Prediction method of the small jerky beetle in wild apples in Xinjiang
A small jerky insect and prediction method technology, applied in the field of identification of wild apples' resistance to insect pests, can solve the problems of unpredictability of the small jerky bug, lack of quickness and convenience, etc., and achieve the effect of low requirements for detection equipment and easy operation
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specific Embodiment approach 1
[0012] Specific implementation mode one: the prediction method of Xinjiang wild apple sense apple small jerky worm in this implementation mode is carried out according to the following steps:
[0013] 1. Measure the diameter, bark thickness and moisture content of the middle section of Xinjiang wild apple branches;
[0014] Two, the proportion of branches with an average diameter of 30~70mm in this strain of Xinjiang wild apple in the middle section of the statistics branch is set as A%; the branch with an average bark thickness of 2~4mm in the middle section of the statistics branch accounts for the proportion in this strain of Xinjiang wild apple Proportion is set as B%; the proportion of branches with an average water content of ≥ 40% in the middle branch of the statistics branch is set as C% in this strain of Xinjiang wild apple;
[0015] Three, (A%+B%+C%) / 3=X%; X ≥ 90, this strain of Xinjiang wild apple is very susceptible to apple stinger; 90> X ≥ 80, this strain of Xinj...
specific Embodiment approach 2
[0016] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is: step 1 is carried out before the outbreak of the apple jerky pest, and the time is from May to June every year. Other steps and parameters are the same as those in Embodiment 1.
specific Embodiment approach 3
[0017] Specific implementation mode three: the difference between this implementation mode and specific implementation mode one is: step one: the number of branches randomly selected from each Xinjiang wild apple tree is ≥10. Other steps and parameters are the same as those in Embodiment 1.
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