Submitted to: European Conference on Precision Agriculture Proceedings
Publication Type: Proceedings
Publication Acceptance Date: May 16, 2006
Publication Date: May 16, 2007
Citation: Sadler, E.J., Jones, J.W., Sudduth, K.A. Modeling for precision agriculture-How good is good enough, and how can we tell? In: Stafford, J.V., ed. Precision Agriculture '07 - Proceedings 6th European Conference on Precision Agriculture, June 4-6, 2007. Skiathos, Greece. 2007. p. 241-248. Technical Abstract: During the development of precision agriculture technology, prior existence of crop simulation models prompted their application to modeling spatial variation in yield. On the face of it, extending a fairly mature 1-D model of crop growth and yield appeared to be a matter of developing spatial suites of input parameters and running a model for each set. For many models, extensive literature had already reported independent tests in multiple combinations of variety, soils, and climate, which was generally considered substantial validation of the performance of the models. However, most prior literature tests had as objectives the evaluation of model performance in simulating mean yields across multiple plots in yield trials, which represented the majority of yield data before yield monitors. Precision agriculture requires not just simulation of the mean, but also a simulation of spatial variation. No real consensus has emerged regarding exactly how to test model performance, nor of what performance constitutes success. In some cases, success simulating inter-annual variability has been asserted as proof of simulating spatial variability. Further, common measures of goodness of fit suffer from dependence on the range of variation in the independent variable. When multiple sources of variation, for example inter-annual and spatial, are combined in a test, commonly used performance measures may fail to support the hypotheses represented in a paper’s objectives. We outline several issues relevant to the topic, specifically 1) fundamental differences between simulating means and simulating variation, and how these results can be evaluated, 2) the need to link performance measures to stated objectives, 3) an example of performance, isolating sources of variation and model performance toward simulating each source, and 4) a discussion of potentially preferable performance measures. By synthesis and example, we provide guidelines and structure for future precision agriculture modeling efforts.