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Toward anticipating pest responses to fruit farms: Revealing factors influencing the population dynamics of the Oriental Fruit Fly via automatic field monitoring

机译:预期对果园的有害生物反应:通过自动田间监测揭示影响东方果蝇种群动态的因素

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摘要

The Oriental Fruit Fly (OFF), Bactrocera dorsalis (Hendel), is one of most devastating insect pests that have periodically caused serious damage to fruit farms in Taiwan and many countries in the world. In the past, many studies reported that the population dynamics of OFF was partially correlated to the weather and the historical population development of OFF in the field. By making the best use of modern info-communication technologies (ICTs), long-term pest population data and microclimate variables measured with uniquely fine spatiotemporal resolution are now available to reveal the population dynamics of OFF. An analysis of data over three years using the Vector Auto-Regressive and Moving-Average model with exogenous variables (VARMAX) was proposed to unravel the regulatory mechanism between the population dynamics of OFF and microclimate factors. In addition, the proposed model provides a 7-day forecast for population dynamics of OFF. The accuracy of 7-day risk level forecasting yielded by the proposed model ranges from 0.87 to 0.97, and the average root-mean squared errors of forecasting the population dynamics fall in the interval between 0.31 and 4.95 per day per farm. The proposed forecasting model can allow authorities to gain a better understanding of the dynamics of OFF and anticipate pest-related problems, so they can make preemptive and effective pest management decisions before the real problems occur. (C) 2014 Elsevier B.V. All rights reserved.
机译:东方果蝇(Bactrocera dorsalis(Hendel))是最具破坏性的害虫之一,已定期对台湾和世界许多国家的果场造成严重破坏。过去,许多研究报告说,OFF的种群动态部分与天气和野外OFF的历史人口发展有关。通过充分利用现代信息通信技术(ICT),现在可以获得以独特的精细时空分辨率测得的长期虫害种群数据和微气候变量,以揭示OFF种群的动态。提出了使用带有外生变量的矢量自回归和移动平均模型(VARMAX)对三年来的数据进行分析,以阐明OFF种群动态与小气候因子之间的调节机制。此外,提出的模型还提供了7天的OFF人口动态预测。所提出的模型得出的7天风险水平预测的准确度在0.87至0.97之间,而预测种群动态的平均均方根误差落在每个农场每天0.31至4.95之间。所提出的预测模型可以使主管部门更好地了解关闭的动态,并预测与虫害有关的问题,因此,他们可以在真正的问题发生之前做出先发制人和有效的虫害管理决策。 (C)2014 Elsevier B.V.保留所有权利。

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