Journal of Plant Diseases and Protection最新文献

The decline of European ash by dieback caused by Hymenoscyphus fraxineus together with stem collar necroses and rots caused by various fungi has been investigated intensively during the last years. Nevertheless, hitherto nearly nothing is known about the species diversity of the fungal rhizobiome of ash trees. Here we investigated the fine roots of affected ash trees on 15 sampling sites in 6 federal countries of Germany. Fine-root samples have been treated in three different sample regimes each as root-adhering soil, unsterilized fine roots and sterilized fine roots. The samples of trees in sampling sites were pooled to get an overview of the species-richness in the area. The next-generation sequencing platform Oxford Nanopore MinION was used to sequence the entire ITS of pooled probes. Most abundant phyla in all samples were the Basidiomycota and Ascomycota. Species richness in sterilized roots was significantly different from unsterilized roots and root-adhering soil. Surprisingly most abundant genera in sterilized roots were the genera Mycena, Mycenella and Delicatula, all of them agaricoids with saprophytic lifestyle. Eleven genera of Glomeromycota have been detected in various abundances, whereas the detection of H. fraxineus was neglectable.

The weevil, Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae) is the most important pest of stored maize worldwide. Experiments were carried out to evaluate the efficacy of Allophylus africanus P. Beauv (Sapindaceae) leaf extracts and Azadirachta indica A. Juss (Meliaceae) seed oil against S. zeamais under farmer storage conditions at the concentrations of 2.5, 5, 7.5, and 10 g/kg of maize. Each concentration is divided into two sets and wrapped in cotton then placed at two different places in 1 kg bag of maize. Maize bags were placed in 5 L containers and 40 adult insects were released inside and covered with a muslin cloth. Adult mortality, progeny inhibition and damage reduction were assessed. The obtained results showed that these tested extracts caused significant mortality to S. zeamais adults. Mortality % recorded 3.33%, 00.00%, 00.00% and 5.88% with neem oil, the methanol, acetone, and hexane extract of A. africanus at the concentration of 10 g/kg after one day post-infestation. Within 12 days of exposure, at the lowest concentration (2.5 g/kg), neem oil, methanol, acetone, and hexane extract caused 76.92%, 100%, 100% and 100% mortality, respectively. At the concentration of 10 g/kg, all the tested extracts completely inhibited F₁ progeny production, respectively. Moreover, these extracts reduced the grain damage and weight losses caused by weevils on maize. Considering the wide availability of A. africanus and neem trees, the application of extracts from both plants could be recommended as phytopesticides against maize weevils under storage conditions.

A molecular tool has been developed for the molecular identification of Ips sexdentatus (Börner 1776) (Coleoptera Curculionidae Scolytidae), the well-known six thooted bark beetle, widely distributed in Eurasia, where it infests several species of the genus Pinus and occasionally a few conifer species of the genera Abies, Larix and Picea. The developed test can be useful both in countries where I. sexdentatus is handled as a quarantine species and, to greater reason, in Europe to discriminate biological traces of this commonly found beetle from those produced by regulated pests. The protocol is based on real-time PCR with TaqMan probe technology and has been developed on whole insect bodies (adults) as well as on artificial frass contaminated by DNA of the beetle. The molecular test developed here for both direct and indirect identification of I. sexdentatus has proven effective in terms of analytical specificity, analytical sensitivity, reliability and reproducibility. The recommended protocol is a practical diagnostic tool allowing a rapid identification of the six toothed bark beetle in the presence of any biological trace of other xylophagous pests collected at points of entry during phytosanitary surveys.

Pistachio cultivations are damaged by many pests such as beetles including several species that had a wide expansion due to climate change. We investigated the biodiversity of pistachio beetles and their parasitoids based on a barcoding approach. The trapping and rearing of insects present in pistachio branches showed the presence of seven species. These insects were identified using two molecular markers, the mitochondrial cytochrome oxidase I (COI) and the region (D2–D3) of the 28S gene of the nuclear ribosomal RNA operon. The obtained sequences allowed the identification of five species with an identity ≥ 97%. However, for the other two species, sequence identity did not exceed 93% and consequently their identification was limited to the genus level. Our results showed that pistachio trees are attacked by four species of beetles, namely Chaetoptelius vestitus (Mulsant & Rey, 1861) (Coleoptera, Curculionidae), Carphoborus perrisi (Chapuis, 1869) (Coleoptera, Curculionidae), Phoracantha semipunctata (Fabricius, 1775) (Coleoptera, Cerambycidae) and Sinoxylon sp. (Coleoptera, Bostrichidae). These beetles are parasitized by three identified species: Doryctes leucogaster (Nees, 1834) (Hymenoptera, Braconidae), Cheiropachus quadrum (Fabricius, 1787) (Hymenoptera, Pteromalidae) and Ecphylus sp. (Hymenoptera, Braconidae). This study is the first to record the species C. perrisi and D. leucogaster in Tunisia.

Opportune detection of Anthonomus eugenii is a fundamental aspect of any management program to this pest. Yellow traps are the most common monitoring strategy, and its efficacy is related with the trap design and placement within crops. However, there’s limited information on this. Two outdoor experiments were performed to determine the efficiency of traps with six different shapes (circle, square, diamond, ellipse, rectangle, and triangle) and seven frame colors (yellow, dark green, black, red, white, purple, and blue); seven to the traps placement at four orientations (North, South, East and West), five aside distances from the bed center (0, 20, 40, 60 and 80 cm aside to the inter-bed space) and six heights (0, 20, 40, 60, 80 and100 cm) on A. eugenii adults. There wasn’t significant difference among shapes and orientations. Traps with yellow, dark green and black frames had the highest number of insects. For the aside distance, the plots were categorized according to their width as narrow (54–65 cm) and wide (71–81 cm), the highest insects caught was exhibited on traps at 0–20 and 0–40 cm, respectively. Regarding the traps height, they were classified according to their height as short (62–64 cm), medium (78–82 cm) and tall (90–92 cm), the highest insects caught was exhibited on traps at 20–40, 40–60, and 40–80 cm, respectively. Regression analyzes indicate that catches decrease as the traps are installed into the inter-bed space and above the canopy. This study provides novel information to improve outdoor programs to monitor A. eugenii.

Fungicide resistance to crop protection products is a critical sustainability issue in modern agriculture that requires constant monitoring of the field situation considering different environmental conditions and agricultural practices. Regulation strategies based on resistance risk ranking of both pathogen and active compound, underpinned by fast and broad field monitoring, shall result in recommendations for suitable practices with the aim to stabilize or even to restore the sensitivity situation. Alternating and mixing various products according to their modes of action belong to the most common and the most efficient mitigation methods. Though, these resistance management measures can be implemented best when precise knowledge of both the molecular target and the cell biology of the pathogen as well as of the resistance mechanism is acknowledged. To this end, we have investigated the molecular target and cellular effects of fluopicolide, one of the most effective Oomyceticides in the market. By combining data of genomic analysis of resistant field isolates of Plasmopara viticola and Phytophthora infestans with UV-mutagenized strains of P. infestans, we identified the enzyme vacuolar H⁺-ATPase as the target protein. Biochemical assays confirmed that fluopicolide inhibits specifically Oomycetes targets and has no residual activity on true fungi or insect orthologs.

Phytophthora infestans (Peronosporaceae, Oomycota) is the causal agent of late blight of potato (Solanum tuberosum) and a native to Central America. When introduced to Europe, it rapidly spread in 1845, triggering the Irish Potato Famine, which claimed millions of lives and led to an exodus of Europeans to North America. The spread of the species was recently traced using historical specimens from various herbaria. However, there are critical spatial and temporal gaps in the documentation of the early spread of the species. Within the framework of a digitalization and restoration project of the mid-nineteenth century fungus collections of the herbarium of the State Museum of Natural History Karlsruhe, several specimens of Phytophthora infestans from North-East Germany collected in 1853, 1855 and 1856 were discovered. In addition, we revised already deposited material and identified a specimen of Ph. infestans that was collected no later than 1852. These specimens are among the oldest from Central Europe and are now available to the scientific public. Further, we searched for thus far overlooked specimens, using online catalogues. We found specimens from 23 European countries, with the oldest material from western Europe and almost no data from eastern Europe, south-eastern Europe and southern Europe. Our results emphasize the need for archiving and digitizing natural history collections in order to document the historical spread of agricultural and forest pathogens and to better understand current-day epidemic spreads.

Over the past decades, European ash trees in Germany have been affected by ash dieback, reducing their vigour and mechanical resistance. Those trees that also have stem collar necroses and the resulting stem rot are particularly affected. In this study, multilocus genotypes (MLGs) of Hymenoscyphus fraxineus and their interactions with other fungi from stem collar necroses were analysed. Ten ash trees from three different adjacent forest stands in central Germany were sampled. A total number of 716 isolates were obtained from stem collar necroses from these ten trees. Microsatellite analysis was successfully performed on 274 isolates identified as H. fraxineus and 26 MLGs were revealed. The number of MLGs varied from one to seven per tree and did not correspond to the number or severity of necroses. A striking result was that five of the MLGs occurred in two trees. All other MLGs occurred independently in only one tree, as expected. Our data show that when multiple MLGs were observed in a tree, one of the MLGs outnumbered the others, indicating that H. fraxineus is a primary coloniser of stem collar necroses. A total of 61 morphotypes, including H. fraxineus, were identified and discussed, comprising endophytic, saprotrophic and pathogenic fungi. Between five and 19 different fungi were found per stem collar necrosis. The majority of all isolated morphotypes were Ascomycota (82%), with the most common orders being Xylariales and Hypocreales. The most frequently isolated morphotypes, apart from H. fraxineus, were Armillaria sp. and Diplodia fraxini. Together they account for more than three quarters of all assigned isolations. Apart from H. fraxineus, only Diplodia fraxini was isolated from all ten trees.

Plant disease diagnosis in smart agriculture is a crucial issue that carries substantial economic significance on a global scale. To address this challenge, intelligent and smart agricultural solutions are currently being developed to assist farmers in implementing preventive measures to increase crop production. As deep learning technology continues to evolve, many convolutional neural network (CNN) models have emerged as highly effective for detecting plant leaf diseases. These CNN-based models require heavy computation and processing cost. So, this paper develops a new lightweight deep convolutional neural network named lightweight DenseNet (LWDN) for detection of plant leaf disease for agricultural applications. Based on the DenseNet121 architecture, the presented model comprises pruned and concatenated architecture of DenseNet121. The presented study involved training and testing a proposed model (LWDN) on the PlantVillage dataset to acquire a knowledge of plant disease features. The model was trained using a combination of partial layer freezing, transfer learning, and feature fusion techniques. Out of several models experimented with, the proposed model has 99.37% classification accuracy, a model size of 13.8 MB, with 1.5 M parameters. The proposed model has 93% fewer parameters than InceptionV3 and Xception and 90% and 50% fewer parameters compared to VGG16 and MobileNetV2, respectively. Furthermore, the proposed method has superior diagnostic capabilities compared to several prior studies and larger state-of-the-art models utilizing plant leaf images. The compact size and competitive accuracy of the LWDN model render it appropriate for real-time plant diagnosis on portable and mobile devices with restricted computational resources.

Plant pathogens are highly adaptable, and have evolved to overcome control measures including multiple classes of fungicides. More effective management requires a thorough understanding of the evolutionary drivers leading to resistance. Experimental evolution can be used to investigate evolutionary processes over a compressed timescale. For fungicide resistance, applications include predicting resistance ahead of its emergence in the field, testing potential outcomes under multiple different fungicide usage scenarios or comparing resistance management strategies. This review considers different experimental approaches to in vitro selection, and their suitability for addressing different questions relating to fungicide resistance. When aiming to predict the evolution of new variants, mutational supply is especially important. When assessing the relative fitness of different variants under fungicide selection, growth conditions such as temperature may affect the results as well as fungicide choice and dose. Other considerations include population size, transfer interval, competition between genotypes and pathogen reproductive mode. However, resistance evolution in field populations has proven to be less repeatable for some fungicide classes than others. Therefore, even with optimal experimental design, in some cases the most accurate prediction from experimental evolution may be that the exact evolutionary trajectory of resistance will be unpredictable.