997 resultados para Soil N Sources
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Report produced by Iowa Departmment of Agriculture and Land Stewardship
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Report of Conservation Program Summary produced by Iowa Departmment of Agriculture and Land Stewardship
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Report produced by Iowa Departmment of Agriculture and Land Stewardship
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Report produced by Iowa Departmment of Agriculture and Land Stewardship
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School district funding sources and amounts
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In recent research, both soil (root-zone) and air temperature have been used as predictors for the treeline position worldwide. In this study, we intended to (a) test the proposed temperature limitation at the treeline, and (b) investigate effects of season length for both heat sum and mean temperature variables in the Swiss Alps. As soil temperature data are available for a limited number of sites only, we developed an air-to-soil transfer model (ASTRAMO). The air-to-soil transfer model predicts daily mean root-zone temperatures (10cm below the surface) at the treeline exclusively from daily mean air temperatures. The model using calibrated air and root-zone temperature measurements at nine treeline sites in the Swiss Alps incorporates time lags to account for the damping effect between air and soil temperatures as well as the temporal autocorrelations typical for such chronological data sets. Based on the measured and modeled root-zone temperatures we analyzed. the suitability of the thermal treeline indicators seasonal mean and degree-days to describe the Alpine treeline position. The root-zone indicators were then compared to the respective indicators based on measured air temperatures, with all indicators calculated for two different indicator period lengths. For both temperature types (root-zone and air) and both indicator periods, seasonal mean temperature was the indicator with the lowest variation across all treeline sites. The resulting indicator values were 7.0 degrees C +/- 0.4 SD (short indicator period), respectively 7.1 degrees C +/- 0.5 SD (long indicator period) for root-zone temperature, and 8.0 degrees C +/- 0.6 SD (short indicator period), respectively 8.8 degrees C +/- 0.8 SD (long indicator period) for air temperature. Generally, a higher variation was found for all air based treeline indicators when compared to the root-zone temperature indicators. Despite this, we showed that treeline indicators calculated from both air and root-zone temperatures can be used to describe the Alpine treeline position.
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Self-potentials (SP) are sensitive to water fluxes and concentration gradients in both saturated and unsaturated geological media, but quantitative interpretations of SP field data may often be hindered by the superposition of different source contributions and time-varying electrode potentials. Self-potential mapping and close to two months of SP monitoring on a gravel bar were performed to investigate the origins of SP signals at a restored river section of the Thur River in northeastern Switzerland. The SP mapping and subsequent inversion of the data indicate that the SP sources are mainly located in the upper few meters in regions of soil cover rather than bare gravel. Wavelet analyses of the time-series indicate a strong, but non-linear influence of water table and water content variations, as well as rainfall intensity on the recorded SP signals. Modeling of the SP response with respect to an increase in the water table elevation and precipitation indicate that the distribution of soil properties in the vadose zone has a very strong influence. We conclude that the observed SP responses on the gravel bar are more complicated than previously proposed semi-empiric relationships between SP signals and hydraulic head or the thickness of the vadose zone. We suggest that future SP monitoring in restored river corridors should either focus on quantifying vadose zone processes by installing vertical profiles of closely spaced SP electrodes or by installing the electrodes within the river to avoid signals arising from vadose zone processes and time-varying electrochemical conditions in the vicinity of the electrodes.
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Current models of brain organization include multisensory interactions at early processing stages and within low-level, including primary, cortices. Embracing this model with regard to auditory-visual (AV) interactions in humans remains problematic. Controversy surrounds the application of an additive model to the analysis of event-related potentials (ERPs), and conventional ERP analysis methods have yielded discordant latencies of effects and permitted limited neurophysiologic interpretability. While hemodynamic imaging and transcranial magnetic stimulation studies provide general support for the above model, the precise timing, superadditive/subadditive directionality, topographic stability, and sources remain unresolved. We recorded ERPs in humans to attended, but task-irrelevant stimuli that did not require an overt motor response, thereby circumventing paradigmatic caveats. We applied novel ERP signal analysis methods to provide details concerning the likely bases of AV interactions. First, nonlinear interactions occur at 60-95 ms after stimulus and are the consequence of topographic, rather than pure strength, modulations in the ERP. AV stimuli engage distinct configurations of intracranial generators, rather than simply modulating the amplitude of unisensory responses. Second, source estimations (and statistical analyses thereof) identified primary visual, primary auditory, and posterior superior temporal regions as mediating these effects. Finally, scalar values of current densities in all of these regions exhibited functionally coupled, subadditive nonlinear effects, a pattern increasingly consistent with the mounting evidence in nonhuman primates. In these ways, we demonstrate how neurophysiologic bases of multisensory interactions can be noninvasively identified in humans, allowing for a synthesis across imaging methods on the one hand and species on the other.
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Oxalate catabolism, which can have both medical and environmental implications, is performed by phylogenetically diverse bacteria. The formyl-CoA-transferase gene was chosen as a molecular marker of the oxalotrophic function. Degenerated primers were deduced from an alignment of frc gene sequences available in databases. The specificity of primers was tested on a variety of frc-containing and frc-lacking bacteria. The frc-primers were then used to develop PCR-DGGE and real-time SybrGreen PCR assays in soils containing various amounts of oxalate. Some PCR products from pure cultures and from soil samples were cloned and sequenced. Data were used to generate a phylogenetic tree showing that environmental PCR products belonged to the target physiological group. The extent of diversity visualised on DGGE pattern was higher for soil samples containing carbonate resulting from oxalate catabolism. Moreover, the amount of frc gene copies in the investigated soils was detected in the range of 1.64x10(7) to 1.75x10(8)/g of dry soil under oxalogenic tree (representing 0.5 to 1.2% of total 16S rRNA gene copies), whereas the number of frc gene copies in the reference soil was 6.4x10(6) (or 0.2% of 16S rRNA gene copies). This indicates that oxalotrophic bacteria are numerous and widespread in soils and that a relationship exists between the presence of the oxalogenic trees Milicia excelsa and Afzelia africana and the relative abundance of oxalotrophic guilds in the total bacterial communities. This is obviously related to the accomplishment of the oxalate-carbonate pathway, which explains the alkalinization and calcium carbonate accumulation occurring below these trees in an otherwise acidic soil. The molecular tools developed in this study will allow in-depth understanding of the functional implication of these bacteria on carbonate accumulation as a way of atmospheric CO(2) sequestration.
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We investigate the problem of finding minimum-distortion policies for streaming delay-sensitive but distortion-tolerant data. We consider cross-layer approaches which exploit the coupling between presentation and transport layers. We make the natural assumption that the distortion function is convex and decreasing. We focus on a single source-destination pair and analytically find the optimum transmission policy when the transmission is done over an error-free channel. This optimum policy turns out to be independent of the exact form of the convex and decreasing distortion function. Then, for a packet-erasure channel, we analytically find the optimum open-loop transmission policy, which is also independent of the form of the convex distortion function. We then find computationally efficient closed-loop heuristic policies and show, through numerical evaluation, that they outperform the open-loop policy and have near optimal performance.
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Tillage systems play a significant role in agricultural production throughout Iowa and the Midwest. It has been well documented that increased tillage intensities can reduce soil organic matter in the topsoil due to increased microbial activity and carbon (C ) oxidation. The potential loss of soil organic matter due to tillage operations is much higher for high organic matter soils than low organic matter soils. Tillage effects on soil organic matter can be magnified through soil erosion and loss of soil productivity. Soil organic matter is a natural reservoir for nutrients, buffers against soil erosion, and improves the soil environment to sustain soil productivity. Maintaining soil productivity requires an agriculture management system that maintains or improves soil organic matter content. Combining cropping systems and conservation tillage practices, such as no-tillage, strip-tillage, or ridge-tillage, are proven to be very effective in improving soil organic matter and soil quality.
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This paper describes a maximum likelihood method using historical weather data to estimate a parametric model of daily precipitation and maximum and minimum air temperatures. Parameter estimates are reported for Brookings, SD, and Boone, IA, to illustrate the procedure. The use of this parametric model to generate stochastic time series of daily weather is then summarized. A soil temperature model is described that determines daily average, maximum, and minimum soil temperatures based on air temperatures and precipitation, following a lagged process due to soil heat storage and other factors.