The application of crop simulation models in precision agriculture research appears to require only the specification of some input parameters and then running the model for each desired location in a field. Reports in the extensive literature on modeling have described independent tests for different cultivars, soil types and weather, and these have been presumed to validate the model performance in general. However, most of these tests have evaluated model performance for simulating mean yields for multiple plots in yield trials or in other large-area studies. Precision agriculture requires models to simulate not only the mean, but also the spatial variation in yield. No consensus has emerged about how to test model performance rigorously, or what level of performance is sufficient. In addition, many measures of goodness of fit between the observed and simulated data (i.e., model performance) depend on the range of variation in the observed data. If, for example, inter-annual and spatial sources of variation are combined in a test, poor performance in one might be masked by good performance in the other. Our objectives are to: (1) examine research aims that are common in precision agriculture, (2) discuss expectations of model performance, and (3) compare several traditional and some alternative measures of model performance. These measures of performance are explained with examples that illustrate their limitations and strengths. The risk of relying on a test that combines spatial and temporal data was shown with data where the overall fit was good (r 2 > 0.8), but the fit within any year was zero. Information gained using these methods can both guide and help to build confidence in future modeling efforts in precision agriculture.
Precision Agriculture – Springer Journals
Published: Nov 29, 2007
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
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
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.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera