What can legacy datasets tell us about soil quality trends? Soil acidity in Victoria

Purpose-built soil monitoring networks have been established in many countries to identify where soil functionality is threatened and to target remediation initiatives. An alternative to purpose-built soil monitoring networks is to use legacy soils information. Such information yields almost instant assessments of soil change but the results should be interpreted with caution since the information was not collected with monitoring in mind. We assess the threat of soil acidification in Victoria using two legacy datasets: (i) the Victorian Soils Information System (VSIS) which is a repository of the results of soil analyses conducted for scientific purposes since the 1950s and (ii) a database of 75 000 routine soil test results requested by farmers between 1973 and 1993. We find that the VSIS measurements are clustered in space and time and are therefore suitable for local rather than broad-scale assessments of soil change. The farmers' results have better spatial and temporal coverage and space-time models can be used to quantify the spatial and temporal trends in the pH measurements. However, careful validation of these findings is required since we do not completely understand how the measured paddocks were selected and we cannot be certain that sampling or laboratory protocols have not changed with time

Increased understandings of ruminal acidosis in dairy cattle

ABSTRACT Ruminal acidosis remains an important and prevalent disorder of economic and welfare concern to the dairy industry worldwide. There are inconsistencies in the diagnostic techniques and definitions of ruminal acidosis and a requirement for further information on the pathogenesis of ruminal acidosis, in particular in regard to the role of feed substrates, such as starch, sugar, and protein (Chapter 1). A greater understanding of changes to the microbiome during ruminal acidosis, feed management, and the possible synergistic effects of feed additive control agents is also required (Chapter 1). Consequently, the overall hypothesis of this thesis, which was supported, is that starch-, sugar-, and protein- or amino acid-based feed substrates would produce different ruminal and blood measures and distinct rumen bacterial community composition associated with different risks of ruminal acidosis. Secondly, that partial mixed ration feeding strategies and feed additive control agents would promote favorable ruminal conditions and reduce the risk of ruminal acidosis, which was also supported; however, whether feed additive control agents reduced the risk of ruminal acidosis was equivocal. Heifers exposed to a single feeding of grain and fructose had an increased risk of ruminal acidosis and accumulated ruminal lactate, compared to those fed grain only (Chapter 2). This highlights that diets with high sugar content should be fed with caution and increase the risk of ruminal acidosis when physically effective fiber is inadequate. Different oxidative stress responses were not observed among treatment groups of heifers fed single exposures of different substrates (Chapter 3) or different feed additives over a 20 d period (Chapter 7), but were evident in a heifer with acute clinical ruminal acidosis (Chapter 8). This suggests oxidative stress responses may only occur during acute clinical ruminal acidosis. Distinct ruminal bacterial community composition occurred among heifers fed a single exposure to different substrates (Chapter 4) and also among lactating cows fed different feeding strategies at different supplementary feeding amounts (Chapter 5) and these communities were associated with rumen fermentation characteristics. Cattle appeared to have host specific rumen bacteria and a core microbiome (Chapters 4 and 5). This suggests that host specificity in rumen ecosystems may be associated with the individual susceptibilities of cattle to ruminal acidosis and a need to tailor feed management and control for ruminal acidosis for individual cattle. Supplementary feeding amount and ruminal concentrations of propionate and valerate appeared to have the largest association with ruminal bacterial communities in Chapter 5 and may be good predictors of ruminal acidosis. A partial mixed ration feeding system, compared with component feeding, decreased ruminal acidosis (Chapter 5), suggesting benefits of this feeding system; however, milk production and milk component benefits were not observed for this feeding system. Feed additive control agents perturbed the rumen by different mechanisms but had minimal synergistic effects when combinations of feed additives were fed and ruminal acidosis control was equivocal (Chapters 6 and 7). Feed additives may not be capable of controlling ruminal acidosis in all cattle when large amounts of readily fermentable carbohydrates are fed (Chapter 7). Concentrations of the volatile fatty acids (VFA): butyrate, propionate, valerate, isobutyrate, isovalerate, and caproate were below detectable limits in a heifer with acute clinical ruminal acidosis 24 h after she consumed a ration with 19.1% sugar and 54.1% starch on a DM basis and her acetate concentration was <20 mM. However, concentrations of these VFA were higher 55 h after she consumed the ration. These findings demonstrate that the rumen is extremely dynamic and can rapidly recover from severe perturbation. Throughout this thesis it has been evident that classic models of ruminal acidosis may not be sufficient to describe the pathogenesis of ruminal acidosis when diets with a high sugar content are fed and uncharacterized rumen bacteria may be involved in the pathogenesis of ruminal acidosis. Definitions of ruminal acidosis to describe acidosis when cattle are fed different substrates, in particular diets with a high sugar content are required. The rumen appears to be better adapted to respond to changes in starch intakes, compared with sugar intakes and cattle have individual rumen responses and susceptibilities to ruminal acidosis during shifts in feed substrates. In summary, this thesis has increased our understandings of the pathogenesis of ruminal acidosis and control strategies for ruminal acidosis in cattle

Smooth electron waveguides in graphene

Copyright © 2010 American Physical SocietyWe present exact analytical solutions for the zero-energy modes of two-dimensional massless Dirac fermions fully confined within a smooth one-dimensional potential V(x)=−α/cosh(βx), which provides a good fit for potential profiles of existing top-gated graphene structures. We show that there is a threshold value of the characteristic potential strength α/β for which the first mode appears, in striking contrast to the nonrelativistic case. A simple relationship between the characteristic strength and the number of modes within the potential is found. An experimental setup is proposed for the observation of these modes. The proposed geometry could be utilized in future graphene-based devices with high on/off current ratios

Non-equilibrium Phonon Generation and Detection in Microstructure Devices

We demonstrate a method to excite locally a controllable, non-thermal distribution of acoustic phonon modes ranging from 0 to ∼200 GHz in a silicon microstructure, by decay of excited quasiparticle states in an attached superconducting tunnel junction (STJ). The phonons transiting the structure ballistically are detected by a second STJ, allowing comparison of direct with indirect transport pathways. This method may be applied to study how different phonon modes contribute to the thermal conductivity of nanostructuresThe authors thank R. B. Van Dover, J. Blakely, S. Baker, K. Schwab, and Cornell LASSP for loan of key equipment, and L. Spietz for photolithography recipes. We thank R. B. Van Dover, K. Schwab, E. Smith, J. Parpia, D. Ralph, B. Plourde, M. Blencowe, D. Westly, R. Pohl, P. Berberich, and C. Mellor for helpful discussions and thank D. Toledo, J. Chang and A. Lin for help with apparatus. The authors acknowledge funding from the National Science Foundation (NSF) (DMR 0520404) and Department of Energy (DOE) (DE-SC0001086). This publication is based on work supported in part by Award No. KUS-C1-018-02, made by King Abdullah University of Science and Technology (KAUST). This work was performed in part at the Cornell NanoScale Facility, a member of the National Nanotechnology Infrastructure Network, which is supported by the National Science Foundation (Grant ECS-0335765