Abstract
Current nitrogen (N) models tend to neglect the contribution of the microbial population to the plant available N pool, resulting in an underestimation of yield and possible over or underestimation of N runoff from natural and agricultural landscapes. We used the measurement of microbial activity coupled with the measurement of their food source, water extractable N and carbon (C), to provide initial N values and mineralization rates to modify N cycling in the Soil Water Assessment Tool (SWAT). Soil test data and spatial analysis of N mineralization values were used to: (1) quantify spatial variation of water extractable organic and inorganic N, soil inorganic N, and microbial activity; (2) develop a field scale model to determine N mineralization using updated soil testing methods for integration into the SWAT model; and (3) predict wheat yield. Simulation results indicate that yearly yield values and the variability of these yield values were consistently greater from the modified N model than from the SWAT model, as would be expected with the addition of N mineralization resulting from microbial activity. The spatial variability in yield results by sample increased with the modified N model as compared to the SWAT model. The yield data resulting from the modified N model simulation were sensitive to soil nutrient values as well as variation in elevation. Temporal and climatic variability is accounted for by including a precipitation trigger for N mineralization. The equations used to model the complex biogeochemical N cycling relationships are elegant in their simplicity, yet capture the spatial complexity associated with their processes. The modified N model may be useful to regulators to help with the simulation of new conservation practices that include the effect of lower fertilizer inputs on nutrient runoff and pollution.
Highlights
All of the major components of environmental modeling have spatial distributions and these distributions affect biogeochemical processes
The objective of this study was to: (1) Quantify spatial variation of water extractable organic and inorganic N, soil inorganic N and microbial activity using updated soil-testing methods; (2) develop a field scale model to determine N mineralization for integration into the Soil Water Assessment Tool (SWAT) model; (3) use Geographic Information System (GIS) to collect and analyze spatial and temporal inputs and outputs; and (4) predict wheat yield based on objectives 1, 2 and 3
The results indicate that the data were mostly normal, excepting water extractable organic carbon, which appeared more normal after a log transformation
Summary
All of the major components of environmental modeling have spatial distributions and these distributions affect biogeochemical processes. A Geographic Information System (GIS) is an important tool in describing the spatial characteristics of the environment, while environmental modeling simulates the environmental processes affected by the spatial distribution (Rao et al, 2000). The HUMUS system is conducted at the watershed scale using a Geographic Information System (GIS) to collect, manage, analyze and display the spatial and temporal inputs and outputs and relational databases for managing the non-spatial data (Arnold et al, 2010). Farm or small watershed scale, the Agricultural Policy/Environmental Extender Model (APEX) simulates N dynamics with varying land management strategies, such as different nutrient management practices, tillage operations and alternative cropping systems
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