| Summary: | Dissertation (Ph.D.) University of Alaska Fairbanks, 2011 The objective of this study was to identify a rapid laboratory technique to predict potentially mineralizable organic N for subarctic Alaska soils. Soil samples were taken from major agricultural area of subarctic Alaska. Laboratory incubation followed by kinetic model fit was first used to select a best model to estimate potential soil N mineralization. By correlating the model estimated organic N pool sizes and different chemical extracted organic N, I then found the best chemical method to estimate soil potentially mineralizable N. Spectroscopic properties of water extractable organic matter were also determined and correlated with model estimated organic N pool sizes in order to improve the estimation of soil mineralizable N pool. Finally, the best chemical method and spectroscopic property were used in the selected best kinetic model for the prediction of soil N mineralization in field incubation. Model comparisons showed that models with fixed rate constants were better than that the ones with rate constants estimated from simulation. Among models with fixed rate constants, fixed double exponential model was best. This model differentiated active mineralizable organic N pool with a fixed rate constant of 0.693 week-1 and slow mineralizable organic N pool with a fixed rate constant of 0.051 week-1. By correlating model estimated organic N pool size and chemical extracted organic N amount, I found that the potentially mineralizable organic N size was closely correlated with hot (80 �C) water extractable organic N or 1 M NaOH hydrolysable organic N. By correlating model estimated organic N size and spectroscopic characteristics of water extractable organic matter, I found that the active mineralizable organic N pool was correlated with humification index in cold (22 �C) water extraction (R 2=0.89, p<0.05), which indicates that characterizing extracted organic matter was a useful tool to improve the estimation of soil organic N pools. In summary, ...
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