The European Commission requested the EFSA Panel on Plant Health to conduct a pest categorisation of (Ellis & Everh) Sutton, following commodity risk assessments of , , and plants from the UK, in which was identified as a pest of possible concern to the EU. When first described, was a clearly defined fungus of the family Schizoparmaceae, but due to lack of a curated type-derived DNA sequence, current identification based only on DNA sequence is uncertain and taxa previously reported to be this fungus based on molecular identification must be confirmed. The uncertainty on the reported identification of this species translates into uncertainty on all the sections of this categorisation. The fungus has been reported on several plant species associated with leaf spots, leaf blights and fruit rots, and as an endophyte in asymptomatic plants. The species is reported from North and South America, Africa, Asia, non-EU Europe and Oceania. is not known to occur in the EU. However, there is a key uncertainty on its presence and geographical distribution worldwide and in the EU due to its endophytic nature, the lack of systematic surveys and possible misidentifications. is not included in Commission Implementing Regulation (EU) 2019/2072 and there are no interceptions in the EU. Plants for planting, fresh fruits and soil and other growing media associated with infected plant debris are the main pathways for its entry into the EU. Host availability and climate suitability in parts of the EU are favourable for the establishment and spread of the fungus. Based on the scarce information available, the introduction and spread of in the EU is not expected to cause substantial impacts, with a key uncertainty. Phytosanitary measures are available to prevent its introduction and spread in the EU. Because of lack of documented impacts, does not satisfy all the criteria that are within the remit of EFSA to assess for this species to be regarded as potential Union quarantine pest.
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http://dx.doi.org/10.2903/j.efsa.2024.8890 | DOI Listing |
Plant Methods
December 2024
School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China.
Background: Pest infestation poses a major challenge in the field of global plant protection, seriously threatening crop safety. To enhance crop protection and optimize control strategies, this study is dedicated to the precise identification of various pests that harm crops, thereby ensuring the efficient use of agricultural pesticides and achieving optimal plant protection.
Results: Currently, pest identification technologies lack accuracy, especially in recognizing pests across different growth stages.
Mol Biol Rep
December 2024
Centre for DNA Taxonomy, Molecular Systematics Division, Zoological Survey of India, Kolkata, West Bengal, India.
Background: Frankliniella schultzei (Trybom) is a serious pest and a carrier of tospoviruses in major agricultural crops. This species is a historical and unresolved species complex that contains genetically different cryptic species across the globe.
Methods And Results: DNA barcodes were generated from freshly collected specimens of F.
Phytopathology
December 2024
University of California Davis Department of Plant Sciences, Davis, California, United States;
is known for causing soft rot in fruit and vegetables during postharvest. Although it has traditionally been considered a saprophyte, it appears to behave more like a necrotrophic pathogen. In this study, we propose that invades host tissues by actively killing host cells and overcoming the host defense mechanisms, as opposed to growing saprophytically on decaying plant matter.
View Article and Find Full Text PDFFront Plant Sci
December 2024
Department of Botany, University of Delhi, Delhi, India.
Introduction: Aphids are phloem sap-sucking insects and are a serious destructive pest of several crop plants. Aphids are categorized as "generalists" or "specialists" depending on their host range. (Sulz.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Biosystems Engineering and Soil Sciences, The University of Tennessee, Knoxville, TN 37996, USA.
Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and financial losses to farmers. Accurate detection and classification of tomato pests are the primary steps of integrated pest management practices, which are crucial for sustainable agriculture.
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