Strategy of statistical model selection for precision farming on-farm experiments

Strategy of statistical model selection for precision farming on-farm experiments Nitrogen (N) fertilization implies two important issues: N enhances grain yields and quality, but applied in excess, nitrous oxide emissions and nitrate leaching may be induced. To reduce environmental impacts, spatial N variability in agricultural fields can be adapted using crop sensors. In on-farm experiments, sensor-based variable rate N application is compared to uniform N application, which is common agricultural practice. On-farm experiments (OFE) provide special considerations as opposed to on-station trials. In OFE, the experimental units in farmer-managed fields are considerably larger, which raises the question if soil heterogeneity may be fully controlled by the experimental design (random treatment allocation and blocking). Grain yield monitoring systems are used increasingly in OFE and provide spatially correlated data. As a consequence, classical analysis of variance is not a valid option. An alternative four-step strategy of statistical model selection is presented, generalizing the assumptions of classical analysis of variance within the framework of linear mixed models. Soil heterogeneity is preliminary identified in step 1 and finalized in step 2 using covariate combinations (analysis of covariance). Yield data correlations are handled in step 3 using geo-statistical models. The last step estimates treatment effects and derives the statistical inference. Analyses of three OFE revealed that different covariate combinations and geo-statistical models were needed for each trial, which involves higher analytical efforts than for on-station trials. These efforts can be minimized by following the steps provided in this study to find a best model approximation. Nevertheless, model selection in precision farming OFE will always accompany some uncertainty. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Strategy of statistical model selection for precision farming on-farm experiments

Loading next page...
 
/lp/springer_journal/strategy-of-statistical-model-selection-for-precision-farming-on-farm-Gyg3iqWkaJ
Publisher
Springer Journals
Copyright
Copyright © 2013 by Springer Science+Business Media New York
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.
10.1007/s11119-013-9306-9
Publisher site
See Article on Publisher Site

Abstract

Nitrogen (N) fertilization implies two important issues: N enhances grain yields and quality, but applied in excess, nitrous oxide emissions and nitrate leaching may be induced. To reduce environmental impacts, spatial N variability in agricultural fields can be adapted using crop sensors. In on-farm experiments, sensor-based variable rate N application is compared to uniform N application, which is common agricultural practice. On-farm experiments (OFE) provide special considerations as opposed to on-station trials. In OFE, the experimental units in farmer-managed fields are considerably larger, which raises the question if soil heterogeneity may be fully controlled by the experimental design (random treatment allocation and blocking). Grain yield monitoring systems are used increasingly in OFE and provide spatially correlated data. As a consequence, classical analysis of variance is not a valid option. An alternative four-step strategy of statistical model selection is presented, generalizing the assumptions of classical analysis of variance within the framework of linear mixed models. Soil heterogeneity is preliminary identified in step 1 and finalized in step 2 using covariate combinations (analysis of covariance). Yield data correlations are handled in step 3 using geo-statistical models. The last step estimates treatment effects and derives the statistical inference. Analyses of three OFE revealed that different covariate combinations and geo-statistical models were needed for each trial, which involves higher analytical efforts than for on-station trials. These efforts can be minimized by following the steps provided in this study to find a best model approximation. Nevertheless, model selection in precision farming OFE will always accompany some uncertainty.

Journal

Precision AgricultureSpringer Journals

Published: Feb 9, 2013

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