School of Agriculture and Environment, Faculty of Science, University of Western Australia,35 Stirling Highway, Crawley, WA 6009, Australia; Institute of Agriculture, Faculty of Science, University of Western Australia,35 Stirling Highway, Crawley, WA 6009, Australia.
Crop Protection Branch, Department of Agriculture and Food Western Australia, Locked Bag No. 4, Bentley Delivery Centre, Perth, WA 6983, Australia.
Virus Res. 2017 Sep 15;241:163-171. doi: 10.1016/j.virusres.2017.05.018. Epub 2017 May 27.
An empirical model was developed to forecast Pea seed-borne mosaic virus (PSbMV) incidence at a critical phase of the annual growing season to predict yield loss in field pea crops sown under Mediterranean-type conditions. The model uses pre-growing season rainfall to calculate an index of aphid abundance in early-August which, in combination with PSbMV infection level in seed sown, is used to forecast virus crop incidence. Using predicted PSbMV crop incidence in early-August and day of sowing, PSbMV transmission from harvested seed was also predicted, albeit less accurately. The model was developed so it provides forecasts before sowing to allow sufficient time to implement control recommendations, such as having representative seed samples tested for PSbMV transmission rate to seedlings, obtaining seed with minimal PSbMV infection or of a PSbMV-resistant cultivar, and implementation of cultural management strategies. The model provides a disease forecast risk indication, taking into account predicted percentage yield loss to PSbMV infection and economic factors involved in field pea production. This disease risk forecast delivers location-specific recommendations regarding PSbMV management to end-users. These recommendations will be delivered directly to end-users via SMS alerts with links to web support that provide information on PSbMV management options. This modelling and decision support system approach would likely be suitable for use in other world regions where field pea is grown in similar Mediterranean-type environments.
建立了一个经验模型,以预测豌豆种传花叶病毒 (PSbMV) 在年度生长季节的关键阶段的发病率,从而预测在类似地中海条件下播种的田间豌豆作物的产量损失。该模型利用生长前季节的降雨来计算 8 月初蚜虫丰度的指数,结合播种种子中的 PSbMV 感染水平,预测病毒作物的发病率。利用 8 月初预测的 PSbMV 作物发病率和播种日期,还预测了从收获种子传播的 PSbMV,但准确性较低。该模型的开发目的是在播种前提供预测,以便有足够的时间实施控制建议,例如对代表种子样本进行 PSbMV 传播率测试以评估对幼苗的影响,获得 PSbMV 感染最小或具有 PSbMV 抗性的品种的种子,以及实施文化管理策略。该模型提供了疾病预测风险指示,考虑到 PSbMV 感染导致的预计产量损失百分比和田间豌豆生产中涉及的经济因素。这种疾病风险预测为最终用户提供有关 PSbMV 管理的具体位置的建议。这些建议将通过带有链接的 SMS 警报直接提供给最终用户,这些链接提供有关 PSbMV 管理选项的信息。这种建模和决策支持系统方法可能适合在其他世界地区使用,这些地区在类似地中海的环境中种植豌豆。