Predicting soil N mineralization: Relevance of organic matter fractions and soil properties

• Published in: Soil biology & biochemistry Vol. 43; no. 8; pp. 1714 - 1722
• Main Authors: Ros, Gerard H., Hanegraaf, Marjoleine C., Hoffland, Ellis, van Riemsdijk, Willem H.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.08.2011; Elsevier
• Subjects: agricultural soils; Agronomy. Soil science and plant productions; availability; Biochemistry and biology; Biological and medical sciences; carbon; chemical methods; Chemical, physicochemical, biochemical and biological properties; Extractable organic nitrogen; forest soils; Fundamental and applied biological sciences. Psychology; grassland soils; indexes; microbial biomass; Mineralizable nitrogen; mineralization; Multivariate analysis; nitrogen; nitrogen fertilizers; nitrogen mineralization; Organic matter; Physics, chemistry, biochemistry and biology of agricultural and forest soils; prediction; respiration; Soil organic matter; soil physical properties; Soil properties; Soil science; statistical models; temperature; uncertainty; water content; Soil organic matter; Mineralizable nitrogen; Extractable organic nitrogen; Multivariate analysis; Soil properties; Organic nitrogen; Organic matter; Extractability; Prediction; Property of soil; Soils; Nitrogen cycle; Mineralization; Soil science
• DOI: 10.1016/j.soilbio.2011.04.017
• ISSN: 0038-0717

Distinct extractable organic matter (EOM) fractions have been used to assess the capacity of soils to supply nitrogen (N). However, substantial uncertainty exists on their role in the N cycle and their functional dependency on soil properties. We therefore examined the variation in mineralizable N and its relationship with EOM fractions, soil physical and chemical properties across 98 agricultural soils with contrasting inherent properties and management histories. Mineralizable N was determined by aerobic incubation at 20 °C and optimum moisture content for 20 weeks. We used multivariate statistical modelling to account for multi-collinearity, an issue generally overlooked in studies evaluating the predictive value of EOM fractions. Mineralization of N was primarily related to the size of OM pools and fractions present; they explained 78% of the variation in mineralizable N whereas other soil variables could explain maximally 8%. Both total and extractable OM expressed the same soil characteristic from a mineralization perspective; they were positively related to mineralizable N and explained a similar percentage of the variation in mineralizable N. Inclusion of mineralizable N in fertilizer recommendation systems should be based on at least one OM variable. The most appropriate EOM fraction can only be identified when the underlying mechanisms are known; regression techniques are not suitable for this purpose. Combination of single EOM fractions is not likely to improve the prediction of mineralizable N due to high multi-collinearity. Inclusion of texture-related soil variables or variables reflecting soil organic matter quality may be neglected due to their limited power to improve the prediction of mineralizable N. ► Mineralization of soil organic N primarily depends on organic matter pools. ► Soil texture, OM quality and acidity have few power to predict N mineralization in soils. ► Current extractable organic matter (EOM) fractions express the same soil characteristic. ► Combining single EOM fractions does not improve the prediction of N mineralization. ► Knowledge of underlying mechanisms is required to select a functional EOM fraction.