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Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica
Crop Protection ( IF 2.8 ) Pub Date : 2020-04-01 , DOI: 10.1016/j.cropro.2019.105046
Isabelle Merle , Philippe Tixier , Elías de Melo Virginio Filho , Christian Cilas , Jacques Avelino

Abstract Coffee leaf rust is a polycyclic disease that causes severe epidemics impacting yield over several years. For this reason, since the 1960s, more than 20 models have been developed to predict different indicators of the disease's development and help manage it. In existing models, standardized periods of influence of the meteorological predictors of the disease are determined a priori, based on strong assumptions. However, the appearance of a symptom or sign can be influenced by complex combinations of meteorological variables acting at different times and for different durations. In our study, we monitored a total of 5400 coffee leaves during a year and a half, in different agroforestry systems, in order to detect the onset dates of the disease symptoms, such as lesion emergence, and signs, such as sporulation and infectious area increase. In these agroforestry systems, we also recorded microclimate. We statistically identified the complex combinations of microclimatic variables responsible for changes in lesion status to construct three models predicting lesion emergence probability, lesion sporulation probability and growth of its infectious area. Our method allowed the identification of different microclimatic variables that fit well with the knowledge about the coffee leaf rust biology. Minimum air temperature from 20 to 18 days before a lesion emergence explained the status change from healthy to emergence of visible lesion, possibly because the short germination phase is stimulated by low temperatures. We also found a unimodal effect of rainfall over a period of 10 days, 33 days before lesion emergence, with a maximum at 10 mm. Below this threshold, uredospore dispersal is efficient, increasing the lesion appearance probability; above this threshold, wash-off effects on uredospores probably occurs, decreasing the probability of lesion emergence. In addition, we identified microclimatic variables whose influence on coffee leaf rust had not been described before. These variables are likely to be involved in the internal development phases of the disease in the coffee leaves: (1) unimodal effects of maximum air temperature in different periods on sporulation and infectious area growth (2) positive and unimodal effects of rainfall in different periods on sporulation and (3) a negative effect of leaf thermal amplitude in different periods on lesion emergence, sporulation and infectious area growth. Although these models do not provide predictors of the level of disease attack, such as incidence, they provide valuable information for warning systems and for mechanistic model development. These models could also be used to forecast risks of infection, sporulation and infectious area growth and help optimize treatment recommendations.

中文翻译:

基于哥斯达黎加以咖啡为基础的农林业系统中确定的小气候组合的咖啡叶锈病症状和体征预测模型

摘要 咖啡叶锈病是一种多循环病害,会在数年内引起严重的流行病影响产量。出于这个原因,自 1960 年代以来,已经开发了 20 多个模型来预测疾病发展的不同指标并帮助管理它。在现有模型中,基于强有力的假设,先验地确定了疾病气象预测因素影响的标准化时期。然而,在不同时间和不同持续时间起作用的气象变量的复杂组合可能会影响症状或迹象的出现。在我们的研究中,我们在一年半的时间里监测了不同农林业系统中的 5400 片咖啡叶,以检测疾病症状的发病日期,例如病斑出现和体征,如孢子形成和感染面积增加。在这些农林业系统中,我们还记录了小气候。我们从统计学上确定了导致病变状态变化的微气候变量的复杂组合,以构建三个模型来预测病变出现概率、病变孢子形成概率和其感染区域的增长。我们的方法允许识别与咖啡叶锈病生物学知识非常吻合的不同小气候变量。病斑出现前 20 至 18 天的最低气温解释了从健康到出现可见病斑的状态变化,可能是因为低温刺激了短的萌发期。我们还发现,在病变出现前 33 天,即 10 天的降雨量具有单峰效应,最大值为 10 毫米。低于这个阈值,葡萄孢子扩散是有效的,增加了病变出现的概率;高于此阈值,可能会发生对尿孢子孢子的冲刷效应,从而降低病灶出现的可能性。此外,我们还确定了对咖啡叶锈病影响的小气候变量,之前没有描述过。这些变量可能与咖啡叶病害的内部发展阶段有关:(1)不同时期最高气温对孢子形成和感染区生长的单峰效应(2)不同时期降雨的正和单峰效应(3) 不同时期叶片热幅度对病斑出现、孢子形成和感染区生长的负面影响。尽管这些模型不提供疾病发作水平的预测指标,例如发病率,但它们为预警系统和机械模型开发提供了有价值的信息。这些模型还可用于预测感染、孢子形成和感染区域增长的风险,并帮助优化治疗建议。
更新日期:2020-04-01
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