What controls the oceanic dimethylsulfide (DMS) cycle ? A modeling approach

We implemented a process-based DMS module into the global carbon cycle ocean model (HAMOCC5) which includes a simple module for plankton dynamics and investigated the regional and seasonal variations of the marine sulfur cycle. The turnover rates within the DMS cycle are only poorly known. Therefore we developed, on the basis of a global DMS data set, an optimization routine for the free parameters controlling DMS production and removal. The resulting seasonal and regional distributions of DMS concentration are fully consistent with the underlying hydrodynamical and biogeochemical processes. We investigated a series of DMS model approaches with various complexities. The distinction between different DMS producing phytoplankton species and the consideration of the regionally and seasonally varying bacterial activity on converting dDMSP to DMS and on DMS consumption appears to have a crucial effect on the quality of the results in the given model conception

Assessing Short‐Term Impacts of Management Practices on N2O Emissions From Diverse Mediterranean Agricultural Ecosystems Using a Biogeochemical Model

Croplands are important sources of nitrous oxide (N2O) emissions. The lack of both long‐term field measurements and reliable methods for extrapolating these measurements has resulted in a large uncertainty in quantifying and mitigating N2O emissions from croplands. This is especially relevant in regions where cropping systems and farming management practices (FMPs) are diverse. In this study, a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC), was tested against N2O measurements from five cropping systems (alfalfa, wheat, lettuce, vineyards, and almond orchards) representing diverse environmental conditions and FMPs. The model tests indicated that DNDC was capable of predicting seasonal and annual total N2O emissions from these cropping systems, and the model\u27s performance was better than the Intergovernmental Panel on Climate Change emission factor approach. DNDC also captured the impacts on N2O emissions of nitrogen fertilization for wheat and lettuce, of stand age for alfalfa, as well as the spatial variability of N2O fluxes in vineyards and orchards. DNDC overestimated N2O fluxes following some heavy rainfall events. To reduce the biases of simulating N2O fluxes following heavy rainfall, studies should focus on clarifying mechanisms controlling impacts of environmental factors on denitrification. DNDC was then applied to assess the impacts on N2O emissions of FMPs, including tillage, fertilization, irrigation, and management of cover crops. The practices that can mitigate N2O emissions include reduced or no tillage, reduced N application rates, low‐volume irrigation, and cultivation of nonleguminous cover crops. This study demonstrates the necessity and potential of utilizing process‐based models to quantify N2O emissions from regions with highly diverse cropping systems

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Transport of Fungal Symbionts by Mountain Pine Beetles

The perpetuation of symbiotic associations between bark beetles (Coleoptera: Curculionidae: Scolytinae) and ophiostomatoid fungi requires the consistent transport of fungi by successive beetle generations to new host trees. We used scanning electron microscopy and culture methods to investigate fungal transport by the mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins. MPB transports its two main fungal associates, Grosmannia clavigera (Robinson-Jeffrey and Davidson) Zipfel, de Beer and Wingfield and Ophiostoma montium (Rumbold) von Arx, in sac-like mycangia on the maxillary cardines as well as on the exoskeleton. Although spores of both species of fungi were observed on MPB exoskeletons, often in pits, O. montium spores were generally more abundant than G. clavigera spores. However, a general scarcity of spores of either species on MPB exoskeletons compared with numbers on scolytines that lack sac-like mycangia indicates that fungal transport exteriorly on MPBs is incidental rather than adaptive. Conidia were the dominant spore type transported regardless of location or species; however, our results suggest that once acquired in mycangia, conidia may reproduce in a yeast-like form and even produce hypha-like strands and compact conidiophore-like structures. Fungi that propagate in mycangia may provide beetles with a continual source of inocula during the extended egg-laying period

NMR Imaging of low pressure, gas-phase transport in packed beds using hyperpolarized xenon-129

Gas-phase magnetic resonance imaging (MRI) has been used to investigate heterogeneity in mass transport in a packed bed of commercial, alumina, catalyst supports. Hyperpolarized 129Xe MRI enables study of transient diffusion for micro- scopic porous systems using xenon chemical shift to selectively image gas within the pores, and, thence, permits study of low-density, gas-phase mass-transport, such that diffusion can be studied in the Knudsen regime, and not just the molecular regime, which is the limitation with other current techniques. Knudsen-regime diffusion is common in many industrial, catalytic processes. Significantly, larger spatial variability in mass transport rates across the packed bed was found compared to techniques using only molecular diffusion. It has thus been found that that these heterogeneities arise over length-scales much larger tha

Interpretation of soil carbon and nitrogen dynamics in agricultural and afforested soils

Includes bibliographical references (pages 1627-1628).Interpretation of soil organic C (SOC) dynamics depends heavily on analytical methods and management systems studied. Comparison of data from long-term corn (Zea mays)-plot soils in Eastern North America showed mean residence times (MRTs) of SOC determined by 14C dating were 176 times those measured with 13C abundance following a 30-yr replacement of C3 by C4 plants on the same soils. However, MRTs of the two methods were related (r 2 = 0.71). Field 13C MRTs of SOC were also related (R 2 = 0.55 to 0.85) to those measured by 13CO2 evolution and curve fitting during laboratory incubation. The strong relations, but different MRTs, were interpreted to mean that the three methods sampled different parts of a SOC continuum. The SOC of all parts of this continuum must be affected by the same controls on SOC dynamics for this to occur. Methods for site selection, plant biomass, soil sampling and analysis were tested on agricultural, afforested-agriculture, and native forest sites to determine the controls on SOC dynamics. Soil-C changes after afforestation were −0.07 to 0.55 Mg C ha−1 yr−1 on deciduous sites and −0.85 to 0.58 Mg C ha−1 yr−1 under conifers. Soil N changes under afforestation ranged from −0.1 to 0.025 Mg N ha−1 yr−1 Ecosystem N accumulation was −0.09 to 0.08 Mg N ha−1 yr−1 Soil C and N sequestration but not plant biomass were related to soil Ca, Mg, and K contents. Comparative, independent assays of long-term plots provides information for concept testing and the confidence necessary for decision-makers determining C-cycle policies

Pathway to cryogen free production of hyperpolarized krypton-83 and xenon-129

yperpolarized (hp) 129Xe and hp 83Kr for magnetic resonance imaging (MRI) are typically obtained through spin-exchange optical pumping (SEOP) in gas mixtures with dilute concentrations of the respective noble gas. The usage of dilute noble gases mixtures requires cryogenic gas separation after SEOP, a step that makes clinical and preclinical applications of hp 129Xe MRI cumbersome. For hp 83Kr MRI, cryogenic concentration is not practical due to depolarization that is caused by quadrupolar relaxation in the condensed phase. In this work, the concept of stopped flow SEOP with concentrated noble gas mixtures at low pressures was explored using a laser with 23.3 W of output power and 0.25 nm linewidth. For 129Xe SEOP without cryogenic separation, the highest obtained MR signal intensity from the hp xenon-nitrogen gas mixture was equivalent to that arising from 15.561.9% spin polarized 129Xe in pure xenon gas. The production rate of the hp gas mixture, measured at 298 K, was 1.8 cm3/min. For hp 83Kr, the equivalent of 4.460.5% spin polarization in pure krypton at a production rate of 2 cm3/min was produced. The general dependency of spin polarization upon gas pressure obtained in stopped flow SEOP is reported for various noble gas concentrations. Aspects of SEOP specific to the two noble gas isotopes are discussed and compared with current theoretical opinions. A non-linear pressure broadening of the Rb D1 transition was observed and taken into account for the qualitative description of the SEOP process

Object-Based Image Classification of Summer Crop with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.This research was partly financed by the TIN2011-22794 project of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds, the P2011-TIC-7508 project of the “Junta de Andalucía” (Spain) and the Kearney Foundation of Soil Science (USA). The research of Peña was co-financed by the Fulbright-MEC postdoctoral program, financed by the Spanish Ministry for Science and Innovation, and by the JAEDoc Program, supported by CSIC and FEDER funds. ASTER data were available to us through a NASA EOS scientific investigator affiliation.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).Peer Reviewe

Object-Based Image Classification of Summer Crops with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification task

Legacy effects of long-term nitrogen fertilizer application on the fate of nitrogen fertilizer inputs in continuous maize

Nitrogen fertilizer management can impact soil organic C (SOC) stocks in cereal-based cropping systems by regulating crop residue inputs and decomposition rates. However, the impact of long-term N fertilizer management, and associated changes in SOC quantity and quality, on the fate of N fertilizer inputs is uncertain. Using two 15-year N fertilizer rate experiments on continuous maize (Zea mays L.) in Iowa, which have generated gradients of SOC, we evaluated the legacy effects of N fertilizer inputs on the fate of added N. Across the historical N fertilizer rates, which ranged from 0 to 269 kg N ha−1 yr−1, we applied isotopically-labeled N fertilizer at the empirically-determined site-specific agronomic optimum rate (202 kg N ha−1 at the central location and 269 kg N ha−1 at the southern location) and measured fertilizer recovery in crop and soil pools, and, by difference, environmental losses. Crop fertilizer N recovery efficiency (NREcrop) at physiological maturity averaged 44% and 14% of applied N in central Iowa and southern Iowa, respectively (88 kg N ha−1 and 37 kg N ha−1, respectively). Despite these large differences in NREcrop, the response to historical N rate was remarkably similar across both locations: NREcrop was greatest at low and high historical N rates, and least at the intermediate rates. Decreasing NREcrop from low to intermediate historical N rates corresponded to a decline in early-season fertilizer N recovery in the relatively slow turnover topsoil mineral-associated organic matter pool (0–15 cm), while increasing NREcrop from intermediate to high historical N rates corresponded to an increase in early-season fertilizer N recovery in the relatively fast turnover topsoil particulate organic matter pool and an increase in crop yield potential. Despite the variation in NREcropalong the historical N rate gradient, we did not detect an effect of historical N rate on environmental losses during the growing season, which averaged 34% and 69% of fertilizer N inputs at the central and southern locations, respectively (69 kg N ha−1 and 185 kg N ha−1, respectively). Our results suggest that, while beneficial for SOC storage over the long term, fertilizing at the agronomic optimum N rate can lead to significant environmental N losses