Predicting CO₂ emissions from farm inputs in wheat production using artificial neural networks and linear regression models - Case study in Canterbury, New Zealand

Abstract

Two models have been developed for simulating CO₂ emissions from wheat farms: (1) an artificial neural network (ANN) model; and (2) a multiple linear regression model (MLR). Data were collected from 40 wheat farms in the Canterbury region of New Zealand. Investigation of more than 140 various factors enabled the selection of eight factors to be employed as the independent variables for final the ANN model. The results showed the final ANN developed can forecast CO₂ emissions from wheat production areas under different conditions (proportion of wheat cultivated land on the farm, numbers of irrigation applications and numbers of cows), the condition of machinery (tractor power index (hp/ha) and age of fertilizer spreader) and N, P and insecticide inputs on the farms with an accuracy of ±11% (± 113 kg CO₂/ha). The total CO₂ emissions from farm inputs were estimated as 1032 kg CO₂/ha for wheat production. On average, fertilizer use of 52% and fuel use of around 20% have the highest CO₂ emissions for wheat cultivation. The results confirmed the ANN model forecast CO₂ emissions much better than MLR model.