Cropping Pattern Scenario based on Global Climate Indices and Rainfall in Banyumas District, Central Java, Indonesia

Although there has been a high relationship between global climate and rainfall in Indonesia, but little evidence is available for relationship among global climate indices, rainfall and crop yield to determine cropping pattern. This study evaluates the relationships between global climate indices, which are representative by Southern Oscillation Index (SOI) and Sea Surface Temperature (SST) Nino3.4, rainfall distribution pattern and crop yields in Banyumas district, Central Java, Indonesia. Monthly SOI was collected from the Bureau of Meteorology, Australia, then SST Nino3.4 was collected from National Oceanic and Atmospheric Administration (NOAA), and monthly rainfall data was collected from Agricultural Government of Banyumas district. Artificial Neural Network (ANN) and Empirical Orthogonal Function (EOF) are used in prediction of rainfall, statistical analyses is using to determine the correlation among global climate indices, rainfall pattern and crop yields. For cropping pattern recommendation in each sub district based on climatic water balance, which has significant correlation between SOI-SST and crop yields. The result shows in macro scale, mostly sub-district has significant correlations between global climate indices and crop yields. In regional scale, prediction of rainfall using ANN and EOF were more reliable in rainfall analysis than rainfall-SOI or rainfall SST Nino3.4 analyses. Based on climatic water balance, and recommendation in cropping pattern are 1) paddy-corn/soybean-paddy 2) paddy-corn/soybean-corn/soybean and 3) corn/soybean-corn/soybean-corn/soybean.