Pesticide Selection Research Articles

Evaluation of Attitudes and Knowledge Levels of Olive Producers in the Application of Agricultural Pharmaceuticals (Mardin Province)

Olives are one of the essential nutrients in human nutrition and health, thanks to the vitamins they contain. There are many diseases, pests and weed species in olive gardens that cause a loss of productivity and quality of this important nutrient. Producers use different methods in plant protection practices to combat these existing pests. This study was conducted to determine the awareness levels of olive producers about plant protection practices in Derik district of Mardin province, where olive production was intense in 2020. For this purpose, the information collected through a 20-question survey method conducted face to face with a total of 90 producers in 15 villages through simple random sampling was evaluated on a percentage basis. According to the results of the current study, it was determined that the olive farmers had high education levels, and the majority of them had other non-agricultural incomes besides agriculture. Those engaged in olive cultivation consult plant protection dealerships and the provincial/district Directorate of Agriculture and Forestry in choosing plant protection products and adjusting their dosage. They stated that the brand and active ingredients are important features in the selection of chemical pesticides, and while they apply pesticides in advance for precautionary purposes without seeing the pests and diseases, they do not use the same pesticides against the same diseases and pests every year and apply the recommended dosage. Producers pay attention to the waiting time between the last spraying and harvest time due to the fact that chemical pesticides leave residues on the products, and they take protective measures for their own health during chemical pesticide application. In order to solve the plant protection problems faced by farmers in the agricultural field, cultural measures are generally applied along with chemical control. However, it is inevitable that chemicals will create many negative situations for human and environmental health over time. For this reason, it is important to carry out an effective, economical and environmentally friendly struggle using appropriate methods in plant protection practices in order to avoid being exposed to undesirable side effects.

Assessment of Pesticide Selection and Application Behaviors of Sugar Beet Farmers in Konya Province

This study is undertaken to determine the behaviors of sugar beet farmers regarding pesticide selection and applications in the Çumra, Altınekin, and Seydişehir districts of Konya province, where sugar beet production is concentrated in 2023. A total of 20 sugar beet farmers were randomly selected from each district through a random sampling method, and evaluations were made based on their responses to 16 face-to-face questions. The results were calculated as percentages and presented in tables. The behaviors of farmers regarding pesticide selection and application practices were assessed in two stages. It was found that sugar beet farmers often seek information from pesticide dealers when choosing and determining the dosage of agricultural chemicals, and they mostly obtain pesticides from agricultural chemical dealers. It was noted that they acquire the chemicals when they start growing the crop, do not spray regularly for pests and diseases as a preventive measure, and pay attention to the legality, cost-effectiveness, and effectiveness of the pesticides they purchase. Half of the sugar beet farmers reported conducting at least two pesticide applications during the season, and the majority stated that they perform pesticide applications in the morning and afternoon. Farmers acknowledged the importance of pesticide residues but emphasized that the effectiveness of the pesticide was more crucial. They also reported not adhering to the waiting periods between the last pesticide application and harvest. Regarding pesticide handling, application, and post-application practices, it was observed that farmers partially protected themselves. They tended to spray excess pesticide-contaminated water at the edge of the orchard or on vacant land and disposed of empty pesticide containers haphazardly on the edge of the field.

Potential Pesticide Misuse in Agriculture Farms from Two Costa Rican Provinces

Pesticide misuse by farmers poses hazards to human, animal, and environmental health. [Objective] This study aimed to document potential pesticide formulation misuse in agricultural farms. [Methodology] Inadequate storage practices, incorrect pesticide selection, and pesticide formulation overuse were documented through interviews and photographs on 13 agricultural farms from the Cartago and Guanacaste provinces of Costa Rica between August 2022 and April 2023. [Results] Storage room characteristics in many farms do not comply with safety standards prescribed by the Costa Rican Ministry of Agriculture and Livestock. Different active ingredients with herbicidal, fungicidal, insecticidal, and bactericidal properties are used in crop species for which they are not recommended. These substances include those from the carbamate, pyridine, cyclohexanedione, pyrethroid, conazole, benzothiazinone, oxadiazine, and phthalimide chemical groups in Cartago province, and from the neonicotinoid and pyrethroid chemical groups in Guanacaste province. Many pesticide formulations are utilized in quantities exceeding the manufacturers' recommendations. Among these formulations were bifenthrin, captan, oxamyl, cypermethrin, mancozeb, dimethoate, and deltamethrin in Cartago province, and imidacloprid in Guanacaste province. [Conclusions] These substances and their secondary metabolites have the potential to move across different environmental compartments such as water, soil, and air, thereby negatively affecting the health of community members rather than just the farmers applying these formulations. Well-established pesticide education programs based on on-site visits to farmers can enhance awareness about implementing good practices and ensure rational use of these substances, with positive results in non-target organisms such as humans and ecosystem service providers as well as natural and anthropogenic ecosystems.

Analysis of cleaning water used in the wine industry: Part I – Pesticide selection

The use of water in wineries is widespread and reaches its peak during the harvest of the vineyard, when it is used to clean and disinfect barrels, tanks, pipes, tools, for pushing the product and dragging the grape juice, for preparing fermentations, and so on. In this sense, according to the European Directive 2020/2184, pesticides in supply water must be analysed to avoid possible contamination in wines. Due to the lack of data on the number of or the pesticides to be analysed in water as required by this Directive, the aim of this work was to select the pesticides to be analysed on the basis of a risk assessment methodology. Information on pesticide water solubility, pesticide water persistence and pesticide oral lethal dose from Pesticide Properties Database was used to build a logistic regression model to predict risk. The pesticides listed in the EURL database for grapes and the information from the pesticides used in the different Spanish Designations of Origin (DOCa) were applied to the logistic regression model. The results obtained showed that only the 25 % of the pesticides pose a high risk and that only the 5 % of these pesticides are used in the different DOCa studied.

MultiFuzzTOPS: A Fuzzy Multi-Criteria Decision-Making Model Using Type-2 Soft Sets and TOPSIS

Effective and optimal decision-making can enhance system performance, potentially leading to a positive reputation and financial gains. Multi-criteria decision-making (MCDM) is an important research topic widely applied to practical decision-making problems. Using the basic idea of symmetry to balance the arrangement where elements or features have an equality or similarity in distribution, MCDM provides robust decisions in such multi-dimensional complex issues. This study proposes MultiFuzzTOPS, a decision-making model to deal with complexity of multi-criteria decision-making. The proposed MultiFuzzTOPS leverages the fuzzy logic and soft sets such as type-2 soft sets (T2SS) and technique for order preference by similarity to ideal solution (TOPSIS) for decision-making. We validate the proposed model by implementing it to solve the pesticide selection problem in food science by considering various criteria for the selection of pesticides. Our proposed MultiFuzzTOPS recommends the best pesticide compared with its counterparts because it covers the maximum information for the selection of the best alternative. Results are ranked on the basis of the Hamming distance and similarity coefficient. We also validate the effectiveness by performing the sensitivity analysis, and the validation shows the reliability and effectiveness of our proposed model.

Agricultural insurance and pesticides selection in China: traditional or green?

PurposeNumerical literature shows that agricultural insurance can affect pesticide investments, but few of them are devoted to explain how agricultural insurance affects farmers’ selection on green or traditional pesticides. This paper aims to develop a theoretical model about how agricultural insurance influences on green pesticides selections and tests our conclusions by using the data from China land economic survey (CLES) from 2020 to 2021.Design/methodology/approachWe employ probit model to capture the effects of agricultural insurance on green pesticides adoption.FindingsWe indicate that green pesticides have a stronger effect on stabilizing yield and increasing income than traditional pesticides, but there are still risks disturbing farmers’ decisions on green pesticides usage. By providing premium subsidies after the farmers are affected by natural risk, agricultural insurance improves the farmers’ expected income and encourages farmers to use green pesticides. Further, we further confirm these conclusions by considering different scenarios such as climate risks, farmers’ entrepreneurship and credit constraints. We find that the effects are more salient if croplands are under higher natural risks and, farmers are equipped with entrepreneurship and formal credit. This paper implies that the agricultural insurance decoupled with green technologies also have salient positive effects on agricultural pollution control.Originality/valueThe potential contributions of this paper can be outlined in three aspects in detail. Firstly, this paper aims to revel the effects of agricultural insurance on pesticide selection by structuring a general theoretical model. By using the CLES data from 2020 to 2021, we confirm that agricultural insurance increases the probability for adopting green pesticides. Secondly, this paper discusses the effects of farmers’ characteristics on the results and finds that if farmers have entrepreneurship, the effects of agricultural insurance on green pesticide usage will be more salient. Thirdly, it uncovers some practices in China, which will supply experiences for other developing countries. For example, this paper further demonstrates that “insurance + credit” plan the present Chinese government carried out will be an important measure for strengthening effects of agricultural insurance on green pesticides usage. Moreover, it shows that decouple agricultural policies will also guide farmers to use green technologies eventually if the technologies are reliable and farmers can afford.

Farmer’s Attitudes on pesticide-use and pest-management practices: A case study of vegetable-cultivators in the vicinity of Lucknow, India

For effective pest management, farmer’s awareness of pesticide toxicity and its safe application is crucial. The use of pesticides in vegetables and fruits is increasing day by day. To assess the awareness of the vegetable and cereal growers in Uttar Pradesh, India on the safety and toxicity management of chemical pesticides, a household survey was carried out using a well-structured questionnaire. According to our survey and observation, more than 99% of farmers preferred chemical pesticides to other alternative approaches for controlling pests. Notably, 80% of farmers were unaware of the harmful consequences that chemical pesticides have on human health and the environment. Nearly 40% of farmers had at least some brief training in integrated pest management (i.e., Communicated through the Kisan Mela and agricultural experts), however, none of them were conscious of the negative effects of pesticide residue or appropriate disposal strategies. For technical information on the selection, handling, and use of pesticides, more than 90% of growers seem to be dependent on local pesticide retailers, radio, and television. The report emphasized the necessity of establishing strict pesticide use rules and awareness campaigns on pesticide selection, its disposal methods, and its toxicity to humans, animals, and overall agrobiodiversity.

Seed fungal endophytes as biostimulants and biocontrol agents to improve seed performance

Seed germination is a major determinant of plant development and final yield establishment but strongly reliant on the plant’s abiotic and biotic environment. In the context of global climate change, classical approaches to improve seed germination under challenging environments through selection and use of synthetic pesticides reached their limits. A currently underexplored way is to exploit the beneficial impact of the microorganisms associated with plants. Among plant microbiota, endophytes, which are micro-organisms living inside host plant tissues without causing any visible symptoms, are promising candidates for improving plant fitness. They possibly establish a mutualistic relationship with their host, leading to enhanced plant yield and improved tolerance to abiotic threats and pathogen attacks. The current view is that such beneficial association relies on chemical mediations using the large variety of molecules produced by endophytes. In contrast to leaf and root endophytes, seed-borne fungal endophytes have been poorly studied although they constitute the early-life plant microbiota. Moreover, seed-borne fungal microbiota and its metabolites appear as a pertinent lever for seed quality improvement. This review summarizes the recent advances in the identification of seed fungal endophytes and metabolites and their benefits for seed biology, especially under stress. It also addresses the mechanisms underlying fungal effects on seed physiology and their potential use to improve crop seed performance.’

Farmers Knowledge on Pesticide Usage in Paddy and Cabbage Crops

The study was conducted to investigate the knowledge of the farmers on pesticide use in selected crops during 2019-22. Two crops namely paddy and cabbage were selected and tow districts that have higher area namely Koppal and Belagavi were selected. In each district, two taluks and eighty farmers were selected for each crop by simple random procedure to form a total sample of 160 farmers. Overall knowledge index of pesticides by farmers was higher in case of paddy (73.57%) and cabbage (64.76%) farmers. Among the various dimensions, knowledge index was highest with respect to concentration and application practice (96.72% & 82.97%), types of sprayers and maintenance (91.25% & 87.50%), IPM practices (73.75% & 70.00) among paddy, and cabbage. Further, knowledge with respect to pest identification (69.06% & 57.66%), toxicity of pesticide and labels signs (68.59% & 61.09%) and disposal of pesticides and personal hygiene (67.86% & 61.09%) was around sixty percent among cabbage and paddy growers respectively. Further, low index was observed with respect to pesticide selection (26.88% & 38.54%) in case of paddy and cabbage growers respectively. Regarding overall knowledge of the pesticide use the findings revealed that Majority (72.50%) of the growers belongs to medium knowledge of pesticide category in case of paddy, low knowledge category in cabbage crop (38.75%) there is need for orientation to the farmers on different insect pests and pathogens through training and digital media. The short video on pests in different crops may be prepared and made access to upload on smart phones of farmers.

Assessment of exposure to pesticides: residues in 24 h duplicate diets versus their metabolites in 24 h urine using suspect screening and target analysis

Human biomonitoring can add value to chemical risk assessment by reducing the assumptions regarding consumption rates, residue occurrence, and processing effects and by integrating exposures from different sources (diet, household use, environmental). However, the relationship between exposure and concentration in human matrices is unknown for most pesticides. Therefore, we conducted a pilot study to gain more insight into the qualitative and quantitative relationship between dietary intake of pesticides (external exposure) and urinary excretion (reflecting internal exposure). In this cross-sectional observational study, 35 healthy consumers aged 18–65 years from the region of Wageningen, Netherlands, collected an exact duplicate portion of their diets during 24 h. On the same day, they also collected all their urine. The duplicate diets were analyzed using target screening by GC- and LC-HRMS; each duplicate diet contained at least five, up to 21, pesticide residues. The 24 h urine samples were analyzed using LC-HRMS in a suspect screening workflow. Metabolites were tentatively detected in all 24 h urine samples, ranging from six metabolites corresponding to four pesticides up to 40 metabolites originating from 16 pesticides in a single urine sample. In total, 65 metabolites originating from 28 pesticides were tentatively detected. After prioritization and additional confirmation experiments, 28 metabolites originating from 10 pesticides were identified with confidence level 1 or 2b. Next, quantitative analysis was performed for a selection of pesticides in duplicate diets and their metabolites in 24 h urine to assess quantitative relationships. In the quantitative comparisons between duplicate diet and 24 h urine, it was found that some metabolites were already present in the duplicate diet, which may give an overestimation of exposure to the parent pesticide based on measurement of the metabolites in urine. Additionally, the quantitative comparisons suggest a background exposure through other exposure routes. We conclude that suspect screening of 24 h urine samples can disclose exposure to mixtures of pesticide on the same day in the general population. However, more research is needed to obtain quantitative relationships between dietary intake and exposure.Graphical

Improvement of the Integral Indicator of the Ecological and Toxicological Assessment of the Danger of the Use of Pesticides in Agriculture and the Creation of an Information System for Their Monitoring

The aim of the work was to assess the ecological and toxicological dangers of pesticides using the value of a complex indicator determined by the calculation method. An integral indicator of the relative ecological and toxicological danger of pesticide use (Hr) is proposed, which allows taking into account the acute oral and chronic toxicity of the pesticide for mammals and its impact on the environment (half-life in soil and chronic toxicity for aquatic organisms), as well as the rate of application of the drug. The computation was performed on fungicides and insecticides that are most commonly used in grain crop cultivation and approved to be applied in the Russian Federation. The research was carried out in 2022–2023. The results showed that the determined indicator takes values from 0.02 for the fungicide benomyl to 26950 for the insecticide chlorpyrifos. Pesticides were ranked according to the Hr index, and four hazard groups were identified, as well as the main factors that determine them. The first hazard group should include drugs with a Hr greater than 1000; the second hazard group—with Hr from 100 to 1000; the third—with Hr from 1 to 100; and the fourth—with Hr less than 1. The first group includes pesticides with multiple adverse safety profiles, such as chlorpyrifos. The second group includes insecticides fipronil, lambda-cyhalothrin, gamma-cyhalothrin, imidacloprid and the fungicide flutriafol. The indicator can be used for agro-ecological substantiation of the choice of pesticides for the treatment of agricultural crops and for the selection of priority pesticides for regular monitoring of their content in the environment—primarily the first and second hazard groups. It can be performed remotely by appropriate detectors and sensors. All data about farm pollution can be monitored using an information server monitoring system, the architecture of which has been proposed.

Decision Support System for Selection of Pesticides in Chili Plants Using the MAUT Method

Diseases in chili plants often cause farmers to experience crop failure. Diseases that are often encountered are leaf spot disease, wilt disease, and anthracnose disease caused by fungi. Therefore, farmers are trying to prevent and reduce the disease by using pesticides. The use of pesticides in agriculture plays a role in preventing and reducing diseases in chili plants. Pesticides for chili plants are quite widely spread on the market and have the advantages of each product offered to farmers, so farmers must be more careful and understanding in choosing pesticides to be used in preventing chili plant diseases. For that we need a decision support system (SPK). The purpose of this research is to apply the MAUT method in SPK which can help chili farmers in selecting pesticides. The data used in this study are 10 types of pesticides as an alternative. While the criteria consist of price, classification, number of diseases eradicated, method of action, shelf life, and formulation concentration. Data analysis uses the MAUT method with the steps of forming a decision matrix, normalizing the decision matrix, determining the utility matrix, calculating the final utility, and ranking. The research results obtained for the three best pesticides are Tridex 80 WP, Ziflo 76 WG, and Cabriotop 60 WG.

The mechanisms of metabolic resistance to pyrethroids and neonicotinoids fade away without selection pressure in the tarnished plant bug Lygus lineolaris.

Heavy selection pressure prompted the development of resistance in a serious cotton pest tarnished plant bug (TPB), Lygus Lineolaris in the mid-southern USA. Conversely, a laboratory resistant TPB strain lost its resistance to five pyrethroids and two neonicotinoids after 36 generations without exposure to any insecticide. It is worthwhile to examine why the resistance diminished in this population and determine whether the resistance fade away has practical value for insecticide resistance management in TPB populations. A field-collected resistant TPB population in July (Field-R1) exhibited 3.90-14.37-fold resistance to five pyrethroids and two neonicotinoids, while another field-collected TPB population in April (Field-R2) showed much lower levels of resistance (0.84-3.78-fold) due to the absence of selection pressure. Interestingly, after 36 generations without exposure to insecticide, the resistance levels in the same population (lab-R) significantly decreased to 0.80-2.09-fold. The use of detoxification enzyme inhibitors had synergistic effects on permethrin, bifenthrin and imidacloprid in resistant populations of L. lineolaris. The synergism was more pronounced in field-R2 than lab-S and lab-R TPB population. Moreover, esterase, GST, and P450 enzyme activities increased significantly by approximately 1.92-, 1.43-, and 1.44- fold in field-R1, respectively, and 1.38-fold increased P450 enzyme activities in field-R2 TPB population, compared to the lab susceptible (lab-S) TPB. In contrast, the three enzyme activities in the lab-R strain were not significantly elevated anymore relative to the lab-S population. Additionally, field-R1 TPB showed elevated expression levels of certain esterase, GST and P450 genes, respectively, while field-R2 TPB overexpressed only P450 genes. The elevation of these gene expression levels in lab-R expectedly diminished to levels close to those of the lab susceptible (lab-S) TPB populations. Our results indicated that the major mechanism of resistance in TPB populations was metabolic detoxification, and the resistance development was likely conferred by increased gene expressions of esterase, GST, and P450 genes, the fadeaway of the resistance may be caused by reversing the overexpression of esterase, GST and P450. Without pesticide selection, resistant gene (esterase, GST, P450s) frequencies declined, and detoxification enzyme activities returned to lab-S level, which resulted in the recovery of the susceptibility in the resistant TPB populations. Therefore, pest's self-purging of insecticide resistance becomes strategically desirable for managing resistance in pest populations. This article is protected by copyright. All rights reserved.

Avoiding Interferences in Advance: Cyclodextrin Polymers to Enhance Selectivity in Extraction of Organic Micropollutants for Carbon Isotope Analysis

Compound-specific isotope analysis (CSIA) of organic water contaminants can provide important information about their sources and fate in the environment. Analyte enrichment from water remains nonetheless a critical yet inevitable step before measurement. Commercially available solid-phase extraction (SPE) sorbents are inherently nonselective leading to co-extraction of concurrent dissolved organic matter (DOM) and in turn to analytical interferences, especially for low-occurring contaminants. Here, we (i) increased extraction selectivity by synthesizing cyclodextrin polymers (α-, β-, γ-CDP) as SPE sorbents, (ii) assessed their applicability to carbon isotope analysis for a selection of pesticides, and (iii) compared them with commonly used commercial sorbents. Extraction with β-CDP significantly reduced backgrounds in gas chromatography-isotope ratio mass spectrometry (GC-IRMS) and enhanced sensitivity by a factor of 7.5, which was further confirmed by lower carbon-normalized CDOM/Canalyte ratios in corresponding extracts as derived from dissolved organic carbon (DOC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Gibbs free energies of adsorption demonstrated weak competition between DOM and analyte on the three CDPs. No isotopic fractionation (Δδ13C within ± 0.3‰) was observed for the investigated pesticides after using β-CDP as an SPE sorbent covering a range of concentrations (5-500 μg L-1), flow velocities (5-40 cm min-1), and sorbent regeneration (up to six times). The present study highlights the benefit of selecting innovative extraction sorbents to avoid interferences in advance. This strategy in combination with existing cleanup approaches offers new prospects for CSIA at field concentrations of tens to hundreds of nanograms per liter.

Multi-criteria Agriculture Recommendation System using Machine Learning for Crop and Fertilizesrs Prediction.

Agriculture plays an essential role in the economies of developing countries such as India and contributes significantly to the gross domestic product (GDP). The escalation in population has led to an upsurge in food demand. Numerous challenges such as the selection of crops, fertilizers, and pesticides without considering the various parameters like types of soil, water requirement, temperature conditions, and profitability analysis of crops for a particular region may lead to degradation in the quality of crop, yield and profitability. With the advancement of Computational technologies, researchers are working on recommending crops according to soil condition, water requirement, and market profitability along with fertilizers recommendation, disease identification, and pesticide recommendation. Through this research, we propose a machine learning-based crop and fertilizer recommendation algorithm called AgriRec. We have utilized soil properties, water level, farm size, and minimum support price of crop and design a machine learning model which predicts crops for different seasons. Further, we propose another mechanism that processes the properties/details of soil, crop, and fertilizer to envisage a combination of fertilizer(s) for a given pair of soil and crop. Our algorithm is tested for 5000 land samples of Gujarat region with 24 different crop and it successfully recommends crops with 95.85% accuracy and fertilizer with 92.11% accuracy with 4 times better performance as compared to existing benchmark recommendation approaches.

Farmers’ knowledge of pests and decision for pesticide selection in cotton: impact on production efficiency levels in southern Pakistan

Pest identification and management in cotton (Gossypium hirsutum L.) cultivation are key factors in cotton production, but farmers’ knowledge of those factors is often poor. The aim of this study was to determine farmers’ knowledge of cotton pests, decision for pesticide selection, and their effect on cotton production efficiency. The stochastic frontier analysis was used to estimate cotton production efficiency and a binary logit model was applied to determine factors associated with cotton production efficiency. Farmers identified insect pests as a major cause of damage to cotton production, followed by weeds and diseases. The majority of the farmers applied chemical pesticides (87.9%) to protect cotton from pests and selected pesticides recommended by their input dealer (58%). Most farmers (58.0%) had moderate technical efficiency (between 0.80 and 0.90). The high levels of education, large farm size, good knowledge of cotton insect pests, and use of mechanical pest management were significant factors (p < 0.05) supporting cotton production efficiency. Training on cotton pest identification, pesticide selection and use is important to enhance cotton production efficiency in Pakistan.

The interaction effects of pesticides with Saccharomyces cerevisiae and their fate during wine-making process

Pesticide residues in grapes could be transferred to fermentation system during the wine-making process, which may interfere the normal proliferation of Saccharomyces cerevisiae and subsequently affect the safety and quality of wine products. However, the interaction between pesticides and Saccharomyces cerevisiae is still poorly understood. Herein, the fate, distribution and interaction effect with Saccharomyces cerevisiae of five commonly-used pesticides during the wine-making process were evaluated. The five pesticides exerted varied inhibition on the proliferation of Saccharomyces cerevisiae, and the order of inhibition intensity was difenoconazole > tebuconazole > pyraclostrobin > azoxystrobin > thiamethoxam. Compared with the other three pesticides, triazole fungicides difenoconazole and tebuconazole showed stronger inhibition and played a major role in binary exposure. The mode of action, lipophilicity and exposure concentration were important factors in the inhibition of pesticides. Saccharomyces cerevisiae had no obvious impacts on the degradation of target pesticides in the simulated fermentation experiment. However, the levels of target pesticides and their metabolite were significantly reduced during the wine-making process, with the processing factors ranged from 0.030 to 0.236 (or 0.032 to 0.257) during spontaneous (or inoculated) wine-making process. As a result, these pesticides were significantly enriched in the pomace and lees, and showed a positive correlation (R2 ≥ 0.536, n = 12, P < 0.05) between the hydrophobicity of pesticides and distribution coefficients in the solid-liquid distribution system. The findings provide important information for rational selection of pesticides on wine grapes and facilitate more accurate risk assessments of pesticides for grape processing products.

Insecticide Resistance, Resistance Mechanisms, and Phylogeny of Three &lt;em&gt;Myzus persicae&lt;/em&gt; Populations in Cabbage from Three Agroclimatic Zones of Sri Lanka

The aphid, Myzus persicae is an important sucking pest and a virus vector in many crops. The management of M. persicae is mainly through synthetic pesticides. The development of resistance against common pesticides has resulted in control failures of M. persicae. The objective of this study was to examine the status of pesticide resistance and the genetic structure of M. persicae populations. Samples were collected from cabbage cultivations in the dry (Anuradhapura), wet (Kandy), and intermediate (Badulla) zones of Sri Lanka during 2017-2019. Bioassays were conducted against five insecticides using the leaf dip method. Biochemical assays examined the activity of insecticide detoxifying enzymes and the insensitivity of the organophosphate/carbamate target site, acetylcholinesterases (AChEs) in the Badulla and Kandy populations. A 636 bp mtCOI gene sequence was used to infer the phylogeny. The resistance spectrum of the populations was low to moderate; 17-25% resistance to thiamethoxam, 11-15% to acetamiprid, 20-28 % to carbosulfan, 20-41 % to etofenprox, and 4-9 % to profenofos. Elevated activity of esterases (specific activities 0.91-1.11 μmol/min/mg protein) and glutathione S-transferases (1.27-1.82 μmol/min/mg protein) were observed for increased metabolism of pesticides. Monooxygenase-based detoxification was noticeable in neonicotinoid resistance. Insensitive AChEs contribute to organophosphate and carbamate resistance. Phylogenetic analysis revealed that 2 haplotypes of the studied mtCOI gene sequence are present in the three populations tested, indicating that environmental and biological pressure, including pesticide selection pressure, may be contributing to the emergence of novel genotypes of M. persicae.

POMEGRANATE DISEASE DETECTION AND PESTICIDES SUGGESTION

Abstract: Pomegranate is a valuable fruit crop that is widely cultivated in various regions. However, its production is often threatened by various diseases that can significantly affect its yield and quality. In this project, we propose a method for pomegranate disease detection and pesticides suggestion using the gray-level co-occurrence matrix (GLCM) algorithm. The proposed approach utilizes GLCM to extract texture features from the pomegranate leaf images and classify them into healthy or diseased categories. Moreover, we suggest the most effective pesticides to control the detected diseases by using the GLCM algorithm. The performance of the proposed method is evaluated using a dataset of pomegranate leaf images with different disease symptoms. The experimental results demonstrate the effectiveness of our approach in detecting and classifying pomegranate diseases with high accuracy. Furthermore, the suggested pesticides show promising results in controlling the diseases. Overall, the proposed method can assist farmers in identifying pomegranate diseases at an early stage and provide appropriate suggestions for pesticide selection, which can reduce crop losses and enhance crop productivity. Keywords: classification, disease detection, GLCM algorithm.

CFD-based pesticide selection for a nozzle used in a six-rotor UAV in hover mode for tea spraying.

A key challenge for unmanned aerial vehicle (UAV) spraying sometimes used in tea plantations is the downwash flow structure there stronger than in crops. In addition, the UAV spray is affected by the relationship between the nozzle design and the pesticide. However, there is little current research on this aspect. As a preliminary step this study focuses on the most appropriate pesticide for a designated nozzle in a six-rotor UAV according to the nozzle-pesticide relationship using a three-dimensional computational fluid dynamics model. This model considers the downwash flow structure effect and nozzle spray performance in hover. Nozzle FVP110-02, widely used in six-rotor UAVs, is used as a representative nozzle and bifenthrin and tea saponin water, commonly used in tea plantations, are used as the pesticides. The downwash flow structure of the six-rotor UAV in hover was conveniently controlled by the flight height and rotational speed, thereby causing the turbulence to be more stable. For nozzle FVP110-02, bifenthrin was more appropriate than tea saponin water at the same concentration, whilst bifenthrin and tea saponin water at a concentration of 1:1000 showed the best performance under identical working conditions. The numerical model developed here was shown to be effective for investigating the relationship between nozzle and pesticide. Our findings will help to not only improve UAV spraying for tea cultivation but also provide guidelines for pesticide selection in crops. Further work will address the comparison of the rigorous qualification of the numerical simulations with the measurements by the field test. © 2023 Society of Chemical Industry.