Lower canopy temperature (CT), resulting from increased stomatal conductance, has been associated with increased yield in wheat. Historically, CT has been measured with hand-held infrared thermometers. Using the hand-held CT method on large field trials is problematic, mostly because measurements are confounded by temporal weather changes during the time required to measure all plots. The hand-held CT method is laborious and yet the resulting heritability low, thereby reducing confidence in selection in large scale breeding endeavors. We have developed a reliable and scalable crop phenotyping method for assessing CT in large field experiments. The method involves airborne thermography from a manned helicopter using a radiometrically-calibrated thermal camera. Thermal image data is acquired from large experiments in the order of seconds, thereby enabling simultaneous measurement of CT on potentially 1000s of plots. Effects of temporal weather variation when phenotyping large experiments using hand-held infrared thermometers are therefore reduced. The method is designed for cost-effective and large-scale use by the non-technical user and includes custom-developed software for data processing to obtain CT data on a single-plot basis for analysis. Broad-sense heritability was routinely >0.50, and as high as 0.79, for airborne thermography CT measured near anthesis on a wheat experiment comprising 768 plots of size 2 × 6 m. Image analysis based on the frequency distribution of temperature pixels to remove the possible influence of background soil did not improve broad-sense heritability. Total image acquisition and processing time was ca. 25 min and required only one person (excluding the helicopter pilot). The results indicate the potential to phenotype CT on large populations in genetics studies or for selection within a plant breeding program.

A knowledge of the structure of the boundary of solutions of the power flow problem is important when analyzing the robustness of operating points. This paper proposes a predictor-corrector technique to assist in exploring that structure. Points on the solution boundary satisfy the power flow equations together with an equation which forces the power flow Jacobian to be singular. Curves of such points result from freeing two parameters of the system. The proposed technique follows those curves. A simple example is used to illustrate the complex nature of the power flow solution space.

Accurate estimates of long-term linear trends of wind speed provide a useful indicator for circulation changes in the atmosphere and are invaluable for the planning and financing of sectors such as wind energy.Here a large number of wind observations over Australia and reanalysis products are analyzed to compute such trends. After a thorough quality control of the observations, it is found that the wind speed trends for 1975-2006 and 1989-2006 over Australia are sensitive to the height of the station: they are largely negative for the 2-m data but are predominantly positive for the 10-m data. The mean relative trend at 2 m is 20.10 6 0.03% yr 21 (20.36 6 0.04% yr 21 ) for the 1975-2006 (1989-2006) period, whereas at 10 m it is 0.90 6 0.03% yr 21 (0.69 6 0.04% yr 21 ) for the 1975-2006 (1989-2006) period. Also, at 10 m light winds tend to increase more rapidly than the mean winds, whereas strong winds increase less rapidly than the mean winds; at 2 m the trends in both light and strong winds vary in line with the mean winds. It was found that a qualitative link could be established between the observed features in the linear trends and some atmospheric circulation indicators (mean sea level pressure, wind speed at 850 hPa, and geopotential at 850 hPa), particularly for the 10-m observations. Further, the magnitude of the trend is also sensitive to the period selected, being closer to zero when a very long period, 1948-2006, is considered. As a consequence, changes in the atmospheric circulation on climatic time scales appear unlikely.

No abstract

Measurements show that on numerous occasions the low-level wind is highly variable across a large portion of south-eastern Australia. Under such conditions the risk of a large rapid change in total wind power is increased. While variability tends to increase with mean wind speed, a large component of wind variability is not explained by wind speed alone. In this work, reanalysis fields from the US National Centers for Environmental Prediction (NCEP) are statistically downscaled to model wind variability at a coastal location in Victoria, Australia. In order to reduce the dimensionality of the problem, the NCEP fields are each decomposed using empirical orthogonal function (EOF) techniques. The downscaling technique is applied to two periods in the seasonal cycle, namely (i) winter to early spring, and (ii) summer. In each case, data representing 2 years are used to form a model that is then validated using independent data from another year. The EOFs that best predict wind variability are examined. To allow for non-linearity and complex interaction between variables, all empirical models are built using random forests. Quantitatively, the model compares favourably with a simple regression of wind variability against wind speed, as well as multiple linear regression models.