Brischetto Chiara, Bove Federica, Fedele Giorgia, Rossi Vittorio
Department of Sustainable Crop Production (DI.PRO.VE.S.), Università Cattolica del Sacro Cuore, Piacenza, Italy.
Horta Srl, Piacenza, Italy.
Front Plant Sci. 2021 Mar 9;12:636607. doi: 10.3389/fpls.2021.636607. eCollection 2021.
A mechanistic model was developed to predict secondary infections of and their severity as influenced by environmental conditions; the model incorporates the processes of sporangia production and survival on downy mildew (DM) lesions, dispersal and deposition, and infection. The model was evaluated against observed data (collected in a 3-year vineyard) for its accuracy to predict periods with no sporangia (i.e., for negative prognosis) or with peaks of sporangia, so that growers can identify periods with no/low risk or high risk. The model increased the probability to correctly predict no sporangia [P(P-O-) = 0.67] by two times compared to the prior probability, with fewer than 3% of the total sporangia found in the vineyard being sampled when not predicted by the model. The model also correctly predicted peaks of sporangia, with only 1 of 40 peaks unpredicted. When evaluated for the negative prognosis of infection periods, the model showed a posterior probability for infection not to occur when not predicted P(P-O-) = 0.87 with only 9 of 108 real infections not predicted; these unpredicted infections were mild, accounting for only 4.4% of the total DM lesions observed in the vineyard. In conclusion, the model was able to identify periods in which the DM risk was nil or very low. It may, therefore, help growers avoid fungicide sprays when not needed and lengthen the interval between two sprays, i.e., it will help growers move from calendar-based to risk-based fungicide schedules for the control of in vineyards.
开发了一个机理模型，以预测由环境条件影响的霜霉病二次感染及其严重程度；该模型纳入了孢子囊在霜霉病（DM）病斑上的产生和存活、传播和沉积以及感染过程。根据观测数据（在一个为期3年的葡萄园收集）对该模型进行评估，以检验其预测无孢子囊时期（即阴性预后）或孢子囊峰值时期的准确性，以便种植者能够识别无风险/低风险或高风险时期。与先验概率相比，该模型将正确预测无孢子囊的概率[P(P-O-)=0.67]提高了两倍，当模型未预测到时，葡萄园采样的孢子囊总数不到3%。该模型还正确预测了孢子囊峰值，40个峰值中只有1个未被预测到。在评估感染期的阴性预后时，该模型显示在未预测到时感染不发生的后验概率P(P-O-)=0.87，108次实际感染中只有9次未被预测到；这些未被预测到的感染较轻，仅占葡萄园观察到的DM病斑总数的4.4%。总之，该模型能够识别DM风险为零或非常低的时期。因此，它可以帮助种植者在不需要时避免喷洒杀菌剂，并延长两次喷洒之间的间隔时间，即它将帮助种植者从基于日历的杀菌剂喷洒计划转向基于风险的葡萄园霜霉病防治计划。
