Interpreting Within-Field Relationships Between Crop Yields and Soil and Plant Variables Using Factor Analysis

Interpreting Within-Field Relationships Between Crop Yields and Soil and Plant Variables Using... Precision farming technologies allow for collection of large amounts of data from producers' fields. This study used grid-sampling techniques and factor analysis to investigate relationships between several site variables and corn (Zea mays L.) yields on five producer's fields. Sampling positions (112 to 258) were at the intersecting points of grid lines spaced 15 m. Variables measured were soil organic matter, pH, P, K, and NO3-N; residue cover; broadleaf and grass weed control; corn height at two dates, plant population, and grain yield. Correlation and multiple regression analyses showed that some variables were related to corn yields but the variables involved in significant relationships varied among fields. Moreover, the site variables often were highly correlated and the correlations varied among fields. In these conditions multiple regression would be an unreliable analysis tool. Study of covariance relationships among the variables using factor analysis showed that some of the variables measured could be grouped to indicate a number of underlying common factors influencing corn yields. These common factors were soil fertility, weed control, and conditions for early plant growth. Their importance in explaining the yield variability differed greatly among fields. Application of factor analysis to data generated by precision-farming technologies has potential for describing and understanding relationships between measured variables. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Interpreting Within-Field Relationships Between Crop Yields and Soil and Plant Variables Using Factor Analysis

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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:1009940700478
Publisher site
See Article on Publisher Site

Abstract

Precision farming technologies allow for collection of large amounts of data from producers' fields. This study used grid-sampling techniques and factor analysis to investigate relationships between several site variables and corn (Zea mays L.) yields on five producer's fields. Sampling positions (112 to 258) were at the intersecting points of grid lines spaced 15 m. Variables measured were soil organic matter, pH, P, K, and NO3-N; residue cover; broadleaf and grass weed control; corn height at two dates, plant population, and grain yield. Correlation and multiple regression analyses showed that some variables were related to corn yields but the variables involved in significant relationships varied among fields. Moreover, the site variables often were highly correlated and the correlations varied among fields. In these conditions multiple regression would be an unreliable analysis tool. Study of covariance relationships among the variables using factor analysis showed that some of the variables measured could be grouped to indicate a number of underlying common factors influencing corn yields. These common factors were soil fertility, weed control, and conditions for early plant growth. Their importance in explaining the yield variability differed greatly among fields. Application of factor analysis to data generated by precision-farming technologies has potential for describing and understanding relationships between measured variables.

Journal

Precision AgricultureSpringer Journals

Published: Oct 6, 2004

References

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