scholarly journals The effectiveness of Shark‐Management‐Alert‐in‐Real‐Time (SMART) drumlines as a tool for catching white sharks, Carcharodon carcharias , off coastal New South Wales, Australia

Author(s):  
Rick D. Tate ◽  
Brendan P. Kelaher ◽  
Craig P. Brand ◽  
Brian R. Cullis ◽  
Christopher R. Gallen ◽  
...  
1994 ◽  
Vol 45 (7) ◽  
pp. 1087 ◽  
Author(s):  
M Krogh

Data for the catches of sharks at each beach meshed in New South Wales are presented for the period from October 1972 to December 1990. High catch rates of sharks were observed at a number of these beaches. Comparison of the catches of sharks at each beach by means of hierarchical clustering and nonmetric multidimensional scaling revealed regional associations as well as some unusual relationships. Beaches at either end of each netted region (i.e. Newcastle, Sydney or Wollongong) tended to have higher catches than did beaches in the centre of each region. Hammerhead sharks (Sphyrna spp.) had significantly higher catch rates on long open beaches. Significantly higher catches of whaler (Carcharhinus spp.), white (Carcharodon carcharias), and tiger (Galeocerdo cuvier) sharks occurred when deeper water was closer to the beach. Limited biological and seasonal data are also presented for the 11 species or species groups: Sphyrna spp., Carcharhinus spp., Squatina austmlis, Heterodontus spp., Carcharodon carcharias, Galeocerdo cuvier, Carcharias taurus, Notorynchus cepedianus, Alopias spp., Isurus oxyrinchus and Orectolobus spp.


2008 ◽  
Vol 48 (3) ◽  
pp. 304 ◽  
Author(s):  
E. Humphreys ◽  
R. J. G. White ◽  
D. J. Smith ◽  
D. C. Godwin

MaizeMan is Windows-based decision support software, derived from CERES Maize and SWAGMAN Destiny, which can be used for real-time irrigation scheduling or strategic analysis. Evaluation of MaizeMan for sprinkler and furrow-irrigated maize (Pioneer 3153) showed good predictive ability for yield, biomass, runoff and soil water depletion between sowing and harvest. MaizeMan simulations using 43 years of weather data from Griffith, New South Wales, suggested that the biggest influence on yield, irrigation requirement and irrigation water productivity is seasonal weather conditions. For example, yield of October-sown 3153 irrigated frequently to avoid soil water deficit varied from about 8 to 16 t/ha, while net irrigation and net irrigation water productivity varied from 7 to 11 ML/ha and 0.8 to 1.6 t/ML, respectively. The optimum sowing window for maximising yield and irrigation water productivity is wide, from late September to mid November. Delaying sowing beyond this may result in higher yield and irrigation water productivity; however, delayed maturity would lead to problems for grain drying and harvesting in winter and increased insect pressure. The simplest management strategy for maximising yield and irrigation water productivity is irrigation scheduling tailored to soil type. Irrigation scheduling can be assisted by real-time scheduling using MaizeMan, provided soil hydraulic properties are accurately characterised. One to two irrigations can also be saved by growing shorter duration hybrids, but the tradeoff is lower yield, while irrigation water productivity is maintained. Simulated sprinkler irrigation increased yield and net irrigation water productivity by small amounts (averages of 0.5 t/ha and 0.2 t/ML, respectively) relative to well-scheduled flood irrigation, through improved soil water and aeration status and reduced deep drainage loss.


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