
%0 Journal Article
%A Meinke, Holger 
%A Hammer, Graeme L. 
%T Forecasting regional crop production using SOI phases: an example for the Australian peanut industry
%J Australian Journal of Agricultural Research
%D 1997/08/07
%V 48
%N 6
%P 789-794
%K crop model, simulation, climate forecast.
%R https://doi.org/10.1071/A96155
%U https://www.publish.csiro.au/paper/A96155 
%X Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing o/Southern bodies the industtry could profitable to adjust their operations stategically. Significantly , physically based lag-relationships exist between an index of ocean/atmospher EI Ni&ntilde;o/southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions. 
