Strategy of statistical model selection for precision
farming on-farm experiments
Published online: 9 February 2013
Ó Springer Science+Business Media New York 2013
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.
le (&) Á D. Ehlert
Department Engineering for Crop Production, Leibniz Institute for Agricultural Engineering,
Max-Eyth-Allee 100, 14469 Potsdam, Germany
Department Biometry and Experimental Design, Faculty of Agriculture and Horticulture,
Humboldt-University of Berlin, Invalidenstr. 42, 10115 Berlin, Germany
Precision Agric (2013) 14:434–449