Spread of invasive migratory pest Spodoptera frugiperda and management practices throughout China

Zhou, Yan; Wu, Qiong; Zhang, Hao-wen; Wu, Kongming

doi:10.1016/s2095-3119(21)63621-3

Cited by 73 publications

(47 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2018, it was reported in India, Myanmar, Thailand, and other Southeast Asian countries [7,8]. In December 2018, the FAW moth was captured in the western part of Yunnan Province in China, and within three months, FAW larvae were confirmed during a maize field survey [9,10]. Subsequently, by October 2019, the FAW rapidly migrated to 1524 counties in 26 provinces (autonomous regions and municipalities) and infested more than 15 crops including maize, sugarcane and sorghum.…”

Section: Introductionmentioning

confidence: 99%

Effects of Different Wheat Tissues on the Population Parameters of the Fall Armyworm (Spodoptera frugiperda)

et al. 2021

View full text Add to dashboard Cite

The fall armyworm (FAW), Spodoptera frugiperda, is an invasive migratory pest that prefers to feed on crops of the Gramineae family such as maize and wheat. It has been recorded in different locations in China since its invasion in 2019. To assess its effect on different wheat tissues and to provide a risk evaluation for wheat fields, FAW larvae were reared on the wheat seedling (WS), spike (SPK), peduncle (PDC), flag leaf blade (F-b), and blade of the first leaf under flag (F-1b). The population parameters were recorded, and the data were analyzed using the age-stage, two-sex life table method. The results showed that the FAW achieved successful development on all the substrates, although those fed on F-1b grew the slowest, had the smallest pupal weight, and deposited the fewest eggs. The larval survival rates of those fed on WS, SPK, and PDC were more than 80%, while for F-b and F-1b they were 56.58% and 32.03%, respectively. Feeding on leaf blades also resulted in lower fertility, reproductive capacity, life expectancy, net reproductive rate, intrinsic rate of increase, and finite rate of increase. These results indicated that feeding on WS, SPK, and PDC were more beneficial for development compared to F-b and F-1b alone. However, leaf blades alone can still support the full FAW lifecycle and thus could play an important role in nutrition, especially when quantities of the preferred host tissues are not sufficient. These results provide guidance for assessing the FAW risk in China.

show abstract

Section: Introductionmentioning

confidence: 99%

Effects of Different Wheat Tissues on the Population Parameters of the Fall Armyworm (Spodoptera frugiperda)

et al. 2021

View full text Add to dashboard Cite

show abstract

“…This algorithm can be summarized as follows: (1) Spectral and polarized images are preprocessed (normalization and filtering) and then co-registered together. (2) The NDVI was calculated with a F680-nm red spectral image and F760-nm infrared spectral image (Equation (1)). (3) The DoLP (Equation ( 3)) and AOP (Equation ( 4)) were calculated with 0, 60 and 120 • polarized images.…”

Section: Fusion Algorithm For Nighttime Plant Detectionmentioning

confidence: 99%

“…Crop diseases and pests are important factors for analyzing crop yields. For example, Spodoptera frugiperda, as one kind of nocturnal animal, spreads extremely fast and seriously threatens human food security [2]. Thus, it is necessary to monitor them efficiently with state-of-the-art techniques.…”

Section: Introductionmentioning

confidence: 99%

Research on Polarized Multi-Spectral System and Fusion Algorithm for Remote Sensing of Vegetation Status at Night

Jiao

Wang

2021

Remote Sensing

View full text Add to dashboard Cite

The monitoring of vegetation via remote sensing has been widely applied in various fields, such as crop diseases and pests, forest coverage and vegetation growth status, but such monitoring activities were mainly carried out in the daytime, resulting in limitations in sensing the status of vegetation at night. In this article, with the aim of monitoring the health status of outdoor plants at night by remote sensing, a polarized multispectral low-illumination-level imaging system (PMSIS) was established, and a fusion algorithm was proposed to detect vegetation by sensing the spectrum and polarization characteristics of the diffuse and specular reflection of vegetation. The normalized vegetation index (NDVI), degree of linear polarization (DoLP) and angle of polarization (AOP) are all calculated in the fusion algorithm to better detect the health status of plants in the night environment. Based on NDVI, DoLP and AOP fusion images (NDAI), a new index of night plant state detection (NPSDI) was proposed. A correlation analysis was made for the chlorophyll content (SPAD), nitrogen content (NC), NDVI and NPSDI to understand their capabilities to detect plants under stress. The scatter plot of NPSDI shows a good distinction between vegetation with different health levels, which can be seen from the high specificity and sensitivity values. It can be seen that NPSDI has a good correlation with NDVI (coefficient of determination R2 = 0.968), PSAD (R2 = 0.882) and NC (R2 = 0.916), which highlights the potential of NPSDI in the identification of plant health status. The results clearly show that the proposed fusion algorithm can enhance the contrast effect and the generated fusion image will carry richer vegetation information, thereby monitoring the health status of plants at night more effectively. This algorithm has a great potential in using remote sensing platform to monitor the health of vegetation and crops.

show abstract

“…FAW is an alien invasive pest in China and quickly invaded the Yunnan Province, southern China in December 2018 ( Sun et al, 2021 ; Wu, Jiang & Wu, 2019 ). At the end of 2020, it had colonized corn fields of 1,338 counties of 27 provinces in China, and the damaged area caused by FAW has reached as high as 11,125–12,780 hectares ( Zhou et al, 2021 ). FAW adult has a strong capability of long-distance migration, and the average flight distance was more than 100 km per night under proper environmental conditions ( Tendeng et al, 2019 ), which aggravating its rapid spreading ( Wan et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…To sum up, the gluttony, strong migration ability and high fertility of FAW have attracted extensive attention. Therefore, it was classified as the top first destructive pest in China ( Zhou et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Full-length transcriptome analysis of Spodoptera frugiperda larval brain reveals detoxification genes

Yang

Xing

et al. 2021

PeerJ

View full text Add to dashboard Cite

Background Spodoptera frugiperda (J. E. Smith), commonly known as fall armyworm (FAW), is one of the most destructive agricultural pests in the world and has posed a great threat to crops. The improper use of insecticides has led to rapid development of resistance. However, the genetic data available for uncovering the insecticide resistance mechanisms are scarce. Methods In this study, we used PacBio single-molecule real-time (SMRT) sequencing aimed at revealing the full-length transcriptome profiling of the FAW larval brain to obtain detoxification genes. Results A total of 18,642 high-quality transcripts were obtained with an average length of 2,371 bp, and 11,230 of which were successfully annotated in six public databases. Among these, 5,692 alternative splicing events were identified.

show abstract

Spread of invasive migratory pest Spodoptera frugiperda and management practices throughout China

Cited by 73 publications

References 35 publications

Effects of Different Wheat Tissues on the Population Parameters of the Fall Armyworm (Spodoptera frugiperda)

Effects of Different Wheat Tissues on the Population Parameters of the Fall Armyworm (Spodoptera frugiperda)

Research on Polarized Multi-Spectral System and Fusion Algorithm for Remote Sensing of Vegetation Status at Night

Full-length transcriptome analysis of Spodoptera frugiperda larval brain reveals detoxification genes

Contact Info

Product

Resources

About