2.4. Statistical analysis
Classification and regression tree (CART) analysis is a machine learning
algorithm, which is able to identify relatively important variables.
CART is a non-parametric model based on graphics, which is easier to
understand than traditional statistical techniques. Use CART to judge
what the environment factors (air temperature (Ta), soil temperature
(Ts), aggregated growing season degree day (GDD), photosynthetic photon
flux density (PPFD), precipitation (PPT), air relative humidity (RH) and
vapor pressure deficit (VPD)) played a major control role on variations
of CO2 fluxes. The maximum number of split was 5, and
the minimum Proportional Reduction in Error (PRE) was 0.01. The growing
season is from May to October, and the other months belong to the
non-growing season (Zhang et al. 2008). Linear regression and
correlation analysis were used to explore the inter-annual variation
characteristics of CO2 fluxes and its response mechanism
to environmental factors. We used SYSTAT 13.0 (Systatsoftware Inc, USA)
for the CART and linear regression analyses.