Fine-scale spatiotemporal variations in bacterial community diversity in agricultural pond water.

Microbial communities in surface waters are affected by environmental conditions and can influence changes in water quality. To explore the hypothesis that the microbiome in agricultural waters associates with spatiotemporal variations in overall water quality and, in turn, has implications for resource monitoring and management, we characterized the relationships between the microbiota and physicochemical properties in a model irrigation pond as a factor of sampling time (i.e., 9:00, 12:00, 15:00) and location within the pond (i.e., bank vs. interior sites and cross-sectional depths at 0, 1, and 2 m). The microbial communities, which were defined by 16S rRNA gene sequencing analysis, significantly varied based on all sampling factors (PERMANOVA P < 0.05 for each). While the relative abundances of dominant phyla (e.g., Proteobacteria and Bacteroidetes) were relatively stable throughout the pond, subtle yet significant increases in α-diversity were observed as the day progressed (ANOVA P < 0.001). Key water quality properties that also increased between the morning and afternoon (i.e., pH, dissolved oxygen, and temperature) positively associated with relative abundances of Cyanobacteria, though were inversely proportional to Verrucomicrobia. These properties, among additional parameters such as bioavailable nutrients (e.g., NH3, NO3, PO4), chlorophyll, phycocyanin, conductivity, and colored dissolved organic matter, exhibited significant relationships with relative abundances of various bacterial genera as well. Further investigation of the microbiota in underlying sediments revealed significant differences between the bank and interior sites of the pond (P < 0.05 for α- and ß-diversity). Overall, our findings emphasize the importance of accounting for time of day and water sampling location and depth when surveying the microbiomes of irrigation ponds and other small freshwater sources.

Modeling the kinetics of manure-borne fecal indicator removal in runoff.

Several manure-borne microorganism removal models have been developed to provide accurate estimations of the number of microorganisms removed from manure or manured soils undergoing rainfall. It has been commonly assumed that these models perform equally well when used to simulate microbe removal in runoff from manures of different consistency and levels of weathering. The objectives of this work were (a) to observe kinetics of the removal of Escherichia coli and enterococci with runoff for two different manure consistencies and three manure weathering durations, and (b) to compare performance of the log-linear, Vadas-Kleinman-Sharpley, and Bradford-Shijven models in simulation of the observed kinetics. Liquid and solid dairy manure were applied to grassed soil boxes that received simulated rainfall immediately after application and subsequently at 1 and 2 wk. Runoff samples were collected for 1 h at increasing time intervals during each event. Only the effective rainfall depth at the start of runoff was significantly affected by manure consistency (p = .033), whereas other parameters were not (p > .05). Substantial differences in microorganism removal kinetics during the initial, 1-, and 2-wk rainfall events were manifested by the significant (p < .05) effect of the degree of manure weathering in about 70% of cases. The log-linear model produced the largest fitting error especially during the initial rainfall event. The Vadas-Kleinman-Sharpley model and the Bradford-Schijven model were comparable in accuracy for all events. The latter model was slightly more accurate, and the former model had better expressed dependencies of parameter values on manure weathering. Ignoring manure weathering may lead to incorrect parameterization of manure removal models.

Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA.

Recently, cyanobacteria blooms have become a concern for agricultural irrigation water quality. Numerous studies have shown that cyanotoxins from these harmful algal blooms (HABs) can be transported to and assimilated into crops when present in irrigation waters. Phycocyanin is a pigment known only to occur in cyanobacteria and is often used to indicate cyanobacteria presence in waters. The objective of this work was to identify the most influential environmental covariates affecting the phycocyanin concentrations in agricultural irrigation ponds that experience cyanobacteria blooms of the potentially toxigenic species Microcystis and Aphanizomenon using machine learning methodology. The study was performed at two agricultural irrigation ponds over a 5-month period in the summer of 2018. Phycocyanin concentrations, along with sensor-based and fluorometer-based water quality parameters including turbidity (NTU), pH, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), conductivity, chlorophyll, color dissolved organic matter (CDOM), and extracted chlorophyll were measured. Regression tree analyses were used to determine the most influential water quality parameters on phycocyanin concentrations. Nearshore sampling locations had higher phycocyanin concentrations than interior sampling locations and "zones" of consistently higher concentrations of phycocyanin were found in both ponds. The regression tree analyses indicated extracted chlorophyll, CDOM, and NTU were the three most influential parameters on phycocyanin concentrations. This study indicates that sensor-based and fluorometer-based water quality parameters could be useful to identify spatial patterns of phycocyanin concentrations and therefore, cyanobacteria blooms, in agricultural irrigation ponds and potentially other water bodies.

Drone-based imaging to assess the microbial water quality in an irrigation pond: A pilot study.

Microbial water quality datasets are essential in irrigated agricultural practices to detect and inform measures to prevent the contamination of produce. Escherichia coli (E. coli) concentrations are commonly used to evaluate microbial water quality. Remote sensing imagery has been successfully used to retrieve several water quality parameters that can be determinants of E. coli habitats in waterbodies. This pilot study was conducted to test the possibility of using imagery from a small unmanned aerial vehicle (sUAV or drone) to improve the estimation of microbial water quality in small irrigation ponds. In situ measurements of pH, turbidity, specific conductance, and concentrations of dissolved oxygen, chlorophyll-a, phycocyanin, and fluorescent dissolved organic matter were taken at depths of 0-15 cm in 23 locations across a pond in Central Maryland, USA. The pond surface was concurrently imaged using a drone with three modified GoPro cameras, and a multispectral MicaSense RedEdge camera with five spectral bands. The GoPro imagery was decomposed into red, blue, and green components. Mean digital numbers for 1-m radius areas in the images were combined with the water quality data to provide input for a regression tree-based analysis. The accuracy of the regression-tree data description with "only imagery" inputs was the same or better than that of trees constructed with "only water-quality parameters" as inputs. From multiple cross-validation runs with "only imagery" inputs for the regression trees, the average (±SD) determination coefficient and root-mean-squared error of the decimal logarithm of E. coli concentrations were 0.793 ±â€¯0.035 and 0.131 ±â€¯0.011, respectively. The results of this study demonstrate the opportunities for using sUAV imagery for obtaining a more accurate delineation of the spatial variation of E. coli concentrations in irrigation ponds.

Intraseasonal variation of E. coli and environmental covariates in two irrigation ponds in Maryland, USA.

The microbial quality of irrigation water is typically assessed by measuring the concentrations of E. coli in irrigation water reservoirs that are variable in space and time. E. coli concentrations are affected by water quality parameters that co-vary with E. coli concentrations and may be easily measured with currently available sensors. The objective of this work was to identify the most influential environmental covariates affecting E. coli concentrations during a three-month biweekly monitoring period within two irrigation ponds in Maryland during the summer of 2017. E. coli levels as well as sensor-based water quality parameters including turbidity, pH, dissolved oxygen, dissolved fluorescent organic matter, conductivity, and chlorophyll were measured at 23 and 34 locations in ponds 1 and 2, respectively. Regression tree analyses were used to determine the most influential water quality parameters for the prediction of E. coli levels. Correlations between E. coli and water quality covariates were not strong and were inconsistently significant. Shoreline sample locations had higher E. coli concentrations than interior pond samples and significant differences were observed when comparing these two groups. Regression trees provided fairly accurate predictions of E. coli levels based on water quality parameters with R2 values ranging from 0.70 to 0.93. Factors identified via the regression trees varied by sampling date but common leading covariates included cyanobacteria, organic matter, and turbidity. Results indicated environmental covariates, sensed either remotely or in situ, could be useful to delineate areas with different E. coli survival conditions across irrigation ponds and potentially other water bodies such as lakes, rivers, or bays.

Depth-Dependent Survival of Escherichia coli and Enterococci in Soil after Manure Application and Simulated Rainfall.

Once released, manure-borne bacteria can enter runoff via interaction with the thin mixing layer near the soil surface. The objectives of this work were to document temporal changes in profile distributions of manure-borne Escherichia coli and enterococci in the near-surface soil layers after simulated rainfalls and to examine differences in survival of the two fecal indicator bacteria. Rainfall simulations were performed in triplicate on soil-filled boxes with grass cover and solid manure application for 1 h with rainfall depths of 30, 60, and 90 mm. Soil samples were collected weekly from depth ranges of 0 to 1, 1 to 2, 2 to 5, and 5 to 10 cm for 1 month. Rainfall intensity was found to have a significant impact on the initial concentrations of fecal indicator bacteria in the soil. While total numbers of enterococci rapidly declined over time, E. coli populations experienced initial growth with concentration increases of 4, 10, and 25 times the initial levels at rainfall treatment depths of 30, 60, and 90 mm, respectively. E. coli populations grew to the approximately the same level in all treatments. The 0- to 1-cm layer contained more indicator bacteria than the layers beneath it, and survival of indicator bacteria was better in this layer, with decimation times between 12 and 18 days after the first week of growth. The proportion of bacteria in the 0- to 1-cm layer grew with time as the total number of bacteria in the 0- to 10-cm layer declined. The results of this work indicate the need to revisit the bacterial survival patterns that are assumed in water quality models.

Performance of Weibull and Linear Semi-logarithmic Models in Simulating Inactivation in Waters.

Modeling inactivation of indicator microorganisms is a necessary component of microbial water quality forecast and management recommendations. The linear semi-logarithmic (LSL) model is commonly used to simulate the dependencies of bacterial concentrations in waters on time. There were indications that assumption of the semi-logarithmic linearity may not be accurate enough in waters. The objective of this work was to compare performance of the LSL and the two-parametric Weibull inactivation models with data on survival of indicator organism in various types of water from a representative database of 167 laboratory experiments. The Weibull model was preferred in >99% of all cases when the root mean squared errors and Nash-Sutcliffe statistics were compared. Comparison of corrected Akaike statistic values gave the preference to the Weibull model in only 35% of cases. This was caused by (i) a small number of experimental points on some inactivation curves, (ii) closeness of the shape parameter of the Weibull equation to one, and (iii) piecewise log-linear inactivation dynamic that could be well described by neither of the two models compared. Based on the Akaike test, the Weibull model was favored in agricultural, lake, and pristine waters, whereas the LSL model was preferred for groundwater, wastewater, rivers, and marine waters. The decimal reduction time parameter of both the LSL and Weibull models exhibited an Arrhenius-type dependence on temperature. Overall, the existing inactivation data indicate that the application of the Weibull model can improve the predictive capabilities of microbial water quality modeling.