Uncovering Price Puzzle in the Wheat Economy of Pakistan: An Application of Artificial Neural Networks

Abstract

Wheat is at the epicenter of global food security. Extreme wheat price volatility can contribute to broader social risks in terms of food security, human development and have a significant influence on farmers' incomes in the coming years especially in developing countries like Pakistan. Wheat is not only the major staple crop of the country's food security, but it also contributes about 10.3% in agriculture which accounts for 2.2% of domestic GDP. However, the presumable intensification in climate change and macroeconomic instability is reputed as a threat to wheat price stability nationwide. Against this backdrop, this research develops a precise wheat price puzzle forecasting model using the Long- Short Term Memory Recurrent Neural Networks (LSTM-RNN) - an application of Artificial Intelligence. LSTM-RNN are proficient in handling non-linear complex systems owing to their special LSTM nodes. An assessment of the planned framework with a handful of prevailing models is also discussed. Results showed that LSTM-RNN outperformed in terms of accuracy and uncovered that wheat prices will progressively swell and shrink by 2030, which will pose menaces to the whole economy. Moreover, our proposed methodology may be used as a guiding principle for other crops as well, to fortify sustainable agriculture development by 2030.