Exploration of underlying patterns among conflict, socioeconomic and political factors.

The emergence of conflict is a complex issue with numerous drivers and interactions playing a role. Exploratory dimension-reduction techniques can reveal patterns of association in such complex data. In this study, an existing dataset was reanalyzed using factor analysis for mixed data to visualize the data in two-dimensional space to explore the conditions associated with high levels of conflict. The first dimension was strongly associated with resilience index, control of corruption, income, income inequality, and regime type, while the second dimension was strongly associated with oil production, regime type, conflict level, political terror level, and water stress. Hierarchical clustering from principal components was used to group the observations into five clusters. Country trajectories through the two-dimensional space provided examples of how movement in the first two dimensions reflected changes in conflict, political terror, regime type, and resilience index. These trajectories correspond to the evolution of themes in research on conflict, particularly in terms of considering the importance of climate or environmental variables in stimulating or sustaining conflict. Understanding conditions associated with high conflict can be helpful in guiding the development of future models for prediction and risk assessment.

Parsimonious Mechanistic Modeling of Bacterial Runoff into Irrigation Ponds To Inform Food Safety Management of Agricultural Water Quality.

Pond irrigation water comprises a major pathway of pathogenic bacteria to fresh produce. Current regulatory methods have been shown to be ineffective in assessing this risk when variability of bacterial concentrations is large. This paper proposes using mechanistic modeling of bacterial transport as a way to identify improved strategies for mitigating this risk pathway. If the mechanistic model is successfully tested against observed data, global sensitivity analysis (GSA) can identify important mechanisms to inform alternative, preventive bacterial control practices. Model development favored parsimony and prediction of peak bacterial concentration events. Data from two highly variable surface water irrigation ponds showed that the model performance was similar or superior to that of existing pathogen transport models, with a Nash-Sutcliffe efficiency of 0.48 and 0.18 for the two ponds. GSA quantified bacterial sourcing and hydrology as the most important processes driving pond bacterial contamination events. Model analysis has two main implications for improved regulatory methods: that peak concentration events are associated with runoff-producing rainfall events and that intercepting bacterial runoff transport may be the best option to prevent bacterial contamination of surface water irrigation ponds and thus fresh produce. This research suggests the need for temporal management strategies. IMPORTANCE Preventive management of agricultural waters requires understanding of the drivers of bacterial contamination events. We propose mechanistic modeling as a way forward to understand and predict such events and have developed and tested a parsimonious model for rain-driven surface runoff contributing to generic Escherichia coli contamination of irrigation ponds in Central Florida. While the model was able to predict the timing of peak events reasonably well, the highly variable magnitude of the peaks was less well predicted. This indicates the need to collect more data on the fecal contamination inputs of these ponds and the use of mechanistic modeling and global sensitivity analysis to identify the most important data needs.

Microbial Quality of Agricultural Water Used in Produce Preharvest Production on the Eastern Shore of Virginia.

Several produce-borne outbreaks have been associated with the use of contaminated water during preharvest applications. Salmonella has been implicated in a number of these outbreaks. The purpose of this study was to evaluate the microbial quality of agricultural surface water used in preharvest production on the Eastern Shore of Virginia in accordance with the Food Safety Modernization Act's Produce Safety Rule water standards. The study also examined the prevalence, concentration, and diversity of Salmonella in those water sources. Water samples (1 L) from 20 agricultural ponds were collected during the 2015 and 2016 growing seasons ( n = 400). Total aerobic bacteria, total coliforms, and Escherichia coli were enumerated for each sample. Population levels of each microorganism were calculated per 100-mL sample and log transformed, when necessary. Samples (250 mL) were also enriched for Salmonella. Presumptive Salmonella isolates were confirmed by PCR ( invA gene) and were serotyped. In 2016, the concentration of Salmonella in each sample was also estimated by most probable number (MPN). Indicator bacteria and environmental and meteorological factors were analyzed for their association with the detection of a Salmonella-positive water sample by using logistic regression analysis. Seventeen of the 20 ponds met the Food Safety Modernization Act's Produce Safety Rule standards for production agricultural water. Three ponds did not meet the standards because the statistical threshold value exceeded the limit. Salmonella was detected in 19% of water samples in each year (38 of 200 in 2015 and 38 of 200 in 2016). Of the 118 Salmonella isolates serotyped, 14 serotypes were identified with the most prevalent being Salmonella Newport. E. coli concentration, farm, and total aerobic bacteria concentration were significantly associated with the likelihood of detecting a Salmonella-positive sample The average concentration of Salmonella in all samples was 4.44 MPN/100 mL, with the limit of detection being 3.00 MPN/100 mL. The highest concentration of Salmonella was 93.0 MPN/100 mL. These data will assist in a better understanding of the risks that production water poses to produce contamination events.

Evaluating the U.S. Food Safety Modernization Act Produce Safety Rule Standard for Microbial Quality of Agricultural Water for Growing Produce.

The U.S. Food and Drug Administration (FDA) has defined standards for the microbial quality of agricultural surface water used for irrigation. According to the FDA produce safety rule (PSR), a microbial water quality profile requires analysis of a minimum of 20 samples for Escherichia coli over 2 to 4 years. The geometric mean (GM) level of E. coli should not exceed 126 CFU/100 mL, and the statistical threshold value (STV) should not exceed 410 CFU/100 mL. The water quality profile should be updated by analysis of a minimum of five samples per year. We used an extensive set of data on levels of E. coli and other fecal indicator organisms, the presence or absence of Salmonella, and physicochemical parameters in six agricultural irrigation ponds in West Central Florida to evaluate the empirical and theoretical basis of this PSR. We found highly variable log-transformed E. coli levels, with standard deviations exceeding those assumed in the PSR by up to threefold. Lognormal distributions provided an acceptable fit to the data in most cases but may underestimate extreme levels. Replacing censored data with the detection limit of the microbial tests underestimated the true variability, leading to biased estimates of GM and STV. Maximum likelihood estimation using truncated lognormal distributions is recommended. Twenty samples are not sufficient to characterize the bacteriological quality of irrigation ponds, and a rolling data set of five samples per year used to update GM and STV values results in highly uncertain results and delays in detecting a shift in water quality. In these ponds, E. coli was an adequate predictor of the presence of Salmonella in 150-mL samples, and turbidity was a second significant variable. The variability in levels of E. coli in agricultural water was higher than that anticipated when the PSR was finalized, and more detailed information based on mechanistic modeling is necessary to develop targeted risk management strategies.