all rankings. Conclusions: This study shows the need to understand factors underlying farmers’ management decisions before recommending shade tree species. Our results led to the upgrade of the online tool (shadetreeadvice.org) which generates lists of potential common shade tree species tailored to local ecological context considering individual farmers’ needs.

Selection of shade tree species for agroforestry systems must take the complexity of these systems into account. Tree species selection should maximize the provision of ecosystem services while minimizing disservices. Selected species must be adapted to local agroecological conditions and cater to farmers' needs, while considering their preferences and constraints. The ShadeTreeAdvice methodology was developed to support said selection process using farmers' local ecological knowledge. It provides the steps to rapidly identify tree species and evaluate their impacts on a range of locally important ecosystem services. Results are uploaded to a decision support tool to tailor tree species recommendations to individual farmers' needs (www.shadetreeadvice.org). During the 5 year timeframe between 2016 and 2020, eight studies following this methodology were conducted in various coffee and cocoa growing regions across Africa, Asia and Central America. This article looks back at these studies to synthesize their findings and evaluate the methodology. We identified similarities in the use of tree species across different study areas, notably regarding leguminous and fruit tree species. We showed that the method was efficient to evaluate tree species' impacts on soil and climate regulation, crop production, and economic benefits. It was less efficient for evaluating impacts related to incidence of pests and diseases, often associated with knowledge gaps. The method also successfully allowed investigating the links between LEK and socio‐economic groups or environmental factors. Furthermore, we suggest a series of improvements in the methodology for future studies. These improvements include (i) broadening the scope of studies beyond tree species provision of ecosystem services to include tree species impact on farming practices; (ii) allowing the comparison of tree performances in agroforestry systems versus in full sun; (iii) providing a clear pathway for validation of the results; (iv) using tree species' functional traits to generalize the results. Read the free Plain Language Summary for this article on the Journal blog.

Selecting the right tree species is a crucial step in designing sustainable and effective agroforestry systems. To support this process, several decision support systems (DSS) have been developed in various countries to help farmers and advisors choose appropriate species. However, these tools are often limited in reach—typically used only within the country where they were created—and tend to focus on different aspects of adaptation to local conditions or achieving specific farming objectives. In this context, we introduce a new framework for agroforestry tree species selection. Its goals are twofold: (i) to compile and organize the knowledge embedded in existing tree selection tools, and (ii) to offer an intuitive, user-friendly graphical interface—AgroforesTreeAdvice. This tool allows users to input local parameters such as soil type, climate, biotic factors, and farm-level or socio-economic constraints, alongside production goals (e.g., timber, fruits) and ecosystem service objectives (e.g., soil conservation, carbon storage). The unified system successfully integrates eight existing agroforestry DSS, improving their (i) findability by centralizing them in one location, (ii) accessibility through a standardized and user-friendly interface, (iii) interoperability by enabling API-based queries across all tools, and (iv) reusability for future DSS development. Finally, we present early user feedback from pilot deployments in three countries, highlighting the tool’s benefits and outlining next steps toward further harmonization of agroforestry databases.