Pest Infestation Research Articles

Occurrence and seasonal variation of organochlorine pesticides in selected vegetable farmlands in Lagos State, Nigeria.

Pest infestation in crop production have increased farmers' interest in pesticides use with short and long term consequences. This study investigated the occurrence and seasonal variations of organochlorine pesticide residues in vegetable farms in selected areas of Lagos State. Non carcinogenic and carcinogenic risk assessment was also evaluated. Soil samples were collected during the wet and dry seasons at soil depth of 0-30 cm. Gas chromatography coupled with an Agilent mass spectrometer was used to analyse organochlorine residues (alpha-lindane,lindane, delta.-lindane, aldrin, heptachlor epoxide, alpha.-endosulfan, p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE), endrin, endosulfan, m,p'-dichlorodiphenyldichloroethane (m,p'-DDD), endosulfan sulfate, o,p'-dichlorodiphenyltrichloroethane (o,p'-DDT) and endrin ketone) in soil. Heptachlor epoxide showed maximum concentration of 43.03 mg/kg in Station 19 in Western zone during the dry season while m,p'-DDD and endosulfan had minimum value of 0.004 mg/kg in Station 2 and Station 5 respectively during the wet season in the Far eastern zone. The concentrations of organochlorine residues were intermediate in the Eastern zone in both seasons. There was significant (p < 0.05) increase in dry season concentrations when compared to wet season. The risk assessment indicated Hazard Quotient (HQ) > 1 for non-cancer risk and cancer risk > 10-6. Thus a need for stringent monitoring programs for pesticides.

Removal of 2,4-dichlorophenoxyacetic acid through nitrogen doped modified palm kernel shell from aqueous solutions

The 2,4-Dichlorophenoxyacetic acid (2,4-D) is extensively used herbicide to boost crop productivity by reducing weed and pest infestation in agricultural fields. Due to the high solubility, mobility and low biodegradability, the ubiquitous presence of 2,4-D in the soil, surface and underground water has become serious threat for environment and human health.The present study addresses removal of hazardous 2,4-D by melamine-modified palm kernel shell biochar. A thorough examination was carried out, encompassing proximate analysis, elemental analysis, X-Ray Diffraction (XRD), Fourier-Transform Infrared Spectroscopy (FTIR), Brunauer-Emmett-Teller (BET) analysis, particle size distribution assessment, SEM (Scanning Electron Microscopy), and TEM (Transmission Electron Microscopy). Additionally, a comparative analysis was conducted, comparing palm kernel shell powder with biochar activated by KOH. The melamine-modified biochar (PKS-BM) exhibited notable characteristics, including nitrogen composition of 4.2 %, enhanced surface groups, a BET surface area 794.70 m2/g, total pore volume 0.449 cm3/g, particle size of 1400 nm, and 85 % removal of 2,4-D. Adsorption kinetics were accurately described by both pseudo-second-order model. According to the Langmuir model, the adsorption process was favorable, demonstrating a maximum adsorption capacity 990.10 mg/g. Enthalpy, entropy, and Gibbs free energy were computed from adsorption data, revealing exothermic and thermodynamically spontaneous 2,4-D adsorption on PKS-BM. These findings confirm the suitability of the synthesized melamine-impregnated palm kernel shell biochar to be used as adsorbent for removal of toxic herbicide from contaminated water.

Detection of Typical Forest Degradation Patterns: Characteristics and Drivers of Forest Degradation in Northeast China

The accurate identification of forest degradation and its driving factors is a prerequisite for implementing high-quality forest management. However, distinguishing degradation patterns is often neglected in large-scale forest quality assessments. The indicators were constructed to identify typical forest degradation patterns using remote sensing indexes, followed by an analysis of the spatiotemporal dynamics of forest degradation and quantification of the contributions from various driving factors. The results indicated that the constructed indicators could effectively distinguish typical forest degradation patterns, with a fire degradation identification accuracy of 90.0% and a fitting accuracy of drought and pest degradation higher than 0.7. The cold temperate conifer forest zone had the largest proportion of fire degradation, accounting for 67.7% of the area, and totals of 99.0% of the subtropical evergreen broadleaf forest zone and 92.8% of the temperate conifer and broadleaf mixed forest zone were moderately to severely affected by drought, with long-term stability. Additionally, 0.1% of the temperate grassland region and 0.1% of the cold temperate conifer forest zone underwent severe pest infestations, with a long-term stable trend. Meteorological factors were the primary contributors to all typical degradation patterns, accounting for 81.35%, 58.70%, and 82.29%, respectively. The research developed an index for assessing forest degradation and explained the importance of natural and anthropogenic factors in forest degradation. The results are beneficial for the scientific management of forest degradation and for improving forest management efficiency.

Comparative Efficacy of Weed Management Practices on Grain and Straw Yields of Wheat

Wheat is central to Afghanistan's food security. It accounts for 76 percent of the annual grain cereal harvest and 57 percent of the country's total food crop production. Several factors, such as low-yielding varieties, low soil fertility, low-quality agrochemicals, widespread pest and disease infestations, and widespread distribution of weeds, are the leading causes of low wheat productivity in the country. The current study was conducted at the Student Research farm of the Faculty of Agriculture at Kabul University to determine the overall effects of weeds on wheat grain and straw yields and to evaluate and compare different weed management practices in the wheat field. After applying the weed management practices, among the treatments, T1, T3, and T4 showed only 8.3, 5, and 2.3 percent weed populations, respectively, compared to the untreated control. Regarding grain and straw yields, mechanical control (hand weeding) ranked first with a grain yield of 4,867 kg/ha, followed by T1 (4,580 kg/ha), T3 (4,513 kg/ha), T2 (4,133 kg/ha), and T5 (3,416 kg/ha), respectively. The difference in straw yield among the treatments was not significant; however, mechanical control ranked first (7,103 kg/ha), followed by T1 (6,783 kg/ha), T3 (6,700 kg/ha), T2 (6,207 kg/ha), and T5 (5,280 kg/ha), respectively. The study findings concluded that by eliminating weeds, wheat grain and straw yields could be increased by an average of 30 and 26 percent, respectively. As broad-leaved weeds are widely distributed in the wheat fields, applying relative herbicides such as 2,4-D is more effective and recommended for all cultivation methods.

Assessing the impact of multi-year droughts on German forests in the context of increased tree mortality

Forests play a crucial role in climate regulation and societal well-being. Despite their significance, the increasing frequency of droughts poses a severe threat to forest ecosystems, impacting carbon sequestration and forest stability. In Germany, the unprecedented 2018–2020 drought resulted in extensive tree mortality and damaged wood volume, with lasting effects observed in subsequent years. As climate models project a continuation of such droughts, understanding the impact of droughts on forests becomes imperative. However, it is unclear how forests will evolve in the future if the drought duration continues to increase.This study employs a forest model to analyze the impact of droughts across various German forest types, focusing on the duration of drought periods and their influence on forest productivity. By utilizing an individual-based forest growth model and national forest inventories, the study addresses critical knowledge gaps regarding the effects of multi-year droughts on biomass and productivity across various forest types, including monocultures and mixed forests. The simulations consider a drought-induced large increase in tree mortality caused by factors such as pest infestations and diseases across Germany.Our simulation results reveal a declining aboveground biomass and gross primary production (GPP) for all simulated drought scenarios, including the three- and six-year drought. GPP is reduced by 46 % in the 3-year drought scenario and by 58 % in the 6-year drought scenario. Notably, prolonged droughts lead to cumulative losses, with a saturation effect in drought scenarios exceeding eight years. Forest stand composition influences these impacts, with greater GPP losses in low-biomass stands. Furthermore, different forest types exhibit varying responses. Monocultures and even-sized forests (mostly planted and managed forests) are more sensitive to drought than mixed and uneven-sized forests.The results provide valuable insights into forest resilience and ecosystem responses to increasingly frequent and prolonged droughts, highlighting the importance of understanding the effects of drought on monocultures and mixed forests to inform future forest management strategies. Modelling the influence of biotic factors on forest dynamics in a process-based manner remains a challenge that requires future research.

Early detection of infestation by mustard aphid, vegetable thrips and two-spotted spider mite in bok choy with deep neural network (DNN) classification model using hyperspectral imaging data

Although exclusion measures (e.g., air filters, biosecurity practices) can be employed to prevent occurrence of pest outbreaks, indoors vegetable farms in Singapore are still susceptible to various arthropod pests. Due to strong interest from the industry to pursue pesticide-free production, indoors pest management is often focused on early detection for timely containment and eradication, implying the importance of robust and vigorous pest monitoring programs. In recent years, application of machine vision technologies, especially hyperspectral imaging (HSI), has been studied for their capacity to early detect pest infestation. However, there is a lack of studies conducted in actual indoor environments and on multiple arthropod pests. Thus, this study aimed to non-destructively collect hyperspectral data of bok choy Brassica rapa subspecies chinensis which were healthy or infested with either mustard aphids Lipaphis erisymi, vegetable thrips Echinothrips americanus or two-spotted spider mites Tetranichus urticae in indoor environment to build deep neural network (DNN) classification model for early detection. Based on HSI data of control and infested plants collected daily over a period of two weeks, we found that point percentage change (PPC) values associated with leaf reflectance in 420–440 nm, 500–520 nm, 620–637 nm, 720–800 nm, and 850 nm were sensitive to infestation by the mentioned arthropod pests. Deep Neural Network (DNN) classification models trained on collected HSI data were found to outperform Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) classification models. DNN models achieved 92.8 ± 0.4 % overall classification accuracy across all days. As early as two days after infestation, DNN models could achieved classification precision values of 96.4 %, 96.9 %, 93.9 % and 100 % for control plants and plants infested with either aphids, spider mites or thrips respectively. These results highlight the feasibility of multiclass early detection of different arthropod pests and the potential of HSI system coupled with DNN classification as an autonomous plant health monitoring tool in indoor crop production.

Impact of Seed Protectants on Seed Quality and Pest Infestation in Pigeon Pea (Cajanus cajan L.) Seeds during Storage

This study investigates the efficacy of diverse seed protectants on key parameters affecting seed quality and pest infestation in pigeon pea seeds during storage. Over 3, 6, and 9-month storage periods, the impact of seed protectants on germination percentage, moisture content, vigor index, insect infestation by pulse beetles, and seed weight loss was assessed. Notably, Emamectin benzoate and Deltamethrin consistently maintained high seed quality, with germination percentages ranging from 88.33% to 84.33% and 87.33% to 83.00%, respectively. These insecticidal treatments also exhibited minimal insect infestation, ranging from 0.33% to 1.00%. In contrast, botanical protectants showed varying effectiveness, with Lantana camara and Vitex negundo displaying germination percentages ranging from 86.67% to 80.67% and 87.00% to 82.67%, and insect infestation levels ranging from 0.33% to 1.33%. These findings highlight the importance of seed protectants in preserving seed viability and minimizing pest damage during storage, thereby enhancing crop yield and reducing post-harvest losses in pigeon pea cultivation.

Development of a 3D point cloud reconstruction-based apple canopy liquid sedimentation model

China leads the world in apple production, contributing to over half of the global total. Annually, extensive pesticide use is employed in apple cultivation, but the uncertain quantities of pesticide deposition and indiscriminate application have resulted in significant pesticide loss and residue. This poses a considerable potential risk to human health. This paper focuses on ground-based plant protection and stratifies the canopy horizontally, using 3D point cloud reconstruction technology and tree deposition experiments to explore the relationship between canopy leaf area index and pesticide deposition. By calculating the droplet penetration coefficient based on the amount of solution deposited at various canopy layers, a functional model linking leaf area index and droplet penetration coefficient has been established, with a correlation coefficient (R2) of 0.92. Based on this model, and by integrating the canopy volume with the amount of solution deposited on the outermost layer of the canopy, the total solution deposition on the entire tree can be estimated. According to the deposition model, there is a negative correlation between the amount of solution deposited on the canopy and the leaf area index of the canopy. Moreover, the experiments have shown that when the spray vehicle travels at a speed of 1.5 m/s, the maximum amount of solution is deposited while still ensuring an effective deposition rate. The establishment of the deposition estimation model allows for the visualization of canopy solution deposition and can guide fruit farmers in determining the appropriate frequency of spraying based on the severity of pest and disease infestations.

Nondestructive detection of fruit fly (Bactrocera dorsalis) infestation in Alphonso and Totapuri varieties of mango fruits by thermal imaging technique

AbstractThermal imaging is a noncontact and nondestructive sensing technique that provides thermal signatures of objects by detecting the infrared radiations emitted by them above 1 K. The nondestructive detection of internal defects and infestations has vast applications in the Food Industry. In the present experiment, a C3 thermal camera (FLIR systems) having a thermal sensitivity of &lt;0.10°C was used for the detection of fruit fly infestation in mango fruits. The healthy mango fruits of both varieties had a uniform surface temperature with a variation of only ±0.1°C. The maximum temperature difference between the healthy and fruit fly infested regions in fruit fly infested Totapuri mango was found to be 4.56°C in the heated and dark conditions. The maximum difference in the surface temperature of 4.3°C was observed between the healthy and infested regions of the infested Alphonso mango under heated and dark conditions. The difference in temperature between the healthy and infested regions of mangoes can be attributed to the changes in the internal structure, thermal properties, and biochemical properties of the healthy and infested regions of the infested mango fruits. The statistical analysis showed a significant difference (p &lt; 0.05) in surface temperature between the infested and healthy regions of fruit fly infested Alphonso and Totapuri mango fruits. This envisages that nondestructive fruit fly infestation in Alphonso and Totapuri varieties of mango fruits can be detected under active thermography.Practical ApplicationsThe quality of mango fruit can be described in terms of external and internal quality. The external quality parameters such as color, size, and weight are usually considered during the sorting of mangoes. The internal defects such as diseases, physiological disorders, and damage due to pests are also significant in mango processing. The visual inspection methods for detecting pest infestation in fruits are nonreliable, time‐consuming, and subjective. Therefore, there is a need for efficient, reliable, and accurate methods to detect fruit fly infestation in mango fruits. A nondestructive method for the detection of fruit fly infestation has the potential to increase fruit quality and acceptability, thereby benefiting processors, traders, and consumers. Thermal imaging has a distinct advantage over other nondestructive quality assessment techniques as it is noncontact, practically feasible, and economically viable.

Skybound Intelligence: AI's Impact on Drone Technology

The integration of Artificial Intelligence (AI) with drone technology has revolutionized various industries, from agriculture and construction to defense and emergency response. This paper aims to provide an overview of the multifaceted role AI plays in enhancing drone capabilities and addressing challenges in different domains.Firstly, the paper discusses how AI-driven autonomy enables drones to perform complex tasks autonomously, such as navigation, obstacle avoidance, and mission planning. Advanced algorithms, including machine learning and computer vision, empower drones to interpret and respond to dynamic environments in real-time, significantly expanding their operational capabilities.Secondly, the paper examines the crucial role of AI in data processing and analysis for drones. AI algorithms enable drones to collect, process, and analyze vast amounts of data quickly and efficiently, extracting actionable insights for various applications. In agriculture, for instance, drones equipped with AI can analyze crop health, identify pest infestations, and optimize resource allocation for improved yields.Furthermore, the paper explores how AI enhances the security and safety of drone operations. AI-powered systems enable drones to detect and mitigate potential risks, such as unauthorized access, airspace violations, and mechanical failures, thereby ensuring safer and more reliable operations, especially in densely populated areas or sensitive environments. Moreover, the paper discusses the implications of AI in enabling swarming capabilities among drones. Through collaborative AI algorithms, drones can coordinate their actions, communicate intelligently, and achieve collective goals efficiently, leading to applications like search and rescue, surveillance, and infrastructure inspection on a large scale. Lastly, the paper addresses the ethical and regulatory considerations associated with the integration of AI in drone technology, including privacy concerns, algorithmic biases, and the need for transparent decision-making processes. Keywords: AI-enabled drones, Autonomous UAVs, Drone autonomy, AI-driven navigation, Machine learning for drones, and Drone surveillance

Revealing early pest source points and spreading laws of Pantana phyllostachysae Chao in Moso bamboo (Phyllostachys pubescens) forests from Sentinel-2A/B images

Pantana phyllostachysae Chao is a leaf-eating pest that poses a significant threat to bamboo forest health. Current research mainly focuses on statically identifying damage using remote sensing images. However, the mechanism behind the damage's traceability remains unclear, making it difficult to pinpoint early infestation sources accurately. Additionally, our understanding of the pest's spreading laws is limited. This study leverages Sentinel-2A/B images from February to November 2021 to investigate P. phyllostachysae infestation traceability through the dynamic age algorithm and indicator analysis method. The results shed light on the distribution of early pest sources over the study period. By analyzing both the overall pest infestation “cluster” and its center of gravity, we dissect P. phyllostachysae infestation characteristics and paths monthly throughout the study period. Our findings reveal three zones with strong spreading momentum, three with slow spreading momentum, and two transitional zones during the February-November period, aligning with P. phyllostachysae occurrence patterns. However, the direction of P. phyllostachysae spreading varies, likely due to a combination of meteorological, topographical, vegetative biochemical, and human activity factors. This study introduces innovative approaches for identifying early pest source points and understand their spreading laws, contributing to more effective pest prevention and control in forest ecosystems.

Study on the Population Density of Insect Pests of Broccoli, Brassica Oleracea, in the Jadiriyah Area of Baghdad, Iraq

A field study was conducted in the fields of the College of Agricultural Engineering Sciences at the University of Baghdad in Al-Jadriya during the 2018 agricultural season. The objective was to survey the population densities of pests affecting broccoli (Brassica oleracea). The results revealed that the broccoli plants in the open field were affected by various pests. On September 24, the highest density of two-spotted red mite (Tetranychus urticae) eggs reached 35 eggs per leaf. On November 12, the highest density of mite nymphs was recorded at 28 nymphs per leaf. The leaf miner, Liriomyza sativae, exhibited the highest density of 23 tunnels per leaf on September 17. Additionally, the highest density of whitefly nymphs (Bemisia tabaci) was observed on October 1 and November 17, reaching 29 nymphs per leaf. The results also indicated the presence of the aphid lion, Chrysoperla carnea, as a natural enemy. Furthermore, the parasitoid Diglyphus isaea was found on the leaf miner, while the parasitoid Eretmocerus mundus was identified on the whitefly. Therefore, understanding the species of pests and their density attacking agricultural crops is crucial. This knowledge allows for the release of natural enemies in the field to mitigate pest infestations.

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

Tea cultivation holds significant economic value, yet the leaves of tea plants are frequently susceptible to various pest and disease infestations. Consequently, there is a critical need for research focused on precisely and efficiently detecting these threats to tea crops. The investigation of a model capable of effectively identifying pests and diseases in tea plants is often hindered by challenges, such as limited datasets of pest and disease samples and the small size of detection targets. To address these issues, this study has chosen TLB, a common pest and disease in tea plants, as the primary research subject. The approach involves the application of transfer learning in conjunction with data augmentation as a fundamental methodology. This technique entails transferring knowledge acquired from a comprehensive source data domain to the model, aiming to mitigate the constraints of limited sample sizes. Additionally, to tackle the challenge of detecting small targets, this study incorporates the decoupling detection head TSCODE and integrates the Triplet Attention mechanism into the E-ELAN structure within the backbone to enhance the model’s focus on the TLB’s small targets and optimize detection accuracy. Furthermore, the model’s loss function is optimized based on the Wasserstein distance measure to mitigate issues related to sensitivity in localizing small targets. Experimental results demonstrate that, in comparison to the conventional YOLOv7 tiny model, the proposed model exhibits superior performance on the TLB small sample dataset, with precision increasing by 6.5% to 92.2%, recall by 4.5% to 86.6%, and average precision by 5.8% to 91.5%. This research offers an effective solution for identifying tea pests and diseases, presenting a novel approach to developing a model for detecting such threats in tea cultivation.

Farmers’ Perceptions of the impact of climate change on apple production in lower Mustang, Nepal

Undoubtedly, climate change is becoming a worldwide concern due to its great sensitivity and detrimental effects on livelihood. The government, policymakers, and other relevant stakeholders are striving to come up with novel and innovative approaches to combat the effects of climate change. Nevertheless, understanding and perceptions of the issue among local farmers are more crucial before making mitigation or adaptation plans. It’s them who are actually more exposed to these environmental works and who are on the frontlines of climate change. So, the study was conducted in 2022 at Lower Mustang to understand how farmers perceive climate change, how it has impacted apple production, and to find discrepancies between their perceptions and the actual change in climatic variability recorded by the Department of Hydrology and Meteorology (DHM). Temperature and precipitation were two climatic variables that were taken into account to investigate the significant effects they have on the rate of apple production. Altogether, 60 households from Lower Mustang were selected randomly for the study. Pre-tested interviews, focus group discussions, key informant interviews, as well as secondary data from DHM, the Ministry of Agriculture and Livestock Development (MoALD), etc. were used to collect the required information. The majority of farmers observed an increase in temperature (83.30%) and an unpredictable increase in rainfall (98%), which are consistent with the actual change in temperature and precipitation recorded between 1991 and 2021. Findings show that the average annual temperature and mean annual precipitation are increasing at a rate of 0.012°C per year and 0.4146 mm per year, respectively. This has resulted in the outbreak of significant amounts of diseases and insect pest infestations that have a direct impact on the quality and quantity of apples. The majority of farmers acknowledged that rainfall was the most significant climatic hazards that negatively impacted apple production, followed with hailstones having an impact on apples during flowering and fruiting. Farmers in the study area are limited to intercropping practices, mostly as an adaptation strategy to combat the impacts of climate change. The study concluded that farmers perceived climate change; they are aware of the term ‘Climate Change’ but haven’t taken any significant adaptation measures towards it. So, there is an immediate need for effective adaptation mechanisms, taking into account farmers' perspectives, to make Lower Mustang self-sufficient in apple production.

The impact and control of Brown marmorated stink bugs on Turkish hazelnut production

The Turkish hazelnut is a crucial commodity both in Turkey and globally, with Turkey accounting for approximately 70% of the world's hazelnut production. However, hazelnut harvesting faces significant challenges, one of which is the presence of stink bug pests such as the brown marmorated stink bug (BMSB). This study utilized a mathematical model to examine the adverse impact of pests such as the brown marmorated stink bug [Halyomorpha halys (Stål, 1855) (Hemiptera: Pentatomidae)] and similar stink bug pests on hazelnut production. The findings revealed that between 2017 and 2022, hazelnut yields in Turkey suffered damage from stink bug pests like the brown marmorated stink bug, amounting to 106,820 tonnes annually and 640,920 tonnes cumulatively. Furthermore, this study demonstrated that the application of control measures, whether through biocontrols, traps, or chemicals, to increase the death rate of BMSB-type pests by 25% or 50%, or to decrease the egg ratio by 25% or 50%, has the potential to save up to 82,506 tonnes of hazelnut production from these pests annually. Overall, this study highlights the importance of managing pest infestations in agricultural production and the potential benefits of using control methods to mitigate their impact.

Farmer’s perception on Chinese citrus fruit fly (CFFs) and its management in Solukhumbu and Sindhuli district of Nepal

This study conducted between January and June 2022 aimed to assess the impact of precipitation on Bactrocera minax, a citrus pest, in Nepal's Solukhumbu and Sindhuli districts. Primary data were gathered from 84 respondents using pre-tested interview schedules, focal group discussions (FGD), and key informant interviews (KII), while secondary information was collected through literature review. The results highlighted the Chinese citrus fruit fly as a significant citrus pest causing fruit drop, particularly impacting Mandarin cultivation after sweet orange displacement. Farmers in Sindhuli exhibited greater motivation and trust in citrus cultivation compared to those in Solukhumbu. Notably, the fruit drop was lower (4.33%) in Sindhuli where the Area-wide Management Program (AWCP) utilizing protein bait and field sanitation was applied, in contrast to Solukhumbu where fruit drop was higher (35.5%), suggesting the effectiveness of AWCP. The PMAMP Sindhuli super zone played a direct role in the study, with respondents in Sindhuli demonstrating higher awareness of Chinese citrus fruit flies and AWCP. Technical and financial support for citrus cultivation was also more prevalent in Sindhuli. Furthermore, the study found that the majority of respondents perceived protein bait (AWCP) as the most straightforward, effective, and expensive management method, despite its limited application in Solukhumbu. This study underscores the importance of AWCP in mitigating citrus pest infestation, particularly in areas with high precipitation, and emphasizes the need for broader adoption of effective management strategies in citrus cultivation regions.

The role of partner species in the crop cocultures: A meta- analysis

Crop cocultures (the growing of one crop with another plant or animal species as partner) are known to increase yield and other ecosystem services. However, whether and how partner species (i.e., the species grown together with the focal crop) affect coculture effects is poorly understood. Here, we test the effects of partner species using a meta-analysis. Two types of cocultures (i.e., crop-crop and rice-fish cocultures) with 19 cereals as focal crops were considered, and 136 plant or animal species were involved. We found that cocultures increased focal crop yield, the land equivalent ratio and soil organic matter (SOM), and decreased insect pest infestation. Farming practices (i.e., fertilization, pesticide use, and irrigation) did not alter these coculture effects on yield, but fertilization decreased the coculture effect on SOM, and pesticide use reduced the coculture effect on pest control. Without irrigation, cocultures increased yield under drought conditions compared to normal moisture conditions. Beneficial coculture effects were obtained with both plant and animal partner species and under both patterns of partner species grown with focal crops (species addition and species replacement). Functional types of partner plants (i.e., C3, C4, legume and non-legume plants) and taxonomic groups of partner animals (i.e., crabs, fish, shrimps, and turtles) affected coculture effects on yield, SOM, and insect pests. Both multiple functional trait distance and single functional trait distance between the focal crops and plant partners correlated with coculture effect on yield. The results suggest that biological types of partner species are important in obtaining beneficial effects of cocultures.

Assessment of Bioefficacy of Chilli Pepper Capsicum frutescens (L.) and its Intercropping on the Performance and Insect Pests Management of Bambara Groundnut (Vigna subterranea L.)

A study was conducted at the Yaba College of Technology Instructional and Research Farm in 2022 and 2023 wet and dry seasons to determine the bio-efficacy of chilli pepper, (Capsicum frutescens (L.)) and its intercropping on the growth, yield and insect pest management of Bambara groundnut (Vigna subterranea (L.)). The experiment was laid out in a 3×4 factorial experiment in Randomized Complete Block Design with 3 treatments (chilli pepper extract at 200l/ha, 250l/ha, and local control) at 3 replications in alternate intercropping, strip cropping, border cropping, and sole cropping arrangement. The chilli pepper aqueous extract at 200 l/ha and 250 l/ha sprayed on the Bambara groundnut plants were sprayed as scheduled from 10 am to 12 noon with the view of protecting it with aqueous extract of chilli pepper on 12 plots of 60 ridges. Data were collected on growth parameters of Bambara groundnut at 2, 4, and 6 weeks after planting, pest population, assessment of thrips and legume pod borer, assessment of both nymph and adult of legume pod borer and pod sucking bugs, pod density, pod damage, grain yield, yield loss and assessment of phytotoxicity signs using Analysis of Variance. Results showed that 250 l/ha aqueous extract treated plots reduced the number of insect pests including thrips (4.67±1.09), legume pod borer (1.76±0.35), legume pod borer nymph (1.08±0.10) adults of pod borers (1.18±0.11) and pod sucking bugs adult (3.33±1.09) in intercropping situation, followed by 200 l/ha respectively as compared to untreated plots (2.69±0.12). There was a significant difference (p &lt; 0.05) between the treated plots (9.08±0.62) and untreated plots (12.44±0.71) in terms of pod damage as well as increased grain yields (343.73±4.71) when compared to the untreated control (322.12±43). However, 250 l/ha causes higher phytotoxicity damage than 200 l/ha although it controlled pest population better than 200 l/ha. Alternate cropping proved to be superior to other cropping system with respect to grain yield (36.78±12.99), reduction in pest infestation (6.33±1.23) and reduction in yield loss (299.58±9.41) when compared to sole cropping of Bambara groundnut with respect to grain yield (100.24±12.99), reduction in pest infestation (15.33±1.23) and reduction in yield loss (395.27±9.41). The bio-pesticidal effect of chilli pepper on Bambara groundnut insects may be due to the presence of active ingredients present in chilli pepper including glycosides, tannin, terpenoides, flavonoids, saponin, reducing sugar, steroids, and resins. This result indicates the potential of using plant extract with insecticidal attributes for the protection of Bambara groundnut in low input agriculture practiced by limited resource farmers in developing countries.

Public Attitudes towards Forest Pest Damage Cost and Future Control Extent: A Case Study from Two Cities of Pakistan

Infestations of pests are perhaps an anthropogenic catastrophe for trees. Aeolesthes sarta (Sart longhorned beetle—SLB) is one of the most severe pests that cause serious damage to a number of hardwood tree species, i.e., Populus, Salix, Acer, Juglans, and Malus. To investigate people’s attitudes towards pest damage cost and future control extent of SLB, a door-to-door method was adopted to survey two major cities (Quetta—QU and Peshawar—PE) of the northwestern region of Pakistan where this pest has caused severe damage. Respondents were asked about SLB pest knowledge, pest damage costs, preferences for control choices, and program extent. According to respondents, more trees (181 ± 1.20 trees/ha/annum) were damaged in QU compared to PE. Populus spp. was the dominant tree genre that attacked and damaged the most. Around 85% of respondents from both cities stated the pest damage cost was calculated as high for QU (480,840.80 ± 4716.94$/annum) compared to PE. Respondents in both locations strongly supported (more than 82%) biological control of future SLB outbreaks. They all agreed that protecting ecologically vulnerable places and wildlife habitats should be the primary priority in a future SLB outbreak. Respondents from both cities who preferred to protect more land area in future SLB outbreaks were calculated to be high for QU (61%) compared to PE (58%). However, city variations in opinions regarding forest-type priority that should be protected and control options were observed. Socio-demographic characteristics were discovered to impact pest damage cost positively, as well as preferred SLB control extent. The findings of this study can help policymakers and forest managers develop publicly permissible pest control plans and make more accurate predictions about future pest outbreaks.

The efficacy of spatio‐temporal predictors in forecasting the risk of Cydia nigricana infestation

AbstractThe ability to estimate the risk of pest infestation can help farmers to reduce pesticide application and provide guidance that would result in better management decisions. This study tested whether different combinations of spatial and temporal risk factors may be used to predict the damage potential of pea moth, Cydia nigricana Fabricius (Lepidoptera: Tortricidae), a major pest in field pea (Pisum sativum L., Fabaceae). Over four consecutive years, the abundance of pea moth was monitored by placing pheromone traps at various field pea‐cultivation sites. We also assessed the phenological development stages and the percentage of damaged seeds per 100 pods collected from each growing pea field in a region of approximately 30 km in diameter. The study found the significant infestation risk indicators to be the time of flowering, the date on which male pea moths are first detected in the monitoring traps and the minimum distance to pea fields that were planted and harvested in the previous growing season. The combination of all three factors using a general additive model approach yielded the best results. The model proposed by this study accurately discriminated between low‐infestation and high‐infestation fields in 95% of cases.