The Agricultural Conservation Planning Framework (ACPF) is a geospatial decision support tool that was developed and is used in many areas of the Midwest of the United States to help with the prioritization and placement of conservation practices within agricultural watersheds.We evaluated the utility and extensibility of ACPF in two US Geological Survey 12-digit scale hydrologic units in the Southern Piedmont of North Carolina. The Southern Piedmont consists of less row crop agriculture and more pasture systems than the Midwest and has generally lower pollutant loads. Also, agricultural fields are comparatively smaller, irregularly shaped, and more sparsely distributed. For this study, local conservation experts were interviewed about conservation practices and their appropriate locations in the landscape. Interviewees demonstrated an extensive working knowledge of the land and producers on over 90% of the farmland. Many of the conservation practices identified by the local experts were "soil health" practices, such as cover crops or nutrient management, and are assumed in use before running ACPF. Results revealed that many of the conservation practices output by ACPF were not identified by the local experts in the Southern Piedmont watersheds due to their limited use in pasture conservation, conservation priorities, and landscape characteristics. Row crop agriculture was sparsely distributed in each study watershed and comprised less than 2% of the total catchment area. Contour buffer strips and grassed waterways were the conservation practices most identified by ACPF and were sited in 75% of cropped fields. A greater number of crop-related conservation practices (48 versus 15) were identified by ACPF than by local experts; however 80% of the conservation practices identified by the experts were outside the scope ofACPF and were mainly nutrient management or soil health practices.To evaluate ACPF for broader utility in the Southern Piedmont, alternative interpretations for existing outputs were considered: (1) ACPF "proxies" were identified to compare locally accepted practices with ACPF outputs that perform a similar function (e.g., strip cropping rather than contour buffer strips) and, (2) placing locally used conservation practices (e.g., exclusion fencing) based on existing ACPF data layers (hydrologically enforced flow paths). Alternative uses and interpretations surrounding ACPF outputs and data layers may provide opportunities for conservation planning outside the scope and intended use of ACPF in the Southern Piedmont.

Advancements in efficient unmanned aerial platforms and affordable sensors has led to renewed interest in remote sensing by agricultural producers and land managers for use as an efficient and convenient method of evaluating crop status and pest issues in their fields. For remote sensing to be employed as a viable and widespread tool for weed management, the accurate detection of distinct weed species must be possible through the use of analytical procedures on the resultant imagery. Additionally, the remote sensing platform and subsequent analysis must be capable of identifying these species across a wide range of heights. In 2017, a field study was performed to identify any weed height thresholds on the accurate detection and subsequent classification of three common broadleaf weed species in the southeastern United States: Palmer amaranth (Amaranthus palmeri), common ragweed (Ambrosia artemisiifolia) and sicklepod Senna obtusifolia) as well as the classification accuracy of image classifications performed on the species scale. Pots of the three species at heights of 5, 10, 15, and 30 cm were randomly arranged in a grid and 5-band multispectral imagery was collected at 15 m. Image analysis was used to identify the spectral reflectance behavior of the weed species and height combinations and to evaluate the accuracy of species based supervised classifications involving the three species. Supervised classification was able to discriminate between the three weed species with between 24-100% accuracy depending on height and species. Palmer amaranth classification accuracy was consistently 100%. Increased height of sicklepod and common ragweed plants did not reliably confer improved accuracy but the species were correctly identified with at least 24% and 60% accuracy, respectively.

The power of citizen science to contribute to both science and society is gaining increased recognition, particularly in physics and biology. Although there is a long history of public engagement in agriculture and food science, the term ‘citizen science’ has rarely been applied to these efforts. Similarly, in the emerging field of citizen science, most new citizen science projects do not focus on food or agriculture. Here, we convened thought leaders from a broad range of fields related to citizen science, agriculture, and food science to highlight key opportunities for bridging these overlapping yet disconnected communities/fields and identify ways to leverage their respective strengths. Specifically, we show that (i) citizen science projects are addressing many grand challenges facing our food systems, as outlined by the United States National Institute of Food and Agriculture, as well as broader Sustainable Development Goals set by the United Nations Development Programme, (ii) there exist emerging opportunities and unique challenges for citizen science in agriculture/food research, and (iii) the greatest opportunities for the development of citizen science projects in agriculture and food science will be gained by using the existing infrastructure and tools of Extension programmes and through the engagement of urban communities. Further, we argue there is no better time to foster greater collaboration between these fields given the trend of shrinking Extension programmes, the increasing need to apply innovative solutions to address rising demands on agricultural systems, and the exponential growth of the field of citizen science.

Catastrophic events require rapid, scientifically sound decision making to mitigate impacts on human welfare and the environment. The objective of this study was to analyze potential impacts of coal ash-derived trace elements on agriculture following a 35,000-tonne release of coal ash into the Dan River at the Duke Energy Steam Station in Eden, North Carolina. We performed scenario calculations to assess the potential for excessive trace element loading to soils via irrigation and flooding with Dan River water, uptake of trace elements by crops, and livestock consumption of trace elements via drinking water. Concentrations of 13 trace elements measured in Dan River water samples within 4 km of the release site declined sharply after the release and were equivalent within 5 d to measurements taken upriver. Mass-balance calculations based on estimates of soil trace-element concentrations and the nominal river water concentrations indicated that irrigation or flooding with 25 cm of Dan River water would increase soil concentrations of all trace elements by less than 0.5%. Calculations of potential increases of trace elements in corn grain and silage, fescue, and tobacco leaves suggested that As, Cr, Se, Sr, and V were elements of most concern. Concentrations of trace elements measured in river water following the ash release never exceeded adopted standards for livestock drinking water. Based on our analyses, we present guidelines for safe usage of Dan River water to diminish negative impacts of trace elements on soils and crop production. In general, the approach we describe here may serve as a basis for rapid assessment of environmental and agricultural risks associated with any similar types of releases that arise in the future.

Environmental impacts of potentially toxic trace elements from coal fly ash are controlled in part by the mineralogy of the ash matrix and the chemical speciation of the trace elements. Our objective was to characterize the chemical and mineralogical composition of fly ash samples that are pertinent to the 2008 release of coal ash from a containment area at the Tennessee Valley Authority (TVA) Kingston fossil plant, which left 4 to 500 t of trace elements in adjoining river systems. Three fly ash samples were analyzed for elemental composition by digestion or neutron activation analysis, mineralogy and macroelement speciation by conventional and synchrotron-based X-ray diffraction (XRD and SXRD) and X-ray absorption spectroscopy (XAS), and for spatial associations of elements by electron probe microanalysis (EPMA). Ash samples were mainly composed of Si (2027% w/w), Al (1014% w/w), Fe (46% w/w), and Ca (46% w/w). Concentrations of selected trace elements ranged from 8 to 1480 mg kg(1), with the following general trend: Sr > Mn approximate to Zn approximate to Cu approximate to Cr > As approximate to Pb > Se approximate to U. XRD and EPMA analyses indicated that fly ash matrices were heterogeneous mixtures of minerals and aluminosilicate glass containing Fe, Ca, Ti, Mg, Na, and K. XAS fitting analyses suggested that Fe was mostly in a poorly ordered, polymerized hydroxyl-Fe(III) phase, with minor proportions of magnetite, and hematite or maghemite. Consistent with XRD data, fits to Ca XAS data included standards of glass, anhydrite, lime, and calcite; and fits to S XAS data included anhydrite and reduced organic S forms. Electron microprobe analysis showed frequent correlations among Ca, Si, and Al (and with Sr), consistent with the glass and mineral phases identified. Ash composition and mineralogy help to define a geochemical basis for projecting the long-term fate of trace elements in residual ash left in sediments following cleanup operations at the TVA-Kingston site.

A semi-empirical bi-directional flux modeling approach is used to estimate NH3 air concentrations and dry deposition fluxes to a portion of the Pocosin Lakes National Wildlife Refuge (PLNWR) downwind of a large poultry facility. Meteorological patterns at PLNWR are such that some portion of the refuge is downwind of the poultry facility 52%, 66%, 57%, and 50% of time during winter, spring, summer, and fall, respectively. Air concentrations and dry deposition rates are highest in the northeasterly direction from the facility, consistent with prevailing wind patterns. Dry deposition rates along the axis of highest concentrations are 10.1 kg N ha(-1) yr(-1) at the refuge boundary closest to the facility, decreasing to 5.4 kg N ha(-1) yr(-1) 1.5 km further downwind and continuing to decrease non-linearly to a deposition rate of 1.4 kg N ha(-1) yr(-1) 8-10 km downwind. Approximately 10% of the refuge model domain receives >= 3.0 kg N ha(-1) yr(-1) as dry NH3 deposition. Depending on the definition of the background air concentration, annual nitrogen loading to the refuge from background NH3 dry deposition and NH3 dry deposition associated with elevated concentrations downwind of the facility is between 41% and 79% higher than background dry NH3 deposition alone. Relative to the total N deposition budget for the refuge, which includes all nitrogen compounds, total background N deposition plus NH3 dry deposition associated with elevated concentrations downwind of the facility is, correspondingly, 6-10% greater than background total N deposition alone. From a process standpoint, predicted fluxes are most sensitive to uncertainty in the parameterization of the cuticular resistance. Published by Elsevier B.V.

Erosion remains a serious problem for agricultural soils throughout the world. Tillage significantly affects a soil's susceptibility to erosion. Erosion research is usually conducted in situ by capturing eroded sediment in brief, natural or artificial rainfall events. Methods for measuring long-term erosion are needed to better understand long-term effects of soil management. Landscape change resulting from erosion may be accurately characterized using ground-based lidar. Ground-based lidar data were collected in 2010 at a long-term (28-yr) trial of nine tillage treatments in the North Carolina Piedmont. Tillage effects on plot-surface elevations were examined after removing large-scale variation in elevation (slope) by detrending with first- through fourth-order polynomials. Residuals represented the elevation difference from the trend for each location. Mean plot elevations were calculated for datasets from each detrending model and used to assess erosion. In the subsequent elevation analysis, data derived from the second-order polynomial had the highest R-2, attributing 66% of the variation in elevation to block and treatment. Treatment elevations relative to no-till (NT) ranged from +3.20 cm in the fall chisel (CHfa) plots to -13.28 cm in the fall moldboard plow plus disk treatment. Weeds in lesser-tilled treatments such as CHfa and no-till plus in-row subsoiling resulted in artificially high elevation measurements. In general, the most intensely-tilled treatments had the lowest elevations and the least-tilled treatments had the highest. NT was used as the reference elevation for no change, and soil loss was calculated using these data along with field-collected estimates of bulk density. The relative elevation differences corresponded to a maximum soil loss of 1891 Mg ha(-1), which corresponds to an average annual soil loss of 67.5 Mg ha(-1) yr(-1). Soil loss estimates were similar to others estimated from soil profile truncation. This research indicates that ground-based lidar data can be used to estimate soil elevation changes and thus soil loss due to tillage-induced erosion. (C) 2012 Elsevier B.V. All rights reserved.

Hydropedology is well positioned to address contemporary issues resulting from climate change. We propose a six step process by which digital, field-scale maps will be produced to show where climate change impacts will be greatest for two land uses: a) home sites using septic systems, and b) wetlands. State and federal laws have defined critical water table levels that can be used to determine where septic systems will function well or fail, and where wetlands are likely to occur. Hydrologic models along with historic rainfall and temperature data can be used to compute long records of water table data. However, it is difficult to extrapolate such data across land regions, because too little work has been done to test different ways for doing this reliably. The modeled water table data can be used to define soil drainage classes for individual mapping units, and the drainage classes used to extrapolate the data regionally using existing digital soil survey maps. Estimates of changes in precipitation and temperature can also be input into the models to compute changes to water table levels and drainage classes. To do this effectively, more work needs to be done on developing daily climate files from the monthly climate change predictions. Technology currently exists to use the NRCS Soil Survey Geographic (SSURGO) Database with hydrologic model predictions to develop maps within a GIS that show climate change impacts on septic system performance and wetland boundaries. By using these maps, planners will have the option to scale back development in sensitive areas, or simply monitor the water quality of these areas for pathogenic organisms. The calibrated models and prediction maps should be useful throughout the Coastal Plain region. Similar work for other climate-change and land-use issues can be a valuable contribution from hydropedologists.

Watershed assessment of nutrient loading from on-site systems is an important issue for a state such as North Carolina, where approximately 50% of the total housing units rely on these systems for household wastewater treatment. Integration of spatially incongruent data sets has always been a major obstacle for environmental studies dealing with interdisciplinary data sets. A geographical information system (GIS)-based area normalization procedure was developed to transfer demographic information from census blocks to watersheds. The GIS-based area normalization procedure improved statewide estimates by 5 and 12%, respectively, for housing units using on-site systems and resulting nitrogen loadings compared to a visualization procedure. A parametric test conducted using a univariate procedure indicated that there were significant differences between the number of housing units using on-site systems (p=0.0014), nitrogen loadings (p=0.0012), and total population (p=0.0065) estimated by the visualization procedure and the normalization procedure at alpha=0.05 level.

Terrain analysis of digital elevation models (DEM) has become an important technique to assess landscape and watershed scale hydrologic and pedologic processes and the spatial variability of soil and ecologic properties. Light detecting and ranging (LIDAR) elevation data sets provide the flexibility needed to produce multiple horizontal resolutions of DEM from the same data source. A series of 61 LIDAR tiles (100 ha) were collected from the North Carolina Flood Mapping Program covering the spatial extent of the Hofmann Forest in the Lower Coastal Plain of Eastern North Carolina. The LIDAR data set was reduced to 50%, 25%, 10%, 5%, and 1% of the original density. We created 5-, 10-, and 30-m DEM with 0.1 m vertical precision for each density level and used paired t-test to determine if the true mean of their differences were equal to zero. Differences indicated that for the 30-m DEM, LIDAR data sets could be reduced to 10% of their original data density without statistically altering the produced DEM. However, the 10-m DEM could only be reduced to 25% of the original data set before statistically altering the DEM. Data reduction was more limited for the 5-m DEM with possible reduction only to 50% of their original density without producing statistically different DEM. Our evaluation provides some indication as to the minimum required LIDAR data density to produce a DEM of a given horizontal resolution. However, evaluation of additional horizontal resolutions and additional density reduction is required to provide a clearer understanding of the effect of LIDAR data density.

Linear interpolation of irregularly spaced LIDAR elevation data sets is needed to develop realistic spatial models. We evaluated inverse distance weighting (IDW) and ordinary kriging (OK) interpolation techniques and the effects of LIDAR data density on the statistical validity of the linear interpolators. A series of 10 forested 1000-ha LIDAR tiles on the Lower Coastal Plain of eastern North Carolina was used. An exploratory analysis of the spatial correlation structure of the LIDAR data set was performed. Weighted non-linear least squares (WNLS) analysis was used to parameterize best-fit theoretical semivariograms oil the empirical data. Tile data were sequentially reduced through random selection of a predetermined percentage of the original LIDAR data set, resulting in data sets with 50%, 25%, 10%, 5% and 1% of their original densities. Cross-validation and independent validation procedures were used to evaluate root mean square error (RMSE) and kriging standard error (SE) differences between interpolators and across density sequences. Review of errors indicated that LIDAR data sets could withstand substantial data reductions yet maintain adequate accuracy (30cm RMSE; 50 cm SE) for elevation predictions. The results also indicated that simple interpolation approaches such as IDW could be sufficient for interpolating irregularly spaced LIDAR data sets.