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Title: THE USE OF REMOTE SENSING TECHNIQUES TO DETERMINE THE IMPACT OF PREFERENTIAL FLOW ON CORN YIELDS

Author
item Dulaney, Wayne
item Daughtry, Craig
item Walthall, Charles
item Gish, Timothy

Submitted to: International Symposium on Preferential Flow
Publication Type: Proceedings
Publication Acceptance Date: 11/1/2000
Publication Date: 1/3/2001
Citation: N/A

Interpretive Summary: Although the influence of soil water dynamics and chemical behavior on crop yield variability is thought to be critical, they are poorly understood at the watershed scale of observation. Preferential flow has recently been identified as the major contributor to the unexpected and rapid transport of agricultural chemicals to groundwater. Its impact on crop growth, however, remains uncertain. Funnel flow, one aspect of preferential flow, can be defined as the movement of water along clay lenses that eventually converges to form small, discreet subsurface flow pathways. This study compared the spatial distribution of the corn grain yield with soil parameters, remotely sensed imagery, and subsurface flow pathways during two consecutive drought years within a geographic information system. Results demonstrated that yield spatial variability was poorly correlated with soil chemical and physical properties, but was highly correlated with color infrared imagery and the primary subsurface flow pathways derived from ground-penetrating radar data. This investigation concluded that the identification of field-scale, subsurface flow pathways can be predictive of high yielding areas during periods of severe drought, therefore, a knowledge of these flow pathways is essential to understanding and managing the spatial and temporal variability of crop yields.

Technical Abstract: Water availability is often the most important factor controlling crop growth in rain-fed agriculture. Although landscape position, soil characteristics, and subsurface stratigraphy are known to influence water availability, the spatial and temporal dynamics of soil moisture are still poorly understood. This study compared the spatial distribution of the corn grain yield with soil parameters, remotely sensed imagery, and subsurface flow pathways during two consecutive drought years within a geographic information system (GIS). Over 12 km of georeferenced, ground-penetrating radar (GPR) data were collected on adjacent, 4 ha watersheds in order to identify subsurface clay lenses which were thought to influence the spatial distribution of available soil moisture. Results demonstrated that the spatial distribution of corn grain yield was poorly correlated with soil chemical and physical properties but was highly correlated with color infrared (CIR) imagery and the primary subsurface flow pathways derived from GPR data.