LWRRDC QPI 20
1.
Volume 6
PROJECT TITLE
QPI20 Development of a National Drought Alert Strategic
Information System (May-1996)
2.
VOLUME 6: WHEAT MODELLING SUB-PROJECT
3.
CONTACT DETAILS:
a) Primary Research Organisation:
b) Principal Investigator:
4.
Climate Impacts and Applications
Resource Sciences Centre,
Queensland Department of Natural Resources
80 Meiers Road, Indooroopilly, 4068.
Mr Ken D. Brook
Principal Scientist
Tel: 07-3896 9379
Fax: 07-3896 9606
DPI research staff:
John Carter, Tim Danaher, Greg McKeon, Cheryl Kuhnell, Neil Flood, Graeme Hammer,
David Butler, Robert Hassett, Helen Wood, Alan Beswick, Alan Peacock, Colin Paull,
Patricia Hugman
5.
Collaborating Organisations
(i) Research Organisations
Greg Beeston, Greg Mlodawski, David Stephens, Agriculture Western Australia.
Dennis Barber (now NSW DLWC), Russell Flavel, Department of Environment and Land
Management, South Australia
Rik Dance, Danny Brock, Don Petty, Department of Primary Industry and Fisheries,
Northern Territory
Daryl Green, David Hart, Rob Richards, Department of Land and Water Conservation, New
South Wales
(ii) Funding Organisations
Land and Water Resources Research and Development Corporation
Grains Research & Development Corporation
Goodman Fielder Mills Ltd.
Evaluation of model performance relative to rainfall, Inter-state model calibration, Extension, and State
comments
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6.
Volume 6
FOR FURTHER INFORMATION:
There are 6 volumes of documentation available on LWRRDC QPI20. The volumes are:
(1) Research summary
(2) Field validation of pasture biomass and tree cover
(3) Development of data rasters for model inputs
(4) Model framework, Parameter derivation, Model calibration, Model validation, Model
outputs, Web technology
(5) Evaluation of model performance relative to rainfall, Inter-state model calibration,
Extension, and State comments.
(6) Wheat modelling sub-project. (this document)
A short video of computer visualisations produced from the project is also available.
The above information sets are available at a nominal charge to cover printing and distribution
costs.
For more detailed information contact:
Spatial rangeland model, drought alerts
Mr John Carter, Resource Sciences Centre,
Queensland Department of Natural Resources, 80 Meiers Rd, Indooroopilly. 4068.
Ph 07 - 3896 9588 Fax: 07 - 3896 9606
GRASP pasture simulation Dr Greg McKeon, Resource Sciences Centre, Queensland
Department of Natural Resources, 80 Meiers Rd, Indooroopilly. 4068.
Ph 07 - 3896 9548 Fax: 07 - 3896 9606
Meteorological data Mr Neil Flood, Resource Sciences Centre, Queensland Department of
Natural Resources, 80 Meiers Rd, Indooroopilly. 4068.
Ph 07 - 3896 9734 Fax: 07 - 3896 9606
Wheat simulation modelling Dr Graeme Hammer, Agricultural Production and Systems
Research Unit, Queensland Department of Primary Industries, Tor Street, Toowoomba. 4068.
Ph 076 - 314 379 Fax: 076 - 332 678
Wheat statistical modelling and yield forecasting Mr David Stephens, c/- Agriculture Western
Australia, Ngala Annex, Baron - Hay Court, South Perth. 6151.
Ph 09 - 368 3983 Fax: 09 - 368 3946
Evaluation of model performance relative to rainfall, Inter-state model calibration, Extension, and State
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Volume 6
SECTION (a) - Development of Predictive Models of
Wheat Production
Contents
1. PROJECT TITLE .............................................................................................................................................. I
2. VOLUME 6: WHEAT MODELLING SUB-PROJECT ................................................................................ I
3. CONTACT DETAILS: ...................................................................................................................................... I
4. DPI RESEARCH STAFF:................................................................................................................................. I
5. COLLABORATING ORGANISATIONS ....................................................................................................... I
6. FOR FURTHER INFORMATION:................................................................................................................ II
7. ABSTRACT........................................................................................................................................................ 1
8. INTRODUCTION.............................................................................................................................................. 1
9. MATERIALS AND METHODS ...................................................................................................................... 4
9.1 STUDY PERIOD AND DATA SETS ..................................................................................................................... 4
9.2 RAINFALL REGRESSION MODEL ..................................................................................................................... 7
9.2.1 Location ................................................................................................................................................ 7
9.2.2 Time ....................................................................................................................................................... 8
9.2.3 Monthly Rain ......................................................................................................................................... 8
9.3 WEIGHTED RAINFALL INDEX .......................................................................................................................... 9
9.4 AGRO-CLIMATIC MODELS ............................................................................................................................ 10
9.5 STRESS INDEX MODEL ................................................................................................................................. 10
9.6 DROUGHT INDEX.......................................................................................................................................... 12
9.7 CALIBRATING THE INDICES - TRENDS IN WHEAT YIELDS ............................................................................. 13
9.8 COMMON WATER BALANCE ........................................................................................................................ 14
9.9 SIMULATION MODELS .................................................................................................................................. 16
9.10 TACT SIMULATION MODEL ....................................................................................................................... 16
9.11 APSIM-WHEAT ......................................................................................................................................... 17
10. RESULTS AND DISCUSSION .................................................................................................................... 18
10.1.1 Individual Shire Results .................................................................................................................... 19
10.1.2 State / National Averages ................................................................................................................ 20
10.2 IMPLICATIONS ............................................................................................................................................ 22
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11. CONCLUSIONS ............................................................................................................................................ 23
12. REFERENCES............................................................................................................................................... 37
List of Figures
FIGURE 1: 127 RAINFALL STATIONS USED BY THE AGRO-CLIMATIC MODELS; + = 70 STATIONS WITH TEMPERATURE
DATA ............................................................................................................................................................... 6
FIGURE 2: AUSTRALIAN CROPPING BOUNDARIES AND RAINFALL STATIONS USED IN THE MULTIPLE REGRESSION
MODEL. ........................................................................................................................................................... 6
FIGURE 3: ABS YIELD (T/HA) FOR 1982 ................................................................................................................. 21
FIGURE 4: ABS YIELD (T/HA) FOR 1983 ................................................................................................................. 21
FIGURE 5: STRESS INDEX YIELD (T/HA) 1982 ......................................................................................................... 21
FIGURE 6: STRESS INDEX YIELD (T/HA) 1983 ......................................................................................................... 21
FIGURE 7 COMPARISON BETWEEN OBSERVED YIELD (ABS) AND PREDICTED YIELD FOR (1) RAINFALL REGRESSION,
(2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) APSIM-WHEAT SIMULATION FOR THE WAGGAMBA SHIRE.24
FIGURE 8 COMPARISON BETWEEN OBSERVED YIELD (ABS) AND PREDICTED YIELD FOR (1) RAINFALL REGRESSION,
(2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) APSIM-WHEAT SIMULATION FOR THE MURILLA SHIRE. .... 25
FIGURE 9 COMPARISON BETWEEN OBSERVED YIELD (ABS) AND PREDICTED YIELD FOR (1) RAINFALL REGRESSION,
(2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) APSIM-WHEAT SIMULATION FOR THE BANANA SHIRE. ..... 26
FIGURE 10 COMPARISON BETWEEN OBSERVED YIELD (ABS) AND PREDICTED YIELD FOR (1) RAINFALL REGRESSION,
(2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) APSIM-WHEAT SIMULATION FOR THE BUNGIL SHIRE. ...... 27
FIGURE 11 COMPARISON BETWEEN OBSERVED YIELD (ABS) AND PREDICTED YIELD FOR (1) RAINFALL REGRESSION,
(2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) TACT SIMULATION FOR THE MERREDIN SHIRE. ................. 28
FIGURE 12 COMPARISON BETWEEN OBSERVED YIELD (ABS) AND PREDICTED YIELD FOR (1) RAINFALL REGRESSION,
(2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) TACT SIMULATION FOR THE CUNDERDIN SHIRE. ............... 29
FIGURE 13 COMPARISON BETWEEN OBSERVED (ABS) AND PREDICTED (WEIGHTED) QUEENSLAND YIELDS FOR (1)
RAINFALL REGRESSION, (2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) WEIGHTED RAIN INDEX. ................ 30
FIGURE 14 COMPARISON BETWEEN OBSERVED (ABS) AND PREDICTED (WEIGHTED) QUEENSLAND YIELDS FOR THE
APSIM-WHEAT MODEL ................................................................................................................................ 31
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FIGURE 15 COMPARISON BETWEEN OBSERVED (ABS) AND PREDICTED (WEIGHTED) NEW SOUTH WALES YIELDS
FOR (1) RAINFALL REGRESSION, (2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) WEIGHTED RAIN INDEX. .... 32
FIGURE 16 COMPARISON BETWEEN OBSERVED (ABS) AND PREDICTED (WEIGHTED) VICTORIAN YIELDS FOR (1)
RAINFALL REGRESSION, (2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) WEIGHTED RAIN INDEX. ................ 33
FIGURE 17 COMPARISON BETWEEN OBSERVED (ABS) AND PREDICTED (WEIGHTED) SOUTH AUSTRALIAN YIELDS
FOR (1) RAINFALL REGRESSION, (2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) WEIGHTED RAIN INDEX ..... 34
FIGURE 18 COMPARISON BETWEEN OBSERVED (ABS) AND PREDICTED (WEIGHTED) WEST AUSTRALIAN YIELDS FOR
(1) RAINFALL REGRESSION, (2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) WEIGHTED RAIN INDEX ............ 35
FIGURE 19 COMPARISON BETWEEN OBSERVED (ABS) AND PREDICTED (WEIGHTED) AUSTRALIAN YIELDS FOR (1)
RAINFALL REGRESSION, (2) STRESS INDEX, (3) DROUGHT INDEX, AND (4) WEIGHTED RAIN INDEX ................. 36
List of Tables
TABLE 1 SUMMARY OF MODELS USED TO ESTIMATE YIELDS .................................................................................... 3
TABLE 2 MAXIMUM MONTHLY RAINFALL AMOUNTS ALLOWED IN MODEL CALCULATIONS FOR DIFFERENT REGIONS
AND PERIODS. ......................................................................................................................................................... 10
TABLE 3 YIELD TRENDS (T/HA/YEAR) FOR (A) A PERIOD WHERE YIELD GROWTH HAD LEVELLED OFF, AND (B)
RECENT INTERVALS CHOSEN TO REPRESENT MORE RECENT TRENDS (EXCLUDING SEQUENCES OF EXTREME YEARS),
WITH ** P<0.01 ...................................................................................................................................................... 14
TABLE 4 CRITERION TO CALCULATE THE MIDPOINT OF DISTRICT SOWING. ............................................................. 16
TABLE 5 R2 AND MAE (T/HA) VALUES FROM A FIT OF OBSERVED (ABS) VS PREDICTED VALUES FOR EACH METHOD
AT 6 SELECTED SHIRES. ........................................................................................................................................... 19
TABLE 6 R2 AND MAE (T/HA) VALUES FROM A FIT OF OBSERVED WEIGHTED YIELD (ABS) VS PREDICTED
WEIGHTED YIELD FOR EACH METHOD FOR EACH STATE AND NATIONALLY. ............................................................. 20
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7.
Volume 6
ABSTRACT
In developing a wheat yield forecasting capability for a National Drought Alert Strategic
Information System, a feasibility study of six yield forecasting models (rainfall regression,
weighted rainfall index, stress index, drought index, TACT simulation model and the APSIMWheat simulation model) from three classes (empirical, agroclimatic and simulation) was
undertaken and evaluated at a number of scales. Essentially it was found that while all classes
have the potential to satisfactorily forecast Australian wheat yields at the shire scale, the simpler
empirical and agroclimatic models performed best overall, particularly when the demand on
computing resources is taken into account. Of these, the empirical approaches were marginally
superior to the agroclimatic models in a number of cases and equivalent in others. It should be
noted, however, that poor knowledge of some input data layers did impact on the performance of
the crop simulation models; efforts to overcome this would need to be balanced against the
expected gain in precision given the performance of the simpler models.
8.
INTRODUCTION
Australia is a major exporter of wheat and coarse grains. Wheat production alone is worth in
excess of $2000 million per annum (AWB, 1993), but this varies as a result of one of the most
variable climates in the world (Russell, 1988).
Large annual fluctuations in yields (and
production) are of major concern to marketing agencies who have to sell this grain on a volatile
world market. While other grain exporting countries have developed systematic techniques and
models to forecast crop yields (Motha and Heddinghaus, 1986; Stephens, 1988; Walker, 1989),
Australia has yet to use any formal forecasting procedure to estimate its own production
(AACM, 1991). Existing forecasts are based on a compilation from various sources of
information and are unreliable and only broadly indicative (AACM, 1991).
To address this problem, this project sought to:
1. develop and apply approaches capable of forecasting Australian wheat yield and its spatial
distribution at the shire scale by considering approaches covering a range in complexity, and
2. compare the predictive ability of the approaches and assess the trade-offs between accuracy
and likely cost of application in a real-time forecasting mode.
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A vast number of crop weather models have been proposed and these have been reviewed in the
Australian context by a number of authors (McMahon, 1983; Rimmington et al., 1986; Angus,
1991; AACM, 1991). Models that can specifically forecast yields have been classified into three
broad categories (Baier, 1977; 1979):
1. Empirical models that relate weather (typically rainfall) and soil variables directly to yield
through a statistical model.
2. Crop weather analysis models that evaluate crop response to variations in derived
agrometeorolgical indices that are usually based on a simple water balance; and
3. Simulation models - that explicitly model plant growth and development with a set of
mathematical equations which have a physical, chemical and physiological basis.
The development of simulation models of ever increasing complexity has been a feature of
research in the last two decades, but there are now signs of real efforts to return to simpler and
more general models (Nix, 1985). The increased cost, effort and complexity of more recent crop
models have not lead to commensurate improvements in predictions (Norman, 1981). Complex
models have often been significantly less successful at simulating grain yield (on a broad scale)
than more empirical models that have been tested locally (White et al., 1993). However, due to
the short history of the field and the difficulty in obtaining adequate data sets, no direct
comparisons of model accuracy has been made for different levels of model complexity.
At the simplest level, Lehane and Staple (1965) found in Canada that multiple regression
equations based on rainfall distribution and soil moisture gave better estimates of yield than did
equations based on total seasonal rainfall alone. Similarly, Nix and Fitzpatrick (1969) showed an
agrometeorological (stress) index based on soil water supply at ear-emergence considerable
improved on equations based on total crop rainfall, soil moisture at sowing and sums of
evapotranspiration. Hashemi (1976) however found that a simple moisture balance did not
improve on growing season rainfall totals in Iran.
Unfortunately, there is a dearth of information on model comparisons as modelling progresses
from water based budgets to simulation models of plant growth. This report therefore reviews
the utility of various yield forecasting models deemed appropriate for Australian conditions.
Modelling approaches are listed in Table 1.
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Table 1 Summary of models used to estimate yields
Class
Model
Description
Empirical
Rainfall Regression
Empirically derived relationship between historical shire yields and
rainfall
Weighted Rainfall Index
Linear function of (biologically based) weighted monthly rainfall for
Australian rainfall districts. (Stephens et al., 1994) The derived
index is calibrated against historical yield data.
Agroclimatic
Stress Index
An index is derived from water stress relative to plant available water
using daily rain, average weekly temperature and radiation data
throughout the growing season. (Stephens et al., 1989) The index is
calibrated against historical yield records.
Drought Index
Calculates a seasonally integrated daily growth index from a
physiologically based model using daily rain and temperature.
(Walker, 1989)
As with the stress index the drought index is
calibrated against historical yields.
Simulation
TACT
A derivative of the CERES-Wheat model. (Robinson and Abrecht,
1994)
APSIM-Wheat
Woodruff-Hammer simulation model (Hammer et al., 1987)
Of these models, most can be run at a shire (or lower) level and predictions scaled up by
weighting to a forecast at a national scale. The exception is the weighted rainfall model which
runs on district rainfall and can only be aggregated to forecast state or national yields. Apart
from the Weighted Rainfall Index, the basic unit for predictive purposes was the shire (or county
in South Australia). No claim is made that any one model is the definitive in its class but rather
they are representative of their type. An exhaustive search, particularly of the empirical class,
was beyond the scope of the project.
Due to data and time constraints this study could only test the simulation models at two shires in
Western Australia (TACT model) and four shires in Queensland (APSIM-Wheat model). To aid
in model comparison the same fallow water balance (Ritchie, 1972) was utilised (from the
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CERES-Wheat model - Ritchie and Otter, 1985) in both the Crop weather analysis models and
the Tact simulation model. A modified form of this is also used in the APSIM-Wheat model.
9.
MATERIALS AND METHODS
9.1 Study Period and Data Sets
In the process of model development many years of data are needed to cover the full range of
conditions and derive the necessary parameters. King (1989) suggests at least twenty are needed
for multiple regression models, if too few are used relative to the number of predictors then
overfitting can be a problem.. The Agroclimatic index and simulation models have a less strict
requirement as only one parameter or index is being regressed against historical yields.
However, in all cases an extended range of years is essential to avoid spurious results from runs
of advantageous seasons.
In this study, the most recent 18 years of available yield data was chosen as this has a good
mixture of drought, average and wet years and yield fluctuations were the greatest on record
(Hamblin and Kyneur, 1993). This period was also chosen because older crop varieties were
replaced from the mid 1970's with varieties that have benefited from the successful
incorporation of the reduced height (dwarfing) and multiple rust resistance genes (Zwer et al.,
1992; Hamblin and Kyneur, 1993). Present varieties have a superior resistance to lodging, higher
yield potential, higher harvest index and less incidence of rust (Richards, 1992; Zwer et al.,
1992). Higher yields closer to potential are now possible in wetter years when yield losses are
highest.
The data sets generated and / or used in the study include:
 Daily rainfall (from the bureau of Meteorology) for 127 rainfall stations, 1975-1992, carefully
selected to evenly spread over the wheat belt. Each station roughly represent 1% of the
national crop (Figure 1). A subset of 70 stations had adequate daily maximum and minimum
temperature data. Years that had missing monthly rainfall data were removed from the
analysis. Unfortunately, due to problems with supplied media, 1992 temperature data was not
available for New South Wales and South Australia. These data were used to derive the
indices from the Stress and Drought models.
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 Monthly rainfall for all available meteorological stations in the Australian (mainland)
cropping belt (Figure 2) from 1974-1992. This data set was a subset of the data used in the
RAINMAN (Clewett et al 1994) project and includes spatially interpolated data (Hutchinson,
1991) to estimate missing values. This data set was used for the multiple regression model.
 Digitised Australian cropping land boundaries (McNaught in Hamblin and Kyneur, 1993;
Figure 2) derived by the Bureau of Resource Sciences from the digital version of the Atlas of
Australian Resources, Volume 6, Vegetation, produced by AUSLIG, with additions from
NSW Agriculture and Queensland Department of Primary Industries.
 Wheat production and area planted from the Australian Bureau of Statistics for each
Statistical Local Area (shire or county) for the years 1975-1992 inclusive. Considerable
effort was put into constructing this data set and reconciling historical SLA boundary changes
(particularly in South Australia) with those currently in use.
Average climate data from the AUSTCLIM dataset (Nix, 1981) gave required average weekly
solar radiation, maximum temperatures and minimum temperatures where these were missing
or not recorded.
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Figure 1: 127 rainfall stations used by the agro-climatic models; + = 70 stations with
temperature data
Figure 2: Australian cropping boundaries and rainfall stations used in the multiple
regression model.
Colour plates enclosed overleaf:
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 Spatially interpolated 5km grid of historical daily climate data for Queensland, 1972-1992.
 Digitised Queensland cropping land boundaries derived from visual interpretation of Landsat
Imagery
9.2 Rainfall Regression Model
Regression models are a static representation of the dynamic crop system. Typically, a yield
series is regressed against various independent variables representing monthly rainfall, location
and time etc.
The general linear regression model can be written as:
Y  X  
where Y is a n1 vector of observations,
X is a np matrix of independent variables,
 is a p1 vector of parameters, and
 is a n1 vector of random errors which is assumed here to be N(0,2I)
In this study the Y consisted of SLA (Statistical Local Government Areas; shire or county) wheat
yields (t/ha) as recorded by the Australian Bureau of Statistics for the period 1975-1992. Only
those shires reporting some production in all years were included in the regression, resulting in
285 shire yields in each of 18 years. That is, n  285  18.
The independent variables included in the regression were:
9.2.1 Location
This factor includes a constant term for each shire and can be interpreted as the long term yield
of an SLA.
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9.2.2 Time
The inclusion of a time trend is a simple way of accounting for the aggregate effects due to
advances in cultural practice, variety performance and other technology advances. A simple
linear term for year was used in this model to account for general increases (or decreases) in
shire yield over the study period. Trends in Australian wheat yields are discussed further in
2.3.3.
9.2.3 Monthly Rain
Rainfall is the primary causal (albeit indirectly) variable considered here in determining final
yield. Only monthly totals were considered as initial candidates for inclusion as the disagregation into smaller (ultimately daily) time steps was unlikely to improve prediction and
would result in a large number of highly correlated predictors. Twelve monthly totals were used
in the analysis: from December in the previous year to November of the year for which yield
was recorded. For example, for the 1975 harvest 12 totals beginning with December 1974
through to November 1975 were used.
As the experimental unit is the SLA, a single rainfall value for each SLA for each month had to
be derived.
Only rainfall stations within the boundary of cropping land (Figure 2) were
included, and, in the absence of any further detailed knowledge it was assumed that yield was
evenly distributed throughout each SLA (or part thereof) within this cropping boundary. An
estimate of the “spatial average” total shire rain for a given shire for each month was calculated
as the weighted average of the rainfall from those stations whose “area of influence” fell within
the shire boundary.
These 12 monthly values can be further aggregated into relatively homogeneous biologically
meaningful periods. In the interests of parsimony, only 4 rainfall variables were ultimately used
in the model fitting. These were:
Rn1
=
Dec+Jan+Feb+Mar
(fallow rain)
Rn2
=
Apr+May+June
(pre-plant)
Rn3
=
Jul+Aug+Sep
(flowering)
Rn4
=
Oct+Nov
(grain fill)
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9.3 Weighted Rainfall Index
Each month the Bureau of Meteorology calculates an average district rainfall for each of its
defined rainfall districts.
An accumulated accounting procedure that gives more importance to rainfall in critical periods
should relate strongly to yields due to various physiological reasons (Hochman, 1982), but also
because the rainfall distribution on a given soil determines how much of the rainfall is available
for plant use, and ultimately, the response of crop yield to rainfall amount (van Keulen, 1987).
Based on these assumptions a Weighted Rainfall Index (WRI) was formed by adding and
weighting district rainfall over the wheat-belt through the growing season. That is,
 n

w

d   w m Rm 
 m 1

d 1
45
WRI 
45
w
d
d 1
where d is the district, m the month, Rm the monthly district rainfall, wm the monthly weighting
factor which assigns a relative value to the month in relation to the crop cycle and wd is the
district weighting factor which is simply the proportion each district makes to the Australian
production. Summation is over the total number of districts (45) and number of months (n)
found between October (in the previous year) and the end of the growing season in each district.
The WRI is equivalent to a regression approach in that it is a weighted average of a linear
predictor, however, the coefficients (wm ) are subjectively determined.
Prior to calculating the index, the district rainfall is screened for extreme amounts. Monthly
totals less than 10 mm were removed as these light amounts spread over a month would mostly
be lost to surface evaporation. Excess rainfall on a regional basis, determined from the soil types
shown in the soil atlas of Australia (Northcote et al., 1975), is removed according to the values
shown in Table 2. Amounts above winter maximums were subtracted from the maximum corresponding to the negative affects of waterlogging on plant germination and growth. In
addition, a final screen was made of the data for two consecutive very wet months as it is
unrealistic to expect the second month’s rainfall to be as useful as the first.
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9.4 Agro-climatic Models
9.5 Stress Index Model
Dugas et al (1983) suggested that if the FAO (Food and Agriculture Organisation) crop
monitoring method (Frere and Popov, 1979) was incorporated into a water balance, it could be
used for crop yield forecasting. This is essentially what Stephens et al. (1989) did when they
joined the CERES-Wheat fallow water balance (Ritchie and Otter, 1985) to the Frere and Popov
(1979) model. This resultant stress index model calculates yields from year to year using daily
rainfall with average weekly radiation, maximum temperatures and minimum temperatures
needed for the water balance. Representative soil data are needed to describe the soil profile and
calculate PAW on a district sowing date.
Table 2 Maximum monthly rainfall amounts allowed in model calculations for different regions and
periods.
REGION
1 Month Total
(mm)
Winter
2 Month Total
(mm)
Growing-season
1 Month
Total (mm)
Non-winter
Queensland
125
200
150
New South Wales
125
200
140
Victoria
100
195
125
South Australia
100
190
125
Western Australia
95
170
125
W.A. District 10
80
150
125
Since the stress index calculates a moisture stress in relation to a PAW value, if too high a soil
water holding capacity is given the model overestimates yields in wet years, while conversely,
too low a value underestimates yields in dry years. Given the importance of this value, the stress
index model was optimised for PAW in relation to final yields. If optimised values were thought
inappropriate for each shire in relation to the dominant soil types shown in the soil atlas of
Australia (Northcote et al.,1975), they were adjusted to a more realistic value.
From sowing to a fixed "maturity date" (date rainfall stops contributing to final yield - just after
soft dough in the grainfilling stage) a weekly potential water balance is calculated. The potential
water balance assumes that rainfall (R) is added to the existing soil water supply and that a crop
water requirement (WR) is removed at a highest yielding (no stress) rate. As such, the soil
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moisture at sowing switches to be come a potential soil water (PSW) and is calculated weekly (i)
by:
PSWi = PSW i-1 + (Ri - WRi )
Usually a negative potential soil water supply is reached and a cumulative stress index (I), which
starts at 100, is reduced by the percentage of the total seasonal water requirement (WRT) not
met. This index is also reduced if the water holding capacity of the soil is exceeded by surplus
rain. If a surplus or deficit is represented as SD then:
I i = I i-1 + (
SDi
)
WRT
For late sowings the same total water requirement is removed from the soil, but because a fixed
maturity date is used for each year the length of the growing season is reduced and individual
weekly water requirements are increased. In fact later sown crops mature a little later and use
less water (French and Scultz, 1984), but because a potential water use is calculated in relation
to a non stressed crop, each year must be related to a potential total water use and what doesn’t
meet that is "stress".
Stephens et al. (1989) describe an elaborate procedure for calculating WR based on a Penman
derivative of potential evapotranspiration and crop coefficients. However, in extending the
model to many locations it became impractical to always adjust the crop coefficients so that WR
always equalled WRT. Instead, the same set of weekly water use values were used at all
locations, with the criteria that the proportion of water use was related to the observations of
French and Schultz (1984). That is, 70% of total water use occurred between sowing and
anthesis, 30% between sowing and tillering (~ 2 months), 40% for the period between tillering
and anthesis (2 months), 20% for the following month up to soft dough, and 10% for the
remaining time to maturity.
The end of the growing season or "maturity date" was reached when rainfall stops being useful
to the crop (just after soft dough in the grainfilling stage). Maturity dates were determined from
discussions with district agronomists from each state. Generally, an optimised model result
based on various maturity dates usually agreed with comments from district agronomist. Final
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dates used were based on a gradual change in optimised values from northern to southern
regions.
Another assumption used in the growing season was that any excess soil moisture above the
water holding capacity of the soil is removed (as does any rainfall greater than 75mm in one day)
- consistent with either runoff or infiltration.
9.6
Drought Index
The drought index was developed at the Canadian Wheat Board for real-time crop yield
projections, and is described in detail by Walker (1989). The model uses as inputs temperature
and precipitation (rainfall and snow) data for available weather stations. Growth of wheat is
simulated, in daily time steps, with a physiological-based approach, and the seasonally integrated
daily growth values (drought indices) that the model calculates are inversely correlated with the
level of drought (Walker, 1989).
Like the stress index, this model does not explicitly calculate actual PAW but instead calculates
the difference between cumulative water supply and transpiration demand over the growing
season. Cumulative water supply is simply the fraction of precipitation from each month that is
accumulated from the end of the previous year. Similarly, cumulative crop demand for water is a
summation of daily transpiration demand which is approximated as
T j = VPD j * G j * C
where for day j, VPD is a mean daytime saturation deficit estimated from mean daily
temperature, G is a mean daytime maximum crop conductance based on a phenology curve (Gp)
and C is a constant to convert to the appropriate units. If the cumulative supply stays far enough
above the cumulative demand throughout the season, then no water stress results. But if the
difference between supply and demand narrows, or becomes negative, a water supply growth
function Gw is progressively reduced. The drought index, D, is calculated by integrating actual
growth over the growing season.
n
D = min(G pj , Gwj )
j=1
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where actual growth is the minimum of growth allowed by phenology and water supply. For this
equation n is the number of days in the growing season, which in turn, is determined by a
summation of growing degree days (base 5°C). This approach has the effect of initially limiting
growth by leaf area, but subsequently by the water supply and demand.
However, some modifications were necessary before this model could be applied to Australian
conditions. Originally, Walker (1989) only adds a percentage of the precipitation to the supply
and has sowing on the same date each year. In Canada these assumptions are quite safe as snow
accumulates with low evaporation rates for much of the year and a short growing season means
the phenological development of wheat is limited to a narrow time frame. In Australia, the
opposite conditions apply leading into the crop season, with high and variable evaporation rates
and sowing varying over a number of months. Therefore, the CERES-Wheat water balance
(Ritchie, 1972) was added to the model, as was an automatic date calculation. The water
balance was run from 1 October on the previous year through to the date that transpiration
exceeded soil evaporation. Transpiration is multiplied by 1.4 so that an actual water use (soil
evaporation and transpiration) can be calculated with all rainfall being added.
9.7 Calibrating the Indices - Trends in Wheat Yields
Before calibration of the stress and drought indices, the climate and yield series should be
analysed to determine if climate trends are contributing to trends in yield in either a positive or
negative way (Sakamoto and Le Duc, 1981). If this is not occurring and a positive trend in yield
is evident, it is usual to attribute this to technology. This trend is removed by a curve fitting
procedure (eg. Wigley and Tu Qipu, 1983), or by using a yearly index (eg. Sakamoto, 1978). In
these cases, it is assumed that departures from trend are associated with weather variability.
In Australia, the rate of wheat yield growth has been one of the lowest in the world (Kogan,
1986; Hamblin and Kyneur, 1993). Both Sakamoto et al. (1980) and Kogan (1986) showed that
Australian yields had levelled from the mid 1950's through to the 1980’s. More recently,
Hamblin and Kyneur (1993) showed that there have been some modest increases in yield since
the mid 1980's, particularly in Western Australia. Better seasons and an absence of major
droughts since 1983 have contributed to this, but higher inputs (Hamblin and Kyneur, 1993) and
the normal lag between the introduction of new technology (new varieties) and higher yield
trends (Kogan, 1986) would have also helped. Table 3 illustrates how longer term trends have
varied with more recent ones.
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The specification of trend in the short term is a difficult problem and may not be resolved
without major assumptions (Sakamoto et al., 1980). Kogan suggests that a time series of yields
should encompass at least 30 years to correctly separate and approximate the climate-technology
interaction and facilitate the proper choice of trend shape and rate. Unfortunately, yields for all
shires selected were not all available for this time and so yield trends were selected from a line
of best fit to the longest possible interval with average years at the end of the interval.
Depending on regional drought, or wet years, these end points varied.
Table 3 Yield trends (t/ha/year) for (a) a period where yield growth had levelled off, and (b) recent
intervals chosen to represent more recent trends (excluding sequences of extreme years), with **
P<0.01
Region
Western Australia
South Australia
Victoria
New South Wales.
Queensland.
Australia
Trend
t/ha/yr
1958-88
(a)
0.0068
0.0037
0.0153
0.0107
0.0070
0.0070
Trend
1958-88
R2
0.089
0.009
0.100
0.053
0.020
0.067
Recent Trend
Years
(b)
1981-91
1975-90
1976-91
1975-91
1975-91
1975-90
Trend
t/ha/yr
Recent
0.050**
0.021
0.010
0.012
0.001
0.020
Recent Trend
R2
0.564
0.067
0.008
0.020
0.000
0.116
The agroclimatic indices were calibrated against yields for the 1976-1988 period assuming that
the effect of technology was minimal.
The year that an additional trend term was first
introduced was 1983 for the eastern states and from 1984 for Western Australia. These years
were chosen as 1983 was the first of a sequence of good years in the east, while in the west 1983
suffered from a very late sowing, whereas 1984 was the first of a sequence of years that
benefited from a swing to earlier sowing. In the latter case, a dramatic increase in the area
planted to nitrogen fixing lupins, nitrogen application, herbicides use and an almost complete
switch to semi-dwarf varieties occurred after 1983. These new adaptations started to level off by
the 1990's - the reason for ending the trend term by 1991.
9.8 Common Water Balance
The tipping bucket two stage evaporation subroutine of CERES-Wheat (Ritchie, 1972; Ritchie
and Otter, 1985) was run to calculate soil moisture at sowing. Daily meteorological data needed
by this model are rainfall, solar radiation, and maximum and minimum temperatures. Parameters
needed to describe a soil profile are the soil albedo (SALB), the maximum amount of stage one
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energy-limiting evaporation (U), transformation term needed to determine stage two evaporation
(C), whole profile drainage rate (SWCON), runoff curve number (CN2) and the number of soil
layers. For each layer, volumetric soil water contents are needed at saturation (SAT), drained
upper limit (DUL), and at the lower limit (LL) of plant Extractable Soil Water. Plant Available
Water PAW (or soil water holding capacity) is calculated as the difference DUL-LL (Ritchie et
al., 1986).
The soil properties used in WATBAL were for three representative soil profiles which all had a
shallow surface layer (10-20cm) and a deeper sub-surface layer. Since about 60% of Western
Australian wheat belt soils are duplex (Tennant et al., 1992) stations in this state were run on a
typical duplex soil with a light textured surface. South Australian stations had a more medium
textured surface, whereas the other states were run on a heavy soil profile. Plant available water
(PAW) was increased for each profile by increasing the depth of the sub-surface layer.
Approximate regional PAW values were therefore deduced by combining a scattering of
measurements at individual sites (eg. Forest et al., 1985) with the geographical distribution of
soil types shown in the soil atlas of Australia (Northcote et al., 1975).
The water balance is started for each model run on 1 October in the previous year with no plant
available water (PAW) assumed. Runoff was assumed to occur prior to sowing via a runoff curve
number and this gave runoff amounts proportional to the amount of rain that fell in a day. The
final estimated water balance is calculated for the day that a mean midpoint of sowing for that
district is determined.
Regional sowing dates are a function of farmers attitudes to early sowing, plus the actual amount
and distribution of rainfall in each year. Based on an analysis of sowing date survey forms and
discussions with agronomists from each state, a mean district midpoint of sowing was calculated
from rainfall amounts or changes in soil moisture over a three day period (Table 4). To simplify
the programming on a national scale, the same sowing date procedure was used for each region.
This assumption is possible because most of the heavier soils of the eastern states are fallowed
leading into the sowing time, while the lighter soils in Western Australia, which normally need
less rain, are commonly unploughed at this time. A region specific (ksd) factor is added to
differentiate regions that sow earlier than others.
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Table 4 Criterion to calculate the midpoint of district sowing.
Period
(Day of Year)
Change in Soil Moisture
(mm)
Sum of 3 day Rainfall (mm)
Sowing Day
101 < day  106
15
-
150 + ksd
106 < day  113
12
-
150 + ksd
113 < day  120
9
12
151 + ksd
120 < day  127
7
10
151 + ksd
127 < day  134
7
9
152 + ksd
134 < day < 189
7
9
day + 21 + ksd
day = 189
-
-
190
9.9 Simulation Models
9.10 TACT Simulation Model
The TACT (Tactical decision support) model is a derivative of the CERES-Wheat model
(version 1) and uses a photosynthetic approach (Robinson and Abrecht, 1994). This fallowcereal crop water balance has a series of sub-routines covering soil water flow, crop growth and
phenological development. Daily dry matter increments are generated by a photosynthesis
algorithm and partitioned into "leaf" and other material. Leaf area index determines light
interception, which is then used both in the photosynthesis sub-routine and to partition
evaporative losses between the soil surface and plant canopy (McMahon, 1983). Final yield is a
function of biomass, being the product of kernel weight times the number of plants.
Meteorological data needed to run the model are daily rainfall, solar radiation, maximum and
minimum temperatures. Management parameters needed are crop variety, sowing depth, sowing
density, and latitude. In addition 10 variety specific genetic coefficients are needed to describe
growth and phenological development. Fortunately, TACT is set up to run on two representative
Western Australian soils with typical crop coefficients for wheat given. An automatic sowing
date calculation is made similar to that given above, but in this case, sowing occurs as soon as
the criteria is met.
The model was run at all available meteorological stations within Merredin and Cunderdin
shires for the years 1976-1992.
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9.11 APSIM-Wheat
APSIM-Wheat (Hammer et al., 1987), is a dynamic wheat model that forms a module of the
APSIM cropping system model (McCown et al., 1995). It contains three interdependent submodels - soil water balance, crop growth and crop phenology. Based on a soil profile with two
layers, the water balance determines crop water use and soil evaporation by combining the
approaches of Ritchie (1972) and Shaw (1962). In the crop growth sub-model, daily crop growth
is expressed as the product of transpiration and transpiration efficiency, with transpiration being
a function of soil water content, leaf area and pan evaporation. Intercepted radiation determines
the partitioning of potential evapotranspiration to potential transpiration and potential soil
evaporation. Final yield is closely related to crop growth around anthesis and is based on the
work by Woodruff and Tonks (1983). They derived a yield index (GYI), such that
GYI =
TRANS
PAN * T m
where TRANS = transpiration (mm), PAN = class A pan evaporation (mm), and Tm = mean daily
temperature (°C), all for the 20-day period centred on the day of anthesis. This relationship
integrates the effects of growth duration, anthesis date, and environment into a single index
which is related to crop yield (Y) by:
Y = min(148,25+0.22* TDWA)+ 2833* GYI + 46291* (GYI )2
where Y = grain yield (g m-2), and TDWA = total dry weight at anthesis (g m-2).
Meteorological data needed to run this model include daily rainfall, maximum temperatures,
minimum temperatures and pan evaporation. The same soil parameters listed above are required,
as are three phenological parameters.
The model was run for the period 1970-1992 assuming continuous wheat cropping. The APSIM
model maintains the soil water balance during fallows between wheat crops and simulates
planting when the specified rainfall and stored soil water thresholds for planting are met within
the defined planting period. The feasible planting period depends on the latitude. The date of
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occurrence of a planting event determines the cultivar maturity used in a manner designed to
avoid frost at flowering (Woodruff, 1992).
The simulation was conducted at each point in a 5km grid across Queensland that fell within the
boundary of cropped land as determined by the interpretation of Landsat Imagery. Spatially
interpolated daily rainfall and temperatures were used at each point.
Modelled shire yields were determined as the weighted (for area of cropped land) average of the
model results of the 25 km2 pixels within each shire. The modelled yields (x) were calibrated
against historical (ABS) shire yields (y) to give predicted shire yields.
10.
RESULTS AND DISCUSSION
A total of 285 SLAs were included in the multiple regression approach and a subset of these
were used in the agro-meteorological models. Data from 127 rainfall stations were used in the
Stress Index model while the Drought Index model used a subset of 70 stations with
temperature records (Figure 1).
Given the number of shires involved, it is difficult to
summarise the results for all methods at the shire scale. Figures 3 and 4 illustrate the 285
observed (ABS) shire yields for 1982 and 1983, for example.
As the Weighted Rainfall Index does not operate at the SLA scale, further levels of
aggregation are necessary to compare the results of the WRI with the other methods. State and
national average yields were also calculated for each year from the shire and rainfall district
(WRI method) predictions.
Six major wheat producing shires from Queensland (4: Waggamba, Murilla, Banana and
Bungil) and Western Australia (2: Merredin and Cunderdin) have been used to illustrate the
results for those methods that can be applied at the SLA scale; with Queensland and Western
Australia chosen because there was ready access to dynamic yield models (APSIM-Wheat and
TACT) applicable to these two regions. Weighted (by area) average yields within states and
nationally have also been used to assess the relative performance of the methods.
The significant predictors in the fitted rainfall regression model were:
shire, year , Rn1 , Rn2 , Rn3 , Rn4 , Rn12 , Rn22 , Rn32 , Rn42 , shire. Rn2 , shire. Rn3 , shire. Rn4 , shire. Rn42
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where shire, year and the Rn are the location, time and rainfall variables, respectively,
described in 2.1.1.
The fitted relationship has an R2 = 0.73 (at the shire level).
The
coefficient of the year term was 0.01528 (SE: 0.00107) which compares reasonably well with
the recent trend data in Table 4.
Although the overall R2 is 0.73, for the purpose of comparison with the other methods
individual R2 values and mean absolute errors (MAE; Mayer and Butler, 1993) have been
calculated on an “observed vs predicted” basis for selected shires and state averages.
10.1.1 Individual Shire Results
By way of comparison between methods, Table 5 lists the R2 and MAE values from a simple
fit of observed (ABS) and predicted yields (t/ha) for each method in each of the 6 shires. The
data for Table 5 is shown in Figures 7 to 12.
No single model was consistently superior over all six shires; the regression model performed
best in the Queensland shires while the Drought Index model was superior in Western
Australia. The performance of the APSIM-Wheat model was comparable to or better than the
Stress and Drought Indices in 3 of the 4 Queensland shires while TACT performed poorly in
both Western Australia shires.
Table 5 R2 and MAE (t/ha) values from a fit of observed (ABS) vs predicted values for each method
at 6 selected shires.
Shire
Regression
SI
R2
MAE
R2
MAE
R2
MAE
R2
MAE
R2
MAE
Waggamba
0.75
0.21
0.73
0.22
0.64
0.25
na
na
0.51
0.32
Murilla
0.84
0.24
0.76
0.26
0.70
0.35
na
na
0.72
0.29
Banana
0.88
0.14
0.64
0.26
0.62
0.24
na
na
0.76
0.20
Bungil
0.88
0.19
0.77
0.26
0.68
0.33
na
na
0.80
0.21
Merredin
0.84
0.09
0.73
0.12
0.93
0.08
0.66
0.13
na
na
Cunderdin
0.64
0.16
0.63
0.19
0.70
0.14
0.50
0.20
na
na
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10.1.2 State / National Averages
Weighted average yields for each state and nationally can be calculated by using ABS reported
crop areas for each shire in each year and the methods compared at this level in the same way
as for individual shires. That is, for a given state,
weighted yield 
 areai  yieldi
total production
i

total area
 areai
i
Table 6 lists the R2 and MAE values from simple regressions of predicted weighted yield vs
observed weighted yield (ABS) for each method for each state. These data are illustrated in
Figures 13 to 19.
The regression model was consistent over states, performing best in New South Wales and
was marginally superior at the national level. The WRI performed best in Queensland, the
Stress Index in Western Australia and the Drought Index in Victoria and South Australia.
Table 6 R2 and MAE (t/ha) values from a fit of observed weighted yield (ABS) vs predicted
weighted yield for each method for each state and nationally.
Region
Regression
WRI
R2
MAE
R2
MAE
R2
MAE
R2
MAE
R2
MAE
QLD
0.86
0.15
0.89
0.14
0.82
0.17
0.84
0.16
0.73
0.22
NSW
0.89
0.12
0.84
0.14
0.87
0.12
0.77
0.17
na
na
VIC
0.88
0.13
0.90
0.13
0.90
0.12
0.91
0.13
na
na
S.A.
0.88
0.11
0.76
0.16
0.85
0.13
0.92
0.08
na
na
W.A.
0.86
0.07
0.86
0.10
0.91
0.08
0.89
0.08
na
na
Australia
0.92
0.07
0.90
0.08
0.90
0.07
0.88
0.07
na
na
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Figure 3: ABS yield (t/ha) for 1982
Figure 4: ABS yield (t/ha) for 1983
Figure 5: Stress Index yield (t/ha) 1982
Figure 6: Stress Index yield (t/ha) 1983
Colour plates for these figures overleaf:
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10.2 Implications
The results indicate that good accuracy and precision are possible in forecasting shire, state
and national wheat yields. All methods achieved a useable level of skill. The simplest
forecasting methods are the rainfall regression and WRI, which require only monthly rainfall
from 45 meteorological districts. (Although, the multiple regression as implemented here
uses monthly rain from 1152 stations; further work is required to determine the minimum
subset of relevant stations.) Both perform as well as any of the more complex approaches and
have similar outcomes (Table 6, Figures 13 - 19). The similarity indicates that the subjective
weights in the WRI are matched by the objective weights determined over the years used in the
analysis. While the WRI method cannot be used at the shire scale, the regression method
performs best at this level (Table 5, Figures 7 - 12).
This finding suggests that there is no need to go beyond simple rainfall relationships.
However, in operational forecasting one is looking ahead, not using historical data. The
extrapolation ability of an approach must be considered. It is likely that these empirical
models would be the poorest when extrapolated. They give good fit within the bounds of the
data on which they are derived, but have no (or few) constraints if very different circumstance
arise.
The agroclimatic and simulation / Geographic Information System (GIS) approaches largely
overcome the concern about extrapolation as they contain enough biophysical rigour to better
mimic the agroecology. However, their performance is not always as good as the empirical
approach. The agroclimatic methods, however, approach the empirical approach in predictive
capability (Tables 2 and 3) particularly at the state level. While these methods require greater
input specification associated with soil and crop factors, this remains relatively simple. The
performance of these agroclimatic methods at the shire level is illustrated in Figures 3 to 6
where actual (ABS) and predicted production from the Stress Index model have been mapped
for the 1982 and 1983 seasons. The simulation / GIS approach is much more demanding of
spatial data and computing technology, but is unable to match the simpler approaches in
utility. This likely relates to the sensitivity of such models to factors such as cropping history
and management (eg. fertility), which cannot be easily specified in spatial data bases.
However, it is unlikely that further effort to refine spatial data bases for improved commodity
forecasting would be worth the payoff given the good performance of the simpler approaches.
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The agroclimatic methods specified in this study are most likely to be successful in an
operational wheat forecasting system by using existing rainfall up to any point in time and
historical data to project into the future.
This approach also provides the basis for
consideration of how to utilise seasonal forecasting in commodity forecasting. The projection
into the future may be based on a seasonal forecast of rainfall or on subsets of analog years.
The historical record provides a means to examine such approaches given that we now have a
method that satisfactorily captures the crop response to environment at this scale.
We
consider this a more robust means to proceed than to seek to incorporate climatic indices
(such as the SOI) directly into the predictive framework.
The methods developed here also provide the basis to rigorously examine production trends in
wheat throughout Australia by first removing the effects of climate variability. Recent studies
(Hamblin and Kyneur, 1993) have used empirical methods to examine trends using ABS
production data and have then used these analyses to suggest likely causes. These analyses
are confounded by seasonal variation. By removing seasonal trends with the agroclimatic
methods outlined in this study the residual component would more closely reflect real
production trends.
11.
CONCLUSIONS
This study has identified empirical, agroclimatic, and simulation / GIS approaches that have
the potential to satisfactorily forecast Australian wheat yield at the shire scale. The empirical
and agroclimatic approaches had better predictive ability and were less demanding of input
requirement than the more complex simulation / GIS approach. The agroclimatic approaches
are likely to be more robust in operational forecasting as they embody sufficient biophysical
rigour to overcome issues of extrapolation into different seasons / years, which would be
likely with the purely empirical approaches. The additional requirement of the agroclimatic
approaches for soil and crop factors (beyond rainfall data) should not be a significant
impediment to development of such methods in an operational context. These methods
provide a basis for a detailed and rigorous evaluation of the implications of seasonal weather
forecasting in commodity forecasting. They also provide a basis to examine real trends in
wheat production after estimating seasonal effects.
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Figure 7 Comparison between observed yield (ABS) and predicted yield for (1) rainfall regression,
(2) stress index, (3) drought index, and (4) APSIM-Wheat simulation for the Waggamba shire.
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Figure 8 Comparison between observed yield (ABS) and predicted yield for (1) rainfall regression,
(2) stress index, (3) drought index, and (4) APSIM-Wheat simulation for the Murilla shire.
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Figure 9 Comparison between observed yield (ABS) and predicted yield for (1) rainfall regression,
(2) stress index, (3) drought index, and (4) APSIM-Wheat simulation for the Banana shire.
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Figure 10 Comparison between observed yield (ABS) and predicted yield for (1) rainfall regression,
(2) stress index, (3) drought index, and (4) APSIM-Wheat simulation for the Bungil shire.
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Figure 11 Comparison between observed yield (ABS) and predicted yield for (1) rainfall regression, (2) stress
index, (3) drought index, and (4) TACT simulation for the Merredin shire.
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Figure 12 Comparison between observed yield (ABS) and predicted yield for (1) rainfall regression,
(2) stress index, (3) drought index, and (4) TACT simulation for the Cunderdin shire.
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Figure 13 Comparison between observed (ABS) and predicted (weighted) Queensland yields for (1)
rainfall regression, (2) stress index, (3) drought index, and (4) weighted rain index.
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Figure 14 Comparison between observed (ABS) and predicted (weighted) Queensland yields for the
APSIM-Wheat model
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Figure 15 Comparison between observed (ABS) and predicted (weighted) New South Wales yields for
(1) rainfall regression, (2) stress index, (3) drought index, and (4) weighted rain index.
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Figure 16 Comparison between observed (ABS) and predicted (weighted) Victorian yields for (1)
rainfall regression, (2) stress index, (3) drought index, and (4) weighted rain index.
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Figure 17 Comparison between observed (ABS) and predicted (weighted) South Australian yields for
(1) rainfall regression, (2) stress index, (3) drought index, and (4) weighted rain index
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Figure 18 Comparison between observed (ABS) and predicted (weighted) West Australian yields for
(1) rainfall regression, (2) stress index, (3) drought index, and (4) weighted rain index
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Figure 19 Comparison between observed (ABS) and predicted (weighted) Australian yields for (1)
rainfall regression, (2) stress index, (3) drought index, and (4) weighted rain index
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Volume 6
REFERENCES
AACM (1991) Review of Crop Forecasting Systems. Australian Agricultural Consulting and
Management Company Pty. Ltd., Adelaide, 35 p.
Angus, J.F. (1991) The evolution of methods for quantifying risk in water limited environments.
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