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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #186845
Title: DETERMINING SPATIAL SOIL PROPERTIES BY OBJECTIVE PARAMETERIZATION OF THE CERES-MAIZE MODEL

Author
item IRMAK, AYSE - U OF NE
item Sadler, Edward
item JONES, JAMES - U OF FL

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/9/2005
Publication Date: 9/27/2005
Citation: Irmak, A., Sadler, E.J., Jones, J.W. 2005. Determining spatial soil properties by objective parameterization of the Ceres-Maize Model [abstract] [CDROM]. ASA-CSSA-SSSA Annual Meeting Abstracts.

Interpretive Summary:

Technical Abstract: Production fields contain complex arrangements of soil and landscape, creating patterns in fields, as they relate to spatial yield variability. However, there is a lack of information about explaining the spatial variability of crop yields across fields and year-to-year consistency of this variability. Our objective was to determine the utility of the inverse modeling approach for describing within field variations in corn yield in a research field in South Carolina in which there were variations in soil, weather, and management over time and space. The CERES-Maize model, when calibrated, helped to determine the magnitude and spatial distribution of yield for most of the grid cells across the field. Results indicated that defining important soil properties is crucial for using crop models for analyzing spatial yield variability. Water stress was one of the leading causes of spatial yield variability within our field. We also found that the inverse modeling approach to estimating soil water holding properties is more difficult for corn than with soybean because of the uncertainties associated with initial soil Nitrogen, its leaching and uptake by the crop. Thus, it was much more difficult to produce reliable results for a research field in which there are a number of treatments in which management varies. Findings from this study provided guidance for improving the use of inverse modeling for diagnosing reasons for spatial variability and for improving the crop model responses to soil, weather, and nutrient, and water management factors.