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Large scale environmental impact studies typically involve the use of simulation models and require a variety of inputs, some of which may need to be estimated in absence of adequate measured data. An important input, soil bulk density (Db) affects conditions for soil aeration, solute transport, and storage as well as the outcome of soil C stock calculations. Correct representation of Db in simulation studies is essential since any bias or uncertainty will propagate through a variety of processes and time steps. We used the U.S.-wide NRCS National Soil Survey Characterization (NSSC) database of point measurements and the ‘k-nearest neighbor’ (k-NN) pattern recognition algorithm combined with random resampling without replacement to estimate Db and its uncertainty. Soil particle-size distribution and organic C content were utilized as inputs and soil taxonomy classification, sample depth, and soil horizon notation were tested as optional grouping and limiting factors to the calibration (reference) data. We obtained an overall root-mean-squared error (RMSE) of 0.17 g cm−3 and mean error (ME) of 0.01 g cm−3 Grouping samples by taxonomic classification proved to be advantageous, while limiting samples by depth helped avoid depth-specific bias in the estimations. Grouping samples by horizon notation did not yield significant improvement due to the great variability of bulk density within horizons of the same notation. Varying the proportion of data used in the resampled data subsets can be used to establish greater or lesser degree of confidence in mean estimates without affecting those mean estimates. Simulation based environmental impact and risk assessment studies can be direct beneficiaries of data with characterized uncertainty.

Large scale environmental impact studies typically involve the use of simulation models and require a variety of inputs, some of which may need to be estimated when adequate measured data are absent. As an example, soil water retention needs to be estimated for a large number of soils that are to be used in the context of the U.S. national scale Conservation Effects Assessment Project (CEAP). Use of a set of well known linear regression based pedotransfer functions (PTFs) developed in 1982 was proposed to address such data need. Examination of the underlying data as well as comparative estimations to an independent US-wide data set revealed that the proposed equations were most likely meant to use organic carbon (OC) data in place of the reported organic matter (OM) data. Other discrepancies--possibly due to misreporting--were also found in a large portion of the OM data. These PTFs were also developed from data originating from only 18 U.S. states--and 48% of them dominated by 3 U.S. states--while major cropland states/regions were barely or not represented at all. Resulting estimations showed non-random distribution of estimation residuals (i.e., bias) that could however be corrected with data transformations and by using a k-Nearest Neighbor algorithm as an alternative PTF technique. We recommend that the PTF equations proposed in 1982 not be used in the context of the U.S. national scale CEAP project. Alternative solutions should ensure the proper representation of U.S. soils and their properties.

Accurate prediction of phenological development in maize (Zea mays L.) is fundamental to determining crop adaptation and yield potential. A number of thermal functions are used in crop models, but their relative precision in predicting maize development has not been quantified. The objectives of this study were (i) to evaluate the precision of eight thermal functions, (ii) to assess the effects of source data on the ability to differentiate among thermal functions, and (iii) to attribute the precision of thermal functions to their response across various temperature ranges. Data sets used in this study represent >1000 distinct maize hybrids, >50 geographic locations, and multiple planting dates and years. Thermal functions and calendar days were evaluated and grouped based on their temperature response and derivation as empirical linear, empirical nonlinear, and process‐based functions. Precision in predicting phase durations from planting to anthesis or silking and from silking to physiological maturity was evaluated. Large data sets enabled increased differentiation of thermal functions, even when smaller data sets contained orthogonal, multi‐location and ‐year data. At the highest level of differentiation, precision of thermal functions was in the order calendar days < empirical linear < process based < empirical nonlinear. Precision was associated with relatively low temperature sensitivity across the 10 to 26°C range. In contrast to other thermal functions, process‐based functions were derived using supra‐optimal temperatures, and consequently, they may better represent the developmental response of maize to supra‐optimal temperatures. Supra‐optimal temperatures could be more prevalent under future climate‐change scenarios, but data sets in this study contained few data in that range.

Elevated carbon dioxide (CO2) influences photosynthesis (AN), transpiration (ET), and water use efficiency (WUE) for well‐watered potato (Solanum tuberosum L.). Little is known regarding effects of short‐term drought and CO2. Two experiments, differing in the quantity of solar radiation, were conducted in soil‐plant‐atmosphere‐research chambers. Plants were grown at ambient (aCO2) or twice‐ambient CO2 (eCO2) and received one of three irrigation treatments: no water stress (C), short‐term (11–16 d) water‐withholding during vegetative and post‐tuber initiation stages (VR), or post‐tuber initiation (R) only. Canopy conductance to CO2 transfer (τ) and water vapor (Gv), light use efficiency (α), daily AN, and ET decreased at the onset of each drought and were correlated with volumetric water content. The rate of decrease was similar for R and VR. Gv declined more sharply than AN, resulting in higher WUE. Seasonal AN declined with the pattern of C > R > VR and was higher for eCO2 C and R treatments. Seasonal WUE was higher for eCO2 at all irrigation treatments. Total dry matter, harvest index, and leaf area were reduced (p < 0.05) for droughted treatments and total dry matter and harvest index were also higher for eCO2 VR pots. Relative responses to drought and CO2 were similar among experiments, with greater magnitude of response under high solar radiation. Findings were similar to those reported under longer‐term water‐withholding studies, suggesting that interactions between CO2 and drought on carbon assimilation and water use are conserved across production zones with varying radiation and rainfall patterns.

Real-time information on salinity levels and transport of fertilizers are generally missing from soil profile knowledge bases. A dual-frequency multisensor capacitance probe (MCP) is now commercially available, for sandy soils, to simultaneously monitor volumetric soil water content (VWC) measured as a percentage and salinity as a unitless volumetric ion content (VIC). The objectives of this research were to assess the relationship of salinity and water content with these dual-frequency MCPs under laboratory conditions, and assess its potential for field use in sandy soils of the mid-Atlantic region of the US. Water and salinity studies were conducted in two sand-filled PVC columns, 1.2 m long by 0.25 m ID. Each column was instrumented with ten dual-frequency capacitance sensors and two thermocouple temperature sensors. Four salinity levels were studied in the two columns using 0.5, 1, 2, and 4 dSm⁻¹ NH₄NO₃ solutions. Water, salinity, and temperature readings were continuously recorded at 1-min intervals. The VIC values were found to be primarily qualitative, but combined with real-time VWC measures the probe could still be an important fertigation management tool to provide near-continuous real-time information on fertilizer penetration, spread and subsequent changes during crop growth.

Crop responses to management practices and the environment, as quantified by leaf area index (LAI), provide decision-making criteria for the delineation of crop management zones. The objective of this work was to investigate whether spatial correlations inferred from remotely sensed imagery can be used to interpolate and map LAI using a relatively small number of ground-based LAI measurements. Airborne imagery was recorded with the Airborne Imaging Spectrometer for Applications (AISA) radiometer over a 3.2 ha corn field. Spectral vegetation indexes (SVI) were derived from the image and aggregated to cells of 2 x 2 m², 4 x 4 m², and 8 x 8 m² resolution. The residual maximum likelihood method was used to estimate the LAI variogram parameters. A generalized least squares regression was used to relate ground truth LAI data and collocated image pixels. This regression result was then used to convert variograms from the imagery to LAI units as well as to interpolate and map LAI. The decrease in resolution by merging pixels led to an increase in the value of the r ² and to a decrease in root mean-squared error (RMSE) values. The accuracy of kriged estimates from the variogram of the measured LAI and that from the image derived variograms was estimated by cross-validation. There was no difference in the accuracy of the estimates using either variograms from measured LAI values or from those of converted SVIs. Maps of LAI from ground-based measurements made by kriging the data with image-derived variogram parameters were similar to those obtained by with kriging with the variogram of measured LAI. Similar coarse spatial trends of high, medium and low LAI were evident for both maps. Variogram parameters from ground-based measurements of LAI compared favorably with those derived from remotely sensed imagery and could be used to provide reasonable results for the interpolation of LAI measurements.

The objective of this study was to quantify relations between preferential transport of a solute and initial water content, infiltration rate, and porosity in a field soil where preferential transport was mainly due to soil heterogeneity. We measured the horizontal and vertical distribution of a tracer chemical applied with ponded water to study the flow paths of the tracer. The soil at the site is a Bosville fine sandy loam (fine-mixed, thermic Albaquic Paleudalfs). Strontium bromide (SrBr2) tracer was applied with a dye (methylene blue) in a 100 or 50-mm pulse of water to soil within eight double ring infiltrometers. After 48 h the soil in each infiltrometer was sampled to 0.7 m. Twelve horizontally oriented, continuous soil samples 0.1 m long were collected at each depth. There were very few dye stains of root hairs, root channels, and pores to a depth of about 50 to 80 mm. Recoveries of Br to 0.5 m ranged from 36 to 56% applied. Bromide recovery was negatively correlated with initial water content and positively with total porosity. Below 0.35 m in depth resident solute concentration at a sampling position was positively correlated with concentration in the layer above indicating preferential vertical flow paths. It appeared that a large fraction of solute transport was through the highly porous areas of the cross-section of soil bounded by the infiltrometer ring. The preferential transport of Br in this study was influenced largely by the properties of the clay layer at 0.35 m that had the lowest conductivity and lowest porosity in the profile and appeared to have cracks filled with sand

We present the X-ray point-source catalogs in two of the XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS) fields, W-CDF-S (4.6 deg\\(^2\\)) and ELAIS-S1 (3.2 deg\\(^2\\)), aiming to fill the gap between deep pencil-beam X-ray surveys and shallow X-ray surveys over large areas. The W-CDF-S and ELAIS-S1 regions were targeted with 2.3 Ms and 1.0 Ms of XMM-Newton observations, respectively; 1.8 Ms and 0.9 Ms exposures remain after flare filtering. The survey in W-CDF-S has a flux limit of 1.0 \\(\\times\\) 10\\(^{-14}\\) erg cm\\(^{-2}\\) s\\(^{-1}\\) over 90% of its area in the 0.5-10 keV band; 4053 sources are detected in total. The survey in ELAIS-S1 has a flux limit of 1.3 \\(\\times\\) 10\\(^{-14}\\) erg cm\\(^{-2}\\) s\\(^{-1}\\) over 90% of its area in the 0.5-10 keV band; 2630 sources are detected in total. Reliable optical-to-IR multiwavelength counterpart candidates are identified for \\(\\approx\\) 89% of the sources in W-CDF-S and \\(\\approx\\) 87% of the sources in ELAIS-S1. 3186 sources in W-CDF-S and 1985 sources in ELAIS-S1 are classified as AGNs. We also provide photometric redshifts for X-ray sources; \\(\\approx\\) 84% of the 3319/2001 sources in W-CDF-S/ELAIS-S1 with optical-to-NIR forced photometry available have either spectroscopic redshifts or high-quality photometric redshifts. The completion of the XMM-Newton observations in the W-CDF-S and ELAIS-S1 fields marks the end of the XMM-SERVS survey data gathering. The \\(\\approx\\) 12,000 point-like X-ray sources detected in the whole \\(\\approx\\) 13 deg\\(^2\\) XMM-SERVS survey will benefit future large-sample AGN studies.