Application of the multiple dependent state sampling strategy to late adolescent suicide rates.

A multiple dependent state sampling plan (MDSSP) is designed when the lifetimes of the variables follow New Lomax Rayleigh Distribution (NLRD). The decision to accept or reject a lot in the proposed methodology is based on the quality of the given present or previous lots. A binomial model-based operating characteristic curve (OC curve) for continuous lots of variables under similar settings in healthcare is used in finding the probability of acceptance, acceptance number, rejection number, and the number of preceding (succeeding) lots to consider. Time truncated life test based on the specified median of the NLRD is used in designing the current acceptance sampling plan. For specified values of the parameters of NLRD, quantile ratios, consumer's risk and producer's risk, average sampling number (ASN) and probability of acceptance of a lot are reported in tables. Real data on worldwide suicide rates of 15-19 years in the year 2019 from the World Health Organization (WHO) website is considered to illustrate this methodology. The minimum sample size required from the selected data to comment on worldwide suicide rates in late adolescents is explained with MDSSP. The results of the proposed acceptance sampling method are compared with the single-stage sampling plan.

Sampling, a New iOS Application for Assessment of Damage by Diseases and Insect Pests Using Sequential Sampling Plans.

Regular scouting for plant diseases and insect pests by growers, crop consultants, extension educators, and researchers (herein defined as stakeholders) is the cornerstone of integrated pest management practices. Sequential sampling plans have the potential to save time and labor in field scouting and reduce the frequency of errors surrounding decision-making. The incorporation of the algorithms behind sequential sampling plans into mobile devices can make scouting for diseases and insect pests more straightforward, practical, and enjoyable. Here, we introduce an iOS application called Sampling. The application was designed for stakeholders to use on a mobile device for assessing disease and insect pest incidence in the field using sequential sampling plans. The application allows users to select a disease or insect pest from a prepopulated list and specify the objective of sampling: Estimation or classification. Conducting sequential sampling depends upon different precision levels and action thresholds within each objective. Detailed instructions for each sequential sampling plan are available as a guide. When sampling begins, users enter the number of diseased individuals at each sampling unit. The specific algorithm developed for the disease or insect pest will inform the user when to stop sampling for the desired goal and return the final incidence and precision or threshold achieved. Results are automatically saved in the application, and the user can inspect and share results by exporting them to a range of compatible programs. The initial version of Sampling (1.1) was released with the sequential sampling plans for Cercospora leaf spot of table beet. Sequential sampling plans for additional diseases or pests will be added to Sampling in subsequent versions. Sampling is available as a free download from the Apple Store (https://apple.co/3pUiYKy) and is compatible with iOS 14.0 or greater on the iPhone or iPad.

Inspection plan for COVID-19 patients for Weibull distribution using repetitive sampling under indeterminacy.

BACKGROUND: This research work is elaborated investigation of COVID-19 data for Weibull distribution under indeterminacy using time truncated repetitive sampling plan. The proposed design parameters like sample size, acceptance sample number and rejection sample number are obtained for known indeterminacy parameter. METHODS: The plan parameters and corresponding tables are developed for specified indeterminacy parametric values. The conclusion from the outcome of the proposed design is that when indeterminacy values increase the average sample number (ASN) reduces. RESULTS: The proposed repetitive sampling plan methodology application is given using COVID-19 data belong to Italy. The efficiency of the proposed sampling plan is compared with the existing sampling plans. CONCLUSIONS: Using the tables and COVID-19 data illustration, it is concluded that the proposed plan required a smaller sample size as examined with the available sampling plans in the literature.

Suitability of centrifuge water for detecting the presence of Escherichia coli versus finished fresh-cut lettuce testing.

Fresh produce causes most foodborne outbreaks in the USA, and it is also considered a hazardous food product in other areas of the world such as Europe. The outbreaks attributed to fresh produce increase the focus of producers on hygiene to minimize exposure to food hazards. The fresh produce industry has the urgent need to detect if there are production lots contaminated with pathogenic microorganisms before distribution. Although the industry is mostly using end-product testing for the detection of target microorganisms, previous studies have evaluated the suitability of different sampling points within the production line of a fresh-cut processing plant. In the present study, the centrifuge effluent water was assessed as an alternative sampling point to end-product testing. E. coli was selected as an index microorganism of the presence of pathogens. The presence of E. coli was assessed in centrifuge effluent water, and fresh-cut lettuce from a commercial fresh-cut produce processing line (n = 95). The rate of false positives and negatives, as well as the specificity, sensitivity, and efficiency of the alternative method were calculated. The mean population of E. coli in positive water samples was 0.86 log cfu/100 mL, while the mean population of E. coli in positive fresh-cut lettuce samples was 0.23 log cfu/g. The proportion of positive samples in centrifuge effluent water and lettuce was similar (≈20%), and most of the results in both matrices were coincident (81.1%). However, the alternative method was not reliable due to its low sensitivity, as only 47.6% of the lettuce samples positive for E. coli could be matched with positive water samples.

Dietary Supplement Ingredient Database (DSID) and the Application of Analytically Based Estimates of Ingredient Amount to Intake Calculations.

OBJECTIVE: We describe the purpose of the Dietary Supplement Ingredient Database (DSID), the statistical methodology underlying online calculators of analytically verified supplement content estimates, and the application and significance of DSID label adjustments in nutritional epidemiology. BACKGROUND AND HISTORY: During dietary supplement (DS) manufacturing, many ingredients are added at higher than declared label amounts, but overages are not standardized among manufacturers. As a result, researchers may underestimate nutrient intakes from DSs. The DSID provides statistical tools on the basis of the results of chemical analysis to convert label claims into analytically predicted ingredient amounts. These adjustments to labels are linked to DS products reported in NHANES. RATIONALE: Tables summarizing the numbers of NHANES DS products with ingredient overages and below label content show the importance of DSID adjustments to labels for accurate intake calculations. RECENT DEVELOPMENTS: We show the differences between analytically based estimates and labeled content for vitamin D, calcium, iodine, caffeine, and omega-3 (n-3) fatty acids and their potential impact on the accuracy of intake assessments in large surveys. Analytical overages >20% of label levels are predicted for several nutrients in 50-99% of multivitamin-mineral products (MVMs) reported in NHANES: for iodine and selenium in adult MVMs, for iodine and vitamins D and E in children's MVMs, and for iodine, chromium, and potassium in nonprescription prenatal MVMs. Predicted overages of 10-20% for calcium can be applied to most MVMs and overages >10% for folic acid in the vast majority of adult and children's MVMs. FUTURE DIRECTIONS: DSID studies are currently evaluating ingredient levels in prescription prenatal MVMs and levels of constituents in botanical DSs. CONCLUSIONS: We estimate that the majority of MVM products reported in NHANES have significant overages for several ingredients. It is important to account for nonlabeled additional nutrient exposure from DSs to better evaluate nutritional status in the United States.

Microbiological and Modeling Approach to Derive Performance Objectives for Bacillus cereus Group in Ready-to-Eat Salads.

In this article, the performance objectives (POs) for Bacillus cereus group (BC) in celery, cheese, and spelt added as ingredients in a ready-to-eat mixed spelt salad, packaged under modified atmosphere, were calculated using a Bayesian approach. In order to derive the POs, BC detection and enumeration were performed in nine lots of naturally contaminated ingredients and final product. Moreover, the impact of specific production steps on the BC contamination was quantified. Finally, a sampling plan to verify the ingredient lots' compliance with each PO value at a 95% confidence level (CL) was defined. To calculate the POs, detection results as well as results above the limit of detection but below the limit of quantification (i.e., censored data) were analyzed. The most probable distribution of the censored data was determined and two-dimensional (2D) Monte Carlo simulations were performed. The PO values were calculated to meet a food safety objective of 4 log10 cfu of BC for g of spelt salad at the time of consumption. When BC grows during storage between 0.90 and 1.90 log10 cfu/g, the POs for BC in celery, cheese, and spelt ranged between 1.21 log10 cfu/g for celery and 2.45 log10 cfu/g for spelt. This article represents the first attempt to manage the concept of PO and 2D Monte Carlo simulation in the flow chart of a complex food matrix, including raw and cooked ingredients.

Microbiological sampling plan based on risk classification to verify supplier selection and production of served meals in food service operation.

Food service operations are confronted with a diverse range of raw materials and served meals. The implementation of a microbial sampling plan in the framework of verification of suppliers and their own production process (functionality of their prerequisite and HACCP program), demands selection of food products and sampling frequencies. However, these are often selected without a well described scientifically underpinned sampling plan. Therefore, an approach on how to set-up a focused sampling plan, enabled by a microbial risk categorization of food products, for both incoming raw materials and meals served to the consumers is presented. The sampling plan was implemented as a case study during a one-year period in an institutional food service operation to test the feasibility of the chosen approach. This resulted in 123 samples of raw materials and 87 samples of meal servings (focused on high risk categorized food products) which were analyzed for spoilage bacteria, hygiene indicators and food borne pathogens. Although sampling plans are intrinsically limited in assessing the quality and safety of sampled foods, it was shown to be useful to reveal major non-compliances and opportunities to improve the food safety management system in place. Points of attention deduced in the case study were control of Listeria monocytogenes in raw meat spread and raw fish as well as overall microbial quality of served sandwiches and salads.

A screening sampling plan to detect Mycobacterium avium subspecies paratuberculosis-positive dairy herds.

Mycobacterium avium ssp. paratuberculosis (MAP) is the etiological agent of paratuberculosis, a chronic contagious bacterial disease primarily affecting dairy cattle. Paratuberculosis represents a dual problem for the milk production chain: in addition to economic losses to affected herds, MAP may have zoonotic potential. Infected herds must be identified in order to implement programs designed to reduce the incidence of disease within and between herds and to prevent MAP from entering the food chain. The objective of this study was to evaluate the sensitivity and specificity of a screening sampling plan (SSP) to detect MAP-positive dairy herds by repetitive analysis of bulk tank milk (BTM) samples by ELISA and in-line milk filter (ILMF) samples by PCR. Samples from BTM and ILMF were collected twice from 569 dairy herds in southern Italy. Additionally, 12,016 individual milk samples were collected: 9,509 from 102 SSP-positive herds (SSP MAP-positive) and 2,507 from 21 randomly selected SSP-negative herds (SSP MAP-negative). There was a total of 126 SSP MAP-positive herds (i.e., 21.3% SSP MAP-positive herds; 95% confidence interval=18.0-24.9); the within-herd apparent prevalence (AP) ranged between 0.00 and 22.73% (mean 6.07%). A significant difference in within-herd AP was shown between SSP MAP-positive herds and SSP MAP-negative herds. A highly significant association was shown between the median AP herd status (>5%) and positivity to at least one ILMF or BTM sample. The SSP detected a minimum of 56.25% of low AP herds (AP ≤ 2.0%) up to a maximum of 100% of herds with a within-herd AP ≥ 8.0%. Overall, the SSP detected 85.57% of herds in which at least one individual milk sample was positive by ELISA. The proposed SSP was an inexpensive and useful tool to detect MAP-positive herds with a higher risk of infection diffusion and milk contamination. Although the SSP cannot be used for MAP-free certification of herds, it could be useful to prioritize appropriate control measures aimed at reducing the prevalence of infection in dairy herds and milk contamination.

Optimal Food Safety Sampling Under a Budget Constraint.

Much of the literature regarding food safety sampling plans implicitly assumes that all lots entering commerce are tested. In practice, however, only a fraction of lots may be tested due to a budget constraint. In such a case, there is a tradeoff between the number of lots tested and the number of samples per lot. To illustrate this tradeoff, a simple model is presented in which the optimal number of samples per lot depends on the prevalence of sample units that do not conform to microbiological specifications and the relative costs of sampling a lot and of drawing and testing a sample unit from a lot. The assumed objective is to maximize the number of nonconforming lots that are rejected subject to a food safety sampling budget constraint. If the ratio of the cost per lot to the cost per sample unit is substantial, the optimal number of samples per lot increases as prevalence decreases. However, if the ratio of the cost per lot to the cost per sample unit is sufficiently small, the optimal number of samples per lot reduces to one (i.e., simple random sampling), regardless of prevalence. In practice, the cost per sample unit may be large relative to the cost per lot due to the expense of laboratory testing and other factors. Designing effective compliance assurance measures depends on economic, legal, and other factors in addition to microbiology and statistics.

The development of an extended Weibull model with applications to medicine, industry and actuarial sciences.

This paper delves into the theoretical and practical exploration of the complementary Bell Weibull (CBellW) model, which serves as an analogous counterpart to the complementary Poisson Weibull model. The study encompasses a comprehensive examination of various statistical properties of the CBellW model. Real data applications are carried out in three different fields, namely the medical, industrial and actuarial fields, to show the practical versatility of the CBellW model. For the medical data segment, the study utilizes four data sets, including information on daily confirmed COVID-19 cases and cancer data. Additionally, a Group Acceptance Sampling Plan (GASP) is designed by using the median as quality parameter. Furthermore, some actuarial risk measures for the CBellW model are obtained along with a numerical illustration of the Value at Risk and the Expected Shortfall. The research is substantiated by a comprehensive numerical analysis, model comparisons, and graphical illustrations that complement the theoretical foundation.

Estimation procedures and optimal censoring schemes for an improved adaptive progressively type-II censored Weibull distribution.

This paper presents an effort to investigate the estimations of the Weibull distribution using an improved adaptive Type-II progressive censoring scheme. This scheme effectively guarantees that the experimental time will not exceed a pre-fixed time. The point and interval estimations using two classical estimation methods, namely maximum likelihood and maximum product of spacing, are considered to estimate the unknown parameters as well as the reliability and hazard rate functions. The approximate confidence intervals of these quantities are obtained based on the asymptotic normality of the maximum likelihood and maximum product of spacing methods. The Bayesian estimations are also considered using MCMC techniques based on the two classical approaches. An extensive simulation study is implemented to compare the performance of the different methods. Further, we propose the use of various optimality criteria to find the optimal sampling scheme. Finally, one real data set is applied to show how the proposed estimators and the optimality criteria work in real-life scenarios. The numerical outcomes demonstrated that the Bayesian estimates using the likelihood and product of spacing functions performed better than the classical estimates.

Life truncated multiple dependent state plan for imprecise Weibull distributed data.

This paper aims to provide a multiple dependent state (MDS) sampling technique for light-emitting diode luminous intensities under indeterminacy by employing time truncated sampling schemes and the Weibull distribution. This indicates that ASN is significantly impacted by the indeterminacy parameter. Furthermore, a comparison is shown between the existing, indeterminate sampling plans and the recommended sample designs. The projected sampling technique is illustrated by calculating the luminous intensities of LEDs using the Weibull distribution. Based on the findings and practical example, we conclude that the suggested strategy needs a smaller sample size than SSP and the current MDS sampling plan.

Improvement of the safety of the red pepper spice with FMEA and post processing EWMA quality control charts.

Although there are numerous decades-old studies drawing attention to the presence of aflatoxins in spices, and particularly in red pepper spice, the problem has not been eradicated. In the present study, information presented in the literature, about production method of red pepper spice, its contamination with aflatoxin, and the uncertainty about the data are assessed to find out the points where improvement may be achieved. Failure Mode and Effect Analysis (FMEA) are performed to assess the risk. The highest total risk attributable to chemical plus physical plus biological causes is associated with the washing stage (RPN=363), which is followed by the receiving (RPN=342) and the storage (RPN=342) stages. The highest risk attributable to biological causes (RPN=180) is associated with microbial growth and aflatoxin production due to insufficient control of drying conditions. The highest chemical risk (RPN=144) is found for the presence of unintentional food additives, such as pesticides, herbicides, hormones, and heavy metals in fresh red pepper fruits. EWMA (exponentially weighted average) charts are employed to monitor aflatoxin production during storage. They successfully distinguished between the batches, which turned to be unsafe. Risk associated with unintentional additives may be reduced by using certified additives only. Better drying control will definitely reduce the risk associated with the drying process. Codex Alimentarius plan has worldwide acceptance for assessing safety of the nuts. Risk of accepting the batches contaminated with aflatoxin may be eliminated by applying the Codex Alimentarius sampling plan before putting the dry pulverized red pepper into the storage facility.

Sampling Twitter users for social science research: evidence from a systematic review of the literature.

All social media platforms can be used to conduct social science research, but Twitter is the most popular as it provides its data via several Application Programming Interfaces, which allows qualitative and quantitative research to be conducted with its members. As Twitter is a huge universe, both in number of users and amount of data, sampling is generally required when using it for research purposes. Researchers only recently began to question whether tweet-level sampling-in which the tweet is the sampling unit-should be replaced by user-level sampling-in which the user is the sampling unit. The major rationale for this shift is that tweet-level sampling does not consider the fact that some core discussants on Twitter are much more active tweeters than other less active users, thus causing a sample biased towards the more active users. The knowledge on how to select representative samples of users in the Twitterverse is still insufficient despite its relevance for reliable and valid research outcomes. This paper contributes to this topic by presenting a systematic quantitative literature review of sampling plans designed and executed in the context of social science research in Twitter, including: (1) the definition of the target populations, (2) the sampling frames used to support sample selection, (3) the sampling methods used to obtain samples of Twitter users, (4) how data is collected from Twitter users, (5) the size of the samples, and (6) how research validity is addressed. This review can be a methodological guide for professionals and academics who want to conduct social science research involving Twitter users and the Twitterverse.

Sequential Sampling Plans for Thrips (Thysanoptera: Thripidae) in Soybean Crops With Insecticide Applications Using Tractor or Airplane.

Sampling plans are an essential part of integrated pest management programs. Sequential sampling plans enable rapid and low-cost assessment of pest densities. Thrips are emerging pests in soybean crops, and the main method used in pest control is chemical. In soybean crops, insecticides are applied mainly using tractors or airplanes. Thus, this work aimed to determine sequential sampling plans for thrips in soybean crops with insecticide applications using a tractor or airplane. Data were collected in 56 soybean fields, and each field was 20 ha. Sampling plans were determined and validated. The lower (m0) and upper (m1) limits of the sequential sampling plans were: m0 = 1.72 and m1 = 3.43 (by tractor applications) and, m0 = 2.27 and m1 = 4.53 thrips. sample-1 (by airplane applications). The slope (S) and the lower (h0) and upper (h1) intercepts of the sequential sampling plans were: S = 2.42, h0 = -5.79, and h1 = 5.79 (by tractor applications) and, S = 3.19, h0 = -6.83, and h1 = 6.83 (by airplane applications). Sequential sampling plans allowed for correct decisions to be made in all situations using a maximum of 10 samples. The sequential plan reduced the sampling effort by over 87% compared to conventional sampling plans. Therefore, these control decision-making systems have proven feasible and advantageous for implementing integrated pest management programs for controlling thrips species in soybean crops.

Distribution and fixed-precision sampling plans for diamondback moth (Lepidoptera: Plutellidae), on winter-spring cabbage.

The diamondback moth (Plutella xylostella L.) is the most destructive insect pest on cabbage (Brassica oleracea var. capitata L.). Infestation by this pest usually results in the indiscriminate use of insecticides by farmers due to a lack of sampling plans for this pest. Sampling plans for P. xylostella management decisions on winter-spring cabbage in the Eastern Cape Province of South Africa were developed, through population monitoring that comprised weekly counts of immature stages of P. xylostella on 60 plants for 11 wk each during the winter and spring seasons. The mean density-variance relationship was used to describe the distribution of the pest, and number of infested plants was used to develop a fixed-precision sampling plan. All plant growth stages preceding maturation were vulnerable to P. xylostella damage resulting in yield losses. A high aggregation of P. xylostella on cabbage was observed in spring than in winter. The average sample number to estimate P. xylostella density within a 15% standard error of the mean was 35 plants. Furthermore, the estimated plant proportion action threshold (AT) was 51% with density action thresholds of 0.50 and 0.80 for spring and winter, respectively. Fitting P. xylostella cumulative counts in the winter and spring sampling plans resulted in 100% and 45% reduction in insecticide treatments. The similarity of sample size and ATs between both seasons provides evidence that a single sampling plan is practical for all cabbage growing seasons. The similarity of the estimated ATs to those acceptable in established integrated pest management programs indicates reliability.

Microbiological and Chemical Analysis of Food Collected Under Official Control in the Emilia-Romagna Region of Northern Italy, 2014-2019.

This study analyzed data from 6 years (2014-2019) of official controls in the Emilia-Romagna region (northern Italy) to investigate the frequencies of human pathogens and chemical hazards in foods during production and distribution. Campylobacter spp. was the most prevalent pathogen, isolated in 4.4% of the 1,078 food samples examined, followed by Salmonella spp. (2.8%), Shiga toxin-producing Escherichia coli (STEC) (1.9%), and Listeria monocytogenes (0.9%). Salmonella serotyping showed that the isolates belonged to the serotypes most commonly isolated from humans in Emilia-Romagna. These serotypes were as follows: S. Infantis (34.8%), mostly isolated from chicken, monophasic S. Typhimurium (1,4, [5],12:i:-) (12.6%), S. Bredeney (8.9%), and S. Derby (8.6%). No Clostridium botulinum, Yersinia spp., and Shigella spp. were isolated. No positivity was detected for hepatitis A virus, while 5.1% of samples taken in the production phase of the food chain were found to be contaminated with norovirus. The chemical analyses identified environmental contaminants within legal limits (heavy metals, 0.6% positive overall; mycotoxins, 0.4% positive overall), analytes subjected to monitoring (perfluoro-alkyl substances (PFASs), 6.2% positive overall; inorganic arsenic, no positives overall) and process contaminants and additives within legal limits (acrylamide, 9.6% positive overall; permitted or nonpermitted additives, 0.9% positive overall). Only one sample showed dioxins and polychlorinated biphenyls (PCBs) at levels higher than the legal limits. The monitoring by competent authorities (CA) of food contamination can generate useful data that can be used as a basis for estimating the exposure to different food contaminants over time and for evaluating the effects of control measures on the contamination of food.

Determination of new multiple deferred state sampling plan with economic perspective under Weibull distribution.

This study focuses on designing a new multiple deferred state sampling plan to ensure products' mean lifetime that complies with Weibull distribution. The parameters that characterize the proposed plan are determined by considering two specified points on the operating characteristic curve. Practical applications of the proposed plan for assuring mean lifetimes of electrical appliances as well as Lithium-ion batteries are explained by using real-time data and simulated data respectively. Sensitivity analysis on testing time of the life test is done and theoretical average sample number is compared with the same obtained by simulation. By comparing the proposed plan with other existing sampling plans based on discriminating power, the number of units required for lot sentencing, it is observed that the new multiple deferred state sampling plan provides quality assurance for the products with low inspection costs compared to the other existing sampling plans. Besides, this study investigates the economic design of a new multiple deferred state sampling plan and compares the total cost needed in the proposed plan with the same required for some other existing sampling plans.

Assessing the effectiveness of performance standards for Salmonella contamination of chicken parts.

The United States Department of Agriculture's Food Safety and Inspection Service implemented Salmonella performance standards for establishments producing chicken parts in 2016. The standards were chosen based on the assumption that a 30 % reduction in the occurrence of Salmonella-contaminated chicken parts samples (i.e., legs, breasts or wings) would result following implementation of the performance standard program. The derivation of the performance standards was based on data collected prior to the implementation of the standards and in the intervening years, so overall changes in the Salmonella contamination of this product can be assessed. This study presents a historical review of changes in Salmonella contamination on chicken parts as these changes relate to the performance standard. The analysis demonstrates that the reduction in Salmonella contaminated chicken parts samples was more than 75 %, so the FSIS risk assessment significantly underestimated the actual reduction in Salmonella contamination. An analysis of chicken parts samples collected at retail demonstrates reductions of a similar magnitude. Changes in the characteristics of Salmonella contamination that are potentially relevant to the occurrence or severity of human illness, such as seasonal changes in contamination, the composition of serotypes and changes in antimicrobial resistance, are also assessed. Small but significant seasonal increases in contamination were observed, with the peaks occurring in late winter rather than the more traditional late summer peak. Rapid changes in both the five most common serotypes and antimicrobial resistance patterns were also observed.

Sampling Optimization and Crop Interface Effects on Lygus lineolaris Populations in Southeastern USA Cotton.

Tarnished plant bug, Lygus lineolaris (Hemiptera: Miridae), is an economically damaging pest in cotton production systems across the southern United States. We systematically scouted 120 commercial cotton fields across five southeastern states during susceptible growth stages in 2019 and 2020 to investigate sampling optimization and the effect of interface crop and landscape composition on L. lineolaris abundance. Variance component analysis determined field and within-field spatial scales, compared with agricultural district and state, accounted for more variation in L. lineolaris density using sweep net and drop cloth sampling. This result highlights the importance of field-level scouting efforts. Using within-field samples, a fixed-precision sampling plan determined 8 and 23 sampling units were needed to determine L. lineolaris population estimates with 0.25 precision for sweep net (100 sweeps per unit) and drop cloth (1.5 row-m per unit) sampling, respectively. A spatial Bayesian hierarchical model was developed to determine local landscape (<0.5 km from field edges) effects on L. lineolaris in cotton. The proportion of agricultural area and double-crop wheat and soybeans were positively associated with L. lineolaris density, and fields with more contiguous cotton areas negatively predicted L. lineolaris populations. These results will improve L. lineolaris monitoring programs and treatment management decisions in southeastern USA cotton.