Importance and inconsistencies of the influence of soil properties on nitrogen mineralization: a systematic review

Climate and soil properties profoundly impact N mineralization (Nmin). Hence, there is a critical need to identify how physical-chemical-biological factors involved in organic matter decomposition influence globally reported predictive models. This paper reflects research focused on those factors considered relevant and used during the construction of Nmin models. The literature data found on factors affecting Nmin or N availability in soils published since 1990 was downloaded to a database in Access. Using different bivariate and multivariate statistical techniques, we compiled results of 785 statistical analyses presented by authors of 90 research articles that related Nmin and environmental factors, management strategies, and soil biological and physicochemical attributes. For organization purposes, we decided to group results according to the similarity of properties related to mineralization into environmental factors (18.6%), ecosystem/vegetation (14.52%), management (7.64%), soil physicochemical properties (34.65%), organic matter (16.05%), and microbiota (6.37%). The measurements of the response variables were 16.2% using N content in soil (as ammonium, nitrates, Organic N and Total N), and 83.88% represent N in the process of mineralization, including potentially mineralized N. As Nmin is the dependent variable, the results included 109 independent variables, of which 47.7% presented seemingly inconsistent results, which means different effects in Nmin. The difference in results was found to be related mostly to a difference in ecosystems or variable interactions. We conclude that acquiring a general prediction model for Nmin or constructing a specific equation for local conditions poses a limitation to optimizing N management for crop production. A more useful strategy is to generate a prediction model for Nmin, including significative soil and weather conditions, within a region and ecosystem; thus, the information can support soil and crop management decisions.