CONTEXT

Coffee leaf rust (CLR) epidemics on Coffea arabica have led to severe socio-economic crises in Latin America starting in 2008. Until now, the scattered nature of scientific and empirical knowledge of the highly complex CLR-coffee pathosystem has been an obstacle to the development of CLR forecasting models.

OBJECTIVE

To help prevent new severe epidemics, we built ExpeRoya, a qualitative model, based on a review of the scientific literature and expert opinion, to forecast the risk of a monthly increase in the incidence of CLR at plot and landscape levels.

METHODS

We adopted the IPSIM (Injury Profile SIMulator) framework, a qualitative and aggregative modeling approach that describes the effects of the cropping system and the plot environment on injuries, thereby making it possible to incorporate scattered knowledge on the system and all its complexity in a simplified way. Involving experts makes this approach powerful and robust because it builds on empirical knowledge based on a very large number of field observations. We argue that broad expert knowledge provides more accurate information on the manifold interactions in the system than existing quantitative models can. The structure of ExpeRoya was discussed with coffee sector experts in 19 workshops and validated in an online survey with 17 CLR experts.

RESULTS AND CONCLUSIONS

ExpeRoya successfully integrates in a simple way 229 multiple interactions that exist within the CLR-coffee pathosystem based on only 12 input variables easily acquired in the field: one incidence monitoring variable; two meteorological variables (temperature and rainfall), four crop management variables (management of shade cover, fungicide application, nutrition and pruning of coffee trees) and five coffee tree characteristics (dates of flowering, beginning and end of harvest, fruit load and cultivar genetic resistance). Coffee institutes in Honduras and Nicaragua now use ExpeRoya, hosted by the platform Pergamino (https://www.redpergamino.net/app-experoya), to assist them in preparing their monthly CLR warning bulletins for growers. ExpeRoya is an improved forecasting model of CLR by fully incorporating the main biophysical factors affecting CLR at the plot and landscape levels.

SIGNIFICANCE

ExpeRoya is both a framework and a proof of concept that improves both forecasting and the comprehensive modeling of CLR. ExpeRoya is a powerful yet user-friendly model designed for all actors of the coffee sector, particularly smallholder farmers and extension agents. ExpeRoya is adaptable: users can modify the model according to advances in knowledge and/or their own expertise of the system. ExpeRoya can help prevent future socio-economic crises.

中文翻译：

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通过定性建模改进咖啡叶锈病的预测：ExpeRoya 模型的设计和专家验证

语境

从 2008 年开始，阿拉比卡咖啡叶锈病 (CLR) 的流行已经导致拉丁美洲严重的社会经济危机。直到现在，高度复杂的 CLR-咖啡病理系统的科学和经验知识的分散性质一直是阻碍开发 CLR 预测模型。

客观的

为了帮助预防新的严重流行病，我们基于对科学文献和专家意见的回顾，建立了一个定性模型 ExpeRoya，以预测在地块和景观水平上 CLR 发病率每月增加的风险。

方法

我们采用了 IPSIM（Injury Profile SIMulator）框架，这是一种定性和聚合建模方法，描述了种植系统和地块环境对伤害的影响，从而可以将关于系统及其所有复杂性的分散知识整合到一个简化的大大地。让专家参与使得这种方法强大而稳健，因为它建立在基于大量实地观察的经验知识之上。我们认为，与现有的定量模型相比，广泛的专家知识可以提供有关系统中多种相互作用的更准确的信息。ExpeRoya 的结构在 19 场研讨会上与咖啡行业专家进行了讨论，并在与 17 位 CLR 专家的在线调查中得到验证。

结果和结论

ExpeRoya 以一种简单的方式成功地整合了存在于 CLR-咖啡病理系统中的 229 种多重相互作用，仅基于现场容易获得的 12 个输入变量：一个发病率监测变量；两个气象变量（温度和降雨量），四个作物管理变量（遮荫管理、杀菌剂应用、营养和咖啡树修剪）和五个咖啡树特征（开花日期、收获开始和结束日期、果实负荷和品种遗传反抗）。洪都拉斯和尼加拉瓜的咖啡机构现在使用由 Pergamino (https://www.redpergamino.net/app-experoya) 平台托管的 ExpeRoya 来帮助他们为种植者准备每月的 CLR 警告公告。

意义

ExpeRoya 既是一个框架又是一个概念证明，它可以改进 CLR 的预测和综合建模。ExpeRoya 是一个功能强大且用户友好的模型，专为咖啡行业的所有参与者，特别是小农和推广代理而设计。ExpeRoya 具有适应性：用户可以根据知识的进步和/或他们自己的系统专业知识来修改模型。ExpeRoya 可以帮助预防未来的社会经济危机。