Australian Prawn Farmers’ Association Pre-proposal Questionnaire
This Questionnaire is used to evaluate how well each proposed project reflects the APFA’s current
research priorities, while weighing up constraints such as cost of project, time to completion and
expected outcomes. The R&D committee will use the responses to the following questions in an
evaluation model, which will then be reviewed and discussed by the committee. If a pre-proposal is
supported, then we will ask for a full project proposal to be drafted for the funding provider.
Project Title: Seasonal forecasting of key environmental drivers for prawn farms
Principle Investigators: Alistair Hobday (CSIRO), Jason Hartog (CSIRO) Claire Spillman (BOM)
Date: June 21, 2011
Please write a concise, 1 to 2 paragraph introduction clearly describing the project and its objectives:
This project will assist economic recovery and future climate risk preparedness for the prawn farming
industry by developing industry-specific seasonal forecasts. Seasonal forecasting is used widely in
terrestrial farming systems to set crop timing, fertilization schedules, harvest periods, rotation schedules
etc (e.g. see Hudson et al 2011). Recent development of forecast models that also include marine
variables such as ocean temperature have seen operational forecasting systems developed for marine
fisheries (southern bluefin tuna; Hobday et al 2011), cage aquaculture (Atlantic salmon; Hobday et al,
FRDC project)and biodiversity management (coral bleaching; Spillman and Alves 2009; Spillman 2011).
Seasonal forecasting has the potential to reduce costs in bad years, and maximize profits in good years.
Any business which has to make decisions that are influenced by the future environment have the
potential to improve profitability by using seasonal forecast information. If you knew it was to be a
warm year, could you increase profits or reduce costs? There are two strong environmental influences
on prawn farming that seasonal forecasting may help to manage. Average conition
The objectives of this project are to test the skill of forecast methods for the pond-based prawn industry
in coastal Queensland (Brisbane to Mackay). In the first year, we would:
1. investigate the skill of forecasts of monthly (and two-week) air temperature (which approximates
pond temperature) at lead times of up to four months for the period September (depending on
when project commenced) to say May. These forecasts would be downscaled to individual prawn
farms in 3-4 coastal locations across the prawn farming region. We would need local historical and
current data on pond and air temperature from these participating farms. We would perform
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historical validation to determine the value and skill of the forecast for each region (say years 20002010). We have done some preliminary evaluation of the POAMA model, and are confident that
there is some skill in the model for the prawn region in terms of forecasting air temperatures at lead
times of between one to four months. For some regions and months, only shorter time scales may
be useful, while for other regions and months we expect useful longer forecasts (out to four
months).
The seasonal forecast model can also represent the ENSO cycle. This is useful because tropical cyclones
can be a major source of rainfall in Queensland, especially in La Niña years. Cyclones also tend to have
different tracks in La Nina and El Nino years, which means different sections of the coast are likely to be
impacted. In La Niña years tropical cyclones have tended to track towards Queensland’s coast and then
deteriorated into rain depressions. In contrast, cyclones paths in El Niño years have been generally
south or east (Figure 1; Hastings 1990).
Figure 1: Cyclone tracks for La Nina (left) and El Nino (right) from Hastings 1990 (source:
http://cawcr.gov.au/publications/BMRC_archive/tcguide/ch5/ch5_figs/figure5_3.htm and
http://cawcr.gov.au/publications/BMRC_archive/tcguide/ch5/ch5_figs/figure5_2.htm)
Thus, the second objective is:
2. To estimate the ENSO cycle up to 5 months ahead of time, and hence the likely latitudinal range of
cyclone activity at the Queensland coast. We would generate POAMA forecasts of the ENSO signal
and then derive statistical relationships to tropical cyclone risks for latitudinal bands along the
Queensland coast. We explore the potential to forecast the rainfall that may accompany cyclones
and subsequent impact on the prawn industry. These forecasts might help prepare the industry
decide on the level of investment for cyclone management in a particular year.
The list below consists of the current APFA research priority areas. Circle / highlight the priority that
your research is most likely to have the most positive influence on.
KPI 1: Stocking density
KPI 2: Growth rates
KPI 3: Days to Harvest (pond longevity)
KPI4: Growth per week
KPI 5: Ha under production/total farm Ha
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Additional Comments: We will need to work with industry/farm representatives to develop targeted
forecasts that deliver maximum value to the farms. There will be critical points in the production cycle
for which forecasts may be particularly useful, say for September and May.
The list below consists of the current APFA research priority areas. Write the expected % increase or
% decrease effect on each KPI as a result of your research. If there will be no effect, write 0%.
Industry Priority
Stocking
Survival
Days to Harvest
(pond longevity)
Growth per week
% increase (+) /
decrease(-)
Industry Priority
Depending on use
of forecast
Depending on use
of forecast
Depending on use
of forecast
Depending on use
of forecast
Admin and sundry /
farm ha
Power cost / ha
Farm FCR
Ha under production Depending on use
/ total farm Ha
of forecast
Price / kg
Reject prawns
Price Reject prawns
Average Farm
labour cost / ha
% increase (+) /
decrease(-)
Pl cost $/Pl
Maintenance Cost /
farm ha
Chemicals, water
cost/farm ha
Feed Cost ($/kg
feed)
Processing cost / kg
prawn
Processing Freight /
kg prawn
Marketing Cost / kg
prawn
Average Process
cost / kg prawn
Additional Comments:
The benefit will depend on how each business uses the forecast. In some years, a forecast will suggest to
a farmer to increase the stocking density and thus maximize profits, while in other years the forecasts
will suggest that reducing the stocking density might be required. The same logic would apply for the
other KPI’s that would be influenced by the environmental conditions during the growing season. For
example, warm years may lead to increased growth rates, and knowing that ahead of time may be an
advantage.
How will knowledge gained as a result of this research will be presented to industry? E.g. a document,
a manual / ‘how to’ guide, a workshop / training seminar, prototype product, commercial – ready
product etc. Forecasts will be targeted at each farm site, and in the case of the salmon forecasts were
delivered by email as a single page document indicate the projected temperatures at each site for the
current month, next month, etc (see Attachment 1). Feedback via phone or meetings during the project
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will be important. A final presentation on the forecasting at an industry meeting would be appropriate.
In the operational phase after year 1, web-based delivery would be appropriate. The coral bleaching
forecasts (http://poama.bom.gov.au/gbr/gbr_coral.shtml) prepared by Spillman and her team include
knowledge building elements, and we could design similar for the prawn industry.
What is the expected total cost of the proposed project?
The two components (Objectives 1 and 2 above) represent a total of 80-100K
Objective 1 – seasonal air temperature forecasting - 40-50K for one year
Salary
Operating (travel, computing)
Objective 2 – cyclone risk forecasting - 40-50 k for one year
Salary
Operating (travel, computing)
How long will the project take (time frame in years)?
We suggest a one year pilot project, followed by a discussion with industry on how to deliver ongoing
seasonal forecasts to an increased number of sites, or to those sites for which the forecasting is
considered valuable. The cost of these forecasts would be approximately 5-10K per site per year into the
future.
What is the realistic length of time between finalizing the project and the industry seeing benefit?
The first preliminary forecasts could be delivered 2 months after the project commenced (this time is
needed to start up the forecasting process). These will steadily improve over the course of the project,
as the methods are developed further. The benefit would be immediate on receipt of forecasts.
Decisions regarding business planning can begin to consider the upcoming seasonal environment based
on the forecasts.
Please provide any reference material to support your proposal. This may include references to
journal articles, reports, word of mouth communications, similar products, similar concepts etc.
References:
Hastings, P. A. (1990) Southern Oscillation influences on tropical cyclone activity in the Australian/southwest Pacific region. Int J. Climatol. 10
291-298
Hobday AJ, Hartog J, Spillman C, Alves O (2011) Seasonal forecasting of tuna habitat for dynamic spatial management. Canadian Journal of
Fisheries and Aquatic Sciences 68, 898–911.
Hudson D, Alves O, Hendon HH, Marshall AG (2011) Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for
Australia. Quarterly Journal of the Royal Meteorological Society 137, 673-689.
Spillman C (2011) Operational real-time seasonal forecasts for coral reef management. Journal of Operational Oceanography 4, 13-22.
Spillman C, Alves O (2009) Dynamical seasonal prediction of summer sea surface temperatures in the Great Barrier Reef. Coral Reefs 28, 197206.
Similar project forecasting water temperatures around Tasmania for coastal salmon farms:

FRDC project 2010/217: Atlantic Salmon Aquaculture Subprogram: Forecasting ocean temperatures for salmon at the farm site
Coral bleaching forecasts: http://poama.bom.gov.au/gbr/gbr_coral.shtml)
Please forward completed form to info@apfa.com.au
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