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

Loading next page...
 
/lp/springer_journal/interpreting-within-field-relationships-between-crop-yields-and-soil-w0THqMIUuK
Publisher
Springer Journals
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

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off