The Centers for Disease Control and Prevention has reported that foodborne disease outbreaks associated with fruits and vegetables increased during the past decade. This study was conducted to characterize the routes of microbial contamination in produce and to identify areas of potential contamination from production through postharvest handling. We report here the levels of bacterial indicator organisms and the prevalence of selected pathogens in produce samples collected from the southern United States. A total of 398 produce samples (leafy greens, herbs, and cantaloupe) were collected through production and the packing shed and assayed by enumerative tests for total aerobic bacteria, total coliforms, total Enterococcus, and Escherichia coli. These samples also were analyzed for Salmonella, Listeria monocytogenes, and E. coli O157:H7. Microbiological methods were based on methods recommended by the U.S. Food and Drug Administration. For all leafy greens and herbs, geometric mean indicator levels ranged from 4.5 to 6.2 log CFU/g (aerobic plate count); less than 1 to 4.3 log CFU/g (coliforms and Enterococcus); and less than 1 to 1.5 log CFU/g (E. coli). In many cases, indicator levels remained relatively constant throughout the packing shed, particularly for mustard greens. However, for cilantro and parsley, total coliform levels increased during the packing process. For cantaloupe, microbial levels significantly increased from field through packing, with ranges of 6.4 to 7.0 log CFU/g (aerobic plate count); 2.1 to 4.3 log CFU/g (coliforms); 3.5 to 5.2 log CFU/g (Enterococcus); and less than 1 to 2.5 log CFU/g (E. coli). The prevalence of pathogens for all samples was 0, 0, and 0.7% (3 of 398) for L. monocytogenes, E. coli O157:H7, and Salmonella, respectively. This study demonstrates that each step from production to consumption may affect the microbial load of produce and reinforces government recommendations for ensuring a high-quality product.

The objective of this study was to perform a systematic review of risk factors for contamination of fruits and vegetables with Listeria monocytogenes, Salmonella, and Escherichia coli O157:H7 at the preharvest level. Relevant studies were identified by searching six electronic databases: MEDLINE, EMBASE, CAB Abstracts, AGRIS, AGRICOLA, and FSTA, using the following thesaurus terms: L. monocytogenes, Salmonella, E. coli O157 AND fruit, vegetable. All search terms were exploded to find all related subheadings. To be eligible, studies had to be prospective controlled trials or observational studies at the preharvest level and had to show clear and sufficient information on the process in which the produce was contaminated. Of the 3,463 citations identified, 68 studies fulfilled the eligibility criteria. Most of these studies were on leafy greens and tomatoes. Six studies assessed produce contamination with respect to animal host-related risk factors, and 20 studies assessed contamination with respect to pathogen characteristics. Sixty-two studies assessed the association between produce contamination and factors related to produce, water, and soil, as well as local ecological conditions of the production location. While evaluations of many risk factors for preharvest-level produce contamination have been reported, the quality assessment of the reviewed studies confirmed the existence of solid evidence for only some of them, including growing produce on clay-type soil, the application of contaminated or non-pH-stabilized manure, and the use of spray irrigation with contaminated water, with a particular risk of contamination on the lower leaf surface. In conclusion, synthesis of the reviewed studies suggests that reducing microbial contamination of irrigation water and soil are the most effective targets for the prevention and control of produce contamination. Furthermore, this review provides an inventory of the evaluated risk factors, including those requiring more research.

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The objective of this study was to determine the effects of farm management and environmental factors on preharvest spinach contamination with generic Escherichia coli as an indicator of fecal contamination. A repeated cross-sectional study was conducted by visiting spinach farms up to four times per growing season over a period of 2 years (2010 to 2011). Spinach samples (n ‫؍‬ 955) were collected from 12 spinach farms in Colorado and Texas as representative states of the Western and Southwestern United States, respectively. During each farm visit, farmers were surveyed about farm-related management and environmental factors using a questionnaire. Associations between the prevalence of generic E. coli in spinach and farm-related factors were assessed by using a multivariable logistic regression model including random effects for farm and farm visit. Overall, 6.6% of spinach samples were positive for generic E. coli. Significant risk factors for spinach contamination with generic E. coli were the proximity (within 10 miles) of a poultry farm, the use of pond water for irrigation, a >66-day period since the planting of spinach, farming on fields previously used for grazing, the production of hay before spinach planting, and the farm location in the Southwestern United States. Contamination with generic E. coli was significantly reduced with an irrigation lapse time of >5 days as well as by several factors related to field workers, including the use of portable toilets, training to use portable toilets, and the use of hand-washing stations. To our knowledge, this is the first report of an association between field workers' personal hygiene and produce contamination with generic E. coli at the preharvest level. Collectively, our findings support that practice of good personal hygiene and other good farm management practices may reduce produce contamination with generic E. coli at the preharvest level.

The potato psyllid, Bactericera cockerelli (Sulc) (Hemiptera: Triozidae), and its associated pathogen "Candidatus Liberibacter solanacearum" (Ca. L. solanacearum), the putative causal agent of zebra chip (ZC) disease in potatoes (Solanum tuberosum L.), were sampled in commercial potato fields and untreated control plots for 3 yr in multiple locations in Texas, Kansas, Nebraska, and Colorado. Populations of the potato psyllid varied across years and across potato growing regions. However, the percentage of potato psyllids infected with Ca. L. solanacearum although variable across years, was consistently highest in the Lower Rio Grande Valley of Texas (LRGV), the reported overwintering location for this pest. The numbers of Ca. L. solanacearum-infected psyllids collected on field traps and large nymphs counted on leaf samples were both positively correlated with the final percentage of ZC in tubers. In the LRGV, where vector and disease pressure is the highest, population levels of immature life stages of the psyllid and percentage of ZC differed greatly between commercial and untreated fields. These results show that the pest management program that was used can be effective at controlling development of the psyllid and ultimately reducing the incidence of ZC.

No abstract

Yield prediction and variety selection are critical components for assessing production and performance in breeding programs and precision agriculture. Since plants integrate their genetics, surrounding environments, and management conditions, crop phenotypes have been measured over cropping seasons to represent the traits of varieties. These days, UAS (unmanned aircraft system) provides a new opportunity to collect high-quality images and generate reliable phenotypic data efficiently. Here, we propose high-throughput phenotyping (HTP) from multitemporal UAS images for tomato yield estimation. UAS-based RGB and multispectral images were collected weekly and biweekly, respectively. The shape of the features of tomatoes such as canopy cover, canopy, volume, and vegetation indices derived from UAS imagery was estimated throughout the entire season. To extract time-series features from UAS-based phenotypic data, crop growth and growth rate curves were fitted using mathematical curves and first derivative equations. Time-series features such as the maximum growth rate, day at a specific event, and duration were extracted from the fitted curves of different phenotypes. The linear regression model produced high R 2 values even with different variable selection methods: all variables (0.79), forward selection (0.7), and backward selection (0.77). With factor analysis, we figured out two significant factors, growth speed and timing, related to high-yield varieties. Then, five time-series phenotypes were selected for yield prediction models explaining 65 percent of the variance in the actual harvest. The phenotypic features derived from RGB images played more important roles in prediction yield. This research also demonstrates that it is possible to select lower-performing tomato varieties successfully. The results from this work may be useful in breeding programs and research farms for selecting high-yielding and disease-/pest-resistant varieties.

This study investigates the potential impact of food safety outbreaks on domestic shipments, imports, and prices of the produce industry. Moreover, the compliance costs associated with new food safety standards were also estimated. Three case studies were analyzed to assess these potential impacts: the muskmelon (Cucumis melo) outbreak of Mar.–Apr. 2008, the spinach (Spinacea oleracea) outbreak of Sept. 2006, and the tomato (Solanum lycopersicum) outbreak of June–July 2008. The results demonstrate that the costs incurred by producers because of food safety outbreaks in produce are far greater than preventing such incidents.