Soil respiration is a biological process in microbes that convert organic carbon to atmospheric CO2. This processis considered to be one of the largest global carbon fluxes and is affected by different physicochemical andbiological properties of soil, land use, vegetation types and climate patterns. Soil respiration recently receivedmuch attention, and it could be measured in two states basal respiration (BR) and substrate induced respiration (SIR) which together gives a good representation of the general soil microbial activity. The aim of this study wasto estimate the BR and SIR of 150 data points obtained from soil samples collected from the surface to 20cm ofdepth under different land use categories using the Artificial Neural Network (ANN) and Linear RegressionMethodology (LRM). This study is bringing data from an arid area, and there is little information on this issue.Soil samples were chosen from three provinces of Iran, with humid subtropical and semi-arid climate patterns. Ineach soil sample a variety of characteristics were measured: soil texture, pH, electrical conductivity (EC), cal-cium carbonate equivalent (CCE), organic carbon (OC), OC fractionation data e.g. light fraction OC (LOC), heavyfraction OC (HOC), cold water extractable OC (COC) and warm water extractable OC (WOC), population offungi, bacteria, actinomycete, BR and SIR. Our goal was to use the most efficient ANN-model to predict soilrespiration with simple soil data and annual precipitation (AP) and mean annual temperature (MAT) andcompare it with LRM. Our results indicated that for an ANN model containing all the measured soil parameters(14 variables), the R2and RMSE values for BR prediction were 0.64 and 0.05 while these statistical indicators forSIR obtained 0.58 and 0.15, respectively; whereas the addition of AP and MAT data to this model (16 variables)caused a decrease in statistical indicators. When the R2and RMSE values of the BR-ANN and SIR-ANN predictedusing an ANN model with only
