NO emission from soils: a neural network approach

January 2006.Soils are considered as an important source for NO emissions, but the uncertainty in quantifying these emissionsworldwide remains large due to the lack of field experiments and high variability in time and space of environmentalparameters influencingNOemissions. In this study, the development of a relationship forNOflux emission from soil withpertinent environmental parameters is proposed. An Artificial Neural Network (ANN) is used to find the best non-linearregression between NO fluxes and seven environmental variables, introduced step by step: soil surface temperature,surface water filled pore space, soil temperature at depth (20–30 cm), fertilisation rate, sand percentage in the soil, pHand wind speed. The network performance is evaluated each time a new variable is introduced in the network, i.e. eachvariable is justified and evaluated in improving the network performance. A resulting equation linking NO flux fromsoil and the seven variables is proposed, and shows to perform well with measurements (R2 = 0.71), whereas otherregression models give a poor correlation coefficient between calculation and measurements (R2 ≤ 0.12 for knownalgorithms used at regional or global scales). ANN algorithm is shown to be a good alternative between biogeochemicaland large-scale models, for future application at regional scale