Agriculture Reference
In-Depth Information
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Currently the method for selecting analogue years is based on phases of
the Southern Oscillation (Stone et al., 1996). Alternative approaches are
also available, such as the SST scheme developed by BoM (Drosdowsky,
2002). Seasonal climate forecasting is a rapidly evolving field, and the spa-
tial modeling framework is flexible enough to incorporate new statistical
forecast schemes or downscaled outputs from global circulation models.
U se of Satellite Imagery
Satellite imagery was initially envisaged as a means of improving the spa-
tial resolution of the modeling framework and of providing an independent
drought assessment. However, the role of satellite imagery has fallen short
of initial expectations, particularly in grazing lands, due to (1) the unrelia-
bility of the signal, (2) the short historical record against which to rank cur-
rent conditions, (3) an inability to project forward from the current situa-
tion to provide warnings, (4) the tree cover confounding the pasture signal,
and (5) the difficulty in distinguishing bare ground from dry pasture. Cur-
rent studies are addressing such difficulties using Landsat data (e.g., Taube,
1999) and normalized difference vegetation index (NDVI) data (Carter et
al., 2000). NDVI data are also used to monitor wheat yields (e.g., Smith
et al., 1995), particularly in western Australia, jointly through Agriculture
Western Australia (http://www.agric.wa.gov.au) and Department of Land
Administration ( http://www.dola.wa.gov.au).
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In corporation of Crop Models in the Modeling Framework
Apart from the GRASP model, the Agricultural Production Systems Simu-
lator (APSIM) model has been tested within the national modeling frame-
work for calculating district crop yields. APSIM is a detailed modeling
framework developed for farm-scale simulations of a range of crop and
farming systems. However, Hammer et al. (1996) found that for modeling
wheat at regional scales, simpler approaches tailored to that the scale (e.g.,
the stress index [STIN] model, Stephens, 1998) were more accurate, robust,
and easier to implement. STIN incorporates a daily soil water balance and
calculates an accumulated crop moisture-stress index (SI) from a nominal
sowing date. SI is sensitive both to moisture deficits and moisture excesses
through the growing season. Inputs for the model include sowing date,
daily rainfall, and average daily climate data (maximum and minimum
temperature and solar radiation). SI is transformed to the district wheat
yields through regression relationships between SI and historical shire yield
data from the Australian Bureau of Statistics.
Yield is generally considered the best measure of drought in cropping
lands (e.g., Stephens, 1998), and calculated district wheat yields from
STIN are used as the basis for a drought alert issued by the Queensland
Department of Primary Industries for Queensland wheat-growing shires
(http://www.dpi.qld.gov.au/climate). Although alerts are only issued in
 
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