Integrating Spatial Data Collection, Modeling, and Analysis for Precision Agriculture

INTEGRATING SPATIAL DATA COLLECTION, MODELING
AND ANALYSIS FOR PRECISION AGRICULTURE*

K.A. Sudduth	C.W. Fraisse	S.T. Drummond	N.R. Kitchen
USDA-Agricultural Research Service Cropping Systems & Water Quality Research		Bio & Ag Engineering Univ. of Missouri	USDA-ARS CS&WQR
Columbia, Missouri, USA

ABSTRACT

Data collected on a 36-ha field in central Missouri were used to investigate methods for relating spatial grain yields to differences in those factors that can affect yields. Nonlinear, non-parametric data analysis methods, including projection pursuit regression and neural network analysis, were able to model yields as a function of soil and topographic data. However, results obtained with these methods were not able to predict future years’ yields and optimum management strategies, due to the uncertainties associated with year-to-year variations in climatic conditions. In order to more robustly evaluate yield-limiting factors across a range of climatic conditions, the CROPGRO-Soybean model was used. The crop growth model was somewhat successful in representing spatial yield patterns, but was deficient in not being able to account for yield variations due to excess water "run-on" from upland areas of the field. In an attempt to overcome this limitation, methods were developed to account for water redistribution based on soil and topographic characteristics.

* Presented at the First International Conference on Geospatial Information in Agriculture and Forestry,
Lake Buena Vista, Florida, 1-3 June 1998.

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