A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks

Agricultural industry contributes to the economic backbone of many countries. Major crops like wheat, cotton, and rice stand out as fulfillment for basic commodities as well as profitable crops. Naturally, the consumption of major crops is increasing every year, influencing many countries to import the staple crops to meet the nutritional requirements of individuals, and thereby, keeping pressure on the economies for the years ahead. This research work addresses the development of an accurate consumption forecasting model for time series data. The proposed methodology uses 18 socio-economic and environmental factors and evaluates their impact on major crop consumption in Pakistan. Most influential factors are differentiated by the Linear Regression Model to forecast next year's upshot. The smart results of the model are beneficial for the farmers to cope with the decisive question of next pragmatic crop. The proposed model was compared with a variant of conventional approaches and verified the efficient performance in terms of forecast accuracy.