Salotti Irene, Bove Federica, Rossi Vittorio
Department of Sustainable Crop Production (DI.PRO.VES.), Università Cattolica del Sacro Cuore, Piacenza, Italy.
Horta Srl, Piacenza, Italy.
Front Plant Sci. 2022 May 27;13:897680. doi: 10.3389/fpls.2022.897680. eCollection 2022.
Stem rust (or black rust) of wheat, caused by f. sp. (), is a re-emerging, major threat to wheat production worldwide. Here, we retrieved, analyzed, and synthetized the available information about to develop a mechanistic, weather-driven model for predicting stem rust epidemics caused by uredospores. The ability of the model to predict the first infections in a season was evaluated using field data collected in three wheat-growing areas of Italy (Emilia-Romagna, Apulia, and Sardinia) from 2016 to 2021. The model showed good accuracy, with a posterior probability to correctly predict infections of 0.78 and a probability that there was no infection when not predicted of 0.96. The model's ability to predict disease progress during the growing season was also evaluated by using published data obtained from trials in Minnesota, United States, in 1968, 1978, and 1979, and in Pennsylvania, United States, in 1986. Comparison of observed versus predicted data generated a concordance correlation coefficient of 0.96 and an average distance between real data and the fitted line of 0.09. The model could therefore be considered accurate and reliable for predicting epidemics of wheat stem rust and could be tested for its ability to support risk-based control of the disease.
由禾柄锈菌小麦专化型（Puccinia graminis f. sp. tritici）引起的小麦秆锈病（又称黑锈病），再度成为对全球小麦生产的重大威胁。在此，我们检索、分析并综合了有关该病菌的现有信息，以开发一个基于天气驱动的机理模型，用于预测由夏孢子引起的秆锈病流行情况。利用2016年至2021年在意大利三个小麦种植区（艾米利亚 - 罗马涅、普利亚和撒丁岛）收集的田间数据，评估了该模型预测一季中首次感染的能力。该模型显示出良好的准确性，正确预测感染的后验概率为0.78，未预测时无感染的概率为0.96。还利用1968年、1978年和1979年在美国明尼苏达州以及1986年在美国宾夕法尼亚州的试验所获得的已发表数据，评估了该模型在生长季节预测病害进展的能力。观测数据与预测数据的比较得出一致性相关系数为0.96，实际数据与拟合线之间的平均距离为0.09。因此，该模型在预测小麦秆锈病流行方面可被认为是准确可靠的，并且可以对其支持基于风险的病害防控的能力进行测试。
