Geostatistical features of streambed vertical hydraulic conductivities in Frenchman Creek Watershed in Western Nebraska

This study evaluated the spatial variability of streambed vertical hydraulic conductivity (Kv) in different stream morphologies in the Frenchman Creek Watershed, Western Nebraska, using different variogram models. Streambed Kv values were determined in situ using permeameter tests at 10 sites in Frenchman, Stinking Water and Spring Creeks during the dry season at baseflow conditions. Measurements were taken both in straight and meandering stream channels during a 5 day period at similar flow conditions. Each test site comprised of at least three transects and each transect comprised of at least three Kv measurements. Linear, Gaussian, exponential and spherical variogram models were used with Kriging gridding method for the 10 sites. As a goodness-of-fit statistic for the variogram models, cross-validation results showed differences in the median absolute deviation and the standard deviation of the cross-validation residuals. Results show that using the geometric means of the 10 sites for gridding performs better than using either all the Kv values from the 93 permeameter tests or 10 Kv values from the middle transects and centre permeameters. Incorporating both the spatial variability and the uncertainty involved in the measurement at a reach segment can yield more accurate grid results that can be useful in calibrating Kv at watershed or sub-watershed scales in distributed hydrological models

LIFE CYCLE ASSESSMENT OF POINT-OF-LAY BIRDS TO FROZEN CHICKEN PRODUCTION IN A TROPICAL ENVIRONMENT

The study examined four scenarios for reduction of environmental impacts with use of 100 % purchased electricity in all processing activities as first scenario while second scenario was the use of 100 % electricity from diesel generators. Third and fourth scenarios were to use 50 % each of both purchased electricity and diesel-generated electricity in all activities. Most of the Energy Use (EU) came during the freezing process. Global Warming Potential (GWP) value for Scenario 2 is predominantly higher than the values for other three scenarios. The results show that the major source for global warming potential for Scenario 2 is the freezing process, whereas for the other three scenarios the animal management represents the main contributor. Similar to GWP, the Acidification Potential and Eutrophication Potential values for Scenario 2 were higher, although very small and this may be attributed to diesel generators emitting slightly higher amounts of NOx and SOx

LIFE CYCLE ASSESSMENT OF POINT-OF-LAY BIRDS TO FROZEN CHICKEN PRODUCTION IN A TROPICAL ENVIRONMENT

The study examined four scenarios for reduction of environmental impacts with use of 100 % purchased electricity in all processing activities as first scenario while second scenario was the use of 100 % electricity from diesel generators. Third and fourth scenarios were to use 50 % each of both purchased electricity and diesel-generated electricity in all activities. Most of the Energy Use (EU) came during the freezing process. Global Warming Potential (GWP) value for Scenario 2 is predominantly higher than the values for other three scenarios. The results show that the major source for global warming potential for Scenario 2 is the freezing process, whereas for the other three scenarios the animal management represents the main contributor. Similar to GWP, the Acidification Potential and Eutrophication Potential values for Scenario 2 were higher, although very small and this may be attributed to diesel generators emitting slightly higher amounts of NOx and SOx

Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the USMeat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means (FCM)clusteringwere also used to developmodels for predicting E. coli. The performances of the predictive models were evaluated and compared using root mean squared log error (RMSLE). Cross-validation and model performance results indicated that although themajority of models predicted E. coli accurately, ANFIS models resulted in fewer errors compared to the othermodels. The ANFISmodels have the potential to be used to predict E. coli concentration for intervention plans and monitoring programs for cascading dams, and to implement effective best management practices for grazing and irrigation during the growing season

Feasibility assessment on use of proximal geophysical sensors to support precision management

A study was conducted at three sites in North Dakota to strengthen understanding of the usefulness of different proximal geophysical data types in agricultural contexts of varying pedology. This study hypothesizes that electromagnetic induction (EMI), gamma-ray sensor (GRS), cosmic-ray neutron sensor (CRNS), and elevation data layers are all useful in multiple linear regression (MLR) predictions of soil properties that meet expert criteria at three agricultural sites. In addition to geophysical data collection with vehicle-mounted sensors, 15 soil samples were collected at each site and analyzed for nine soil properties of interest. A set of model training data was compiled by pairing the sampled soil property measurements with the nearest geophysical data. Eleven models passed expert-defined uncertainty criteria at Site 1, 16 passed at Site 2, and 14 passed at Site 3. Electrical conductivity (EC), organic matter (OM), available water holding capacity, silt, and clay were predicted at Site 1 with an R-squared of prediction (2 ) \u3e .50 and acceptable root mean square error of prediction (RMSEP). Bulk density (BD), OM, available water capacity, silt, and clay were predicted with 2\u3e .50 and acceptable RMSEP at Site 2. At Site 3, no soil properties were predicted with acceptable RMSEP and an 2\u3e .50. These results confirm feasibility of our method, and the authors recommend the prioritization of EMI data collection if geophysical data collection is limited to a single mapping effort and calibration soil samples are few

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Modeling Streambed Vertical Hydraulic Conductivity, Water Quality Pollutants, and Best Management Practices Using Machine Learning and the Soil and Water Assessment Tool

Spatio-temporal variability of natural and man-made watershed characteristics controls hydrological processes. With respect to quantity and quality, water resources management in a watershed involves understanding these variations in order to make better water policy changes and for the implementation of best management practices (BMPs). Two watersheds were studied at different spatial and temporal scales to model streambed vertical hydraulic conductivity (Kv), water quality pollutants, and BMPs. At Frenchman Creek, a new approach was used to develop pedo-transfer functions (PTFs) for simulating the effects of complex sediment routing on Kv variability across ten sites in multiple stream orders using watershed characteristics as predictors. Coupling Kv and drainage area as a response variable reduced the fuzziness in selecting the best PTFs. Four variograms were also used to determine the spatial distribution of Kv. Cross-validation results showed differences in the median absolute deviation of the cross-validation residuals. Geostatistical analysis showed that using the ten geometric means of the sites performed better than using either the Kv values from 93 permeameter tests (at least 9 tests per site) or ten Kv values from the middle transects and center permeameters. BMPs were simulated at hotspots within the Big Sandy Creek to determine reductions in pollutant loads, and to determine if water-quality standards would be met at the watershed outlet. With scaled-down acreage based on the proposed implementation plan, a combination of filter strips, grassed waterways and atrazine application rate reduction would most likely yield measureable improvement both in the hotspots and at the outlet. To predict daily Escherichia coli concentrations in two cascading dams at US Meat Animal Research Center (a subwatershed of Big Sandy Creek), a novel approach that integrated hydro-climatic variables with animal density and grazing pattern was used to increase prediction accuracy. Models were developed using regression, artificial neural network, and adaptive neuro-fuzzy inference system (ANFIS). Cross-validation and model performance results indicated that ANFIS models resulted in fewer errors compared to other models. ANFIS models have the potential to be used for predicting E. coli concentration for monitoring programs and for implementing BMPs for grazing and irrigation during the growing season

Modeling Streambed Vertical Hydraulic Conductivity, Water Quality Pollutants, and Best Management Practices Using Machine Learning and the Soil and Water Assessment Tool

Spatio-temporal variability of natural and man-made watershed characteristics controls hydrological processes. With respect to quantity and quality, water resources management in a watershed involves understanding these variations in order to make better water policy changes and for the implementation of best management practices (BMPs). Two watersheds were studied at different spatial and temporal scales to model streambed vertical hydraulic conductivity (Kv), water quality pollutants, and BMPs. At Frenchman Creek, a new approach was used to develop pedo-transfer functions (PTFs) for simulating the effects of complex sediment routing on Kv variability across ten sites in multiple stream orders using watershed characteristics as predictors. Coupling Kv and drainage area as a response variable reduced the fuzziness in selecting the best PTFs. Four variograms were also used to determine the spatial distribution of Kv. Cross-validation results showed differences in the median absolute deviation of the cross-validation residuals. Geostatistical analysis showed that using the ten geometric means of the sites performed better than using either the Kv values from 93 permeameter tests (at least 9 tests per site) or ten Kv values from the middle transects and center permeameters. BMPs were simulated at hotspots within the Big Sandy Creek to determine reductions in pollutant loads, and to determine if water-quality standards would be met at the watershed outlet. With scaled-down acreage based on the proposed implementation plan, a combination of filter strips, grassed waterways and atrazine application rate reduction would most likely yield measureable improvement both in the hotspots and at the outlet. To predict daily Escherichia coli concentrations in two cascading dams at US Meat Animal Research Center (a subwatershed of Big Sandy Creek), a novel approach that integrated hydro-climatic variables with animal density and grazing pattern was used to increase prediction accuracy. Models were developed using regression, artificial neural network, and adaptive neuro-fuzzy inference system (ANFIS). Cross-validation and model performance results indicated that ANFIS models resulted in fewer errors compared to other models. ANFIS models have the potential to be used for predicting E. coli concentration for monitoring programs and for implementing BMPs for grazing and irrigation during the growing season

Influence of watershed characteristics on streambed hydraulic conductivity across multiple stream orders

Streambeds are critical hydrological interfaces: their physical properties regulate the rate, timing, and location of fluxes between aquifers and streams. Streambed vertical hydraulic conductivity (Kv) is a key parameter in watershed models, so understanding its spatial variability and uncertainty is essential to accurately predicting how stresses and environmental signals propagate through the hydrologic system. Most distributed modeling studies use generalized Kv estimates from column experiments or grain-size distribution, but Kv may include a wide range of orders of magnitude for a given particle size group. Thus, precisely predicting Kv spatially has remained conceptual, experimental, and/or poorly constrained. This usually leads to increased uncertainty in modeling results. There is a need to shift focus from scaling up pore-scale column experiments to watershed dimensions by proposing a new kind of approach that can apply to a whole watershed while incorporating spatial variability of complex hydrological processes. Here we present a new approach, Multi-Stemmed Nested Funnel (MSNF), to develop pedo-transfer functions (PTFs) capable of simulating the effects of complex sediment routing on Kv variability across multiple stream orders in Frenchman Creek watershed, USA. We find that using the product of Kv and drainage area as a response variable reduces the fuzziness in selecting the “best” PTF. We propose that the PTF can be used in predicting the ranges of Kv values across multiple stream orders

Modeling and Prioritizing Interventions Using Pollution Hotspots for Reducing Nutrients, Atrazine and E. coli Concentrations in a Watershed

Excess nutrients and herbicides remain two major causes of waterbody impairment globally. In an attempt to better understand pollutant sources in the Big Sandy Creek Watershed (BSCW) and the prospects for successful remediation, a program was initiated to assist agricultural producers with the implementation of best management practices (BMPs). The objectives were to (1) simulate BMPs within hotspots to determine reductions in pollutant loads and (2) to determine if water-quality standards are met at the watershed outlet. Regression-based load estimator (LOADEST) was used for determining sediment, nutrient and atrazine loads, while artificial neural networks (ANN) were used for determining E. coli concentrations. With respect to reducing sediment, total nitrogen and total phosphorus loads at hotspots with individual BMPs, implementing grassed waterways resulted in average reductions of 97%, 53% and 65% respectively if implemented all over the hotspots. Although reducing atrazine application rate by 50% in all hotspots was the most effective BMP for reducing atrazine concentrations (21%) at the gauging station 06883940, this reduction was still six times higher than the target concentration. Similarly, with grassed waterways established in all hotspots, the 64% reduction in E. coli concentration was not enough to meet the target at the gauging station. With scaled-down acreage based on the proposed implementation plan, filter strip led to more pollutant reductions at the targeted hotspots. Overall, a combination of filter strip, grassed waterway and atrazine rate reduction will most likely yield measureable improvement both in the hotspots (\u3e20% reduction in sediment, total nitrogen and total phosphorus pollution) and at the gauging station. Despite the model’s uncertainties, the results showed a possibility of using Soil and Water Assessment Tool (SWAT) to assess the effectiveness of various BMPs in agricultural watersheds