910 research outputs found
Radio to Gamma-Ray Emission from Shell-type Supernova Remnants: Predictions from Non-linear Shock Acceleration Models
Supernova remnants (SNRs) are widely believed to be the principal source of
galactic cosmic rays. Such energetic particles can produce gamma-rays and lower
energy photons via interactions with the ambient plasma. In this paper, we
present results from a Monte Carlo simulation of non-linear shock structure and
acceleration coupled with photon emission in shell-like SNRs. These
non-linearities are a by-product of the dynamical influence of the accelerated
cosmic rays on the shocked plasma and result in distributions of cosmic rays
which deviate from pure power-laws. Such deviations are crucial to acceleration
efficiency and spectral considerations, producing GeV/TeV intensity ratios that
are quite different from test particle predictions. The Sedov scaling solution
for SNR expansions is used to estimate important shock parameters for input
into the Monte Carlo simulation. We calculate ion and electron distributions
that spawn neutral pion decay, bremsstrahlung, inverse Compton, and synchrotron
emission, yielding complete photon spectra from radio frequencies to gamma-ray
energies. The cessation of acceleration caused by the spatial and temporal
limitations of the expanding SNR shell in moderately dense interstellar regions
can yield spectral cutoffs in the TeV energy range; these are consistent with
Whipple's TeV upper limits on unidentified EGRET sources. Supernova remnants in
lower density environments generate higher energy cosmic rays that produce
predominantly inverse Compton emission at super-TeV energies; such sources will
generally be gamma-ray dim at GeV energies.Comment: 62 pages, AASTeX format, including 1 table and 11 figures, accepted
for publication in The Astrophysical Journal (Vol 513, March 1, 1999
A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding
Remote sensing (RS) of plant canopies permits non-intrusive, high-throughput monitoring of plant physiological characteristics. This study compared three RS approaches using a low flying UAV (unmanned aerial vehicle), with that of proximal sensing, and satellite-based imagery. Two physiological traits were considered, canopy temperature (CT) and a vegetation index (NDVI), to determine the most viable approaches for large scale crop genetic improvement. The UAV-based platform achieves plot-level resolution while measuring several hundred plots in one mission via high-resolution thermal and multispectral imagery measured at altitudes of 30-100 m. The satellite measures multispectral imagery from an altitude of 770 km. Information was compared with proximal measurements using IR thermometers and an NDVI sensor at a distance of 0.5-1m above plots. For robust comparisons, CT and NDVI were assessed on panels of elite cultivars under irrigated and drought conditions, in different thermal regimes, and on un-adapted genetic resources under water deficit. Correlations between airborne data and yield/biomass at maturity were generally higher than equivalent proximal correlations. NDVI was derived from high-resolution satellite imagery for only larger sized plots (8.5 x 2.4 m) due to restricted pixel density. Results support use of UAV-based RS techniques for high-throughput phenotyping for both precision and efficiency
Genomic Prediction with Pedigree and Genotype x Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico
Developing genomic selection (GS) models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation (CV) scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY), thousand-grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 international environments (year-location combinations) in major wheat-producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random CV schemes were applied to predict a subset of lines that were not observed in any of the 18 environments (CV1), and a subset of lines that were not observed in a set of the environments, but were observed in other environments (CV2). Genomic prediction models, including genotype x environment (GxE) interaction, had the highest average prediction ability under the CV1 scenario for GY (0.31), GN (0.32), GW (0.45), and TTF (0.27). For CV2, the average prediction ability of the model including the interaction terms was generally high for GY (0.38), GN (0.43), GW (0.63), and TTF (0.53). Wheat lines in siteyear combinations in Mexico and India had relatively high prediction ability for GY and GW. Results indicated that prediction ability of lines not observed in certain environments could be relatively high for genomic selection when predicting GxE interaction in multi-environment trials
Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology
The green area displayed by a crop is a good indicator of its photosynthetic capacity, while chlorophyll retention or âstay-greenâ is regarded as a key indicator of stress adaptation. Remote-sensing methods were tested to estimate these parameters in diverse wheat genotypes under different growing conditions. Two wheat populations (a diverse set of 294 advanced lines and a recombinant inbred line population of 169 sister lines derived from the cross between Seri and Babax) were grown in Mexico under three environments: drought, heat, and heat combined with drought. In the two populations studied here, a moderate heritable expression of stay-green was foundâwhen the normalized difference vegetation index (NDVI) at physiological maturity was estimated using the regression of NDVI over time from the mid-stages of grain-filling to physiological maturityâand for the rate of senescence during the same period. Under heat and heat combined with drought environments, stay-green calculated as NDVI at physiological maturity and the rate of senescence, showed positive and negative correlations with yield, respectively. Moreover, stay-green calculated as an estimation of NDVI at physiological maturity and the rate of senescence regressed on degree days give an independent measurement of stay-green without the confounding effect of phenology. On average, in both populations under heat and heat combined with drought environments CTgf and stay-green variables accounted for around 30% of yield variability in multiple regression analysis. It is concluded that stay-green traits may provide cumulative effects, together with other traits, to improve adaptation under stress further
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