Visual Data Analysis, Center For Earth System Research and Sustainability, Regional Computing Center, University of Hamburg, Hamburg, Germany.
Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.
PLoS One. 2021 Feb 3;16(2):e0245697. doi: 10.1371/journal.pone.0245697. eCollection 2021.
Wheat rusts are the key biological constraint to wheat production in Ethiopia-one of Africa's largest wheat producing countries. The fungal diseases cause economic losses and threaten livelihoods of smallholder farmers. While it is known that wheat rust epidemics have occurred in Ethiopia, to date no systematic long-term analysis of past outbreaks has been available. We present results from one of the most comprehensive surveillance campaigns of wheat rusts in Africa. More than 13,000 fields have been surveyed during the last 13 years. Using a combination of spatial data-analysis and visualization, statistical tools, and empirical modelling, we identify trends in the distribution of wheat stem rust (Sr), stripe rust (Yr) and leaf rust (Lr). Results show very high infection levels (mean incidence for Yr: 44%; Sr: 34%; Lr: 18%). These recurrent rust outbreaks lead to substantial economic losses, which we estimate to be of the order of 10s of millions of US-D annually. On the widely adopted wheat variety, Digalu, there is a marked increase in disease prevalence following the incursion of new rust races into Ethiopia, which indicates a pronounced boom-and-bust cycle of major gene resistance. Using spatial analyses, we identify hotspots of disease risk for all three rusts, show a linear correlation between altitude and disease prevalence, and find a pronounced north-south trend in stem rust prevalence. Temporal analyses show a sigmoidal increase in disease levels during the wheat season and strong inter-annual variations. While a simple logistic curve performs satisfactorily in predicting stem rust in some years, it cannot account for the complex outbreak patterns in other years and fails to predict the occurrence of stripe and leaf rust. The empirical insights into wheat rust epidemiology in Ethiopia presented here provide a basis for improving future surveillance and to inform the development of mechanistic models to predict disease spread.
埃塞俄比亚是非洲最大的小麦生产国之一，其小麦生产的主要生物制约因素是小麦锈病。这些真菌病害导致经济损失，并威胁到小农的生计。尽管已知小麦锈病在埃塞俄比亚发生过，但迄今为止，还没有对过去爆发情况进行系统的长期分析。我们呈现了非洲最全面的小麦锈病监测活动之一的结果。在过去 13 年中，已经调查了超过 13000 个田地。我们使用空间数据分析和可视化、统计工具以及经验模型，确定了小麦秆锈病 (Sr)、条锈病 (Yr) 和叶锈病 (Lr) 分布的趋势。结果显示感染水平非常高（Yr 的平均发病率为 44%；Sr 为 34%；Lr 为 18%）。这些反复出现的锈病爆发导致了巨大的经济损失，我们估计每年损失达数千万美元。在广泛采用的小麦品种 Digalu 上，随着新的锈病传入埃塞俄比亚，病害的流行率显著增加，这表明主要基因抗性呈明显的兴衰循环。使用空间分析，我们确定了所有三种锈病的疾病风险热点，显示了海拔高度与疾病流行率之间的线性相关性，并发现了茎锈病流行率的明显南北趋势。时间分析显示，在小麦季节期间，疾病水平呈指数增长，且存在强烈的年际变化。虽然在某些年份，简单的逻辑曲线在预测茎锈病方面表现良好，但它无法解释其他年份复杂的爆发模式，也无法预测条锈病和叶锈病的发生。本文对埃塞俄比亚小麦锈病流行病学的实证见解为改进未来的监测提供了基础，并为开发预测疾病传播的机制模型提供了信息。
