Empirical Modeling of Relationships Between Sorghum Yield and Soil Properties

Empirical Modeling of Relationships Between Sorghum Yield and Soil Properties A crucial part of any site-specific management is the identification of causes of yield variability and assessment of crop requirements. Therefore, relationships between yield and soil properties must be identified. In this study, relationships between sorghum yield and soil properties on a verbosols within a field located in Moree, in northern NSW, Australia, were examined. Measured soil properties included pH; available phosphorus; percent clay, silt and sand; gravimetric moisture content of air-dry soil and at matric potentials corresponding to −1 500 kPa and −33 kPa; percent organic carbon; CEC and exchangeable calcium, magnesium, sodium and potassium and copper, zinc, manganese and iron contents. The exchangeable sodium percentage (ESP) and the Ca/Mg ratio were calculated. We used a number of empirical methods and found that neural networks, projection pursuit regression, generalized additive models and regression trees are good techniques for modeling yield response. However, further comparison of these techniques is needed. By modeling yield response to individual soil properties and using kriging to map yields predicted from these models, it was possible to identify which soil properties limited production in different areas of the field. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Empirical Modeling of Relationships Between Sorghum Yield and Soil Properties

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 1999 by Kluwer Academic Publishers
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1023/A:1009968907612
Publisher site
See Article on Publisher Site

Abstract

A crucial part of any site-specific management is the identification of causes of yield variability and assessment of crop requirements. Therefore, relationships between yield and soil properties must be identified. In this study, relationships between sorghum yield and soil properties on a verbosols within a field located in Moree, in northern NSW, Australia, were examined. Measured soil properties included pH; available phosphorus; percent clay, silt and sand; gravimetric moisture content of air-dry soil and at matric potentials corresponding to −1 500 kPa and −33 kPa; percent organic carbon; CEC and exchangeable calcium, magnesium, sodium and potassium and copper, zinc, manganese and iron contents. The exchangeable sodium percentage (ESP) and the Ca/Mg ratio were calculated. We used a number of empirical methods and found that neural networks, projection pursuit regression, generalized additive models and regression trees are good techniques for modeling yield response. However, further comparison of these techniques is needed. By modeling yield response to individual soil properties and using kriging to map yields predicted from these models, it was possible to identify which soil properties limited production in different areas of the field.

Journal

Precision AgricultureSpringer Journals

Published: Oct 6, 2004

References

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