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Analysis of Spatial Soil, Crop and Yield Data in a Winter Wheat Field
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Paper number 031080, 2003 ASAE Annual Meeting . (doi: 10.13031/2013.13725) @2003
Authors: Els Vrindts, Mieke Reyniers, Paul Darius, Marc Frankinet, Bernard Hanquet, Marie-France Destain and Josse De Baerdemaeker
Keywords: spatial variability, precision agriculture, clusters, correlation, soil physical properties, yield map, wheat, field variability, crop map, soil variability
In 2001 and 2002, soil and crop parameters were measured on a winter wheat field in
Sauveniere, as part of a precision farming research project. One of the objectives was to study the
processing of precision farming data for correct use in precision management. Different methods to
study the relation between soil and crop were tested: correlation analysis, principal component
analysis of soil parameters and clustering of soil and yield parameters.
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Crop and soil data were interpolated to a 6m grid and a 10 m grid to check the effect of grid size on
the correlations between field data. Correlations were very similar for the 2 datasets, with slightly
higher values for the 10 grid data (difference of 0 to 0.02 in correlation values). Grain and straw yield
in 2001 were correlated to soluble phosphate, texture parameters, soil electrical conductivity, and
potassium (coefficient of determination R values up to 0.30 for grain yield). Crop optical
measurements in May 2001 had lower correlation to soil parameters than yield (coefficient of
determination R= 0 to 0.18). Correlations were higher in March 2002, with coefficient of
determination values up to 0.66 for optical mesurements. Correlation of grain yield to soil was very
low in 2002, in part because of the incidence of eye spot disease.
Principal component analysis of soil parameters resulted in three principal components describing
the overall soil texture variation over the field, soil organic matter and soil nitrogen in early spring and
acidity and phosphate variability. Correlations between yield and crop measurements and soil
principal components were as expected from the correlation analysis.
Clustering soil parameters resulted in soil zones that did not coincide with crop variability. Clustering
yield and soil electrical conductivity did lead to zones that could be used to set up management
zones. The average soil properties of these zones could be used to find parameters linked to yield
variability and as a start to determine causes of variability in crop growth and yield, using a broader
knowledgde on the soil-plant interaction.
The grid size influenced the results of the analysis and should be further investigated, to determine
the best methods for processing precision farming data. The soil data collected from the top 30 cm
layer and the soil electrical conductivity only explain a limited part of variability in crop growth and
yield. This means that either the top 30 cm was not representative of growth conditions or that other,
non-measured soil parameters were important for crop growth. Root depth, water availability and soil
compaction were probably important for the crop growth in 2001.