Control measures of pine wilt disease

2014 ◽  
Vol 35 (2) ◽  
pp. 519-531 ◽  
Author(s):  
Muhammad Ozair ◽  
Xiangyun Shi ◽  
Takasar Hussain
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Control Measures ◽  
Pine Wilt

Dynamical aspects of pine wilt disease and control measures

2021 ◽  
Vol 145 ◽  
pp. 110764
Author(s):  
Takasar Hussain ◽  
Adnan Aslam ◽  
Muhammad Ozair ◽  
Fatima Tasneem ◽  
J.F. Gómez-Aguilar
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Control Measures ◽  
Pine Wilt ◽  
And Control

scholarly journals Dynamical features of pine wilt disease through stability, sensitivity and optimal control

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Riaz Ahmad Khan ◽  
Takasar Hussain ◽  
Muhammad Ozair ◽  
Fatima Tasneem ◽  
Muhammad Faizan
Keyword(s):  
Lyapunov Functional ◽  
Reproduction Number ◽  
Control Strategies ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Control Measures ◽  
Disease Eradication ◽  
Graph Theoretic ◽  
Pine Wilt ◽  
Dynamical Features

AbstractThis work investigates the dissemination mechanism of pine wilt disease. The basic reproduction number is computed explicitly, and an ultimate invariable level of contagious hosts and vectors, without and with disease, is discussed by using this number. Highly effective techniques, Lyapunov functional and graph theoretic, are utilised to obtain the ultimate constant level of the whole population. The idea of complete disease eradication and reduction of endemic level is explored through the utilisation of two efficient methods. Using sensitivity analysis approach, necessary control measures are suggested to overcome the disease. Using the literature data, the robustness of control strategies is shown graphically.

scholarly journals Effects of cavitation on the development of pine wilt disease caused by Bursaphelenchus xylophilus.

1988 ◽  
Vol 54 (5) ◽  
pp. 606-615 ◽  
Author(s):  
Keiko KURODA ◽  
Toshihiro YAMADA ◽  
Kazuhiko MINEO ◽  
Hirotada TAMURA
Keyword(s):  
Pine Wilt Disease ◽  
Bursaphelenchus Xylophilus ◽  
Wilt Disease ◽  
Pine Wilt

scholarly journals Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Run Yu ◽  
Lili Ren ◽  
Youqing Luo
Keyword(s):  
Early Stage ◽  
Vegetation Indices ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pinus Tabulaeformis ◽  
Pine Tree ◽  
Preventative Measures ◽  
Pine Wilt ◽  
Pine Trees ◽  
Airborne Vehicle

Abstract Background Pine wilt disease (PWD) is a major ecological concern in China that has caused severe damage to millions of Chinese pines (Pinus tabulaeformis). To control the spread of PWD, it is necessary to develop an effective approach to detect its presence in the early stage of infection. One potential solution is the use of Unmanned Airborne Vehicle (UAV) based hyperspectral images (HIs). UAV-based HIs have high spatial and spectral resolution and can gather data rapidly, potentially enabling the effective monitoring of large forests. Despite this, few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine. Method To fill this gap, we used a Random Forest (RF) algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data (data directly collected from trees in the field). We compared relative accuracy of each of these data collection methods. We built our RF model using vegetation indices (VIs), red edge parameters (REPs), moisture indices (MIs), and their combination. Results We report several key results. For ground data, the model that combined all parameters (OA: 80.17%, Kappa: 0.73) performed better than VIs (OA: 75.21%, Kappa: 0.66), REPs (OA: 79.34%, Kappa: 0.67), and MIs (OA: 74.38%, Kappa: 0.65) in predicting the PWD stage of individual pine tree infection. REPs had the highest accuracy (OA: 80.33%, Kappa: 0.58) in distinguishing trees at the early stage of PWD from healthy trees. UAV-based HI data yielded similar results: the model combined VIs, REPs and MIs (OA: 74.38%, Kappa: 0.66) exhibited the highest accuracy in estimating the PWD stage of sampled trees, and REPs performed best in distinguishing healthy trees from trees at early stage of PWD (OA: 71.67%, Kappa: 0.40). Conclusion Overall, our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage, although its accuracy must be improved before widespread use is practical. We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data. We believe that these results can be used to improve preventative measures in the control of PWD.

scholarly journals Spatiotemporal Pattern of Pine Wilt Disease in the Yangtze River Basin

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 731
Author(s):  
Zhuoqing Hao ◽  
Jixia Huang ◽  
Yantao Zhou ◽  
Guofei Fang
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Spatiotemporal Pattern ◽  
Pinewood Nematode ◽  
The Yangtze River ◽  
Pine Wilt ◽  
The Yangtze River Basin

The Yangtze River Basin is among the river basins with the strongest strategic support and developmental power in China. As an invasive species, the pinewood nematode (PWN) Bursaphelenchus xylophilus has introduced a serious obstacle to the high-quality development of the economic and ecological synchronization of the Yangtze River Basin. This study analyses the occurrence and spread of pine wilt disease (PWD) with the aim of effectively managing and controlling the spread of PWD in the Yangtze River Basin. In this study, statistical data of PWD-affected areas in the Yangtze River Basin are used to analyse the occurrence and spread of PWD in the study area using spatiotemporal visualization analysis and spatiotemporal scanning statistics technology. From 2000 to 2018, PWD in the study area showed an “increasing-decreasing-increasing” trend, and PWD increased explosively in 2018. The spatial spread of PWD showed a “jumping propagation-multi-point outbreak-point to surface spread” pattern, moving west along the river. Important clusters were concentrated in the Jiangsu-Zhejiang area from 2000 to 2015, forming a cluster including Jiangsu and Zhejiang. Then, from 2015–2018, important clusters were concentrated in Chongqing. According to the spatiotemporal scanning results, PWD showed high aggregation in the four regions of Zhejiang, Chongqing, Hubei, and Jiangxi from 2000 to 2018. In the future, management systems for the prevention and treatment of PWD, including ecological restoration programs, will require more attention.

Can insecticide applications used to kill vector insects prevent pine wilt disease?

2021 ◽  
Author(s):  
Jong‐Kook Jung ◽  
Ung Gyu Lee ◽  
Deokjea Cha ◽  
Dong Soo Kim ◽  
Chansik Jung
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wilt

scholarly journals The Microbiome of Endophytic, Wood Colonizing Bacteria from Pine Trees as Affected by Pine Wilt Disease

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Diogo Neves Proença ◽  
Romeu Francisco ◽  
Susanne Kublik ◽  
Anne Schöler ◽  
Gisle Vestergaard ◽  
...  
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wilt ◽  
Pine Trees

scholarly journals A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fengdi Li ◽  
Zhenyu Liu ◽  
Weixing Shen ◽  
Yan Wang ◽  
Yunlu Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Detection System ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Edge Computing ◽  
Pine Wilt

Suppression of pine wilt disease by an antibacterial agent, oxolinic acid

2010 ◽  
Vol 66 (6) ◽  
pp. 634-639 ◽  
Author(s):  
Hyeok Ran Kwon ◽  
Gyung Ja Choi ◽  
Yong Ho Choi ◽  
Kyoung Soo Jang ◽  
Nack-Do Sung ◽  
...  
Keyword(s):  
Antibacterial Agent ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Oxolinic Acid ◽  
Pine Wilt

scholarly journals Pseudomonas associated with Bursaphelenchus xylophilus, its insect vector and the host tree: A role in pine wilt disease?

2019 ◽  
Vol 49 (6) ◽  
pp. e12564
Author(s):  
Marta Salgueiro Alves ◽  
Anabela Pereira ◽  
Cláudia Vicente ◽  
Manuel Mota ◽  
Isabel Henriques
Keyword(s):  
Pine Wilt Disease ◽  
Bursaphelenchus Xylophilus ◽  
Wilt Disease ◽  
Host Tree ◽  
Insect Vector ◽  
Pine Wilt
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