Search Results (18)

Agricultural drainage ditches contain a variety of non-crop vegetation, including potential sources of alternate hosts and food for hymenopteran parasitoids that provide conservation biological control on adjacent farm fields. To assess the patterns of family-level diversity of hymenopteran parasitoids, we surveyed ditch and adjacent crop habitats during June, July, and August 2021–2023, using yellow sticky traps over one week. We sampled two agricultural drainage ditches on each of five farms on the Delmarva Peninsula, eastern USA. We collected 36,725 specimens and identified 29 families across 738 sticky traps. Parasitoid diversity was greater in agricultural ditches than in adjacent fields. While parasitoid family diversity and abundance varied across the farms, ditches within a farm were similar. Within crop fields, diversity was greater at 1.5 m from agricultural ditches than at 9.1 m from the ditches. For several well-sampled families, greater abundance on one farm relative to others extended to both ditches and adjacent crops. Our findings indicate that agricultural drainage ditches serve as an existing beneficial semi-natural habitat for parasitoids on farms. Further research into ditch management practices may reveal methods of enhancing parasitoid abundance and conservation biological control while requiring relatively little investment from farm managers. Full article

Excessive land application of poultry litter (PL) may lead to surface runoff of nitrogen (N) and phosphorus (P), which cause eutrophication, fish death, and water pollution that ultimately have negative effects on humans and animals. Increases in poultry production in the Delmarva Peninsula underscore the need for more efficient, cost-effective, and sustainable disposal technologies for processing PL instead of direct land application. The pyrolysis conversion process can potentially produce nutrient-rich poultry litter biochar (PLB), while the pyrolysis process can change the N and P to a more stable component, thus reducing its runoff. Pyrolysis also kills off any microorganisms that would otherwise trigger negative environmental health effects. This study is to apply an integrated method and investigate the effect of pyrolysis temperature (300 °C, 500 °C), poultry litter source (different feedstock composition), and bedding material mixture (10% pine shavings) on PLB qualities and quantities. Proximate and ultimate analysis showed PL sources and bedding material addition influenced the physicochemical properties of feedstock. The SEM and BET surface results indicate that pyrolysis temperature had a significant effect on changing the PLB morphology and structure, as well as the pH value (7.78 at 300 °C vs. 8.78 at 500 °C), extractable phosphorus (P) (18.73 ppm at 300 °C vs. 11.72 ppm at 500 °C), sulfur (S) (363 ppm at 300 °C vs. 344 ppm at 500 °C), and production yield of PLBs (47.65% at 300 °C vs. 60.62% at 500 °C). The results further suggest that adding a bedding material mixture (10% pine shavings) to PLs improved qualities by reducing the content of extractable P and S, as well as pH values of PLBs. This study also found the increment in both the pore volume and the area of Bethel Farm was higher than that of Sun Farm. Characterization and investigation of qualities and quantities of PLB using the integrated framework suggest that PL from Bethel Farm could produce better-quality PLB at a higher pyrolysis temperature and bedding material mixture to control N and P runoff problems. Full article

We examine several different features of DSDs based on data and observations from two mid-latitude coastal locations: (a) the Delmarva peninsula, USA, and (b) Incheon, South Korea. In each case, the full DSD spectra were obtained from two collocated disdrometers. Two events from location (a) and one event from location (b) are presented. For (a), observations and retrievals from NASA’s S-band polarimetric radar are included in the analyses as well as retrieved DSD parameters from the dual-wavelength precipitation radar onboard the Global Precipitation Measurement satellite. For (b), the disdrometer-based DSD data are compared with measurements from another sensor. Our main aim is to examine the underlying shape of the DSDs and their representation by the generalized gamma model. Full article

Concerns about atmospheric ammonia have been expressed recently by some on the Lower Eastern Shore (LES) of Maryland, which lies between the Chesapeake Bay and the Atlantic Ocean on the Delmarva peninsula. Agriculture, seafood and tourism are responsible for a significant fraction of the economic activity on the LES. The USDA 2017 census reported there were ~100 Concentrated Animal Feeding Operations (CAFOs) raising nearly 63 M chickens per year across Somerset and Worcester Counties. We report air quality data collected from sites near Princess Anne, Somerset County, and near Pocomoke City, Worcester County, to address air quality concerns by examining the influence of chicken farms on ammonia in ambient air on the LES. Within a two-mile radius of the Worcester County site, CAFO operations house ~1.6 million birds. The Princess Anne site is comparable to the Pocomoke City site in agricultural use and population demographics but has only a few chicken houses within two miles. The first 33 months of LES ammonia data are presented, and their significance is discussed relative to other ammonia studies. The 33-month average concentration of ammonia in Pocomoke was 10.3 ± 0.08 ppb, more than double that in Princess Anne, which was 4.7 ± 0.04 ppb. Full article

Repeated manure additions containing phosphorus (P) in excess of crop needs have led to many agricultural soils with high levels of soil P (i.e., legacy P), particularly in the Delmarva region (USA). Due to the potential for P release, it is important to gain a better understanding of the mechanisms of P desorption and solubilization. Agricultural soils with high legacy P were collected from the Delmarva Peninsula, and soil P pools were determined using a suite of wet chemical and spectroscopic techniques, including a modified Hedley sequential extraction and X-ray absorption near-edge structure (XANES) spectroscopy. Five different desorption solutions were used to investigate P removal efficiency to assess release mechanisms. The results indicate that sulfate can have a stronger competition for P desorption than silicate, especially in the ditch sample with 21% labile P and 44% P adsorbed to iron and aluminum (via Hedley extraction). Additionally, linear combination fitting results of the ditch sample indicate 10.5% organic P and 73.9% P associated with iron and aluminum. This is an important finding because sulfate is a prevalent ion in sea water, and many agricultural soils with high legacy P in the Delmarva coastal area are threatened by sea level rise and inundation. Full article

Drainage water management (DWM), also known as controlled drainage, is a best management practice (BMP) deployed on drainage ditches with demonstrated success at reducing dissolved nitrogen export from agricultural fields. By slowing discharge from agricultural ditches, subsequent anaerobic soil conditions provide an environment for nitrate to be reduced via denitrification. Despite this success, incomplete denitrification might increase nitrous oxide (N₂O) emissions and more reducing conditions might increase methanogenesis, resulting in increased methane (CH₄) emissions. These two gases, N₂O and CH₄, are potent greenhouse gases (GHG) and N₂O also depletes stratospheric ozone. This potential pollution swapping of nitrate reduction for GHG production could negatively impact the desirability of this BMP. We conducted three years of static chamber measurements of GHG emissions from the soil surface in farm plots with and without DWM in a corn–soybean rotation on the Delmarva Peninsula. We found that DWM raised the water table at the drainage ditch edge, but had no statistically significant effect on water-filled pore space in the field soil surface. Nor did we find a significant effect of DWM on GHG emissions. These findings are encouraging and suggest that, at least for this farm site, DWM can be used to remove nitrate without a significant tradeoff of increased GHG emissions. Full article

A novel method for retrieving the moments of rain drop size distribution (DSD) from the dual-frequency precipitation radar (DPR) onboard the global precipitation mission satellite (GPM) is presented. The method involves the estimation of two chosen reference moments from two specific DPR products, namely the attenuation-corrected Ku-band radar reflectivity and (if made available) the specific attenuation at Ka-band. The reference moments are then combined with a function representing the underlying shape of the DSD based on the generalized gamma model. Simulations are performed to quantify the algorithm errors. The performance of methodology is assessed with two GPM-DPR overpass cases over disdrometer sites, one in Huntsville, Alabama and one in Delmarva peninsula, Virginia, both in the US. Results are promising and indicate that it is feasible to estimate DSD moments directly from DPR-based quantities. Full article

The Delmarva Peninsula in the eastern United States is partially characterized by thousands of small, forested, depressional wetlands that are highly sensitive to weather variability and climate change, but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and their occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art U-Net semantic segmentation network to map forested wetland inundation in the Delmarva area by integrating leaf-off WorldView-3 (WV3) multispectral data with fine spatial resolution light detection and ranging (lidar) intensity and topographic data, including a digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation labels generated from lidar intensity were used for model training and validation. The wetland inundation map results were also validated using field data, and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% (Kappa = 0.90) compared to field data and high overlap (IoU = 70%) with lidar intensity-derived inundation labels. The integration of topographic metrics in deep learning models can improve the classification accuracy for depressional wetlands. This study highlights the great potential of deep learning models to improve the accuracy of wetland inundation maps through use of high-resolution optical and lidar remote sensing datasets. Full article

Infectious bronchitis virus (IBV) infection in chickens can lead to an economically important disease, namely, infectious bronchitis (IB). New IBV variants are continuously emerging, which complicates vaccination-based IB control. In this study, five IBVs were isolated from clinical samples submitted to a diagnostic laboratory in Ontario, Canada, and subjected to detailed molecular characterization. Analysis of the spike (S)1 gene showed that these five IBVs were highly related to the Delmarva (DMV/1639) strain (~97.0% nucleotide sequence similarity) that was firstly isolated from an IB outbreak in the Delmarva peninsula, United States of America (USA), in 2011. However, the complete genomic sequence analysis showed a 93.5–93.7% similarity with the Connecticut (Conn) vaccine strain, suggesting that Conn-like viruses contributed to the evolution of the five Canadian IBV/DMV isolates. A SimPlot analysis of the complete genomic sequence showed evidence of recombination for at least three different IBV strains, including a Conn vaccine-like strain, a 4/91 vaccine-like strain, and one strain that is yet-unidentified. The unidentified strain may have contributed the genomic regions of the S, 3, and membrane (M) genes of the five Canadian IBV/DMV isolates. The study outcomes add to the existing knowledge about involvement of recombination in IBV evolution. Full article

We evaluated the prevalence of influenza A virus (IAV) in different species of bivalves inhabiting natural water bodies in waterfowl habitat along the Delmarva Peninsula and Chesapeake Bay in eastern Maryland. Bivalve tissue from clam and mussel specimens (Macoma balthica, Macoma phenax, Mulinia sp., Rangia cuneata, Mya arenaria, Guekensia demissa, and an undetermined mussel species) from five collection sites was analyzed for the presence of type A influenza virus by qPCR targeting the matrix gene. Of the 300 tissue samples analyzed, 13 samples (4.3%) tested positive for presence of influenza virus A matrix gene. To our knowledge, this is the first report of detection of IAV in the tissue of any bivalve mollusk from a natural water body. Full article

Retreat of coastal forests in relation to sea level rise has been widely documented. Recent work indicates that coastal forests on the Delmarva Peninsula, United States, can be differentiated into persistence and regenerative zones as a function of sea-level rise and storm events. In the lower persistence zone trees cannot regenerate because of frequent flooding and high soil salinity. This study aims to verify the existence of these zones using spectral remote sensing data, and determine whether the effect of large storm events that cause damage to these forests can be detected from satellite images. Spectral analysis confirms a significant difference in average Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) values in the proposed persistence and regenerative zones. Both NDVI and NDWI indexes decrease after storms triggering a surge above 1.3 m with respect to the North American Vertical Datum of 1988 (NAVD88). NDWI values decrease more, suggesting that this index is better suited to detect the effect of hurricanes on coastal forests. In the regenerative zone, both NDVI and NDWI values recover three years after a storm, while in the persistence zone the NDVI and NDWI values keep decreasing, possibly due to sea level rise causing vegetation stress. As a result, the forest resilience to storms in the persistence zone is lower than in the regenerative zone. Our findings corroborate the ecological ratchet model of coastal forest disturbance. Full article

Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional- to global-scale surface water products lack the spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic classification tree approach to classify surface water extent using Sentinel-1 synthetic aperture radar (SAR) data and training datasets derived from prior class masks. Prior classes of water and non-water were generated from the Shuttle Radar Topography Mission (SRTM) water body dataset (SWBD) or composited dynamic surface water extent (cDSWE) class probabilities. Classification maps of water and non-water were derived over two distinct wetlandscapes: the Delmarva Peninsula and the Prairie Pothole Region. Overall classification accuracy ranged from 79% to 93% when compared to high-resolution images in the Prairie Pothole Region site. Using cDSWE class probabilities reduced omission errors among water bodies by 10% and commission errors among non-water class by 4% when compared with results generated by using the SWBD water mask. These findings indicate that including prior water masks that reflect the dynamics in surface water extent (i.e., cDSWE) is important for the accurate mapping of water bodies using SAR data. Full article

Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height and the aboveground biomass at a 30 m spatial resolution in Maryland by combining Geoscience Laser Altimeter System (GLAS) data and Landsat spectral imageries. The processes for calculating the forest biomass included the following: (i) processing the GLAS waveform and calculating spatially discrete forest canopy heights; (ii) developing canopy height models from Landsat imagery and extrapolating them to spatially contiguous canopy heights in Maryland; and, (iii) estimating forest aboveground biomass according to the relationship between canopy height and biomass. In our study, we explore the ability to use the GLAS waveform to calculate canopy height without ground-measured forest metrics (R² = 0.669, RMSE = 4.82 m, MRE = 15.4%). The machine learning models performed better than the principal component model when mapping the regional forest canopy height and aboveground biomass. The total forest aboveground biomass in Maryland reached approximately 160 Tg. When compared with the existing Biomass_CMS map, our biomass estimates presented a similar distribution where higher values were in the Western Shore Uplands region and Folded Application Mountain section, while lower values were located in the Delmarva Peninsula and Allegheny Mountain regions. Full article

We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications. Full article

Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality. Full article