The expected value of seasonal stream-flow forecasts to a grain-cotton
irrigator in the Condamine-Balonne catchment
Power B1, Rodriguez D2, Perkins J3, Hawksworth C3
1
Agri-Science Queensland. PO Box 102 Toowoomba Qld (4350) Australia;
[email protected]
2
Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of
Queensland, PO Box 102 Toowoomba QLD 4350, Australia
3
Bureau of Meteorology, GPO Box 413, Brisbane QLD 4001, Australia
Keywords: seasonal stream-flow forecast, whole farm systems modelling, APSIM
Introduction
Water for irrigation is often the limiting resource that prevents grain-cotton growers from
planting more area and increasing farm business returns. The potential exists for grain-cotton
irrigators with river harvesting entitlements to increase farm profits by utilising the Bureau of
Meteorology’s (BOM) seasonal stream-flow forecasts (BOM, 2010). However, for any
climate forecast to be effective it must provide information that generates actionable and
beneficial changes in the management of the targeted system (Hammer, et al. 2000). This
paper provides an example of how a seasonal forecast of river stream-flow could be used to
support a relevant and actionable decision, in this case varying cotton area by adjusting the
amount of water at sowing required to plant each hectare of cotton. This results in a potential
increase in the profitability of an irrigated farm business from the Darling Downs in
Queensland, Australia through capitalising on relatively high summer river flow forecasts by
increasing the area sown to cotton.
Methods
Case study farm
Semi-structured interviews with the case study farmer from Dalby, Queensland, Australia,
were used to describe farm resources and its management. The farm is 616 ha of furrow
irrigated cropping land, with 3 on-farm water storages having a combined capacity of 2,400
ML. Sources of irrigation water include bores (170ML total annual allocation), water
captured from on-farm runoff, and water-harvesting entitlements from the Condamine River.
Here, for simplicity we assumed cotton was grown as a monoculture.
Modelling framework
The information collected during the interviews with the farm manager was used to
parameterise the multi-field configuration of the process-based model APSIM (Keating et al.
2005), described in Power et al. (2011). Farm operating constraints, such as pumping
capacities and machinery work rates, as described by the farmer, were included in the model.
We ran the model using patched historical climate records (Jeffrey et al., 2001) for 51 years
(1958 to 2009) using 2010/2011 prices and costs. Fallow costs are driven by a simple weed
emergence model as in Power et al., (2011). Integrated Quality and Quantity Model (IQQM)
provided daily Condamine River flow data (ML/day) near the case study farm (State of
Queensland, 2010).
Stream flow forecast
At the time of writing this manuscript BOM’s Seasonal Stream-flow Forecasting service
(www.bom.gov.au/water/ssf/) was not yet operational in Queensland catchments, therefore a
climate index was used to provide proxy forecasts. The NINO3 index with a 2 month lag has
the best skill at predicting summer rainfall (DJF) in the area under consideration (Schepen et
al., in review). This corresponds to the value of the NINO3 index for October, which
coincides with cotton sowing month in southern Queensland. We used historical October
NINO3 values in a hindcast to evaluate its ability to predict Condamine River flows for
summer. A relatively high summer river flow is more likely when October’s NINO3 anomaly
is less than -0.8 and conversely a relatively low flow season is more likely when the NINO3
anomaly is greater than 0.8. Otherwise (-0.8≤NINO3≤0.8) there is no indication of either
above or below median flow for the coming season. Figure 1 shows cumulative distribution
functions for aggregate flows for the Condamine River separated into analogue prediction
types. It demonstrates the NINO3 index has reasonable ability (F test p-value = 0.03) to
predict the relative size of summer Condamine River flows, especially for the prediction of
high flow seasons and justifies its use as a predictor of stream flow.
Figure 1 (left). Aggregate modelled Condamine River
flows (GL) for summer (D,J,F) for the NINO3 predicted:
low flow seasons (dashed curve); high flow seasons
(dotted curve); and when no prediction is available of
either above or below median flows (solid curve).
Change in management
For the case study farm, the area planted to cotton each year depends on the amount of stored
water available for irrigation at the time of sowing. Here, we incorporated NINO3 predictions
of river flows in the farmer’s decision framework by adjusting the amounts of stored water
required at sowing for each hectare of cotton being considered for planting. When a higher
river flow than normal was expected less stored water at sowing per hectare of cotton was
required due to the increased probability of both additional river flows and in-crop rainfall.
This then allows for a greater area of cotton to be sown for that season. Similarly, when a
relatively lower river flow is expected for the season, then more stored water per hectare of
cotton is required and the area sown is reduced. The actual amounts of stored water (ML/ha)
were optimised to maximise farm returns. When no additional information about the
subsequent seasons is available from the NINO3 climate index, management is un-changed.
Results and Discussion
Twenty out of the 51 seasons simulated a forecast of either high or low flows, and hence the
potential for a change in area of the farm allocated to cotton in order to maximise farm
returns. Figure 2a, shows probability density functions of farm gross margins for those
seasons when the stream-flow forecasts indicated either higher or lower flows as the most
likely outcome, and adaptive management is thus undertaken (dashed curve); and those
seasons when the farmer’s current management strategy is employed (solid line). In those 20
years farm returns increased significantly (one sided t-test p-value = 0.05). However, when
all years were compared (Figure 2b) the statistical significance was lost. The optimal amounts
of stored water for sowing cotton were 0 ML/ha when a high flow season was the more likely
outcome of the forecast i.e. corresponding to sowing all 616 ha of the farm to cotton. This
indicates that during high flow seasons available cropping area limits farm profits. When a
low river flow season was the most likely, the optimal strategy was found to be the farmer’s
current management (i.e. setting cotton area based on 4ML/ha), which indicates no change in
the cotton area for the season (ca. 232ha). This agrees with the results in Figure 1, which
shows the NINO3 index has no ability in forecasting low flow seasons.
Figure 2. Current management (solid curve) and adaptive management (dashed curve) for those
seasons when a NINO3 based prediction of either high or low flow was available a) and all years b).
We conclude that for this case study farm the expected value of a seasonal river stream flow
that uses NINO3 as a predictor would be ca AU$31,000/year. For simplicity, here we only
considered changes in sown cotton areas. However, many other strategies relevant to the
farmer could have also been explored e.g. crop choice, sowing densities, etc.
Acknowledgments
This research was jointly funded by the Nation Program for Sustainable Irrigation (NPSI) and
the Grains Research and Development Corporation (GRDC). The authors acknowledge the
valuable contribution made by the farmer who volunteered his time to participate in this
study.
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