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Temperature and rainfall are important climatological parameters, and knowledge of their temporal and spatial patterns is useful for researchers working in many disciplines. In this study, spatial interpolation techniques were implemented in a Geographic Information System (GIS) to study the spatial variability of climate variables (maximum air temperature, minimum air temperature, and seasonal and annual rainfall) in Nebraska. Thirty years (1971-2000) of climate data (average monthly maximum and minimum temperatures and rainfall) from 215 National Weather Service Cooperative Observer Network (COOP) weather stations distributed throughout Nebraska and surrounding states were used in the analyses. Literature suggests that there is no single preferred method of interpolation, and the selection of interpolation method is usually based on the available data, desired level of accuracy, and available resources. We analyzed three different commonly used interpolation methods (inverse distance weighted, spline, and kriging) and evaluated their performance. Overall, the summary of all statistical parameters showed no significant difference between interpolation techniques in predicting the spatial variability in 30-year climate normals. Investigation of interpolation errors at individual weather stations agreed with summary statistics. Spatial variability, in this instance, is likely smoothed due to long-term averaging of the data (30 years), resulting in similar errors for all the interpolation techniques. Subjective assessment of maps for all climate variables showed that the kriging method produced smoother maps compared to spline and inverse distance weighted. Considering the degree to which accurate spatial interpolation could be accomplished with relative ease and less bias, the spline method proves the better option. [ABSTRACT FROM AUTHOR]

A method to rapidly measure dopamine (DA), dihydroxyindolphenylacetic acid, homovanillic acid, serotonin (5-HT) and 5-hydroxyindoleacetic acid concentrations in cerebrospinal fluid (CSF) has not yet been reported. A rapid, sensitive, and specific HPLC method was therefore developed using electrochemical detection. CSF was mixed with an antioxidant solution prior to freezing to prevent neurotransmitter degradation. Separation of the five analytes was obtained on an ESA MD-150 × 3.2 mm column with a flow rate of 0.37 mL/min and an acetonitrile-aqueous (5 : 95, v/v) mobile phase with 75 mM monobasic sodium phosphate buffer, 0.5 mM EDTA, 0.81 mM sodium octylsulfonate and 5% tetrahydrofuran. The optimal electrical potential settings were: guard cell +325 mV, E1 −100 mV and E2 +300 mV. Within-day and between-day precisions were <10% for all analytes and accuracies ranged from 91.0 to 106.7%. DA, 5-HT, and their metabolites were stable in CSF with antioxidant solution at 4°C for 8 h in the autoinjector. This method was used to measure neurotransmitters in CSF obtained from children enrolled on an institutional medulloblastoma treatment protocol. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The objectives of this study were to evaluate the effects of feeding high-protein distillers dried grains (HPDDG) on rumen degradability, dry matter intake, milk production, and milk composition. Sixteen lactating Holstein cows (12 multiparous and 4 primiparous) averaging 80 ± 14 d in milk were randomly assigned to 1 of 2 dietary treatments in a 2 × 2 crossover design. A portion of forage and all soy-based protein in the control diet were replaced by HPDDG (20% dry matter). Milk production and dry matter intake were recorded daily and averaged for d 19 to 21 of each 21-d period. Milk samples were collected on d 20 to 21 of each period. Milk yield increased with the inclusion of HPDDG (33.4 vs. 31.6 ± 2.13 kg/d), and 3.5% FCM was higher for the ration containing HPDDG (36.3 vs. 33.1 ± 2.24 kg/d). Percentage protein was not affected by treatment (average 3.04 ± 0.08%), but protein yield increased with inclusion of HPDDG (0.95 to 1.00 ± 0.05 kg/d). Milk fat concentration was not different between treatments (average 3.95 ± 0.20%), but fat yield increased for the ration containing HPDDG (1.35 vs. 1.21 ± 0.09 kg/d). Dry matter intake was not affected and averaged 21.9 ± 0.80 kg across treatments. Because of greater milk production, feed conversion was improved by the inclusion of HPDDG (1.47 to 1.73 ± 0.09). Milk urea N was greater for the HPDDG ration than the control (14.5 vs. 12.8 ± 0.67 mg/dL). This research suggests that HPDDG may effectively replace soy-based protein in lactating dairy cow diets. [ABSTRACT FROM AUTHOR]

The effect of missing data can result in errors that exhibit temporal and spatial patterns in climatological and meteorological research applications. Many climate related tools perform best with a serially complete dataset (SCD). To support the National Agricultural Decision Support System (NADSS), a SCD with no missing data values for daily temperature and precipitation for the United States was developed using a self-calibrating data quality control (QC) library. The library performs two primary functions: identifies outliers and provides estimates to replace missing data values and outliers. This study presents the development of the SCD and the QC library in detail. An in-depth evaluation in terms of root mean square error (RMSE) and mean absolute error (MAE) for the SCD for the period of 1975 - 2004 is provided. The study shows an impressively low average RMSE in the range of 2.27 to 3.58°F for temperature and 0.07 to 0.23 inch for precipitation for the whole country for 30 years. The goal of this study is to enhance drought risk assessment and environmental risk analysis. [ABSTRACT FROM AUTHOR]

The biases of four commonly used air temperature sensors are examined and detailed. Each temperature transducer consists of three components: temperature sensing elements, signal conditioning circuitry, and corresponding analog-to-digital conversion devices or dataloggers. An error analysis of these components was performed to determine the major sources of error in common climate networks. It was found that, regardless of microclimate effects, sensor and electronic errors in air temperature measurements can be larger than those given in the sensor manufacturer's specifications. The root-sum-of-squares (RSS) error for the HMP35C sensor with CR10X datalogger was above 0.2°C, and rapidly increases for both lower (<-20°C) and higher temperatures (>30°C). Likewise, the largest errors for the maximum–minimum temperature system (MMTS) were at low temperatures (<-40°C). The temperature linearization error in the HO-1088 hygrothermometer produced the largest errors when the temperature was lower than -20°C. For the temperature sensor in the U.S. Climate Reference Networks (USCRN), the error was found to be 0.2° to 0.33°C over the range -25° to 50°C. The results presented here are applicable when data from these sensors are applied to climate studies and should be considered in determining air temperature data continuity and climate data adjustment models. [ABSTRACT FROM AUTHOR]