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Integrating seasonal climate forecasts into farm management decisions: the case of Gymbowen farmers in north western Victoria

De-Anne Price1, Muhuddin Anwar1, De Li Liu2, Chris Souness1 and Garry O’Leary1

1 Primary Industries Research Victoria - Horsham, 110 Natimuk Rd, Horsham, VIC 3400, Australia deanne.price@dpi.vic.gov.au, muhuddin.anwar@dpi.vic.gov.au
2
Department of Primary Industries of New South Wales, Wagga Wagga, NSW 2650, Australia

Abstract

Rainfall variability represents one of the biggest challenges facing the management of rainfed farming across Victoria. During the past few decades climatologists have improved, and continue to improve their ability to predict the seasonal weather. Seasonal climate forecasts that are probabilistic predictions of how much rain is expected during the season, based on the principle that the ocean controls atmospheric behaviour, predict some of this climate variability. Farmers can potentially use forecasts to plan farm management strategies and allocate resources in anticipation of likely outcomes. With improved climate forecasts farmers have the ability to make informed on-farm management decisions. With improved climate knowledge farmers can better match cropping inputs to seasonal requirements. In this paper, results from a case study of a Gymbowen farming community in Victoria will be used to highlight some of the challenges faced by both farmers and forecasters in the effective application of seasonal climate forecasts. We will also discuss, farmers’ ability to bear risk, their understanding of probabilistic forecasts, and benefits of forecasts. Further, we will show how the El Nio Southern Oscillation (ENSO) phase system, when combined with cropping systems models, can be used operationally in quantification of farmers management options in terms of economic and environmental consequences in relation to seasonal conditions.

Three key learnings: (1) There are significant ENSO signals on the climate and grain yields in Gymbowen region of Victoria. (2) Using phases of Southern Oscillation Index allows a probabilistic forecast of future rainfall can be useful for farmers’ decision making. (3) This work explores how a small faming community is benefiting from seasonal climate forecasts and how researchers are tailoring research findings to their needs.

Key Words

Seasonal climate forecast, crop models, forecast quality, predictive accuracy.

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