Short-term effects of tillage on mineralization of nitrogen and carbon in soil

Tillage is known to decrease soil organic nitrogen (N) and carbon (C) pools with negative consequences for soil quality. This decrease is thought partly to be caused by exposure of protected organic matter to microbial degradation by the disturbance of soil structure. Little is known, however, about the short-term effects of tillage on mineralization of N and C, and microbial activity. We studied the short-term effects of two types of tillage (conventional plough- and a non-inverting-tillage) on mineralization and microbial N and C pools in a sandy loam under organic plough-tillage management. The release of active and protected (inactive) N by tillage was further studied in the laboratory by use of 15N labelling of the active pool of soil N followed by simulation of tillage by sieving through a 2 mm sieve. Results showed that the two types of tillage as well as the simulation of tillage had very few effects on mineralization and microbial pools. The simulation of tillage caused, however, a small release of N from a pool which was otherwise protected against microbial degradation. The use of soil crushing for disruption of larger macroaggregates (>425 µm) and chloroform fumigation for perturbation of the microbial biomass increased the release from both active and protected N pools. The relative contribution from the protected N pool was, however, similar in the three treatments (22-27%), thus the pools subjected to mineralization were characterised by similar degree of protection. On the basis of isotopic composition the pools of N mineralised were indistinguishable. This suggests that the released N originated from the same pool, that is the soil microbial biomass. The study points to the microbial pool as the main source of labile N which may be released by tillage, and thus to its importance for sustained soil fertility in agricultural systems

Surface soil quality in five midwestern cropland Conservation Effects Assessment Project watersheds

Soil quality (SQ) assessment is a proactive process for evaluating soil and crop management effects on biological, chemical, and physical indicators of soil health. Our objectives were to evaluate several SQ indicators within five Agricultural Research Service (ARS) experimental watersheds (WS) and determine if those indicators were affected by manure, tillage, or crop rotation histories. Ten soil quality indicators were measured within each of 600 0 to 5 cm (0 to 2 in) depth and 398 5 to 15 cm (2 to 6 in) depth increment samples, evaluated statistically, and then scored using the Soil Management Assessment Framework. Except for soil organic carbon (C) at both depth increments or microbial biomass C and β-glucosidase within the 5 to 15 cm increment, the indicators showed significant WS differences. Except for surface soil-test phosphorous (P), Soil Management Assessment Framework indicator scores and overall soil quality index values also showed significant (p ≤ 0.05) WS differences. Microbial biomass C was significantly affected by crop rotation at both sampling depths and by WS within the surface 5 cm. β-glucosidase was significantly affected by all four factors (WS, manure, tillage, and crop rotation) and their interactions within the 0 to 5 cm increment. The water-stable macroaggregate indictor within the 0 to 5 cm increment and within the 5 to 15 cm increment, however, were not significantly different for the tillage and manure application treatments, respectively. Our study showed that the ARS Conservation Effects Assessment Project (CEAP) watersheds provided a moderately controlled example that watershed-scale monitoring of soil quality is feasible and should be used to monitor soil health and/or conservation program effectiveness

Descriptive Epidemiology of Collegiate Men's Baseball Injuries: National Collegiate Athletic Association Injury Surveillance System, 1988–1989 Through 2003–2004

Objective: To review 16 years of National Collegiate Athletic Association (NCAA) injury surveillance data for men's baseball and identify potential areas for injury prevention initiatives

Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring

Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-megapixel camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the camera-UAV system may provide important information for site-specific agriculture

Use of High-Resolution Land Cover Maps to Support the Maintenance of the NWI Geospatial Dataset: A Case Study in a Coastal New Orleans Region

The National Wetlands Inventory (NWI) is the most comprehensive wetland geospatial dataset in the United States. However, it can be time-consuming and costly to maintain. This study introduces automated algorithms and methods to support NWI maintenance. Through a wall-to-wall comparison between NWI and Coastal Change Analysis Program (C-CAP) datasets, a pixel-level difference product was generated at 1 m resolution. Building upon this, supplementary attributes describing wetland changes were incorporated into each NWI polygon. Additionally, new water polygons were extracted from C-CAP data, and regional statistics regarding wetland changes were computed for HUC12 watersheds. The 1 m difference product can indicate specific wetland change locations, such as wetland loss to impervious surfaces, the gain of open water bodies from uplands, and the conversion of drier vegetated wetlands to open water. The supplementary attributes can indicate the amount and percentage of wetland loss or water regime change for NWI polygons. Extracted new water polygons can serve as preliminary materials for generating NWI standard-compliant products, expediating NWI maintenance processes while reducing costs. Regional statistics of wetland change can help target watersheds with the most significant changes for maintenance, thereby reducing work areas. The approaches we present hold significant value in supporting NWI maintenance

Characterizing Wetland Inundation and Vegetation Dynamics in the Arctic Coastal Plain Using Recent Satellite Data and Field Photos

Arctic wetlands play a critical role in the global carbon cycle and are experiencing disproportionate impacts from climate change. Even though Alaska hosts 65% of U.S. wetlands, less than half of the wetlands in Alaska have been mapped by the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) or other high-resolution wetlands protocols. The availability of time series satellite data and the development of machine learning algorithms have enabled the characterization of Arctic wetland inundation dynamics and vegetation types with limited ground data input. In this study, we built a semi-automatic process to generate sub-pixel water fraction (SWF) maps across the Coastal Plain of the Arctic National Wildlife Refuge (ANWR) in Alaska using random forest regression and 139 Sentinel-2 images taken in ice-free seasons from 2016 to 2019. With this, we characterized the seasonal dynamics of wetland inundation and explored their potential usage in determining NWI water regimes. The highest levels of surface water expression were detected in June, resulting from seasonal active layer thaw and snowmelt. Inundation was most variable in riverbeds, lake and pond margins, and depressional wetlands, where water levels fluctuate substantially between dry and wet seasons. NWI water regimes that indicate frequent inundation, such as permanently flooded wetlands, had high SWF values (SWF ≥ 90%), while those with infrequent inundation, such as temporarily flooded wetlands, had low SWF values (SWF < 10%). Vegetation types were also classified through the synergistic use of a vegetation index, water regimes, synthetic-aperture radar (SAR) data, topographic data, and a random forest classifier. The random forest classification algorithms demonstrated good performance in classifying Arctic wetland vegetation types, with an overall accuracy of 0.87. Compared with NWI data produced in the 1980s, scrub-shrub wetlands appear to have increased from 91 to 258 km2 over the last three decades, which is the largest percentage change (182%) among all vegetation types. However, additional field data are needed to confirm this shift in vegetation type. This study demonstrates the potential of using time series satellite data and machine learning algorithms in characterizing inundation dynamics and vegetation types of Arctic wetlands. This approach could aid in the creation and maintenance of wetland inventories, including the NWI, in Arctic regions and enable an improved understanding of long-term wetland dynamics

Characterizing Wetland Inundation and Vegetation Dynamics in the Arctic Coastal Plain Using Recent Satellite Data and Field Photos

Arctic wetlands play a critical role in the global carbon cycle and are experiencing disproportionate impacts from climate change. Even though Alaska hosts 65% of U.S. wetlands, less than half of the wetlands in Alaska have been mapped by the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) or other high-resolution wetlands protocols. The availability of time series satellite data and the development of machine learning algorithms have enabled the characterization of Arctic wetland inundation dynamics and vegetation types with limited ground data input. In this study, we built a semi-automatic process to generate sub-pixel water fraction (SWF) maps across the Coastal Plain of the Arctic National Wildlife Refuge (ANWR) in Alaska using random forest regression and 139 Sentinel-2 images taken in ice-free seasons from 2016 to 2019. With this, we characterized the seasonal dynamics of wetland inundation and explored their potential usage in determining NWI water regimes. The highest levels of surface water expression were detected in June, resulting from seasonal active layer thaw and snowmelt. Inundation was most variable in riverbeds, lake and pond margins, and depressional wetlands, where water levels fluctuate substantially between dry and wet seasons. NWI water regimes that indicate frequent inundation, such as permanently flooded wetlands, had high SWF values (SWF ≥ 90%), while those with infrequent inundation, such as temporarily flooded wetlands, had low SWF values (SWF < 10%). Vegetation types were also classified through the synergistic use of a vegetation index, water regimes, synthetic-aperture radar (SAR) data, topographic data, and a random forest classifier. The random forest classification algorithms demonstrated good performance in classifying Arctic wetland vegetation types, with an overall accuracy of 0.87. Compared with NWI data produced in the 1980s, scrub-shrub wetlands appear to have increased from 91 to 258 km2 over the last three decades, which is the largest percentage change (182%) among all vegetation types. However, additional field data are needed to confirm this shift in vegetation type. This study demonstrates the potential of using time series satellite data and machine learning algorithms in characterizing inundation dynamics and vegetation types of Arctic wetlands. This approach could aid in the creation and maintenance of wetland inventories, including the NWI, in Arctic regions and enable an improved understanding of long-term wetland dynamics.https://doi.org/10.3390/rs1308149

The Blackwell Companion to Digital Humanities: a Roundtable Discussion.

This session will reflect on the recently published Blackwell Companion to Digital Humanities by six of its contributors and its three editors. This collection marks a turning point in the field of digital humanities: for the first time, a wide range of theorists and practitioners, those who have been active in the field for decades, and those recently involved, disciplinary experts, computer scientists, and library and information studies specialists, have been brought together to consider digital humanities as a discipline in its own right, as well as to reflect on how it relates to areas of traditional humanities scholarship