Agriculture Reference
In-Depth Information
Figure 10.2
. Measured and simulated changes in soil carbon after 18-21 years of different
management systems at the KBS LTER Main Cropping System Experiment (MCSE) and at
the Interactions Experiment, an adjacent continuous corn tillage (conventional vs. no-till)
× nitrogen fertilizer (fertilized vs. not fertilized) experiment. Modified from Senthilkumar
et al. (2009).
Linking Crop Models with Digital Terrain Analysis for Assessing
Spatially Connected Processes
The assessment of soil water spatial patterns is crucial for understanding crop yield
variation across the landscape. Soil water within a field is highly variable in space
and time as a result of several processes that occur at different scales and because
of complex interactions among weather, topography, soil, and vegetation. The
effect of topographic convergence and divergence in natural landscapes has a major
impact on soil water balance (Moore and Grayson 1991). Without consideration
of the terrain characteristics, accurate simulation of soil water balance in entire,
nonuniform fields is not possible. Spatial variability of soil water content is often
the cause of yield variation over space and time. Accurate estimation of the spatial
variability of soil water is also important for other applications including soil ero-
sion, groundwater flow models, and precision agriculture.
The dynamics of soil water balance and crop growth have been extensively mod-
eled to assess the risk associated with uncertainty in water availability (Jones et al.
1993). Soil-plant-atmosphere models often simulate vertical drainage but not lat-
eral movement and water routing across the landscape (Basso 2000).
Existing digital terrain models are able to partition the landscape into a series of
interconnected elements to spatially route water flow (Moore et al. 1993, Vertessy
et al. 1993). Most digital terrain models fill the depressions in landscapes to provide
