Comparative water use by Dorycnium hirsutum-, lucerne-, and annual-based pastures in the Western Australian wheatbelt

Dryland salinity in southern Australia has been caused by inadequate water use by annual crops and pastures. The purpose of this study was to compare the water use of annual pastures and Medicago sativa L. (lucerne) with Dorycnium hirsutum (L.) Ser., a potential new perennial forage species. The soil water dynamics under bare ground, annual legume-, lucerne-, and D. hirsutum-based pastures were compared at 2 sites in the low- (Merredin) and medium- (New Norcia) rainfall wheatbelt of Western Australia between September 2002 and February 2005. Soil under D. hirsutum was drier than under annual pastures by 8–23 mm in Year 1, 43–57 mm in Year 2, and 81 mm in Year 3. Lucerne used little additional water (<19 mm, n.s.) compared with D. hirsutum and profile soil water content was similar under both species throughout the experiment. At Merredin, annual pastures used water to a depth of 1.0 m, whereas under both D. hirsutum and lucerne in the first 3 years after establishment the successive maximum depth of water use was 1.0, 1.8, and 2.2 m. At New Norcia, additional soil water was extracted by lucerne and D. hirsutum at depths <1.0 m and no difference between treatments was detected below 1.0 m. Biomass of D. hirsutum pasture harvested in autumn contained minimal annual components and was 15–50% of that produced by lucerne- or annual legume-based pastures. D. hirsutum and lucerne plant density declined each summer (25–80%), but D. hirsutum density was lower than lucerne due to poorer establishment. Nonetheless, the comparable water use of lucerne and D. hirsutum suggests that D. hirsutum could make reductions in recharge similar to those of lucerne in the Western Australian wheatbelt.

Regression Model for the 22-year Hale Solar Cycle Derived from High Altitude Tree-ring Data

Initially some simple analytical properties based on the annual Zürich relative sunspot number are established for the 22-year Hale solar magnetic cycle. Since about AD1850, successive maximum sunspot numbers in a Hale cycle are highly correlated. Also, a regression model for the reconstruction of the 22-year Hale cycle has been formulated from proxy tree-ring data, obtained from spruce trees growing at a high altitude site in White River National Forest in Colorado. Over a considerable fraction of the past 300 years to AD1986, the ring-index time series power spectrum exhibits a strong 22-year periodicity, and more recently a significant spectral peak (at the 95% confidence level) at approximately 11 years. The model shows that the greatest variation in ‘amplitude’ in the magnetic cycle occurs over the early decades of the eighteenth century, when the sample size is small. Thereafter, a nearly constant amplitude is maintained until about AD1880 when a break occurs in both phase correspondence and amplitude, extending over the next three cycles. From AD1950 the signal recovers phase with the solar cycle, with reduced but increasing amplitude.

The relationship between maximum entropy and maximum likelihood spectra

Burg (1972) established an analytical relationship between maximum entropy and maximum likelihood spectral density estimates. If [Formula: see text], j = 0, 1,… M − 1 and M successive maximum entropy spectral density estimates at frequency f, then the M‐length maximum likelihood spectral density estimate [Formula: see text] is given by [Formula: see text]. (1) The maximum entropy estimates are made via Burg's well‐known formula,[Formula: see text], (2) where [Formula: see text], k = 0, 1,…,N are the coefficients of the N‐length prediction‐error filter, [Formula: see text] is its error power, and Δt is the sample interval. These estimates can be recursively calculated as shown by Burg (Smylie et al, 1973).

The exponential rate of convergence of the distribution of the maximum of a random walk. Part II

Let Gn (x) be the distribution of the nth successive maximum of a random walk on the real line. Under conditions typical for complete exponential convergence, the decay of Gn (x) – limn→∞ Gn (x) is asymptotically equal to H(x) γn n–3/2 as n → ∞where γ &lt; 1 and H(x) a function solely depending on x. For the case of drift to + ∞, G ∞(x) = 0 and the result is new; for drift to – ∞we give a new proof, simplifying and correcting an earlier version in [9].

The exponential rate of convergence of the distribution of the maximum of a random walk. Part II

Let Gn (x) be the distribution of the nth successive maximum of a random walk on the real line. Under conditions typical for complete exponential convergence, the decay of Gn (x) – limn→∞ Gn(x) is asymptotically equal to H(x) γn n–3/2 as n → ∞where γ < 1 and H(x) a function solely depending on x. For the case of drift to + ∞, G∞(x) = 0 and the result is new; for drift to – ∞we give a new proof, simplifying and correcting an earlier version in [9].

Decrement and Recovery with Repetitive Maximal Muscular Exertions

Sixty subjects exerted ten successive maximum pulls of 12 1/2 sec. duration on an isometric dynamometer handle. All trials in a series were separated by a constant duration intertrial interval of 12 1/2, 25, 50, 100, or 200 sec. For all intertrial intervals there was a rapid initial reduction in output followed by an essentially linear decline. The effect of the intertrial intervals on the within-trial decrements were quite small with a difference of less than 2% of maximum between the means for the shortest and longest intervals. For the longer rest conditions there was a reduction in the within-trial decrement over trials. For the shorter intertrial intervals, recovery tended to increase with successive rests, but for the longer intervals there was a tendency for recovery to decrease with repeated rests. The amount of strength recovery with rest was found to be influenced not only by the length of rest but also by the degree to which the response was degraded by prior performance.