Irrigation waters may facilitate the spread of antibiotic-resistant bacteria or genes to humans a... more Irrigation waters may facilitate the spread of antibiotic-resistant bacteria or genes to humans and animals. Monitoring of resistance in irrigated waters has become common; however, many studies do not incorporate a spatial component into sampling designs. The objective of this work was to assess spatiotemporal variations in tetracycline-resistant E. coli in an irrigation pond. Water samples were collected at 10 locations and two different water depths, and in situ and laboratory water quality measurements were performed. The percentage of E. coli resistant to the low (4 μg mL−1) and high (16 μg mL−1) tetracycline doses varied by date and location but were observed to be as high as 12.7% and 6.3% of the total population throughout the study, respectively. While significant differences were not observed between resistance levels measured at different depths, on one date resistant E. coli were only detected in samples collected at depth. Nitrate, fluorescent dissolved organic matter, ...
Small to medium irrigation ponds provide substantial quantities of water for irrigation in the Mi... more Small to medium irrigation ponds provide substantial quantities of water for irrigation in the Mid-Atlantic region of the U.S. The concentrations of the fecal indicator organism Escherichia coli (E. coli) are used to evaluate the microbial water quality of irrigation sources. Little is known about the spatiotemporal variability of E. coli concentrations in pond water and the possible effects on monitoring and management of the microbial quality of irrigation water from these ponds. The objective of this work was to test the hypotheses that (a) spatial patterns of E. coli concentrations exist that are preserved both intra- and interannually, and (b) persistent spatial patterns in water quality parameters exist and correlate with persistent patterns of E. coli concentrations. Sampling was conducted fortnightly during the summer months in 2016 to 2018 and consisted of taking water quality measurements at 23 and 34 locations in ponds P1 and P2, respectively. Interannual variability of E. coli was observed in both ponds as was substantial spatial variability of E. coli concentrations within each year. The mean relative difference (MRD) analysis was used to identify temporally stable patterns of E. coli concentrations within the ponds. These patterns found for individual years showed significant positive correlations with each other and with the overall pattern derived from the 3-year dataset. Correlation coefficients of patterns varied from 0.487 to 0.842 in P1 and from 0.467 to 0.789 in P2 (p < 0.05). MRD patterns of water quality parameters and of E. coli concentrations were also significantly correlated. Within the 3-year dataset, the highest positive correlations were observed for chlorophyll-a and turbidity while the dissolved oxygen concentrations demonstrated the greatest negative correlations. Results of the present study emphasize the advisability and feasibility of finding temporally stable spatial patterns in microbial water quality within irrigation ponds.
The anthropogenic increase in radiatively active gases in the atmosphere has been well documented... more The anthropogenic increase in radiatively active gases in the atmosphere has been well documented. Recently the impact of this increase on the earth`s climate has been confirmed. Agriculture is vulnerable to climatic change, and estimating the likely response to such changes is critical. Many studies of these responses have included soybeans both because they are an important commodity and because they are sensitive to changes in atmospheric CO, concentration. Such studies have generally focused on yield response. While this is critical it does not provide information on the underlying causal link between climate and atmospheric change and changes in soybean yield. The current work examines the impact of climatic change on water stress during the critical periods of soybean reproductive development.
&amp;lt;p&amp;gt;Phytoplankton is known to affect freshwater habitats of pathogenic and i... more &amp;lt;p&amp;gt;Phytoplankton is known to affect freshwater habitats of pathogenic and indicator organisms in irrigation water sources. Cyanobacteria are associated with producing harmful toxins which can be transferred to crops, and the gene transfer between phytoplankton and pathogens is of interest particularly in connection with the antibiotic resistance in microorganisms. The objective of this work was to evaluate the possibilities of estimating phytoplankton populations in irrigation ponds by using separate and combined in situ water quality sensing/sampling and sUAS imagery. The study was conducted during a 5-month summer-early fall period at a working irrigation pond on Maryland&amp;amp;#8217;s Eastern shore, USA. In situ physical, biochemical, and nutrient measurements were taken at 34 locations in the pond with a total of 21 parameters. Phytoplankton species were enumerated using a modified &amp;amp;#220;termohl method and then grouped into green algae, diatoms, and cyanobacteria. The imagery was obtained from an altitude of 120 meters using three modified GoPro cameras and a MicaSense camera. It was then clipped to represent the area around locations of sensing/sampling. The measured parameters were grouped into physical, biochemical, nutrient, and imagery datasets as inputs. Various combinations of these inputs constituted 17 different datasets used with machine learning algorithms. The target variables were the three groups of phytoplankton and the proportion of cyanobacteria in the total count of observed phytoplankton cells. The regression tree (RT) algorithm was applied to research the structure of the dataset and to determine the major influential variables. The random forest (RF) algorithm was applied to estimate the target variables for each of the 15 total datasets. With the RT analysis, nutrient concentrations appear to be influential for green algae and cyanobacteria proportion. After the nutrients were added to the physical and biochemical parameters in the RT analysis for these specific variables, the R&amp;lt;sup&amp;gt;2 &amp;lt;/sup&amp;gt;went from 0.782 to 0.869 and from 0.678 to 0.758, respectively. The imagery alone provided moderate RT accuracy for green algae (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.661) and cyanobacteria (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.586), but less for diatoms (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.483). The RT analysis provided good estimates for green algae with the R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; of 0.756 but was not efficient for diatoms (avg. R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.524), cyanobacteria (avg. R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.284), nor the proportion of cyanobacteria (avg. R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.524). In the random forest study, the most important predictors for green algae and cyanobacterial proportion were nutrient concentrations of potassium and calcium, respectively. MicaSense imagery at the red edge and near-infrared parts of the spectrum were among the most important predictors. The drone-based imagery provided information useful for the estimation and prediction of green algae. Influential input variables were different amongst phytoplankton groups. For the 17 input datasets, the overall accuracy increased in the sequence imagery &amp;lt; physical &amp;lt; biochemical &amp;lt; nutrient water quality parameters as inputs. &amp;amp;#160;&amp;lt;/p&amp;gt;
Fecal indicator organisms (FIOs), such as Escherichia coli and enterococci, are often used as sur... more Fecal indicator organisms (FIOs), such as Escherichia coli and enterococci, are often used as surrogates of contamination in the context of beach management; however, bacteriophages may be more reliable indicators than FIO due to their similarity to viral pathogens in terms of size and persistence in the environment. In the past, mechanistic modeling of environmental contamination has focused on FIOs, with virus and bacteriophage modeling efforts remaining limited. In this paper, we describe the development and application of a fate and transport model of somatic and F‐specific coliphages for the Washington Park beach in Lake Michigan, which is affected by riverine outputs from the nearby Trail Creek. A three‐dimensional model of coliphage transport and photoinactivation was tested and compared with a previously reported E. coli fate and transport model. The light‐based inactivation of the phages was modeled using organism‐specific action spectra. Results indicate that the coliphage...
Understanding spatial patterns of Escherichia coli in freshwater sediments is necessary to charac... more Understanding spatial patterns of Escherichia coli in freshwater sediments is necessary to characterize sediments as microbial reservoirs and to evaluate the impact of sediment resuspension on microbial water quality in watersheds. Sediment particle size distributions and streambed E. coli concentrations were measured along a 500‐m‐long reach of a first‐order creek 1 d before and on Days 1, 3, 6, and 10 after each of two artificial high‐flow events, with natural high‐flow events also occurring within the sampling periods. Spatial variability of E. coli was greater in sediments than in water within any given sampling; however, variation between sampling days was greater for water than for sediment. The mean relative difference analysis revealed temporally stable patterns of E. coli concentrations in sediments. Escherichia coli–rich locations along the reach corresponded to areas with higher organic matter and fine particle contents. Although low (k < 0.5 d−1) or negative survival ...
Hydropedology is a microcosm for what is happening in Soil Science. Once a staid discipline found... more Hydropedology is a microcosm for what is happening in Soil Science. Once a staid discipline found in schools of agriculture devoted to increasing crop yield, soil science is transforming itself into an interdisciplinary mulch with great significance not only for food production but also climate change, ecology, preservation of natural resources, forestry, and carbon sequestration. "Hydropedology" brings together pedology (soil characteristics) with hydrology (movement of water) to understand and achieve the goals now associated with modern soil science. This is the first book of its kind in the market. It is highly interdisciplinary, involving new thinking and synergistic approaches. It includes stimulating case studies that demonstrate the need for hydropedology in various practical applications. Future directions and new approaches are presented to advance this emerging interdisciplinary science.
The determination of soil water retention curves (SWRC) in the laboratory is a slow and tedious t... more The determination of soil water retention curves (SWRC) in the laboratory is a slow and tedious task, which is especially challenging for sandy soils due to their low water retention capacity and large water content changes for small pressure head differences. Due to spatial variability within larger areas and difficulties to obtain minimally disturbed soil samples, especially under dry conditions,
Irrigation waters may facilitate the spread of antibiotic-resistant bacteria or genes to humans a... more Irrigation waters may facilitate the spread of antibiotic-resistant bacteria or genes to humans and animals. Monitoring of resistance in irrigated waters has become common; however, many studies do not incorporate a spatial component into sampling designs. The objective of this work was to assess spatiotemporal variations in tetracycline-resistant E. coli in an irrigation pond. Water samples were collected at 10 locations and two different water depths, and in situ and laboratory water quality measurements were performed. The percentage of E. coli resistant to the low (4 μg mL−1) and high (16 μg mL−1) tetracycline doses varied by date and location but were observed to be as high as 12.7% and 6.3% of the total population throughout the study, respectively. While significant differences were not observed between resistance levels measured at different depths, on one date resistant E. coli were only detected in samples collected at depth. Nitrate, fluorescent dissolved organic matter, ...
Small to medium irrigation ponds provide substantial quantities of water for irrigation in the Mi... more Small to medium irrigation ponds provide substantial quantities of water for irrigation in the Mid-Atlantic region of the U.S. The concentrations of the fecal indicator organism Escherichia coli (E. coli) are used to evaluate the microbial water quality of irrigation sources. Little is known about the spatiotemporal variability of E. coli concentrations in pond water and the possible effects on monitoring and management of the microbial quality of irrigation water from these ponds. The objective of this work was to test the hypotheses that (a) spatial patterns of E. coli concentrations exist that are preserved both intra- and interannually, and (b) persistent spatial patterns in water quality parameters exist and correlate with persistent patterns of E. coli concentrations. Sampling was conducted fortnightly during the summer months in 2016 to 2018 and consisted of taking water quality measurements at 23 and 34 locations in ponds P1 and P2, respectively. Interannual variability of E. coli was observed in both ponds as was substantial spatial variability of E. coli concentrations within each year. The mean relative difference (MRD) analysis was used to identify temporally stable patterns of E. coli concentrations within the ponds. These patterns found for individual years showed significant positive correlations with each other and with the overall pattern derived from the 3-year dataset. Correlation coefficients of patterns varied from 0.487 to 0.842 in P1 and from 0.467 to 0.789 in P2 (p < 0.05). MRD patterns of water quality parameters and of E. coli concentrations were also significantly correlated. Within the 3-year dataset, the highest positive correlations were observed for chlorophyll-a and turbidity while the dissolved oxygen concentrations demonstrated the greatest negative correlations. Results of the present study emphasize the advisability and feasibility of finding temporally stable spatial patterns in microbial water quality within irrigation ponds.
The anthropogenic increase in radiatively active gases in the atmosphere has been well documented... more The anthropogenic increase in radiatively active gases in the atmosphere has been well documented. Recently the impact of this increase on the earth`s climate has been confirmed. Agriculture is vulnerable to climatic change, and estimating the likely response to such changes is critical. Many studies of these responses have included soybeans both because they are an important commodity and because they are sensitive to changes in atmospheric CO, concentration. Such studies have generally focused on yield response. While this is critical it does not provide information on the underlying causal link between climate and atmospheric change and changes in soybean yield. The current work examines the impact of climatic change on water stress during the critical periods of soybean reproductive development.
&amp;lt;p&amp;gt;Phytoplankton is known to affect freshwater habitats of pathogenic and i... more &amp;lt;p&amp;gt;Phytoplankton is known to affect freshwater habitats of pathogenic and indicator organisms in irrigation water sources. Cyanobacteria are associated with producing harmful toxins which can be transferred to crops, and the gene transfer between phytoplankton and pathogens is of interest particularly in connection with the antibiotic resistance in microorganisms. The objective of this work was to evaluate the possibilities of estimating phytoplankton populations in irrigation ponds by using separate and combined in situ water quality sensing/sampling and sUAS imagery. The study was conducted during a 5-month summer-early fall period at a working irrigation pond on Maryland&amp;amp;#8217;s Eastern shore, USA. In situ physical, biochemical, and nutrient measurements were taken at 34 locations in the pond with a total of 21 parameters. Phytoplankton species were enumerated using a modified &amp;amp;#220;termohl method and then grouped into green algae, diatoms, and cyanobacteria. The imagery was obtained from an altitude of 120 meters using three modified GoPro cameras and a MicaSense camera. It was then clipped to represent the area around locations of sensing/sampling. The measured parameters were grouped into physical, biochemical, nutrient, and imagery datasets as inputs. Various combinations of these inputs constituted 17 different datasets used with machine learning algorithms. The target variables were the three groups of phytoplankton and the proportion of cyanobacteria in the total count of observed phytoplankton cells. The regression tree (RT) algorithm was applied to research the structure of the dataset and to determine the major influential variables. The random forest (RF) algorithm was applied to estimate the target variables for each of the 15 total datasets. With the RT analysis, nutrient concentrations appear to be influential for green algae and cyanobacteria proportion. After the nutrients were added to the physical and biochemical parameters in the RT analysis for these specific variables, the R&amp;lt;sup&amp;gt;2 &amp;lt;/sup&amp;gt;went from 0.782 to 0.869 and from 0.678 to 0.758, respectively. The imagery alone provided moderate RT accuracy for green algae (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.661) and cyanobacteria (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.586), but less for diatoms (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.483). The RT analysis provided good estimates for green algae with the R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; of 0.756 but was not efficient for diatoms (avg. R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.524), cyanobacteria (avg. R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.284), nor the proportion of cyanobacteria (avg. R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.524). In the random forest study, the most important predictors for green algae and cyanobacterial proportion were nutrient concentrations of potassium and calcium, respectively. MicaSense imagery at the red edge and near-infrared parts of the spectrum were among the most important predictors. The drone-based imagery provided information useful for the estimation and prediction of green algae. Influential input variables were different amongst phytoplankton groups. For the 17 input datasets, the overall accuracy increased in the sequence imagery &amp;lt; physical &amp;lt; biochemical &amp;lt; nutrient water quality parameters as inputs. &amp;amp;#160;&amp;lt;/p&amp;gt;
Fecal indicator organisms (FIOs), such as Escherichia coli and enterococci, are often used as sur... more Fecal indicator organisms (FIOs), such as Escherichia coli and enterococci, are often used as surrogates of contamination in the context of beach management; however, bacteriophages may be more reliable indicators than FIO due to their similarity to viral pathogens in terms of size and persistence in the environment. In the past, mechanistic modeling of environmental contamination has focused on FIOs, with virus and bacteriophage modeling efforts remaining limited. In this paper, we describe the development and application of a fate and transport model of somatic and F‐specific coliphages for the Washington Park beach in Lake Michigan, which is affected by riverine outputs from the nearby Trail Creek. A three‐dimensional model of coliphage transport and photoinactivation was tested and compared with a previously reported E. coli fate and transport model. The light‐based inactivation of the phages was modeled using organism‐specific action spectra. Results indicate that the coliphage...
Understanding spatial patterns of Escherichia coli in freshwater sediments is necessary to charac... more Understanding spatial patterns of Escherichia coli in freshwater sediments is necessary to characterize sediments as microbial reservoirs and to evaluate the impact of sediment resuspension on microbial water quality in watersheds. Sediment particle size distributions and streambed E. coli concentrations were measured along a 500‐m‐long reach of a first‐order creek 1 d before and on Days 1, 3, 6, and 10 after each of two artificial high‐flow events, with natural high‐flow events also occurring within the sampling periods. Spatial variability of E. coli was greater in sediments than in water within any given sampling; however, variation between sampling days was greater for water than for sediment. The mean relative difference analysis revealed temporally stable patterns of E. coli concentrations in sediments. Escherichia coli–rich locations along the reach corresponded to areas with higher organic matter and fine particle contents. Although low (k < 0.5 d−1) or negative survival ...
Hydropedology is a microcosm for what is happening in Soil Science. Once a staid discipline found... more Hydropedology is a microcosm for what is happening in Soil Science. Once a staid discipline found in schools of agriculture devoted to increasing crop yield, soil science is transforming itself into an interdisciplinary mulch with great significance not only for food production but also climate change, ecology, preservation of natural resources, forestry, and carbon sequestration. "Hydropedology" brings together pedology (soil characteristics) with hydrology (movement of water) to understand and achieve the goals now associated with modern soil science. This is the first book of its kind in the market. It is highly interdisciplinary, involving new thinking and synergistic approaches. It includes stimulating case studies that demonstrate the need for hydropedology in various practical applications. Future directions and new approaches are presented to advance this emerging interdisciplinary science.
The determination of soil water retention curves (SWRC) in the laboratory is a slow and tedious t... more The determination of soil water retention curves (SWRC) in the laboratory is a slow and tedious task, which is especially challenging for sandy soils due to their low water retention capacity and large water content changes for small pressure head differences. Due to spatial variability within larger areas and difficulties to obtain minimally disturbed soil samples, especially under dry conditions,
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