Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: July 19, 2000
Publication Date: N/A
Crop yield and soil properties are strongly related to landscape position. As a result, terrain information should be useful for interpreting yield maps and identifying recurring spatial patterns in agricultural fields. The objectives of our study were to examine the relationship between six years of corn (Zea mays L.) yield data and basic terrain attributes and to develop a regression model to explain spatial variability of corn yield based on elevation, slope, curvature for a 16 ha field in central Iowa. Corn grain yield was measured in six crop years along eight transects using a combine equipped with a weigh tank. Soil surface elevation was measured at thousands of points using a kinematic global positioning system and elevation, slope, and curvature were then determined using digital terrain analysis. Our data showed that in the four years with less than normal growing season precipitation there was a negative correlation between corn yield and higher and more sloping field positions would have less water infiltration, less water elevation, slope, and curvature. We hypothesized that in years of less than normal precipitation, higher and more sloping field positions would have less water infiltration, less water storage, and lower yields. In the two years with greater than normal precipitation, yield was positively correlated with yield. In those years, yield was reduced in low and level field positions because of periods of standing water and high water tables. A multiple linear regression model based on elevation, slope, and curvature was developed that explained 78% of the spatial variability of corn yield in the four dry years.