An incorporative statistic and neural approach for crop yield modelling and forecasting

Abstract

An incorporative framework is proposed in this study for crop yield modelling and forecasting. It is a complementary approach to traditional time series analysis on modelling and forecasting by treating crop yield and associated factors as a non-temporal collection. Statistics are used to identify the highly related factor(s) among many associates to crop yield and then play a key role in data cleaning and a supporting role in data expansion, if necessary, for neural network training and testing. Wheat yield and associated plantation area, rainfall and temperature in Queensland of Australia over 100 years are used to test this incorporative approach. The results show that well-trained multilayer perceptron models can simulate the wheat production through given plantation areas with a mean absolute error (MAE) of ~2%, whereas the third-order polynomial correlation returns an MAE of ~20%. However, statistical analysis plays a key role in identifying the most related factor, detecting outliers, determining the general trend of wheat yield with respect to plantation area and supporting data expansion for neural network training and testing. The combination of these two methods provides both meaningful qualitative and accurate quantitative data analysis and forecasting. This incorporative approach can also be useful in data modelling and forecasting in other applications due to its generic nature.