Safa, MajeedRenwick, ARauf, S2021-06-282021-05-112021-05https://hdl.handle.net/10182/13954The link between energy prices and agricultural products has attracted considerable academic attention (Harri et al, 2009; AFBI, 2012, Defra, 2009, Silvennoinen and Thorp, 2015) with some studies showing a link between energy value and agricultural products. Of particular, interest to New Zealand is that monitoring of the relationship between milk price and global oil price suggests a correlation between the two. There are a number of possible reasons why the two price series are correlated. For example, most farm inputs are based directly or indirectly on energy and any changes in oil prices can change the price of farm products. Of course, there is a range of other supply and demand factors that can influence milk prices as well. Based on a review of the current literature, the plan is to pull together a comprehensive dataset comprising the key variables that are used within the study. These include global milk prices, global oil prices, exchange rates, economic growth indicators, supply and demand data, and other possible important factors. Also, all data fluctuations and extreme values should be investigated carefully. This study proposes to develop an Artificial Neural Network (ANN) model to estimate milk prices based on global oil value and other important factors. ANNs have developed into a powerful approach that can approximate any nonlinear input-output mapping function to any degree of accuracy in an iterative manner. This is compared with more tradition econometric modeling techniques to assess whether it outperforms these techniques in terms of prediction1 pagesenmilkmodeloilConference Contribution - published