| "Candidatus Phytoplasma prunorum" Seemuller and Schneider 2004| Candidatus Phytoplasma prunorum| European stone fruit yellows mycoplasma-like organism| European stone fruit yellows phytoplasma| Phytoplasma prunorum
Economic impact: Phytoplasma diseases caused by Candidatus Phytoplasma prunorum can lead to significant economic losses in agricultural production, particularly in regions where affected crops are grown commercially. Infected fruit trees may exhibit symptoms such as stunted growth, yellowing or reddening of leaves, proliferation of shoots, reduced fruit quality and yield, and eventually, tree death. These symptoms can result in reduced crop yields and financial losses for growers.
Food security: Diseases caused by Candidatus Phytoplasma prunorum and other phytoplasmas can threaten food security by reducing the availability and quality of agricultural produce. In regions where fruit trees are important crops for local consumption or export, outbreaks of phytoplasma diseases can disrupt food supplies and trade, leading to food shortages and economic instability.
Environmental impact: Phytoplasma diseases can affect the health and diversity of plant communities, including native and cultivated species. The loss of fruit trees due to Candidatus Phytoplasma prunorum infections can disrupt ecosystems, reduce habitat quality for wildlife, and alter ecosystem services provided by plants, such as carbon sequestration and soil stabilization.
Pesticide use: In some cases, growers may resort to increased use of pesticides to control insect vectors and manage phytoplasma diseases in affected crops. However, excessive pesticide use can have negative impacts on human health and the environment, including pesticide exposure among agricultural workers, contamination of soil and water resources, and adverse effects on non-target organisms.
A lot more information is available when you are logged in and raise the display level
Other Sources for more information:
Statistics | NCBI | Data Punk | End Products Produced |
Different labs use different software to read the sample. See this post for more details.
One lab may say you have none, another may say you have a lot! - This may be solely due to the software they are using to estimate.
We deem lab specific values using values from the KM method for each specific lab to be the most reliable.
Lab | Frequency | UD-Low | UD-High | KM Low | KM High | Lab Low | Lab High | Mean | Median | Standard Deviation | Box Plot Low | Box Plot High | KM Percentile Low | KM Percentile High |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Other Labs | 0.04 | 0 | 6 | 6 | 6 | 6 | 6 | 0 %ile | 99 %ile | |||||
biomesight | 67.39 | 0 | 70 | 20 | 460 | 0 | 4451 | 586.9 | 60 | 1971.4 | 0 | 340 | 9.6 %ile | 90.6 %ile |
thryve | 0.09 | 0 | 530 | 530 | 530 | 530 | 530 | 0 %ile | 99 %ile |
Source of Ranges | Low Boundary | High Boundary | Low Boundary %age | High Boundary %age |
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Lab | Frequency Seen | Average | Standard Deviation | Sample Count | Lab Samples |
---|---|---|---|---|---|
BiomeSight | 71.743 % | 0.056 % | 0.19 % | 2054.0 | 2863 |
Medivere | 14.286 % | 0.001 % | % | 1.0 | 7 |
Thryve | 0.145 % | 0.269 % | 0.305 % | 2.0 | 1383 |
Click on Impact for information if high or low levels are causing the impact
Magnitude | Impact | Symptom |
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And display level must be raised above public.
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