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The carbon emission prediction model of agroforestry ecosystem based on least squares regression method has no learning process for data samples, it is difficult to accurately describe the nonlinear relationship among them, and the prediction accuracy is poor. A carbon emission prediction model of agroforestry ecosystem based on support vector regression was designed. Seven carbon sources, including root decomposition, chemical fertilizer, pesticide, agricultural film, agricultural irrigation, agricultural machinery and farmland tillage, were selected as influencing factors of carbon emissions in agroforestry ecosystem. The SVR model for carbon emissions prediction in agroforestry ecosystem was constructed, and the regression function of sample data was solved by optimizing the training sample data based on the basic principle of support vector regression machine. On this basis, the data of influencing factors of agroforestry ecosystem were normalized and substituted into the model to predict the carbon emission results of agroforestry ecosystem system. Experiments show that the errors of the designed model in predicting the average carbon emissions of agroforestry ecosystems in the two experimental provinces from 2010 to 2017 are 5.9/10 t and 6.4/10 t, respectively, which are lower than those of other models. The average time of predicting the carbon emissions of agroforestry ecosystems in the two experimental provinces from 2006 to 2015 is about 4.15 s, which shows that the model can predict agriculture. The results of forest ecosystem carbon emissions have the advantages of high efficiency, high precision and comprehensiveness.

CARBON EMISSION PREDICTION MODEL OF AGROFORESTRY ECOSYSTEM BASED ON SUPPORT VECTOR REGRESSION MACHINE