Jung Joon Park

Unbiased estimation of greenhouse whitefly, Trialeurodes vaporariorum, mean density using yellow sticky trap in cherry tomato greenhouses

The spatial distribution of the count of adult greenhouse whiteflies, Trialeurodes vaporariorum (Westwood), on yellow sticky traps was analyzed using Taylors power law and spatial autocorrelation statistics in the cherry tomato greenhouses from 1998–1999. Samples were collected weekly using a cylindrically shaped yellow sticky trap placed in a 5 by 8 grid covering 0.10–0.15 ha in each of five cherry tomato greenhouses. Taylors (1961) power law indicated that counts of T. vaporariorum on traps were aggregated within greenhouses. Spatial autocorrelation analysis showed that trap catches were similar (positively autocorrelated) to a distance of 12.5 m, and then dissimilar (negatively autocorrelated) at >12.5 m. Autocorrelation‐lag plots showed a globally significant spatial relation in 34 of 57 sample‐weeks according to Bonferronis approximation. The presence of this spatial relation was not related to the changes of mean density. Trap counts at the second lag distance (12.5–25 m) showed little spatial autocorrelation and tended to be the most spatially independent. A fixed‐precision‐level sequential sampling plan was developed using the parameters from Taylors power law. The presence of spatial dependency in data sets degraded the sampling plans precision relative to performance in data sets lacking significant spatial autocorrelation. Therefore, to obtain an unbiased mean density of T. vaporariorum per greenhouse, sticky traps should be placed at least >12.5 m apart to ensure that they are spatially independent.

Evaluation of Data Transformations and Validation of a Spatial Model for Spatial Dependency of Trialeurodes vaporariorum Populations in a Cherry Tomato Greenhouse

Abstract An evaluation of data transformations was made for constructing the reliable spatial models of the greenhouse whitefly {Trialeurodes vaporariorum Westwood) populations in a commercial cherry tomato greenhouse. A Box-Cox power transformation that is useful family of transformations was applied to original data sets. The ability of the transformations to correct for the heterogeneity of variance was tested with Shapiro-Wilk W statistics. After finding the appropriate transformations, empirical variograms were calculated and fitted to spherical model. In this study, the data transformations can stable variogram modeling by means of converting non-normal data to normal. The model was validated with new data set by comparing the deviation between observed and predicted values, using a leave-one-out method. Among the data transformations tested, loge (x+0.5) and (x+0.5) transformations were found to be appropriate at correcting for the heterogeneity of variance. According to the leave-one-out cross validation, the (x+0.5) transformation was better than the loge(x+0.5) transformation. However, both transformations produced a systemic deviation: the predicted mean was always smaller than the observed mean. No transformations were found to be appropriate, when a proportion of empty sample units (no individuals were observed) was higher than 0.2. Moreover, in this study, the abnormal high density of sample units made inappropriate spherical variogram modeling.

Detecting and cleaning outliers for robust estimation of variogram models in insect count data

Outlier detection and cleaning procedures were evaluated to estimate mathematical restricted variogram models with discrete insect population count data. Because variogram modeling is significantly affected by outliers, methods to detect and clean outliers from data sets are critical for proper variogram modeling. In this study, we examined spatial data in the form of discrete measurements of insect counts on a rectangular grid. Two well-known insect pest population data were analyzed; one data set was the western flower thrips, Frankliniella occidentalis (Pergande) on greenhouse cucumbers and the other was the greenhouse whitefly, Trialeurodes vaporariorum (Westwood) on greenhouse cherry tomatoes. A spatial additive outlier model was constructed to detect outliers in both the isolated and patchy spatial distributions of outliers, and the outliers were cleaned with the neighboring median cleaner. To analyze the effect of outliers, we compared the relative nugget effects of data cleaned of outliers and data still containing outliers after transformation. In addition, the correlation coefficients between the actual and predicted values were compared using the leave-one-out cross-validation method with data cleaned of outliers and non-cleaned data after unbiased back transformation. The outlier detection and cleaning procedure improved geostatistical analysis, particularly by reducing the nugget effect, which greatly impacts the prediction variance of kriging. Consequently, the outlier detection and cleaning procedures used here improved the results of geostatistical analysis with highly skewed and extremely fluctuating data, such as insect counts.

Estimation of Leafmine Density of Liriomyza trifolii (Diptera: Agromyzidae) in Cherry Tomato Greenhouses using Fixed Precision Sequential Sampling Plans

This study was conducted to develop sequential sampling plans to estimate leafmine density by Liriomyza trifolii (Burgess) at three fixed-precision levels in commercial tomato greenhouses. The within-greenhouse spatial patterns of leafmines were aggregated. The slopes and intercepts of Taylors power law did not differ between greenhouses and years. A fixed-precision level sampling plan was developed using the parameters of Taylors power law generated from total number of leafmines in a tomato leaf (consisted of 7 leaflets) at three precision levels (D) of 0.20, 0.25, and 0.30. The resulting sampling plans were tested with sequential bootstrap simulations (n = 500) using 9 independent data sets for validation. Bootstrap simulation within a wide range of densities demonstrated that actual D′ values at desired D = 0.30 averaged < 0.29 in all cases. even at the lowest density of leafmine (0.16 mines per leaf), the actual mean D′ value was 0.24 at D = 0.30. This result shows that the sampling plan developed in this study is effective and reliable for estimating the mine densities in tomato greenhouses.

Characterization of Leaf Mining Damage of Liriomyza trifolii (Diptera: Agromyzidae) in Cherry-Tomato Greenhouse

Abstract The development periods and areas of the leaf mines by larvae Liriomyza trifolii (Burgess) were studied in the experimental greenhouse. The mean development periods of the mines was 3.4 days (58.9 DD), and the mean area of individual mines was 0.6 cm2. Incidence of the foliar leaf mines was examined three times in the commercial tomato greenhouse. The mine numbers in each tomato leaflet varied from 1 to 9, but the leaflets with 2 mines were the most frequent case. The area of individual mine remained relatively constant regardless of the increase of mine numbers per leaflet (0.37-0.41 cm2). There was a significant relationship between the total mine number and total leaf area mined (r2=0.72). Counting the total mines provides the baseline information for the decision making and assessing yield loss in the tomato greenhouses.

Evaluation of binomial sequential classification sampling plan for leafmine of Liriomyza trifolii (Diptera: Agromyzidae) in greenhouse tomatoes

Abstract Sequential binomial sampling plans for classifying the leafmine density of Liriomyza trifolii (Burgess) were developed and evaluated in tomato greenhouses in southern Korea during 2003 – 2004. Two action thresholds (m AT), three and seven leafmines per tomato leaf, were set by examining the relationship between the leafmine density and the decrease in the tomato yield. An empirical P T -m model, which is expressed as ln(−ln(1−P T )) = γ + δ ln(m), was used to examine the relationship between the proportion of the infested tomato leaves (P T ) with at least T (tally threshold) leafmines and the mean leafmine density (m). The empirical model showed an improvement in the fit up to T = 5, which then stabilised at T > 5. Walds sequential probability ratio test was used to formulate the sequential sampling stop lines relative to m AT = 3 and 7 with T = 1, 2, 3, 4, and 5. The sampling plans were evaluated using the operating characteristic (OC) and the average sample number (ASN) functions. The robustness of the sampling plans improved with increasing T values but the improvements were negligible at T ≥ 3. Simulation analysis with the OC and ASN functions indicated that a binomial model with T = 3 was optimal and less than 70 samples were required to classify the number of leafmines relative to either m AT values. Resampling simulation of eight independent data sets with T = 3 showed that the performance of the sampling plans was superior to that expected from the established OC and ASN functions. The correct classification rate was at least 99% and the matching sample size was <57.

Gastrointestinal: Impacted gastric balloon in small bowel

A 32-year-old woman presented to the emergency department complaining of 2 days of severe abdominal colic and vomiting. An intragastric balloon (END BALL®, Endalis lab, France) had been implanted for weight reduction at an obesity clinic three months prior. Abdominal CT scan revealed a metallic foreign body with an air-fluid level at the level of the mid-jejunum with dilatation of the small bowel (Figure 1). The patient had severe abdominal pain and fever of 38.5°C 8 hrs after admission. The patient refused surgery. Because of the high suspicion of impending bowel obstruction and infarction, we performed an enteroscopy to extract the intragastric balloon. Antegrade double balloon enteroscopy (EN450T5, Fujinon, Saitama, Japan) observed a dark impacted fully inflated balloon in the jejunum (Figure 2). Because the extraction kit supplied by the intragastric balloon company was not available for the enteroscope, we used a flexible snare (Kachu Tech Co. Seoul, Korea) designed for endoscopic submucosal dissection to deflate the balloon. A giant polypectomy snare was used to grasp the proximal end of the balloon, which was then easily extracted (Figure 3). The total procedure time was 60 minutes. No immediate complications developed after the procedure and the patient’s abdominal pain subsided. Oral feeding was permitted on the day after surgery and she was discharged home thereafter. Endoscopic intragastric balloon deployment is an accepted minimally-invasive and effective temporary bariatric procedure. However, procedure-related complications have been reported including bowel obstruction, gastric ischemia and acute necrotizing pancreatitis. Small bowel obstruction due to migration of the deflated intragastric balloon is the most serious complication. Although some migrating intragastric balloons can spontaneously pass through the GI tract, most cases have to be extracted to prevent bowel obstruction surgically. In this case, we successfully extracted the impacted balloon using DBE and to our knowledge, this is the first report of a successful removal of the impacted gastric balloon in small bowel using this technique. This procedure can substitute for more invasive surgery for the extraction of migrated intragastric balloon.