Crop Pests Research Articles

A Framework for Enhancing Adoption of Mobile-based Surveillance for Crop Pest and Disease Management by Farmers in Kenya

Crop pests and diseases are ranked as some of the world’s leading threats to agricultural productivity. Though different digital solutions have been developed in the last decade to monitor and control biosecurity threats, their adoption in developing nations remain sluggish. The need to improve adoption of digital solutions prompted a review on the applicability of emerging digital solutions in large-scale surveillance of crop pest and diseases in developing nations. The study presents findings on key requirements for achieving digitized large-scale pest surveillance, fitness for purpose of common autonomous biosecurity surveillance technologies, and prospects of smartphones as alternative surveillance solution. Firstly, the research identified appropriateness of the solution, availability of supporting infrastructure and level of stakeholder involvement in solution formulation as some of the key determinants of digital solution adoption. Secondly, though most common autonomous biosecurity surveillance technologies are promising, their adoption in developing nations are limited by operational cost, legal requirements, required skillset, and operational environment among others. Thirdly, recent advancements in smartphones and wide spread ownership among farmers provide a unique opportunity for advancing Mobile Crowd-Sensing solutions in achieving large-scale pest surveillance. Lastly, we recommend designing an incentive mechanism to motivate farmers’ participation in MCS based surveillance solution.

A Review on the High-Efficiency Detection and Precision Positioning Technology Application of Agricultural Robots

The advancement of agricultural technology has increasingly positioned robotic detection and localization techniques at the forefront, ensuring critical support for agricultural development through their accuracy and reliability. This paper provides an in-depth analysis of various methods used in detection and localization, including UWB, deep learning, SLAM, and multi-sensor fusion. In the domain of detection, the application of deep algorithms in assessing crop maturity and pest analysis is discussed. For localization, the accuracy of different methods in target positioning is examined. Additionally, the integration of convolutional neural networks and multi-sensor fusion with deep algorithms in agriculture is reviewed. The current methodologies effectively mitigate environmental interference, significantly enhancing the accuracy and reliability of agricultural robots. This study offers directional insights into the development of robotic detection and localization in agriculture, clarifying the future trajectory of this field and promoting the advancement of related technologies.

HCFormer: A Lightweight Pest Detection Model Combining CNN and ViT

Pests are widely distributed in nature, characterized by their small size, which, along with environmental factors such as lighting conditions, makes their identification challenging. A lightweight pest detection network, HCFormer, combining convolutional neural networks (CNNs) and a vision transformer (ViT) is proposed in this study. Data preprocessing is conducted using a bottleneck-structured convolutional network and a Stem module to reduce computational latency. CNNs with various kernel sizes capture local information at different scales, while the ViT network’s attention mechanism and global feature extraction enhance pest feature representation. A down-sampling method reduces the input image size, decreasing computational load and preventing overfitting while enhancing model robustness. Improved attention mechanisms effectively capture feature relationships, balancing detection accuracy and speed. The experimental results show that HCFormer achieves 98.17% accuracy, 91.98% recall, and a mean average precision (mAP) of 90.57%. Compared with SENet, CrossViT, and YOLOv8, HCFormer improves the average accuracy by 7.85%, 2.01%, and 3.55%, respectively, outperforming the overall mainstream detection models. Ablation experiments indicate that the model’s parameter count is 26.5 M, demonstrating advantages in lightweight design and detection accuracy. HCFormer’s efficiency and flexibility in deployment, combined with its high detection accuracy and precise classification, make it a valuable tool for identifying and classifying crop pests in complex environments, providing essential guidance for future pest monitoring and control.

Effect of the Botanical Insecticides on <i>Amegilla</i> Friese, 1897 (Hymenoptera: Apidae) Foraging on the Cowpea Flowers in Dang (Adamaoua, North-Cameroon)

Synthetic pesticides present risks of pollution of the environment, humans and livestock and the alternative proposed today is to use botanical extracts in the fields against crop pests. But in North Cameroon, little information exists concerning the effect of these extracts on useful pollinating insects in general and no information exists in particular on foragers of the genus <i>Amegilla</i> Friese, 1897 (Apidae: Apinae: Anthophorini). The frequency and foraging activities of <i>Amegilla</i>, on newly blooming flowers of <i>Vigna unguiculata </i>(L.) Walp., 1843 (Fabales: Fabaceae) were recorded during five consecutive days in 2021 and 2022 planting campaigns. Plants were divided into untreated plots and plots treated using the synthetic insecticide Parastar (l p.c..ha<sup>-1</sup>) or 10%, 20% and 30% aqueous leaf extracts of <i>Calotropis procera</i> (Aiton) Aiton, 1811 (Gentianales: Apocynaceae), <i>Eucalyptus camaldulensis </i>Dehnh., 1832 (Myrtales: Myrtaceae) and <i>Tithonia diversifolia</i> (Hemsley) Gray, 1883 (Asterales: Asteraceae) respectively. Among 8,987 insects collected (48.9% in 2021), <i>Amegila calens</i> Le Peletier. 1841 with stockier foragers (2021 campaign: 2.2% of the total collection, entomophily F<sub><i>A. calens</i></sub>=4.5%; 2022 campaign: 0.7%, F<sub><i>A. calens</i></sub>=1.3%; pooled campaigns: 2.9%, F<sub><i>A. calens</i></sub>=2.9%) and <i>Amegilla</i> sp. with slender foragers (2021: 3.8%, F<sub><i>Amegilla </i>sp.</sub>=7.7%; 2022: no data) were recorded. Foragers started activity from 6 a.m. and stopped foraging before noon, with a peak of activity in 8 to 9 a.m. time slot for <i>A. calens </i>and 10 to 11 a.m. time slot for <i>Amegilla</i> sp.. During the five consecutive days from the first blooming day of the flowers, 598 visits (89.8% in 2021 and 10.2% in 2022) were recorded with a peak of visits during the 3<sup>rd</sup> day and then declined until it stopped during the 5<sup>th</sup> day. Treatments including the synthetic insecticide (which was the most repellent to the wild bees), did not significantly reduce the frequency of visits. But 20% aqueous extract of <i>Ca. procera</i> showed a significant increased of the mean duration of visits of the bees, compare to the results recorded in Parastar-treated plots. Therefore, the tested extracts, especially 20% aqueous leaves extract of <i>Ca. procera</i> may be recommended to control field insect pests and for preservation of foraging activities of <i>Amegilla </i>genus.

A Lightweight Crop Pest Detection Method Based on Improved RTMDet

To address the issues of low detection accuracy and large model parameters in crop pest detection in natural scenes, this study improves the deep learning object detection model and proposes a lightweight and accurate method RTMDet++ for crop pest detection. First, the real-time object detection network RTMDet is utilized to design the pest detection model. Then, the backbone and neck structures are pruned to reduce the number of parameters and computation. Subsequently, a shortcut connection module is added to the classification and regression branches, respectively, to enhance its feature learning capability, thereby improving its accuracy. Experimental results show that, compared to the original model RTMDet, the improved model RTMDet++ reduces the number of parameters by 15.5%, the computation by 25.0%, and improves the mean average precision by 0.3% on the crop pest dataset IP102. The improved model RTMDet++ achieves a mAP of 94.1%, a precision of 92.5%, and a recall of 92.7% with 4.117M parameters and 3.130G computations, outperforming other object detection methods. The proposed model RTMDet++ achieves higher performance with fewer parameters and computations, which can be applied to crop pest detection in practice and aids in pest control research.

Utilization of Information and Communication Technologies (ICTs) Among Women Farmers in North-Eastern Nigeria

This research focused on evaluating the utilization of ICTs by women farmers in North-Eastern Nigeria. A multi-stage sampling method, which included both purposive and simple random sampling techniques, was used to select 359 women farmers for the study. Primary data were gathered and analysed using descriptive and inferential statistics to meet the study's objectives. The majority of respondents (78.0%) were married, with a mean age of 41 years. Most of the respondents (52.5%) were involved in trading, and the average farm size was 3.4 hectares. Additionally, 88.3% had a high social participation score, and 51.7% and 58.9% were aware of old and new ICTs, respectively. Majority of the participants benefited from access to information on fertilizer sources and application (74.9%), credits, grants, and loans (70.8%), market rates and locations (63.1%), and crop pests, diseases, and control (62.1%) through the use of ICTs. Most of the respondents faced several constraints: high cost of gadgets (73.5%), insufficient time allotted to agricultural programmes (73.5%), lack of or inadequate ICT infrastructure (62.3%), expensive power/electricity prices (61.3%), defective equipment (57.8%), and multiple responsibilities of women farmers (54.3%). The regression analysis showed that age, marital status, religion, farm size, household size, and occupations did not significantly affect ICT utilization. However, educational attainment, participation in social organizations, and awareness were significant factors influencing the use of ICTs among women farmers. Based on these findings, it was recommended that the Federal Government of Nigeria should prioritize the establishment of essential infrastructure aimed at promoting and facilitating the utilization of ICTs among women farmers.

Decision fusion-based system to detect two invasive stink bugs in orchards

Accurate and early detection of insect pests plays an important role in crop protection and pest management in agriculture, especially in orchards. This paper is focused on evaluating and improving the performance of insect detection algorithms by adopting an ensemble approach of artificial neural networks. A set of advanced object detection models including YOLOv8, Faster R-CNN, RetinaNet, SSD, and FCOS were selected, and the models were trained and evaluated on a common dataset representing digital images of different insect species pests. Two classes were considered represented by quite similar invasive stink bugs, Halyomorpha Halys and Nezara Viridula. These architectures were optimized to identify significant peculiarities and variations between reference insects, including size, shape, and color. Each model has been implemented and optimized to achieve the best possible performance before integrating into an ensemble system. By integrating the predictions of these models through a weighted ensemble mechanism that leverages the F1 Score of each model, a more performant global system was developed capable of detecting insect pests with improved performance over individual models. This significant improvement in insect detection highlights the potential of the proposed ensemble system in efficient and rapid insect pest identification, ultimately providing valuable opportunities for implementing crop monitoring technologies. The research highlights the importance of implementing and developing deep-learning technologies for solving specific challenges in agriculture and brings innovative ways of strategic pest management for sustainable agricultural practices.

New insights on entomopathogenic bacteria isolated from soil of citrus crops to combat the polyphagus aphid pest Hyalopterus pruni (Geoffroy 1762) (Hemiptera, Aphididae)

BackgroundThe employment of entomopathogenic microorganisms is a promising approach for ensuring optimal phytosanitary protection in the framework of biological management of insect crop pests. Among these microbes, entomopathogenic soil-borne bacteria are preferred over pesticides because they help successfully in the natural regulation of arthropod populations, as soil has a favorable ecology for the availability and richness of many beneficial bacterial species. In this study, it was focused on the isolation, identification and characterization of entomopathogenic bacteria isolated from cultivated citrus soils and on the evaluation of their insecticidal potential in the laboratory against the mealy plum aphid, Hyalopterus pruni (Geoffroy 1762) (Hemiptera: Aphididae), a polyphagous and major pest attacking and virus-transmitting of several Prunus crops.ResultsIsolation results gave 11 bacterial isolates, which were more than 50% effective in selection tests on H. pruni aphids at 1 × 108 CFU/ml. A total of seven isolates generated significant insecticidal potential at different concentrations, and their molecular identification based on 16S rRNA genome sequencing yielded the following results: Lysinibacillus fusiformis (B4), Bacillus thuringiensis (B13), B. thuringiensis (B22), B. thuringiensis (B23), B. thuringiensis (B24), Pseudomonas sp. (P2) and Enterococcus gallinarum (P4). The most pathogenic potential of these strains was for E. gallinarum (P4), which induced 100% H. pruni mortality after 72 h of treatment at the concentration (C3 = 1 × 106 CFU/ml), followed by B. thuringiensis (B23), (B22), (B4), (P2), (B13) and (B24) isolates, which caused 96, 91, 85, 83, 65 and 50% mortality rates, respectively, at the lowest concentration (C4 = 1 × 105 CFU/ml). The LC50 and LT50 values were calculated for the entomopathogenic isolates of (P2), (P4), (B23) and (B24). A lowest LC50 value was 1.08 × 102 CFU/ml for (P4) E. gallinarum after 72 h of treatment, whereas (P2) Pseudomonas sp. presented the shortest LT50 of 33.6 h at concentration (C4 = 1 × 105 CFU/ml).ConclusionsThe present study's outcomes have confirmed the existence, abundance, and variety of entomopathogenic bacteria at the soil level in citrus groves. Interestingly, these bacteria could be useful for aphids’ population control on a wide scale through the utilization of their toxins and enzymes, even against insect pests of a broad order.

GLU-YOLOv8: An Improved Pest and Disease Target Detection Algorithm Based on YOLOv8

In the contemporary context, pest detection is progressively moving toward automation and intelligence. However, current pest detection algorithms still face challenges, such as lower accuracy and slower operation speed in detecting small objects. To address this issue, this study presents a crop pest target detection algorithm, GLU-YOLOv8, designed for complex scenes based on an enhanced version of You Only Look Once version 8 (YOLOv8). The algorithm introduces the SCYLLA-IOU (SIOU) loss function, which enhances the model generalization to various pest sizes and shapes by ensuring smoothness and reducing oscillations during training. Additionally, the algorithm incorporates the Convolutional Block Attention Module (CBAM) and Locality Sensitive Kernel (LSK) attention mechanisms to boost the pest target features. A novel Gated Linear Unit CONV (GLU-CONV) is also introduced to enhance the model’s perceptual and generalization capabilities while maintaining performance. Furthermore, GLU-YOLOv8 includes a small-object detection layer with a feature map size of 160 × 160 to extract more features of small-target pests, thereby improving detection accuracy and enabling more precise localization and identification of small-target pests. The study conducted a comparative analysis between the GLU-YOLOv8 model and other models, such as YOLOv8, Faster RCNN, and RetinaNet, to evaluate detection accuracy and precision. In the Scolytidae forestry pest dataset, GLU-YOLOv8 demonstrated an improvement of 8.2% in mAP@0.50 for small-target detection compared to the YOLOv8 model, with a resulting mAP@0.50 score of 97.4%. Specifically, on the IP102 dataset, GLU-YOLOv8 outperforms the YOLOv8 model with a 7.1% increase in mAP@0.50 and a 5% increase in mAP@0.50:0.95, reaching 58.7% for mAP@0.50. These findings highlight the significant enhancement in the accuracy and recognition rate of small-target detection achieved by GLU-YOLOv8, along with its efficient operational performance. This research provides valuable insights for optimizing small-target detection models for various pests and diseases.

Seasonal Dynamics of Major Insect Pests of Rice Crop and Impact of Various Abiotic Factors on Their Incidence

Seasonal Dynamics of Major Insect Pests of Rice Crop and Impact of Various Abiotic Factors on Their Incidence

Establishing the distribution of Carpophilus truncatus in Australia using an integrative approach for an emerging global pest

The nitidulid beetle Carpophilus truncatus is rapidly becoming a major pest of nut crops around the world. This insect first infested Australian almonds in 2013 and has since escalated to be the preeminent insect pest for the industry. Data pertaining to C. truncatus distribution are scant, but without awareness of its origin, distribution, and ecological factors that influence distribution, efforts to understand and manage the insect as a pest are stymied. Here, we employ an integrative approach to gain a multifaceted understanding of the distribution of C. truncatus in Australia. Methods employed were (1) reviewing historical records in insect collections to establish the presence of C. truncatus prior to commercial almond horticulture, (2) field trapping of insects to establish presence in regions of interest, (3) laboratory trials to determine the thermal limits of the organism, and (4) correlative species distribution modelling to describe its current distribution. We find that C. truncatus is more widespread across Australia than was previously known, with historical records preceding commercial almond production in Australia by a century. The methods developed in this study can be applied elsewhere in the world where C. truncatus is an emerging pest, or to novel pest species as they arise with increasing frequency in a globalised and warming world.

Identification and Functional Insights of Knickkopf Genes in the Larval Cuticle of Leptinotarsa decemlineata.

The Colorado potato beetle (Leptinotarsa decemlineata) is a major pest of potato crops. While Knickkopf (Knk) genes are essential for insect cuticle formation, their roles in pests like L. decemlineata remain unclear. This study aims to identify and characterize Knk genes in L. decemlineata and explore their functions in larval development and cuticle integrity. We used genomic and transcriptomic databases to identify LdKnk-family genes, validated through RT-PCR and RACE. Gene expression was analyzed at various developmental stages and tissues using qRT-PCR. RNA interference (RNAi) and Transmission electron microscopy (TEM) were applied to determine the functional roles of these genes. Four LdKnk-family genes were identified. Spatio-temporal expression analysis indicated significant gene expression during larval molting and pupal stages, especially in the epidermis. RNAi experiments showed that silencing LdKnk and LdKnk3-5' led to reduced larval weight, cuticle thinning, and increased mortality, while LdKnk3-FL knockdown caused abnormal cuticle thickening and molting disruptions. LdKnk2 knockdown increased epicuticle and endocuticle thickness without visible phenotypic changes. The study highlights the essential roles of LdKnk-family genes in maintaining cuticle structure and integrity, suggesting their potential as targets for RNAi-based pest control.

Development of an agroinfiltration-based transient hairpin RNA expression system in pak choi leaves (Brassica rapa ssp. chinensis) for RNA interference against Liriomyza sativae

The vegetable leafminer (Liriomyza sativae) is a devastating invasive pest of many vegetable crops and horticultural plants worldwide, causing serious economic loss. Conventional control strategy against this pest mainly relies on the synthetic chemical pesticides, but widespread use of insecticides easily causes insecticide resistance development and is harmful to beneficial organisms and environment. In this context, a more environmentally friendly pest management strategy based on RNA interference (RNAi) has emerged as a powerful tool to control of insect pests. Here we report a successful oral RNAi in L. sativae after feeding on pak choi (Brassica rapa ssp. chinensis) that transiently express hairpin RNAs targeting vital genes in this pest. First, potentially lethal genes are identified by searching an L. sativae transcriptome for orthologs of the widely used V-ATPase A and actin genes, then expression levels are assessed during different life stages and in different adult tissues. Interestingly, the highest expression levels for V-ATPase A are observed in the adult heads (males and females) and for actin in the abdomens of adult females. We also assessed expression patterns of the target hairpin RNAs in pak choi leaves and found that they reach peak levels 72 h post agroinfiltration. RNAi-mediated knockdown of each target was then assessed by letting adult L. sativae feed on agroinfiltrated pak choi leaves. Relative transcript levels of each target gene exhibit significant reductions over the feeding time, and adversely affect survival of adult L. sativae at 24 h post infestation in genetically unmodified pak choi plants. These results demonstrate that the agroinfiltration-mediated RNAi system has potential for advancing innovative environmentally safe pest management strategies for the control of leaf-mining species.

Exploring Virus Diversity in the Potato leafhopper (Empoasca fabae), an Economically Important Agricultural Pest.

The potato leafhopper (Empoasca fabae, PLH) is a serious pest that feeds on a wide range of agricultural crops and is found throughout the United States but is not known to be a vector for plant-infecting viruses. We probed the diversity of virus sequences in field populations of PLH collected from four Midwestern states: Illinois, Indiana, Iowa, and Minnesota. High-throughput sequencing data from total RNAs extracted from PLH were used to assemble sequences of fifteen positive-stranded RNA viruses, two negative-stranded RNA viruses, and one DNA virus. These sequences included ten previously described plant viruses and eight putative insect-infecting viruses. All but one of the insect-specific viruses were novel and included three solemoviruses, one iflavirus, one phenuivirus, one lispivirus, and one ambidensovirus. Detailed analyses of the novel genome sequences and their evolutionary relationships with related family members were conducted. Our study revealed a diverse group of plant viruses circulating in the PLH population and discovered novel insect viruses, expanding knowledge on the untapped virus diversity in economically important crop pests. Our findings also highlight the importance of monitoring the emergence and circulation of plant-infecting viruses in agriculturally important arthropod pests.

Ex vivo delivery of dsRNA targeting ryanodine receptors for control of Tuta absoluta.

RNA interference (RNAi) is an endogenous eukaryote viral defence mechanism representing a unique form of post-transcriptional gene silencing. Owing to its high specificity, this technology is being developed for use in dsRNA-based biopesticides for control of pest insects. Whilst many lepidopteran species are recalcitrant to RNAi, Tuta absoluta, a polyphagous insect responsible for extensive crop damage, is sensitive. Ryanodine receptors (RyRs) are intracellular calcium channels regulating calcium ion (Ca2+) release. The chemical pesticide class of diamides functions agonistically against lepidopteran RyR, resulting in uncontrolled Ca2+ release, feeding cessation and death. Resistance to diamides has emerged in T. absoluta, derived from RyR point mutations. RNAi was used to target RyR transcripts of T. absoluta. Data presented here demonstrate the systemic use of exogenous T. absoluta RyR-specific (TaRy) dsRNA in tomato plants (Solanum lycopersicum) to significantly downregulate expression of the target gene, resulting in significant insect mortality and reduced leaf damage. Using a leaflet delivery system, daily dosing of 3 μg TaRy dsRNA for 72 h resulted in 50% downregulation of the target gene and 50% reduction in tomato leaf damage. Corrected larval mortality and adult emergence were reduced by 38% and 33%, respectively. TaRy dsRNA demonstrated stability in tomato leaves ≤72 h after dosing. This work identifies TaRy as a promising target for RNAi control of this widespread crop pest. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Effect of hyaluronic acid-stabilized silver nanoparticles on lettuce (Lactuca sativa L.) seed germination

Nanotechnology has brought significant advancements to agriculture through the development of engineered nanomaterials (ENPs). Silver nanoparticles (AgNPs) capped with polysaccharides have been applied in agricultural diagnostics, crop pest management, and seed priming. Hyaluronic acid (HA), a natural polysaccharide with bactericidal properties, has been considered a growth regulator for plant tissues and an inducer of systemic resistance against plant diseases. Additionally, HA has been employed as a stabilizing agent for AgNPs. This study investigated the synthesis and effects of hyaluronic acid-stabilized silver nanoparticles (HA-AgNPs) as a seed priming agent on lettuce (Lactuca sativa L.) seed germination. HA-AgNPs were characterized using several techniques, exhibiting spherical morphology and good colloidal stability. Germination assays conducted with 0.1, 0.04, and 0.02 g/L of HA-AgNPs showed a concentration-dependent reduction in seed germination. Conversely, lower concentrations of HA-AgNPs significantly increased germination rates, survival, tolerance indices, and seed water absorption compared to silver ions (Ag+). SEM/EDS indicated more significant potential for HA-AgNPs internalization compared to Ag+. Therefore, these findings are innovative and open new avenues for understanding the impact of Ag+ and HA-AgNPs on seed germination.

Dataspace Integration for Agrobiodiversity Digital Twins with RO-Crate

A severe impact of human-induced climate change is the global decrease in crop yields, threatening the United Nations sustainable development goal (SDG) to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture (SDG 2: “Zero Hunger Goal”). Decision-making on appropriate mitigation strategies requires data integration from heterogeneous data-rich contexts including Earth observation (EO), biodiversity monitoring, gene banks as well as socio-economic data. Accordingly, large-scale initiatives for thematic data spaces, such as the European projects Destination Earth (DestinE) and the Green Deal Data Space, have been launched. Underutilized crops and crop wild relatives (CWR) can contribute to mitigating the impacts of climate change since their greater genetic diversity allows adaptation of agro-food sources to extreme weather events such as flooding and drought or emerging crop pests. Within the framework of Biodiversity Digital Twin (BioDT), a prototype digital twin for crop genetic resources modeling is under development. This prototype combines the scanning of genetic resources of CWRs and traditional cultivars for promising pest resistance and abiotic tolerance traits with advanced species distribution modeling and actual climate scenarios from EO. The aim is to generate habitat suitability maps and identify potential areas where relevant populations are potentially growing (Chala et al. 2024). The talk gives an overview of data models used to facilitate the required interoperability of both Earth models and environmental data within and across different data spaces such as the DestinE Data Lake (Duatis Juarez et al. 2023). Based on our findings from the use case of agrobiodiversity digital twins, we will discuss approaches for processing machine-actionable digital objects (Fig. 1) containing workflow information (i.e., in the Common Workflow Language format, Crusoe et al. 2022), and for sharing resulting research data in a FAIR (Findable, Accessible, Interoperable, and Reusable) manner by leveraging on so-called webby FAIR Digital Objects involving Research Object Crate (RO-Crate, Soiland-Reyes et al. 2022), FAIR Signposting (Soiland-Reyes et al. 2024) and schema.org and its Bioschemas extension respectively (Castro et al. 2020),

A pollinator crisis can decrease plant abundance despite pollinators being herbivores at the larval stage

Pollinating insects are decreasing worldwide due to various environmental stresses (so-called pollinator crisis), raising concerns that plant productivity could be undermined in natural and agricultural ecosystems. To date, however, few studies have reported a concurrent decline in both pollinators and plants, and little is known about when a “plant crisis” occurs. Here, we propose that anthropogenic environmental stresses on pollinating insects (e.g. climate change, habitat loss, and pesticide usage) can negatively affect herbivorous insects (e.g., pollinator larvae and crop pests) as well, and effects of pollinator declines may be masked by positive effects of herbivore declines. To test the idea, we theoretically investigated plant population dynamics mediated by two insect groups: one representing a pollinator that is mutualistic at the adult stage but antagonistic at the larval stage, and the other representing a non-structured pest herbivore. Our model revealed that environmental stresses (increasing insect mortality) can have counterintuitive effects on plants. Nonetheless, plant abundance generally decreases with decreasing pollinator abundance, especially when plant populations grow slowly without pollinators, when pollinators are effective mutualists, or when pollinators are susceptible to environmental stresses. These findings offer a theoretical basis for assessing the pollinator crisis for biodiversity conservation and agricultural management.

Design, synthesis, and biological activities of arecoline derivatives containing 1,3,4-oxadiazole structure

Pesticides play an important role in the development of agriculture, as they can prevent and control crop diseases and pests, improve crop yield and quality. However, the abuse and improper use of pesticides can lead to negative impacts such as environmental pollution and pest resistance issues. There is an urgent need to develop green, safe, and efficient pesticides. In this work, natural product arecoline was selected as parent structure, a series of arecoline derivatives were designed, synthesized, and systematically investigated antiviral activities against tobacco mosaic virus (TMV). These compounds were found to have good to excellent anti-TMV activities for the first time. The antiviral activities of 4a, 4 h, 4 l, 4p, 6a, 6c, and 6f are higher than that of ningnanmycin. Compounds 4 h (EC50 value 146 µg/mL) and 4p (EC50 value 161 µg/mL) with simple structures and excellent activities emerged as new antiviral candidates. We chose 4 h to further investigate the antiviral mechanism, which revealed that it can cause virus fragmentation by acting on the viral coat protein (CP). We further validated this result through molecular docking. These compounds also displayed broad-spectrum fungicidal activities against 8 plant pathogenic fungi. This work lays the theoretical foundation for the application of arecoline derivatives in the agricultural field.

Spatial Econometrics in Agriculture: Modelling Spatial Dependencies in Data

Spatial econometrics in agriculture focuses on modelling spatial dependencies in data, recognizing that agricultural outcomes are often influenced by geographic proximity and spatial interactions. This approach acknowledges that agricultural phenomena, such as crop yields, pest outbreaks, and soil quality, can exhibit spatial patterns that traditional econometric models may overlook. By incorporating spatial elements into econometric analysis, researchers can better understand how neighbouring regions or locations influence each other's agricultural outcomes. This is crucial for policymakers and farmers seeking to optimize resource allocation, manage environmental impacts, and enhance productivity in agriculture. Spatial econometrics provides a robust framework to uncover hidden relationships and spatial interactions within agricultural data, thereby supporting informed decision-making and sustainable agricultural practices in a spatially interconnected world.