2007/02/20 PM
Invited Lecture 1
Best practices on the application of climate information
in the agricultural sector
Roger C. Stone1 and Holger Meinke2.
1.Queensland Department of Primary Industries and Fisheries, 203 Tor Street,
Toowoomba, Australia, and,
Faculty of Sciences, University of Southern Queensland, Darling Heights,
Toowoomba, Australia, 4350.
2.Agricultural Productions Systems Research Unit,
Queensland Department of Primary Industries and Fisheries, 203 Tor Street,
Toowoomba, Australia, 4350
Email: Roger.Stone@dpi.qld.gov.au
ABSTRACT
Current understanding of climate variability and improved skill in seasonal climate
forecasting is sufficient to justify its use in investigating potential application in improving
management in the agricultural sector world-wide. Additionally, recent developments in
connecting numerical weather prediction models and general circulation models with
quantitative crop growth models also offer the potential for development of integrated
systems that incorporate components of long-term climate change.
However, we point out that climate information and operational seasonal climate
forecasting systems have little or no value unless they are able to change management
decisions in the agricultural sector. Changed decision-making in agricultural industries
through incorporation of seasonal climate forecasting ultimately has to demonstrate
improved long-term performance of the agricultural enterprise. Simulation analyses
conducted on specific production scenarios are especially useful in improving decisions,
particularly if this is done in conjunction with development of decision support systems
and associated facilitated discussion groups.
Importantly, improved management of the agricultural production system requires
an interdisciplinary approach where climate scientists, agricultural scientists, and
extension specialists are intimately linked with agricultural production managers in
development of targeted seasonal forecast systems. The same principal applies in
developing improved operational management systems for commodity trading
organisations, milling companies, and agricultural marketing organisations. Application
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2007/02/20 PM
Invited Lecture 1
of seasonal forecast systems across the whole value chain in agricultural production
offers considerable benefits in improving overall operational management of agricultural
production.
Agricultural businesses, associated government systems and farmers depending on
agriculture for sustenance, may all be significantly responsive to fluctuations in climate.
These systems involve farms, input supply businesses, marketing, and government
policy systems. Skill in operational seasonal climate forecasting offers considerable
opportunities to agricultural managers through the potential to provide improvements in
the overall system involved. This may be through increased production and farm
profitability or through reduction in risks. However, capturing the opportunities
associated with climate and crop forecasting is not necessarily straightforward as
climate forecasting skill, while nevertheless improving over recent years, remains
imperfect and methods used to apply this type of skill level to operational management
issues in agricultural production have not generally been developed or tested
extensively.
A key issue in fitting agricultural production models to seasonal climate models is in
dealing with differences in scale between crop models (normally developed for
field-level application) with the new generation of general circulation models which
provide output at national or regional scales. A further key aspect related to seasonal
forecasting of crop performance is that the outputs of the combined seasonal
crop-climate forecasting system must have direct application for crop production
managers to apply this type of information to modify their actions ahead of likely impacts
of climate variability or climate change (Hammer et al, 2001; Hansen, 2002; Challinor et
al., 2004).
Finally, extreme weather events over the past 30 years have already caused severe
crop damage and induced significant economic toll for United States’ farmers alone.
This has occurred against a back-drop of greater variability in crop yields, price and
farmer income, part of which can be related to long-term climate change (Rosenzweig et
al., 2000). Additionally, it is the authors’ experience that farmer needs and requests in
eastern Australia for climate-related information (while engaging in participative
workshops) have shifted markedly over recent years from the need for issues only
associated with managing risks due to climate variability on a 3-7 year time scale to be
addressed to the needs for longer-term, high-level strategic issues associated with
climate change to be addressed. These types of practical issues range from the need to
find ways of coping with perceived more extreme weather events in tactical day-to-day
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farm management to more complex whole-farm economic issues associated with 5-20
year planning horizons. Typical management examples often cited relate to long-term
reduction of cattle stocking rates on available land because of perceived long-term
decline in rainfall and pasture availability (with some expectation these conditions will,
more or less, continue) or to, otherwise, make the high-risk decision to purchase an
adjoining property in order to maintain constant stocking rates under potentially
increasing drying and warming conditions. We note the work of Schneider et al., (2000)
who state that ‘farmers in the real world will need to adapt to climate change trends
embedded in a very noisy background of natural climatic variability’. They argue this
variability can mask slow trends and delay necessary adaptive responses by farmers. In
this respect we suggest actions need to be develop now to aid agricultural industries in
their ability to cope with potential impacts from long-term climate change.
The aim of the presentation is to provide an overview of a number of best practice
techniques, especially those developed in Queensland, Australia, that are already
successfully being applied in the application of climate information and operational
climate forecasting for the agricultural sector.
References:
Challinor, A.J., Wheeler, T.M. Osborne and Slingo, J.M. (2004) ‘Assessing the vulnerability of
food systems to climate change thresholds using an integrated crop-climate model’. Agric, For.
Meteor; 124: 99-120.
Hammer, G.L., Hansen, J.W., Phillips, J.G., Mjelde, J.W., Hill, H., Love, A., and Potgieter, A.
(2001) ‘Advances in application of climate prediction in agriculture’. Agricultural Systems, 70, 2-3,
515-553.
Hansen, J.W. (2002b) ‘Realising the potential benefits of climate prediction to agriculture: issues,
approaches, challenges’. Agricultural Systems, 74, 3, 309-330.
Rosenzweig, C., Iglesias, A., Yang, X.B., Epstein, P.R., and Chivian, E. (2000) ‘Climate change
and U.S. agriculture: The impacts of warming and extreme weather events on productivity, plant
diseases, and pests’. Center for Health and the Global Environment, Harvard Medical School,
Boston. 47pp.
Schneider, S.H., Easterling, W.E., and Mearns, L.O. (2000) ‘Adaptation: sensitivity to natural
variability, agent assumptions and dynamic climate changes’. Climatic Change, 45, 203-221.
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