The future of public sector forecasting in Australian agriculture

​​Author: Rohan Nelson

ABARES and its predecessors have been providing commodity market forecasts for Australia’s agricultural sector since the 1940s, as part of a wider suite of evidence‑based analysis and advice to policymakers and industry stakeholders. The operational context of ABARES and the industries we serve has changed a great deal over this period, and there are no signs that future changes will be slower or less profound.

This paper explores the future of public sector agricultural forecasting in the context of the past evolution of ABARES work. It identifies a range of challenges and opportunities for people involved in public sector forecasting, including the usual suspects of globalisation and global change, rapid improvements in information and communication technology, the emergence of big data (in all its forms), and new technology-enabled possibilities for interaction and co-production of knowledge.

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Australian governments since 1945 have provided commodity market forecasts for Australia’s agricultural sector. Since then the operating and policy context of Australian agriculture has changed dramatically. Public investment in agricultural forecasting fell from the 1990s onwards as technology made forecasting more efficient and as agriculture’s falling share of the economy reduced its priority within government. The acceleration of global change into the 21st century brings into question the future role of public sector forecasting in Australian agriculture and what these forecasting services should look like into the future.

Public sector forecasts for Australian agriculture have almost exclusively been provided by the Bureau of Agricultural Economics (BAE, 1945–1987) and its successors, the Australian Bureau of Agricultural and Resource Economics (ABARE, 1987–2010) and the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES, from 2010). The idea of creating a BAE surfaced during World War II and was initially driven by the need to provide an evidence base for policy development. Prior to the war, public investment in agricultural economics and market analysis was limited to research that supported sporadic industry inquiries.

Government commitment to forecasting began during World War II

The Australian Government’s commitment to providing forecasting services for agriculture emerged from 1943 onwards as the scale of dismantling wartime policy became clear. This was intensified by anticipation of the problems associated with resettling returned soldiers with limited farming experience onto farms acquired by governments.

An early soldier settler and family at the post World War I Beerburrum soldier settlement in Queensland, established to grow pineapples and other fruit, which failed and closed by 1929 due to low pineapple prices, poor soil, and holdings that were economically too small.
Queensland State Archives, Digital Image ID 2606, An early settler, Beerburrum, December 1916

Many soldier settlement farms established after World War I had failed, partly as a result of naive commodity forecasts. The bureau’s flagship publication, the Quarterly Review of Agricultural Economics, was first published in January 1948 and included quarterly updates of expected trends in agricultural markets.

Australia’s BAE owed much in its original design to similar institutions that had already been established in the United States and Canada. Governments in the United States and Australia both saw the public provision of production and price forecasts as a means of addressing a perceived imbalance in information between farmers and traders. In Australia, the public provision of forecasting services was complemented by statutory marketing. Statutory marketing was designed to provide farmers with countervailing market power in their negotiations with traders. However, it also removed responsibility for marketing from farmers and incentives to innovate in response to market signals.

Statutory marketing was just one manifestation of a high level of post-war government involvement in agriculture that sustained policy demand for agricultural forecasts by central government agencies. BAE’s forecasting services expanded from the 1960s to the 1980s in terms of commodity coverage and methodological capability, with public investment in forecasting services peaking in the mid-1980s. In 1981–82, 65 staff were involved in commodity and marketing economics research and a further 42 in collecting and processing commodity data. The commitment of resources during the 1980s allowed experimentation with increasingly sophisticated structural and programming models.

Forecasts provide economic value through market efficiency

The market efficiency value of forecasts
Adapted from Freebairn (1976a), The value and distribution of the benefits of commodity price outlook information’, Economic Record, vol. 52, no. 2, pp. 199–212.

Economists have always emphasised the economic value that forecasts provide through the efficient operation of markets, with benefits to consumers and producers. They also tend to be equivocal about whether and to what extent these services should have been exclusively funded by governments. Reliable forecasts can help reduce the uncertainty that farmers face when committing resources to production well in advance of knowing demand at the time of marketing. Consumers benefit through the timely availability of high quality and reasonably priced food. This provides an incentive for both consumers and producers to contribute to the cost of forecasts. Economic thinking can also be used to understand the value derived from using forecasts to make markets more equitable, and to support policy development.

Policy demand for agricultural forecasts remains strong into the 21st century. Although the emphasis on industries and issues changes constantly, the basic policy applications of ABARES forecasts have changed little since 1945. Forecasts and related market information are used to respond to stakeholder concerns and form policy responses to emerging issues, and continue to be used by central agencies for macroeconomic forecasting. ABARES forecasts remain essential for policy applications where independence from industry and markets is essential.

Globalisation has changed policy arguments for public forecasting

Beyond this policy role, globalisation has dramatically changed the policy arguments shaping the future public provision of forecasting services for Australian agriculture.

The growing sophistication of forecast users and development of interactive web-based technologies means that public sector forecasting services should focus on providing intermediate data and analyses that users can recombine to produce their own forecasts. In government, this would build the capacity of policy advisers to tailor advice in response to emerging issues. In the private sector, this would boost the value of public good data and analyses that consultants and others use to tailor commercial forecasts.

Policy arguments for providing public sector forecasting into the 21st century

The growing expertise of users increases their independence from expert forecasters and creates a store of knowledge that forecasters should draw on. This will require ABARES to evolve from an expert-centric institution to one via which expert forecasters engage meaningfully with diverse groups of forecast users. This is likely to involve greater use of social media and interactive web-technologies that support innovative approaches to consensus-based foresighting of deeply uncertain future market scenarios.

Future forecasting services need to evolve to meet changing user needs

Globalisation has fundamentally altered relationships between farmers and other participants along vertically integrated value chains. Former adversaries in spot markets are now mutually dependent on one another in vertically integrated value chains, which potentially improves the flow of information between them. This means that future forecasting services can no longer focus exclusively on farmgate prices, but need to inform the creation of value along vertically integrated value chains.

Public sector forecasting services also need to evolve a complementary focus on foresighting, using scenarios built through consultation that draws on diverse types of expertise and multiple perspectives. Scenario analysis that anticipates the future vulnerability of agricultural industries would support proactive policy design, particularly for significant and uncertain step-wise change that is difficult for markets and industries to assess and adapt to. It would also support the investigation of significant new investments and forays into unfamiliar markets by increasingly sophisticated forecast users.

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ABS 2018a, 6227.0 Education and Work, Australia, Australian Bureau of Statistics, Canberra.

ABS 2018b, Australian National Accounts: National Income, Expenditure and Product, cat. no. 5206.0, Australian Bureau of Statistics, Canberra.

ACCC 2017a, Cattle and beef market study - Final report, Australian Competition and Consumer Commission, Canberra.

ACCC 2017b, Dairy inquiry interim report, Australian Competition and Consumer Commission, Canberra.

Allen, P 1994, ‘Economic forecasting in agriculture’, International Journal of Forecasting, vol. 10, no. 1, pp. 81–135.

Anderson, K 2014, ‘The intersection of trade policy, price volatility, and food security’, Annual Review of Resource Economics, vol. 6, no. 1, pp. 513–532.

Arnstein, S 1969, A ladder of citizen participation, Journal of the American Institute of planners, vol. 35, no. 4, pp. 216–224.

Baldwin, R 2016, The Great Convergence, Harvard University Press, Cambridge, Massachusetts.

Bozeman, B & Sarewitz, D 2011, ‘Public value mapping and science policy evaluation’, Minerva, vol. 49, no. 1, pp. 1–23.

Brorsen, B & Irwin, S 1994, ‘Research on Price Forecasting and Marketing Strategies: Improving Our Relevance’, paper presented at NCR-134 Conference: Applied Commodity Price Analysis, Forecasting, and Market Risk Management, Chicago, Illinois, pp. 1–14.

Bunn, D & Wright, G 1991, ‘Interaction of judgemental and statistical forecasting methods: issues & analysis’, Management science, vol. 37, no. 5, pp. 501–518.

Bureau of Agricultural Economics 1950, The functions of the Bureau of Agricultural Economics, Canberra.

Bureau of Agricultural Economics 1962, ‘Organisation and functions of the Bureau of Agricultural Economics’, Canberra.

Burgess, M 2014, ‘From ‘trust us’ to participatory governance: Deliberative publics and science policy’, Public understanding of science, vol. 23, no. 1, pp. 48–52.

Cash, D, Borck, J & Patt, A 2006, ‘Countering the loading-dock approach to linking science and decision making: comparative analysis of El Niño/Southern Oscillation (ENSO) forecasting systems’, Science, technology, & human values, vol. 31, no. 4, pp. 465–494.

CFARE 2013, From farm income to food consumption: valuing USDA data products, Council on Food, Agricultural and Resource Economics, Washington DC.

Chambers, R 1994, ‘The origins and practice of participatory rural appraisal’, World development, vol. 22, no. 7, pp. 953–969.

Chambers, R & Ghildyal, B 1985, ‘Agricultural research for resource-poor farmers: the farmer-first-and-last model’, Agricultural administration, vol. 20, no. 1, pp. 1–30.

CIE 2013, Contribution of live exports to the Australian Wool Industry, consulting report for Australian Wool Innovation, Centre for International Economics, Canberra.

Copland, D & Janes, C 1938, Australian marketing problems: a book of documents 1932-1937, Angus & Robertson, Sydney.

Crawford, J 1945, Paper by J.G. Crawford on establishment of the Bureau of Agricultural Economics, Cabinet Submission, Ministry of Post-War Reconstruction, Division of Agricultural Economics, Canberra.

Cuhls, K 2003, ‘From forecasting to foresight processes—new participative foresight activities in Germany’, Journal of Forecasting, vol. 22, no. 2–3, pp. 93–111.

Davidson, BR 1981, European farming in Australia: An economic history of Australian farming, Elsevier, New York.

den Hertog, P 2000, ‘Knowledge-intensive business services as co-producers of innovation’, International journal of innovation management, vol. 4, no. 4, pp. 491–528.

Dewbre, J, Shaw, I, Corra, G & Harris, D 1985, EMABA. Econometric Model of Australian Broadacre Agriculture, Bureau of Agricultural Economics, Canberra.

Dietz, T, Ostrom, E & Stern, PC 2003, ‘The struggle to govern the commons’, Science, vol. 302, no. 5652, pp. 1907–1912.

Dilling, L & Lemos, M 2011, ‘Creating usable science: Opportunities and constraints for climate knowledge use and their implications for science policy’, Global environmental change, vol. 21, no. 2, pp. 680–689.

DPI 1982, Annual Report 1981-82, Department of Primary Industries, Canberra.

Ebling, WH 1939, ‘Why the government entered the field of crop reporting and forecasting’, Journal of Farm Economics, vol. 21, no. 4, pp. 718–734.

Fairclough, I & Fairclough, N 2012, Political Discourse Analysis: A Method for Advanced Students, Taylor & Francis.

Freebairn, J 1975, ‘Forecasting for Australian agriculture’, Australian Journal of Agricultural and Resource Economics, vol. 19, no. 3, pp. 154–174.

Freebairn, J 1976a, ‘The value and distribution of the benefits of commodity price outlook information’, Economic Record, vol. 52, no. 2, pp. 199–212.

Freebairn, J 1976b, ‘Welfare implications of more accurate rational forecast prices’, Australian Journal of Agricultural Economics, vol. 20, no. 2, pp. 92-102.

Freebairn, J 1978, ‘An evaluation of outlook information for Australian agricultural commodities’, Review of Marketing and Agricultural Economics, vol. 46, no. 3, pp. 295–314.

Friedman, T 2005, The world is flat: A brief history of the globalized world in the 21st century, Allen Lane, London.

Gropp, L, Hallam, T & Manion, V 2000, Single-desk marketing: assessing the economic arguments, Staff Research Paper, Productivity Commission, Canberra.

Harper, I, Anderson, P, McCluskey, S, & O’Bryan, M 2015. Competition policy review - Final report, Australian Government, Canberra.

Hirshleifer, J 1988, Price theory and applications 4th edition, Prentice Hall, Englewood Cliffs.

IC 1991, Statutory marketing arrangements for primary products, Industry Commission, Canberra.

Isengildina-Massa, O, Karali, B & Irwin, S 2013, ‘When do the USDA forecasters make mistakes?’, Applied Economics, vol. 45, no. 36, pp. 5086–5103.

Jasanoff, S, Wynne, B, Buttel, F, Charvolin, F, Edwards, P, Elzinga, A, Haas, P, Kwa, C, Lambright, W & Lynch, M 1998, ‘Science and decision making’, in S Rayner & E Malone (eds), Human choice & climate change, Battelle Press, Columbus, pp. 1–87.

Just, D & Zilberman, D 2002, Information Systems in Agriculture, ARE Update 6:1, Giannini Foundation of Agricultural Economics, San Francisco.

Kahneman, D & Tversky, A 1984, ‘Choices, values, and frames’, American Psychologist, vol. 39, no. 4, pp. 341–350.

Kerin, J 2017, The way I saw it; the way it was: The making of national agricultural and natural resource management policy, Analysis & Policy Observatory, Melbourne.

Kilpatrick, S & Rosenblatt, T 1998, ‘Information vs training: issues in farmer learning’, The Journal of Agricultural Education and Extension, vol. 5, no. 1, pp. 39–51.

Kingma, O, Longmire, J & Stoeckel, A 1980, ‘A review of three research programs in quantitative modelling in the Bureau of Agricultural Economics’, Australian Journal of Agricultural Economics, vol. 24, no. 3, pp. 224–247.

Klein, K & Kerr, W 1995, ‘The globalization of agriculture: A view from the farm gate’, Canadian Journal of Agricultural Economics, vol. 43, no. 4, pp. 551–563.

Klerkx, L & Leeuwis, C 2008, ‘Matching demand and supply in the agricultural knowledge infrastructure: Experiences with innovation intermediaries’, Food policy, vol. 33, no. 3, pp. 260–276.

Koontz, S & Ward, C 2011, ‘Livestock mandatory price reporting: A literature review and synthesis of related market information research’, Journal of Agricultural & Food Industrial Organization, vol. 9, no. 1, pp. 1-31.

Lewis, J 1961, ‘Organized marketing of agricultural products in Australia’, Australian Journal of Agricultural and Resource Economics, vol. 5, no. 1, pp. 1–8.

Lewis, J 1967, ‘Agricultural price policies’, in D Williams (eds), Agriculture in the Australian economy, Sydney University Press, Sydney, pp. 299–314.

Llewellyn, R 2007, ‘Information quality and effectiveness for more rapid adoption decisions by farmers’, Field Crops Research, vol. 104, no. 1, pp. 148–156.

Lloyd, A 1982, ‘Agricultural Price Policy’, in D Williams (eds), Agriculture in the Australian economy, Sydney University Press, Sydney, pp. 353–382.

Longmire, J & Watts, G 1981 On evaluating forecasts and forecasting methods, BAE Working Paper 81–10, Bureau of Agricultural Economics, Canberra.

Luehrman, T 1998, ‘Strategy as a portfolio of real options’, Harvard Business Review, vol. 76, no. pp. 89-101.

Mann, C 2006, Accelerating the globalization of America: The role for information technology, Columbia University Press, Washington.

Marsh, S & Pannell, D 2000, ‘Agricultural extension policy in Australia: the good, the bad and the misguided’, Australian Journal of Agricultural and Resource Economics, vol. 44, no. 4, pp. 605–627.

Massy, C 2011, Breaking the Sheep's Back: The Shocking True Story of the Decline and Fall of the Australian Wool Industry, University of Queensland Press, Brisbane.

Miller, G & Harris, S 1972, ‘Price Formation, Price Projections and Commodity Marketing Research’, paper presented at 16th Annual Conference of the Australian Agricultural Economics Society, Sydney, New South Wales, Bureau of Agricultural Economics.

Nelson, R, Byron, R, & Stafford-Smith, M 2011. Adaptation as a public policy agenda, Adaptation Policy Forum, Discussion Paper 4, Department of Climate Change and Energy Efficiency, Canberra.

Pannell, D 2006, ‘Flat earth economics: the far-reaching consequences of flat payoff functions in economic decision making’, Review of Agricultural Economics, vol. 28, no. 4, pp. 553–566.

Parcell, J & Tonsor, G 2013, ‘Information and Market Institutions’, in W Armbruster & R Knutson (eds), US Programs Affecting Food and Agricultural Marketing, Springer, pp. 375–400.

Parish, R 1967, ‘Marketing agricultural products’, in D Williams (eds), Agriculture in the Australian economy, Sydney University Press, Sydney, pp. 278–298.

PC 2016, Regulation of Australian Agriculture, report no. 79, Productivity Commission, Canberra.

Picciotto, R & Anderson, J 1997, ‘Reconsidering agricultural extension’, The World Bank Research Observer, vol. 12, no. 2, pp. 249–259.

Robinson, J 1933, The economics of imperfect competition, Macmillan and Co., London.

Sapiro, A 1923, ‘Cooperative marketing’, Iowa Law Bulletin, vol. 8, no. 4, pp. 193–210.

Secretary of the Department of Commerce and Agriculture 1950, Notes of the Bureau of Agricultural Economics provided by the Secretary of the Department to the Prime Minister 11th March, 1950, Canberra.

Sulaiman, V, Hall, A, Kalaivani, N, Dorai, K & Reddy, T 2012, ‘Necessary, but not sufficient: critiquing the role of information and communication technology in putting knowledge into use’, The Journal of Agricultural Education and Extension, vol. 18, no. 4, pp. 331–346.

Taylor, H 1924, ‘Agricultural Forecasting’, American Journal of Agricultural Economics, vol. 6, no. 2, pp. 156–163.

Taylor, H 1939, ‘A century of agricultural statistics’, Journal of Farm Economics, vol. 21, no. 4, pp. 697–706.

Umali-Deininger, D 1997, ‘Public and private agricultural extension: partners or rivals?’, The World Bank Research Observer, vol. 12, no. 2, pp. 203–224.

Vinning, G 1980, Statutory agricultural marketing authorities of Australia: a compendium, Standing Committee on Agriculture, Canberra.

Watson, A & Parish, R 1982, ‘Marketing agricultural products’, in D Williams (eds), Agriculture in the Australian economy, Sydney University Press, Sydney, pp. 326–352.

Weimer, D & Vining, A 2015, Policy Analysis: Concepts and Practice, Taylor & Francis, London.

Willis, S & Tranter, B 2006, ‘Beyond the ‘digital divide’ – internet diffusion and inequality in Australia’, Journal of Sociology, vol. 42, no. 1, pp. 43–59.

Working, E 1930, ‘Evaluation of methods used in commodity price forecasting’, Journal of Farm Economics, vol. 12, no. 1, pp. 199–133.

Xie, R, Isengildina-Massa, O, Dwyer, G & Sharp, J 2016, ‘The impact of public and semi-public information on cotton futures market’, Applied Economics, vol. 48, no. 36, pp. 3416–3431.

Last reviewed:
22 Nov 2018