Pest Dynamics Research Articles

Seasonal Incidence of Major Insect Pests of Pea in Malwa Region of Madhya Pradesh

The seasonal variation or dynamics of insect pests are major constraints in the production of field pea. This study revealed that the seasonal incidence of pests in the Malwa region of Madhya Pradesh, during 2021-22 in the rabi season is related to ecological factors such as temperature, rainfall, and relative humidity. Three major pests of field pea were recorded, viz., gram pod borer (Helicoverpa armigera Hubner), leaf miner (Phytomyza horticola Goureau) and pea aphid (Acyrthosiphon pisum Harris). The H. armigera infestation occurred during the vegetative to pod-forming or reproductive stage from 50th SMW of 2021 to 12th SMW of 2022 (Standard Meteorological Week), which shows a significant negative correlation with minimum temperature (-0.637*) and was significantly influenced by A. pisum and P. horticola during the seedling to maturity stage.

Agent-Based Spatial Dynamic Modeling of Diatraea saccharalis and the Natural Parasites Cotesia flavipes and Trichogramma galloi in Sugarcane Crops

In recent years, sugarcane production areas in Brazil have experienced a slower evolution in productivity, and one of the reasons for this is related to the increase in phytosanitary problems, such as the presence of pests. Nevertheless, limited attention has been paid to the development of tools for simulating the spatial dynamics of pests, including their impact on production. This study aims to simulate the potential population growth and dispersal of Diatraea saccharalis (sugarcane borer) in sugarcane crop fields and its estimated impacts on this crop production and to simulate biological control strategies. We developed an agent-based model to simulate the pest population and its dispersal in a one-hectare sugarcane crop field in Pederneiras, São Paulo, Brazil, delimited with the aid of satellite imagery, considering two scenarios: the first without biological control and the second with biological control using the parasites Trichogramma galloi and Cotesia flavipes. The model was developed using the NetLogo 6.3.0 software. The results indicate that the model accurately reproduced the infestation rates reported in the literature. Additionally, it provided insights into expected pest dispersal, potential production losses, and how the use of T. galloi in association with C. flavipes could mitigate production losses. We believe that the model can be used to simulate different biological control strategies and the implementation of integrated pest management (IPM) to achieve adequate control levels and greater productivity in sugarcane production.

Ecological Impact of American Chestnut Hybrid Restoration on Invertebrate Communities Above- and Belowground

The cross-hybridization of American chestnut (Castanea dentata (Marsh.) Borkh.) with Chinese chestnut (Castanea mollissima Bl.) is a promising strategy for restoring a blight-resistant strain of this keystone species to the Appalachian mountains. To assess the ecological impacts of hybridization on invertebrate communities, we conducted a study across chestnut plots with varying degrees of hybridization (75%, 94%, or 100% American chestnut). Our findings indicate American chestnut hybridization impacted invertebrate communities above- and belowground. Aboveground insect community composition, insect herbivory, gall infestation, and belowground invertebrate diversity were all altered. While some of these differences could be explained by different growth habits or environmental differences, stark differences in Asian chestnut gall wasp infestation (Dryocosmus kuriphilus Yasumatsu.) suggest a genetic component. These results suggest that chestnut hybridization, and particularly expanded restoration efforts using chestnut hybrids, could impact invertebrate communities above- and belowground in addition to pest dynamics. Understanding these effects is crucial for successful chestnut restoration and ecosystem management.

Population dynamics of major sucking pests and their natural enemies of pomegranate (Punica granatum L.) under north Gujarat conditions

Population dynamics of major sucking pests and their natural enemies of pomegranate (Punica granatum L.) under north Gujarat conditions

Population Dynamics of Major Sucking Pests of Mung bean in Western Rajasthan and Their Management Using Newer Insecticides

Population Dynamics of Major Sucking Pests of Mung bean in Western Rajasthan and Their Management Using Newer Insecticides

An Autoregulatory Model of Forest Insect Population Dynamics and Forest Stand Damage Dynamics in Different Habitats: An Example of Lymantria dispar L.

This paper addresses the problem of constructing a mathematical model of population density dynamics and the dynamics of forest areas damaged by spongy moth (Lymantria dispar L.) outbreaks in the United States, Europe, Russia, and Japan. The key variable of the model is either the pest population density or the area of forests damaged by spongy moths during a season. This variable can be considered proportional to the total current pest abundance in the study area. For the purposes of modeling, data from a number of different authors was used (see bibliography), as well as data from surveys conducted at the egg or caterpillar stage. The complexity of modeling the dynamics of L. dispar abundance is largely due to the fact that, when studying the dynamics of spongy moth population density, the values of external factors such as parasites, predators, and the amount of available food are often unknown. A simple model was proposed using only two types of data: population density and monthly weather characteristics. Our analysis demonstrated that, even in the absence of knowledge regarding the characteristics of ecosystem components interacting with the spongy moth population (parasites, predators, and the state of forage trees), it is possible to introduce models that characterize the regulatory processes in the population in terms of (i) the presence of negative and positive feedbacks in the system and (ii) the influence of external weather factors. The system under investigation was described as an autoregressive system, whereby the current state of the population is dependent on its state in previous years. The order of autoregression in the system was estimated using the order of the maximum significant partial autocorrelation function. It was found that the regulation of spongy moth population density was characterized by the presence of two feedback loops: positive feedback between the current population density and the population density in the previous season and negative feedback between the current population density and the population density two years ago. To evaluate the model, its stability margin was calculated and found to be directly proportional to the positive feedback coefficient and inversely proportional to the negative feedback coefficient. The model was demonstrated to explain up to 90% of the observed variance of real data. Although the model coefficients for different local populations (North America, Europe, and Asia) differ, the general form of the equation describing both direct data on population densities and indirect data on pest dynamics characterized by areas of stand damage is consistent. Consequently, the form of the ADL model is general, irrespective of the location of the local population.

Assessing the Impact of Climate Change on Global Crop Yields and Farming Practices

Climate change is recognized as one of the most pressing challenges of our time, with profound implications for global food security. This review article delves into the multifaceted impacts of climate change on crop yields and farming practices on a global scale. By synthesizing recent research findings, identifying key challenges and opportunities, and exploring adaptation strategies, this review provides a comprehensive analysis of the complex interplay between climate change and agriculture. The effects of climate change on agriculture are diverse and far reaching. Alterations in weather patterns, including shifts in temperature, precipitation, and frequency of extreme events, pose significant challenges to agricultural productivity and food security. These changes disrupt traditional farming practices, exacerbate water stress, soil erosion, and crop loss, and threaten the livelihoods of millions of farmers worldwide. Additionally, changes in pest and disease dynamics have further compounded the challenges faced by agricultural systems under pressure from climate variability. These challenges, there are opportunities for innovation and adaptation in the agricultural systems. This review highlights the potential of climate-smart agricultural practices such as crop diversification, conservation agriculture, and precision farming to enhance resilience and mitigate the impacts of climate change on crop yields. investments in climate-resilient crop varieties, improved water management techniques, and sustainable land management practices offer pathways for building adaptive capacities and ensuring food security in a changing climate. addressing the complex challenges of climate change adaptation in agriculture requires concerted action at multiple levels. This review emphasizes the importance of coordinated efforts among policymakers, researchers, farmers, and other stakeholders in developing and implementing effective adaptation strategies. Such strategies should be context-specific, socially equitable, and environmentally sustainable, considering the diverse needs and priorities of different regions and communities. This review underscores the urgent need for coordinated action to build resilient and sustainable food systems in the face of a changing climate. By synthesizing the latest research findings, identifying key challenges and opportunities, and exploring adaptation strategies, this review aims to inform policymakers, researchers, and practitioners about the imperative of addressing climate change’s impacts on agriculture. Only through collaborative and transformative action can we ensure food security for the present and future generations in a rapidly changing world.

The Potential Distribution Prediction of the Forestry Pest Cyrtotrachelus buqueti (Guer) Based on the MaxEnt Model across China

Exploring the geographical distribution of forestry pests is crucial for formulating pest management strategies. Cyrtotrachelus buqueti (Guer) stands out as one of the primary pests among China’s forestry hazards. This study employs the MaxEnt model, along with 19 bioclimatic variables and habitat characteristics, to predict the current and future distribution of C. buqueti under three typical emission scenarios for 2050 and 2070 (2.6 W/m2 (SSP1-2.6), 7.0 W/m2 (SSP3-7.0), and 8.5 W/m2 (SSP5-8.5)). Among the 19 bioclimatic variables, BIO 14 (precipitation of the driest month), BIO 8 (mean temperature of the wettest quarter), Elev, slope, and aspect were identified as significant contributors. These five variables are critical environmental factors affecting the suitability of habitats for C. buqueti and are representative of its potential habitat. The results indicate that C. buqueti predominantly inhabits southern regions such as Chongqing, Guizhou, Yunnan, Sichuan, Guangxi, Shaanxi, Hubei, Hainan, and Taiwan. Among them, Chongqing, Guizhou, and Yunnan are the primary distribution areas of high suitability. In the future, the centroid’s movement direction will generally shift southward, with an expansion trend observed in the distribution areas of each province. This study enhances researchers’ understanding of forestry pest dynamics and promotes proactive management strategies to mitigate their impact on forest ecosystems and agricultural productivity.

Seasonal incidence of major sucking pests of Bt cotton and their natural enemies with relation to weather parameters

Study on population dynamics of major sucking pests infesting Bt cotton showed that aphid, jassid, whitefly and thrips initiated from 28th SMW (2nd week of July), 28th SMW (2nd week of July), 29th SMW (3rd week of July) and 27th SMW (1st week of July), respectively. Peak infestation of aphid (21.20 aphid per 3 leaves) observed during 1st week of December (49th SMW). Jassid population observed highest in 40th SMW (14th WAS) with 9.88 jassid per 3 leaves, while the maximum whitefly population was observed during 43rd with 11.84 whitefly per 3 leaves. Population of thrips reached its peak during 34th SMW (8th WAS) with 15.32 thrips per 3 leaves. The peak population of lady bird beetles (4.52 grubs and adults per plant) was recorded during 43rd SMW (17th WAS) and the peak population of spider was recorded during 34th SMW (8th WAS) i.e. 1.36 spider per plant. Among the different abiotic environmental factors, jassid and whitefly had significant positive correlation with maximum temperature. Significant positive correlation was showed by thrips with minimum temperature. Morning relative humidity and evening relative humidity showed non-significant negative correlation with all sucking pest except thrips. Rainfall had non-significant positive correlation with jassid, whitefly and thrips. Bright sunshine had non-significant positive correlation with all sucking pests except thrips.

Enhancing Crop Resilience to Climate Change through Biochar: A Review

Crop resilience is crucial in the face of climate change, as agricultural regions face unprecedented challenges such as rising global temperatures, altered precipitation patterns, and increased extreme weather events. These changes impact food security, crop yields, and the livelihoods of millions of farmers worldwide. Crops face threats from heat stress, changing pest and disease dynamics, water scarcity, and unpredictable growing seasons. Crop resilience involves a complex interplay of genetics, environmental factors, and agricultural practices. Researchers and agricultural scientists are exploring innovative approaches like selective breeding, genetic modification, and precision agriculture to enhance crop resilience. Integrating traditional knowledge and indigenous farming practices into modern agricultural strategies is essential for securing food production, ensuring the sustainability of agricultural systems, conserving biodiversity, and supporting community resilience in an uncertain climate future. Crop resilience is central to ensuring global food security, supporting rural livelihoods, preserving ecosystems, and advancing sustainable agriculture in the face of climate change challenges. Biochar, a climate-resilient agricultural amendment, is gaining attention for its role in enhancing agricultural sustainability and mitigating climate change. Its porous structure and high carbon content sequester carbon dioxide, improve soil health, and reduce nutrient leaching. Biochar's porous nature fosters a rich microbial community, aids in nutrient cycling, and aids in rehabilitating degraded soils. It also reduces synthetic fertilizer requirements and environmental pollution. Climate change significantly impacts crop agriculture, disrupting traditional growth patterns and threatening global food security. High temperatures cause heat stress, while droughts and floods cause soil desiccation, impairing crop yields. Increased plant diseases and pests spread, while higher CO2 levels stimulate photosynthesis but reduce essential nutrients. Rising temperatures disrupt vegetative and reproductive growth phases, pollination, and seed formation, compromising crop quality and market value. Biochar is a porous material formed through pyrolysis, a process where organic biomass is decomposed under limited oxygen conditions. It is primarily carbon-rich but contains hydrogen, oxygen, nitrogen, and minerals. As a soil amendment, biochar is a stable carbon sink with a high carbon content of 70-90%. Its porous structure allows it to efficiently adsorb and retain water, nutrients, and organic compounds. Its large surface area facilitates interactions with soil microbes and nutrient ions, and its high CEC helps in nutrient retention and soil fertility.

Existence and Turing instability of positive solutions for a predator–pest model with additional food

To better explore the dynamics of pests, this paper deals with a brand-new predator–pest model with diffusion and additional food. The existence and diffusion-driven Turing instability of positive constant solutions are discussed. We obtain that for additional food of a certain quality and quantity, there exists a critical value such that the model can produce four forms of positive constant solutions as the predation rate of predators is greater than the critical value, and only one form of positive constant solution as the predation rate is less than the critical value. For predator–pest model, which is a new finding indeed. Meanwhile, we conclude that the introduction of diffusion can lead to Turing instability of positive constant solutions. This indicates that the model is likely to produce spatial pattern with certain conditions.

Biomonitoring of biocontrol across the full annual cycle in temperate climates: Post‐harvest, winter and early‐season interaction data and methodological considerations for its collection

Abstract Conservation biocontrol, the regulation of crop pests by naturally occurring biocontrol agents (e.g., predators and parasitoids), is predominantly monitored throughout periods of primary crop growth when pests exert the most observable impact on yields. Pest‐focused agricultural biomonitoring often overlooks post‐harvest, winter and even early‐season biocontrol, despite the significant predator–pest interactions during these periods that profoundly affect pest abundance and, consequently, crop yields. Rapid advances in biomonitoring, particularly in the detection of predator–pest interactions that underpin biocontrol, provide an opportunity to reconsider how and when we monitor these interactions. Advances in agricultural biomonitoring must transcend methodological innovation and encompass conceptual changes in the monitoring of agricultural systems. Here, we assess existing evidence supporting the importance of periods beyond primary crop growth for biocontrol and how predator–pest interactions are likely to evolve during these periods, subsequently influencing pest population dynamics during the primary crop growth period. We advocate for a greater concerted effort to establish continuous monitoring of biocontrol interactions, particularly beyond primary crop growth periods in temperate climates. To facilitate this, we also summarise the methodological approaches that can make it possible and explore how extending sampling across the full annual cycle might impact the practicalities and outcomes of these approaches. Year‐round monitoring of biocontrol interactions, both in crops and adjacent semi‐natural habitats, will provide a previously intractable understanding of predator–pest dynamics, offering significant potential to enhance our ability to optimise and manipulate these systems. This would manifest in reduced crop yield losses, pest infestation rates and disease transmission, with concomitant long‐term financial, environmental and land‐use benefits.

Influence of weather factors on population dynamics of major insect pests in moringa (Moringa oleifera Lam.) in south Tamil Nadu

Influence of weather factors on population dynamics of major insect pests in moringa (Moringa oleifera Lam.) in south Tamil Nadu

Exploring pest, disease, and weed dynamics in cotton cultivation & assessing the field efficacy of biopesticides and the impact of food spray on beneficial insects in Dhule and Nandurbar districts, Maharashtra

Exploring pest, disease, and weed dynamics in cotton cultivation & assessing the field efficacy of biopesticides and the impact of food spray on beneficial insects in Dhule and Nandurbar districts, Maharashtra

Non-Consumptive Effects of Harmonia axyridis on the Reproduction and Metabolism of Spodoptera frugiperda.

An increasing body of research has underscored the significant impact of non-consumptive effects on the dynamics of prey pests, encompassing growth, development, reproduction, and metabolism across various vertebrate and invertebrate taxa, rivaling the influence of consumption effects. In our investigation, we delved into the non-consumptive effects exerted by the natural predatory enemy Harmonia axyridis on the reproductive capacity and metabolism of Spodoptera frugiperda adults. Our findings revealed a substantial decrease in the reproductive ability of S. frugiperda adults when exposed to the non-consumptive effects of H. axyridis. Concurrently, we observed an elevation in hydrogen peroxide (H2O2) content and the activities of antioxidant enzymes such as superoxide dismutases (SODs), catalases (CATs), and peroxidases (PODs). Furthermore, notable alterations were detected in energy metabolism, characterized by heightened triglyceride levels and diminished glycogen and trehalose concentrations. These outcomes underscored the adaptive response of the pest aimed at mitigating non-consumptive adverse effects by augmenting antioxidant enzyme activity to counteract oxidative stress and minimize cellular damage. Nonetheless, this defensive mechanism entails a significant expenditure of energy resources, resulting in shifts in energy utilization. Elevated triglyceride levels and reduced glycogen and trehalose concentrations diminish available resources for reproductive processes, such as egg laying, ultimately culminating in decreased fecundity. This study contributes novel insights into the non-consumptive effects observed in insects, while also furnishing valuable insights into the mechanisms underlying insect stress responses.

Fine-Tuning Artificial Neural Networks to Predict Pest Numbers in Grain Crops: A Case Study in Kazakhstan

This study investigates the application of different ML methods for predicting pest outbreaks in Kazakhstan for grain crops. Comprehensive data spanning from 2005 to 2022, including pest population metrics, meteorological data, and geographical parameters, were employed to train the neural network for forecasting the population dynamics of Phyllotreta vittula pests in Kazakhstan. By evaluating various network configurations and hyperparameters, this research considers the application of MLP, MT-ANN, LSTM, transformer, and SVR. The transformer consistently demonstrates superior predictive accuracy in terms of MSE. Additionally, this work highlights the impact of several training hyperparameters such as epochs and batch size on predictive accuracy. Interestingly, the second season exhibits unique responses, stressing the effect of some features on model performance. By advancing our understanding of fine-tuning ANNs for accurate pest prediction in grain crops, this research contributes to the development of more precise and efficient pest control strategies. In addition, the consistent dominance of the transformer model makes it suitable for its implementation in practical applications. Finally, this work contributes to sustainable agricultural practices by promoting targeted interventions and potentially reducing reliance on chemical pesticides.

Climate Change and Global Agriculture: Addressing Challenges and Adaptation Strategies

Climate change poses significant challenges to global agriculture, affecting crop production, food security, and the livelihoods of millions worldwide. This paper reviews the current understanding of the impact of climate change on agriculture and explores adaptation strategies to mitigate its adverse effects. Climate change-induced phenomena such as rising temperatures, altered precipitation patterns, increased frequency of extreme weather events, and shifts in pest and disease dynamics threaten crop yields and agricultural productivity. To address these challenges, various adaptation measures are being implemented, including the development and adoption of climate-resilient crop varieties, improved water management techniques, agroforestry practices, and enhanced early warning systems. Furthermore, policy interventions and international collaborations are crucial for building resilience and promoting sustainable agricultural practices in the face of climate change. This review highlights the urgent need for concerted efforts at local, national, and global levels to safeguard agricultural systems and ensure food security in a changing climate.

Euwallacea perbrevis (Schedl, 1951) and associated novel fungal symbiont, Fusarium sp.: A potential cause of wilting in cocoa, Theobroma cacao in India

Cocoa is a commercially important beverage crop that faces new challenges in the context of climate change, which can alter pest and disease dynamics in cocoa plantations. In the Dakshina Kannada district of Karnataka, India, an abrupt increase in cocoa wilt was observed during the 2017–18 and 2018–19 seasons. Systematic examination of affected trees revealed consistent attacks of Euwallacea perbrevis (Schedl, 1951; Coleoptera: Curculionidae: Scolytinae). In-depth studies of the wilted trees revealed the association of the plant pathogenic fungus Fusarium sp. with Euwallacea perbrevis. The identity of the obtained pure culture from the insect and infected tree was confirmed as Fusarium sp. using amplification of ribosomal DNA, RNA polymerase II largest subunit (RPB1) and translation elongation factor 1 (Tef1) genes. Notably, this study reports a remarkable case of mutualistic association between ambrosia beetles and Fusarium sp., belonging to the Fusarium solani species complex (FSSC), rather than the Ambrosia Fusariumclade (AFC). This finding highlights the intriguing diversity of mutualistic relationships in nature and sheds new light on the complex interactions between organisms. The present study also investigated the impact of rainfall on the colonization and spread of the Euwallacea-Fusarium complex. The findings revealed that rainfall was identified as a predisposing factor for the incidence and spread of attacks. Additionally, the study conducted a comprehensive analysis of the modeling of Euwallacea-Fusarium complex spread over a specific period of time.

Integrating Omics Approaches for Climate-Resilient Crops: A Comprehensive Review

Climate change is a looming threat to global agriculture, impacting temperature, rainfall patterns, pest dynamics, and soil quality. These challenges transcend numerous crops vital for global food security. Drought, soil acidity, and nutrient fluctuations induced by climate change impede crop productivity, necessitating the development of breeding strategies to produce climate-resilient varieties. This review delves into the integration of omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, to bolster breeding programs across diverse crops. By harnessing high-throughput technologies, researchers gain insights into the genetic and molecular mechanisms underlying traits such as stress resistance, yield, and disease tolerance. The cultivation of elite cultivars with enhanced stress tolerance is paramount for sustainable agriculture in the face of climate change. Various breeding approaches, encompassing functional genomics and mutagenomics, are explored alongside the application of genome editing tools like CRISPR/Cas9 and TALEN for targeted trait enhancement. Through the integration of multi-omics data, novel genetic targets are unearthed, facilitating the development of crop varieties resilient to climate-induced stressors beyond maize. This review underscores the significance of multi-omics in crop breeding and highlights strides made toward climate-resilient crop production. Understanding crop responses to abiotic stresses induced by climate change is imperative for the development of resilient varieties. The integration of modern genetics into classical breeding methods aims to cultivate stress-resistant cultivars, mitigating food security risks. Multi-omics approaches play pivotal roles in unraveling crop performance and stress tolerance mechanisms under diverse environmental conditions. Moving forward, the integration of multi-omics approaches will pinpoint candidate genes and pathways, enabling precision breeding to enhance crop performance amidst changing climates. These endeavors will propel the advancement of climate-resilient crops, safeguarding global food security in the face of climate change's challenges.

Exploring the Dynamic Invasion Pattern of the Black-Headed Fall Webworm in China: Susceptibility to Topography, Vegetation, and Human Activities.

The fall webworm (FWW), H. cunea (Drury) (Lepidoptera: Erebidae: Arctiidae), is an extremely high-risk globally invasive pest. Understanding the invasion dynamics of invasive pests and identifying the critical factors that promote their spread is essential for devising practical and efficient strategies for their control and management. The invasion dynamics of the FWW and its influencing factors were analyzed using standard deviation ellipse and spatial autocorrelation methods. The analysis was based on statistical data on the occurrence of the FWW in China. The dissemination pattern of the FWW between 1979 and 2022 followed a sequence of "invasion-occurrence-transmission-outbreak", spreading progressively from coastal to inland regions. Furthermore, areas with high nighttime light values, abundant ports, and non-forested areas with low vegetation cover at altitudes below 500 m were more likely to be inhabited by the black-headed FWW. The dynamic invasion pattern and the driving factors associated with the fall webworm (FWW) provide critical insights for future FWW management strategies. These strategies serve not only to regulate the dissemination of insects and diminish migratory tendencies but also to guarantee the implementation of efficient early detection systems and prompt response measures.