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Development and statistical evaluation of models in forecasting moth phenology of major lepidopterous peach pest complex for Integrated Pest Management programs.

机译:开发和统计评估用于综合害虫治理计划的主要鳞翅目桃有害生物群的蛾物候预测模型。

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摘要

A 3-parameter Boltzman and a 4-parameter Logistic non-linear regression model were constructed to simulate the emergences and seasonal dynamics of the major moth pest complex of peach including Anarsia lineatella (Lepidoptera: Gelechiidae), Grapholitha (Cydia) molesta (Lepidoptera: Tortricidae) and Adoxophyes orana (Lepidoptera: Tortricidae). Model development used 4 yr (2004-2007) climatic data and field observations in peach orchards located in two separate regions in northern Greece. Model performance was evaluated by using the Adjusted r2 and the Akaike and Bayes-Schwartz information criteria. Applied models had a very high prediction capability in most of the cases. The Akaike and Bayes-Schwartz information values of the 4-parameter Logistic model provided better estimates in most data sets. According to the Logistic equation, 50% of the cumulative number of male moths for the 1st, 2nd and 3rd generations of A. lineatella occurred at 151, 785 and 1513 degree-days respectively, for G. molesta at 654, 785 and 1251, and for A. orana at 406, 1260 and 2141 degree-days (Biofix: 1st March in all cases). In addition, a simplified theoretical degree-day model adjustment was attempted to generate simultaneously a multispecies moth phonological model for the above species, in order to outline perspectives in Integrated Pest management (IPM). The unique proposed model revealed differences in the phenology of the three species, and can simplify calculations and recommendations to pest control advisors, based on a common base temperature and biofix.
机译:构建了一个三参数Boltzman模型和一个四参数Logistic非线性回归模型,以模拟桃的主要蛾蛾害虫复合物的出现和季节动态,包括 Anarsia lineatella (鳞翅目:Gelechiidae),< i> Grapholitha ) molesta (鳞翅目:Tor科)和 Adoxophyes orana (鳞翅目:Tor科)。模型开发使用了希腊北部两个独立地区的桃园的4年(2004-2007年)气候数据和实地观察。使用调整后的 r 2 以及Akaike和Bayes-Schwartz信息标准对模型性能进行评估。在大多数情况下,应用模型都具有很高的预测能力。 4参数Logistic模型的Akaike和Bayes-Schwartz信息值在大多数数据集中提供了更好的估计。根据Logistic方程,第一代,第二代和第三代A的雄蛾累积数量的50%。 Lineiella 分别在151、785和1513度日发生。分别在654、785和1251进行摩尔破坏,并且对于 A。 orana 在406、1260和2141度-天(Biofix:在所有情况下均为3月1日)。此外,为了概述上述综合害虫管理(IPM)的观点,尝试了简化的理论度日模型调整,以同时生成上述物种的多物种蛾语音模型。提出的独特模型揭示了这三种物种的物候差异,并且可以基于共同的基准温度和生物固定剂简化对害虫防治顾问的计算和建议。

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