Abstract In complex coffee-based agroforestry systems, quantifying the impact of shade trees on coffee disease regulation and coffee yield is crucial for improving these systems and designing more sustainable ones. To this end, we analyzed interactions amongst shade trees, coffee plants (cv. Catimor), the coffee foliar disease complex and soil characteristics. We studied systems characterized by 40 variables measured in 60 plots located on three farms (monitored for 2 years) in Nicaragua. These variables characterized six system components grouped in six statistical blocks: shade trees (shade percentage and species abundancy), soil characteristics (fertility), foliar diseases, coffee plant characteristics (age and size), coffee growth and yield. We used partial least square path modelling (PLS-PM), i.e. a structural equation modelling approach used to understand and quantify interactions between the six blocks. Shade trees (mostly the associated shade percentage) had direct positive effects on foliar disease severity and incidence and soil quality, while having negative effects on coffee growth and yield. Soil characteristics (carbon, nitrogen, litter index, water infiltration potential) were negatively correlated with foliar diseases. An excessive shade percentage then had an indirect negative effect on coffee growth and yield due to the increased prevalence of foliar diseases. Finding the optimal shade cover can help reduce foliar diseases and enhance coffee berry production. The ‘dose effect’ of shade cover must also be considered because excessive shade, as well as lack of shade, have negative impacts on coffee growth and yield. Overall, effective shade management requires an analysis of trade-offs between soil quality, disease regulation and yield gains. In conclusion, PLS-PM turned out to be a good tool for studying agroecosystem networks and enabled us to put forward some foliar disease management and coffee yield enhancement guidelines.

中文翻译：

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多层次农林复合系统中遮荫树、咖啡叶病和咖啡产量之间的相互作用分析

摘要在复杂的基于咖啡的农林业系统中，量化遮荫树对咖啡病害调节和咖啡产量的影响对于改进这些系统和设计更可持续的系统至关重要。为此，我们分析了遮荫树、咖啡植物（cv. Catimor）、咖啡叶病复合体和土壤特征之间的相互作用。我们研究了以在尼加拉瓜三个农场（监测 2 年）的 60 个地块中测量的 40 个变量为特征的系统。这些变量表征了分为六个统计块的六个系统组件：遮荫树（树荫百分比和物种丰富度）、土壤特征（生育力）、叶病、咖啡植物特征（年龄和大小）、咖啡生长和产量。我们使用了偏最小二乘路径建模（PLS-PM），即 一种用于理解和量化六个模块之间相互作用的结构方程建模方法。遮荫树（主要是相关的遮荫百分比）对叶病严重程度、发病率和土壤质量有直接的积极影响，同时对咖啡的生长和产量有负面影响。土壤特征（碳、氮、凋落物指数、水分渗透潜力）与叶面病害呈负相关。由于叶病的流行增加，过多的遮荫百分比会对咖啡的生长和产量产生间接的负面影响。找到最佳的遮荫罩有助于减少叶面病害并提高咖啡浆果的产量。还必须考虑遮荫的“剂量效应”，因为过度遮荫和缺乏遮荫都会对咖啡的生长和产量产生负面影响。全面的，有效的遮荫管理需要分析土壤质量、疾病调节和产量收益之间的权衡。总之，PLS-PM 是研究农业生态系统网络的好工具，使我们能够提出一些叶面病害管理和咖啡产量提高指南。