Sampling methods, dispersion patterns, and fixed precision sequential sampling plans for western flower thrips (Thysanoptera: Thripidae) and cotton fleahoppers (Hemiptera: Miridae) in cotton

A 2-yr field study was conducted to examine the effectiveness of two sampling methods (visual and plant washing techniques) for western flower thrips, Frankliniella occidentalis (Pergande), and five sampling methods (visual, beat bucket, drop cloth, sweep net, and vacuum) for cotton fleahopper, Pseudatomoscelis seriatus (Reuter), in Texas cotton, Gossypium hirsutum (L.), and to develop sequential sampling plans for each pest. The plant washing technique gave similar results to the visual method in detecting adult thrips, but the washing technique detected significantly higher number of thrips larvae compared with the visual sampling. Visual sampling detected the highest number of fleahoppers followed by beat bucket, drop cloth, vacuum, and sweep net sampling, with no significant difference in catch efficiency between vacuum and sweep net methods. However, based on fixed precision cost reliability, the sweep net sampling was the most cost-effective method followed by vacuum, beat bucket, drop cloth, and visual sampling. Taylor's Power Law analysis revealed that the field dispersion patterns of both thrips and fleahoppers were aggregated throughout the crop growing season. For thrips management decision based on visual sampling (0.25 precision), 15 plants were estimated to be the minimum sample size when the estimated population density was one thrips per plant, whereas the minimum sample size was nine plants when thrips density approached 10 thrips per plant. The minimum visual sample size for cotton fleahoppers was 16 plants when the density was one fleahopper per plant, but the sample size decreased rapidly with an increase in fleahopper density, requiring only four plants to be sampled when the density was 10 fleahoppers per plant. Sequential sampling plans were developed and validated with independent data for both thrips and cotton fleahoppers.     

Sequential sampling of Aphis gossypii Glover (Hemiptera: Aphididae) and Frankliniella schultzei Trybom (Thysanoptera: Thripidae) on cotton crop

The goal of this research was to use the sequential probability ratio test to establish a sequential sampling plan for Aphis gossypii Glover and Frankliniella schultzei Trybom infesting cotton. Field work was conducted at the agricultural experimental station of the Universidade Federal da Grande Dourados during the 2003/2004 and 2004/2005 agricultural years. Aphid colonies and individual thrips in the sampling area were counted and their numbers were recorded. The spatial distribution pattern of A. gossypii and F. schultzei in the cotton culture was aggregated. Sequential sampling plans were developed for aphids and thrips with type I and type II errors set at 0.1, common Kc = 0.6081 (aphids) and = 0. 9449 (thrips), and safety and management levels of 20% (aphids) and 40% (thrips) of infested plants. The sampling plans resulted in two decision boundaries for each species, as follows: the upper boundary, indicating when management (population control) is recommended: S1 = 4.6546 + 0.2849n (aphids), and S1 = 3.6514 + 0.1435n (thrips); and the lower boundary, indicating when population control is not necessary: S0 = -4.6546 + 0.2849n (aphids) and S0 = - 3.6514 + 0.1435n (thrips). The highest probability of error when making a decision was 3% for aphids and 2% for thrips, respectively. The maximum number of samples required to reach a decision was 63 for aphids and 95 for thrips.     

Enumerative and binomial sampling plans for citrus mealybug (Homoptera: pseudococcidae) in citrus groves

The spatial distribution of the citrus mealybug, Planococcus citri (Risso) (Homoptera: Pseudococcidae), was studied in citrus groves in northeastern Spain. Constant precision sampling plans were designed for all developmental stages of citrus mealybug under the fruit calyx, for late stages on fruit, and for females on trunks and main branches; more than 66, 286, and 101 data sets, respectively, were collected from nine commercial fields during 1992-1998. Dispersion parameters were determined using Taylor's power law, giving aggregated spatial patterns for citrus mealybug populations in three locations of the tree sampled. A significant relationship between the number of insects per organ and the percentage of occupied organs was established using either Wilson and Room's binomial model or Kono and Sugino's empirical formula. Constant precision (E = 0.25) sampling plans (i.e., enumerative plans) for estimating mean densities were developed using Green's equation and the two binomial models. For making management decisions, enumerative counts may be less labor-intensive than binomial sampling. Therefore, we recommend enumerative sampling plans for the use in an integrated pest management program in citrus. Required sample sizes for the range of population densities near current management thresholds, in the three plant locations calyx, fruit, and trunk were 50, 110-330, and 30, respectively. Binomial sampling, especially the empirical model, required a higher sample size to achieve equivalent levels of precision.     

棉花苗期棉蚜的二项式抽样设计及其精度分析(英文)

1991年4月—7月在河北省北京农业大学曲周试验站的棉花地进行苗期棉蚜（Aphisgossypii）的田间抽样调查,共收集到24组抽样数据。用泰勒幂法则对数据进行拟合,得到棉花苗期棉蚜为聚集分布。利用每样方（株）虫口不超过数阈值T（分别为0,1,2…9,10,15,20,30）头蚜虫的植株比例（PT）与种群密度（m,头／株）的关系,拟合经验关系式ln（m）=a十bln[-ln（PT）],通过对不同数阈值T的回归决定系数（r2）、种群的回归估计方差（Var（m））和抽样精度（用d估计）等进行综合分析,结果表明该蚜虫在数阈值T为15时,回归估计方差最小,回归决定系数和d值最大,因而T=15为该蚜虫的理想数阈值;而小的T值尤其是T为O时,由于产生太大的回归估计方差,很小的回归决定系数和d值即抽样的精度极低,因而不宜在实际中应用于棉蚜的二项式抽样设计。     

Sequential sampling plans to Orthezia praelonga Douglas (Hemiptera: Sternorrhyncha, Ortheziidae) in citrus

The sequential sampling is characterized by using samples of variable sizes, and has the advantage of reducing sampling time and costs if compared to fixed-size sampling. To introduce an adequate management for orthezia, sequential sampling plans were developed for orchards under low and high infestation. Data were collected in Mat?o, SP, in commercial stands of the orange variety 'Pêra Rio', at five, nine and 15 years of age. Twenty samplings were performed in the whole area of each stand by observing the presence or absence of scales on plants, being plots comprised of ten plants. After observing that in all of the three stands the scale population was distributed according to the contagious model, fitting the Negative Binomial Distribution in most samplings, two sequential sampling plans were constructed according to the Sequential Likelihood Ratio Test (SLRT). To construct these plans an economic threshold of 2% was adopted and the type I and II error probabilities were fixed in alpha = beta = 0.10. Results showed that the maximum numbers of samples expected to determine control need were 172 and 76 samples for stands with low and high infestation, respectively.     

Strategies and Decision-Support Tools for Optimizing Long-Term Groundwater Monitoring Plans—MAROS 2.0

Existing long-term groundwater monitoring programs can be optimized to increase their effectiveness/efficiency with the potential to generate considerable cost savings. The optimization can be achieved through an overall evaluation of conditions of the contaminant plume and the monitoring network, focused spatial and temporal sampling analyses, and automated and efficient management of data, analyses, and reporting. Version 2.0 of the Monitoring and Remediation Optimization System (MAROS) software, by integrating long-term monitoring analysis strategies and innovative optimization methods with a data management, processing, and reporting system, allows site managers to quickly and readily develop cost-effective long-term groundwater monitoring plans. The MAROS optimization strategy consists of a hierarchical combination of analysis methods essential to the decision-making process. Analyses are performed in three phases: 1) evaluating site information and historical monitoring data to obtain local concentration trends and an overview of the plume status; 2) developing optimal sampling plans for future monitoring at the site with innovative optimization methods; and 3) assessing the statistical sufficiency of the sampling plans to provide insights into the future performance of the monitoring program. Two case studies are presented to demonstrate the usefulness of the developed techniques and the rigor of the software.     

Strategies and Decision-Support Tools for Optimizing Long-Term Groundwater Monitoring Plans—MAROS 2.0

Existing long-term groundwater monitoring programs can be optimized to increase their effectiveness/efficiency with the potential to generate considerable cost savings. The optimization can be achieved through an overall evaluation of conditions of the contaminant plume and the monitoring network, focused spatial and temporal sampling analyses, and automated and efficient management of data, analyses, and reporting. Version 2.0 of the Monitoring and Remediation Optimization System (MAROS) software, by integrating long-term monitoring analysis strategies and innovative optimization methods with a data management, processing, and reporting system, allows site managers to quickly and readily develop cost-effective long-term groundwater monitoring plans. The MAROS optimization strategy consists of a hierarchical combination of analysis methods essential to the decision-making process. Analyses are performed in three phases: 1) evaluating site information and historical monitoring data to obtain local concentration trends and an overview of the plume status; 2) developing optimal sampling plans for future monitoring at the site with innovative optimization methods; and 3) assessing the statistical sufficiency of the sampling plans to provide insights into the future performance of the monitoring program. Two case studies are presented to demonstrate the usefulness of the developed techniques and the rigor of the software.     

BINOMIAL COUNT ANALYSIS BASED ON THE SPATIAL DISTRIBUTION AND POPULATION DENSITY ESTIMATION OF COTTON APHIDS1)

Abstract  Based on field population sampling of Aphis gossypii on cotton seedlings in Quzhou Experiment Station of China Agricultural University in Hebei Province in 1991, we obtained a data set consisting of 24 estimates of mean aphid density ( m , number of aphids per plant), variance (s²) and the proportion of plants (P_T) with no more than T aphids (T=0, 1, 2,…, 8, 10, 15, 20, respectively and defined as tally threshold). Taylor's power law fitted the data well (r²= 0. 958). The resulting slope (1. 515) was significantly greater than 1, indicating that the spatial distribution of this aphid was in aggregated pattern. An empirical relationship between m and Pr was developed for each T value using the parameters from the linear regression In( m )= a +bln[- ln( P_T )}]. The importance of the T values in reduction of sampling errors and their application to binomial sampling plans are discussed. Small T values, particularly aphid-free plant (T = 0, conventional binomial sample), could lead to spurious estimates of m from P_T . A value of T from 10 to 15 was recommended to develop binomial sampling plans for the aphids on cotton seedlings because of the relatively small sampling errors.     

The influence of management practice on the spatial distribution of Lepidopteran pests in <Emphasis Type="Italic">Brassica</Emphasis> crops in the Democratic People’s Republic of Korea: implications for sequential sampling plans

Plutella xylostella and Pieris rapae are the key components of a pest complex that attacks Brassica crops in the Democratic People’s Republic of Korea (DPRK). We examined the spatial distributions of these insects within crops both as individual species and when combined as a standard insect that was derived from their relative feeding rates. The influence of standard co-operative management practice and an integrated pest management (IPM) strategy on the dispersion of the standard insect was tested. Iwao’s m* − m relation was then used to describe the distribution of standard insects by management categories and of Pieris rapae using all data. Pest management practices only affected the distribution of the species when they were combined into standard insects. Enumerative sampling plans were therefore designed for standard insects based on population data derived from IPM-managed fields and for Pieris rapae from population data from all experimental fields. The presented plans have the potential to make a significant contribution to managing lepidopteran pests in the DPRK. The approach will be useful in the design of sequential sampling plans for other geographical regions where these pests co-occur and can also contribute to the development of sequential sampling plans for other pest complexes for which standard insects can be derived.     

Evaluation of sampling methods and development of sample plans for estimating predator densities in cotton

The cost-reliability of five sampling methods (visual search, drop cloth, beat bucket, shake bucket, and sweep net) was determined for four groups of predatory arthropods on cotton plants in Texas. The beat bucket sample method was the most cost-reliable sampling method for Orius adults, and the beat bucket and drop cloth were the most cost-reliable methods for Orius nymphs. The drop cloth and beat bucket were the most cost-reliable methods for sampling spiders. For sampling adult Coccinellidae, the sweep net and the beat bucket were the most cost-reliable. The visual sample method was the least cost-reliable method for Orius adults and nymphs and spiders. No one sampling method was identified as the optimum method for all four predator groups. However, the relative cost-reliability of the beat bucket method ranked first or second among the five sampling methods and this method was chosen for further evaluation in field studies in Texas and Arizona. The relative cost-reliability of 1-, 3-, 5-, and 10-plants per beat bucket sample varied with predator group, but multiple plant sample units were equal to or more cost-reliable than the one plant sample unit. Fixed sample plans for the beat bucket method were developed for Orius adults, Orius nymphs, spiders, and adult Coccinellidae, and the sum of these groups using the 3-, 5-, and 10-plant sample unit sizes. The greater cost-reliability of the beat bucket sampling method and its ease of use is of particular advantage in assessing predator densities in a commercial cotton field monitoring program.     

Fixed precision sampling plans for white apple leafhopper (Homoptera: Cicadellidae) on apple

Constant precision sampling plans for the white apple leafhopper, Typhlocyba pomaria McAtee, were developed so that it could be used as an indicator species for system stability as new integrated pest management programs without broad-spectrum pesticides are developed. Taylor's power law was used to model the relationship between the mean and the variance, and Green's constant precision sequential sample equation was used to develop sampling plans. Bootstrap simulations of the sampling plans showed greater precision (D = 0.25) than the desired precision (Do = 0.3), particularly at low mean population densities. We found that by adjusting the Do value in Green's equation to 0.4, we were able to reduce the average sample number by 25% and provided an average D = 0.31. The sampling plan described allows T. pomaria to be used as reasonable indicator species of agroecosystem stability in Washington apple orchards.     

Evaluation of sequential presence-absence sampling plans for the diamondback moth (Plutellidae: Lepidoptera) in cruciferous crops in Australia

Two sets of sequential presence-absence sampling plans for decision-making in the management of diamondback moth, Plutella xylostella (L.), were developed and evaluated. One set of sampling plans targeted the classification of proportions of infested plants, and the other set of sampling plans targeted the classification of larval density. The action thresholds investigated were 0.15, 0.25, 0.35, and 0.45 proportion of plants infested with larvae, and 0.2, 0.4, 0.6, and 0.8 larvae per plant. They are representative of the action thresholds currently practiced by Australian crucifer growers. For each sampling plan, the population range within which a minimal correct decision rate of 95% can be expected at a maximal average sample size of 50 plants (OC95ASN50) was specified. The closeness of an OC95ASN50 range to the target action threshold is a measure of the expected performance of the sampling plan. A closer distance reflects better performance. The OC95ASN50 ranges of the proportion-classification sampling plans were within 33-53% of the target action thresholds. The width of these OC95ASN50 ranges represents 73-87% of the entire population range (0-1). For the classification of larval density, an empirical proportion-density model was first established using data from different states and different cruciferous crops. The OC95ASN50 ranges of the density-classification sampling plans were within 57-75% of the target action threshold. Simulated sampling of 20 independent data sets showed that for most data sets the correct classification rate was at least 98% and the matching average sample size was <50 plants.     

Binomial sequential sampling plans for late instars of European corn borer (Lepidoptera: Crambidae), corn earworm (Lepidoptera: Noctuidae), and damaged kernels in sweet corn ears

Late-season infestations of European corn borer, Ostrinia nubilalis (Hübner), and corn earworm, Helicoverpa zea (Boddie), were sampled to develop binomial sequential sampling plans for larval infestations and damaged kernels in sweet corn, Zea mays L., ears, near harvest. Fields were sampled to obtain a range of larval densities likely to be encountered over a range of infestation levels and field conditions. Binomial sampling plans were developed for O. nubilalis larvae, H. zea larvae, O. nubilalis, and H. zea larvae combined, and for damaged sweet corn kernels. Observed densities ranged from 0.01 to 4.40 larvae per ear for O. nubilalis, 0.005-1.62 larvae per ear for H. zea, and 0.004-36.12 damaged kernels per ear. Results of resampling analyses, based on the proportion of ears infested with one or more larvae, or damaged kernels, indicated an average sample size of 34-37 ears was necessary to classify whether larval infestations, or the incidence of damaged kernels, exceeded 5%. Two operating characteristic curves are presented for each of the four sampling plans. Initial results, with upper bounds of 0.10, and alpha (type I) and beta (type II) error rates at 0.10 and 0.05, respectively, resulted in a 90% probability of making the correct management decision at infestation levels >10%. To improve performance of the sampling plans, we modified the binomial plans by reducing the upper bound to 0.075, while maintaining the same error rates. This plan resulted in a higher probability (>95%) of making the correct management decision to reject a sweet corn load when infestation levels are >10%.     

Occupancy Modeling Reveals Interspecific Variation in Habitat Use and Negative Effects of Dogs on Lemur Populations

International Journal of Primatology - Effective, targeted management and conservation plans for wildlife populations require reliable population sampling and estimation. Unfortunately, robust,...     

Temporal variation in arthropod sampling effectiveness: the case for using the beat sheet method in cotton

Predatory insects and spiders are key elements of integrated pest management (IPM) programmes in agricultural crops such as cotton. Management decisions in IPM programmes should to be based on a reliable and efficient method for counting both predators and pests. Knowledge of the temporal constraints that influence sampling is required because arthropod abundance estimates are likely to vary over a growing season and within a day. Few studies have adequately quantified this effect using the beat sheet, a potentially important sampling method. We compared the commonly used methods of suction and visual sampling to the beat sheet, with reference to an absolute cage clamp method for determining the abundance of various arthropod taxa over 5 weeks. There were significantly more entomophagous arthropods recorded using the beat sheet and cage clamp methods than by using suction or visual sampling, and these differences were more pronounced as the plants grew. In a second trial, relative estimates of entomophagous and phytophagous arthropod abundance were made using beat sheet samples collected over a day. Beat sheet estimates of the abundance of only eight of the 43 taxa examined were found to vary significantly over a day. Beat sheet sampling is recommended in further studies of arthropod abundance in cotton, but researchers and pest management advisors should bear in mind the time of season and time of day effects.     

Enumerative and binomial sequential sampling plans for the multicolored Asian lady beetle (Coleoptera: Coccinellidae) in wine grapes

To develop a practical integrated pest management (IPM) system for the multicolored Asian lady beetle, Harmonia axyridis (Pallas) (Coleoptera: Coccinellidae), in wine grapes, we assessed the spatial distribution of H. axyridis and developed eight sampling plans to estimate adult density or infestation level in grape clusters. We used 49 data sets collected from commercial vineyards in 2004 and 2005, in Minnesota and Wisconsin. Enumerative plans were developed using two precision levels (0.10 and 0.25); the six binomial plans reflected six unique action thresholds (3, 7, 12, 18, 22, and 31% of cluster samples infested with at least one H. axyridis). The spatial distribution of H. axyridis in wine grapes was aggregated, independent of cultivar and year, but it was more randomly distributed as mean density declined. The average sample number (ASN) for each sampling plan was determined using resampling software. For research purposes, an enumerative plan with a precision level of 0.10 (SE/X) resulted in a mean ASN of 546 clusters. For IPM applications, the enumerative plan with a precision level of 0.25 resulted in a mean ASN of 180 clusters. In contrast, the binomial plans resulted in much lower ASNs and provided high probabilities of arriving at correct "treat or no-treat" decisions, making these plans more efficient for IPM applications. For a tally threshold of one adult per cluster, the operating characteristic curves for the six action thresholds provided binomial sequential sampling plans with mean ASNs of only 19-26 clusters, and probabilities of making correct decisions between 83 and 96%. The benefits of the binomial sampling plans are discussed within the context of improving IPM programs for wine grapes.     

Sequential sampling plans for western flower thrips (Thysanoptera: Thripidae) on greenhouse cucumbers

The development of cost-effective and reliable sampling programs for the management of western flower thrips, Frankliniella occidentalis (Pergande), on greenhouse cucumbers is important for getting growers to adopt economic injury levels and economic thresholds. The objectives of this study were to develop two sequential sampling plans. A fixed-precision sequential sampling plan was designed for estimating F. occidentalis adult density at a fixed-precision level on cucumber flowers. Also, a sequential sampling plan for classifying thrips population levels as below or above economic thresholds was developed to assist in decision making for the timing of pesticide applications. Both sequential sampling plans were validated using a resampling simulation technique on nine independent data sets ranging in density from 1.25 to 12.95 adults per flower. With the fixed-precision sampling plan, average means obtained in 100 repeated simulation runs were within the 95% CI of the estimated mean for all data sets. Appropriate levels of precision for the different population densities were recommended based on the simulation results. With sequential sampling for classifying the population levels of thrips in terms of an economic threshold, it has the advantage of requiring smaller sample sizes to determine the population status when the population densities differ greatly from the critical density (i.e., economic threshold). However, this plan needs a great number of samples when population density is close to the critical density. In this case, use of a combination of both sampling plans is recommended.     

Development and economic evaluation of spatial sampling plans for corn rootworm Diabrotica spp. (Col., Chrysomelidae) adults

Abstract  To develop spatial sampling plans for corn rootworm ( Diabrotica spp.) adults, their spatial distributions were characterized and economics of sampling plans were evaluated by comparing sampling costs between spatial and conventional (non-spatial) sampling plans. Semivariogram modelling and spatial by with distance indices showed that corn rootworm adults were significantly (P < 0.05) aggregated at peak population densities and any two samples were spatially correlated within approximately 45 m, with 39–90% of the variability explained by spatial dependence. Sampling costs for spatial sampling plans linearly increased as the sampling distance decreased and exponentially increased as the field size increased. Although sampling costs for non-spatial sampling plans were generally lower, spatial sampling plans could be more economical when the mean insect density became lower and the field size became smaller. This study demonstrated that spatial sampling plans could be optimized to minimize the sampling costs and maximize the spatial resolution.     

Standardized sampling plan for Aphis gossypii based on the cotton cultivar,plant phenology and crop size

A standardized sampling plan is the starting point for developing a decision‐making system for pest control. Aphis gossypii (Hemiptera: Aphididae) is a destructive sap‐feeding pest on cotton worldwide. However, research addressing cotton cultivar, plant phenology and field size with the aim of developing a sampling plan for A. gossypii has not been done. Therefore, in this study, we developed a standardized sampling for A. gossypii as a function of these factors. To accomplish this, A. gossypii densities in four experimental cotton cultivars were sampled weekly during year one to determine the ideal aphid characteristic to sample (by individual or colony). During year one and two, A. gossypii densities were sampled weekly in the same cultivars to determine sampling unit, sampling technique and the number of samples for an A. gossypii sampling plan. Using the sample number determined, the sampling time was recorded for cotton field size of 1, 5, 10, 50, 100 and 150 ha in order to estimate the sampling cost. In cotton, the count of individuals was the best characteristic for the assessment of A. gossypii. Leaves of the most apical branches for the vegetative and reproductive cotton plant stage were the best sampling units. The best sampling technique was direct counting. The cotton cultivar did not affect the development of the sampling plan. The A. gossypii sampling plan involved the evaluation of 58 samples per zone and required 20 min (<0.35 min/sample) for the evaluation of these samples. However, the walking time between samples was the main factor responsible for the total sampling time and cost in cotton fields, and this factor strongly depends on the size of the cotton field.