Variable rate N management strategies are often based on information about soil texture or from canopy sensors, mounted on ground-based vehicles or satellites. However, disentangling the effect of each information type on N management strategy with experimental studies is often difficult, as
results are only valid for the specific experimental conditions as well as the weather conditions for
specific years. An alternative for this is to use deterministic crop growth models. This study examines
whether ‘static’ soil profile information or ‘dynamic’ canopy sensor type information provide the
best basis for decision making concerning N-application at the subfield level. The DAISY model was
used for simulating crop growth on six soil profiles found in a heterogeneous loamy sand field and a
five-year crop rotation. A range of management descriptions and simulations were made using
5x500 years of synthetic weather data with each crop in the rotation set at the first year of the five
parallel simulations. Simulated growth was used as a proxy for a ‘dynamic’ canopy sensor based
information system. The net revenue was then calculated for a range of price relations between
fertilizer (model input) and wheat yield (model output), including wheat price adjustments according
to protein content. Based on regressions and backward induction analysis, the N application that
maximizes the expected net revenue were estimated for four information cases; Case 1) Uniform
application, assuming no prior information, Case 2) application based only on soil type , Case 3)
application based on canopy sensor information only and Case 4) application based on combined soil
and canopy sensor information. Findings from this study indicate that decisions with soil information
alone provide an annual net revenue (without considering cost of collecting information and variable
rate technology) of variable rate application (VRA) between 3.88 and 13.30 € ha-1 across price and
soil variation. This net revenue is approximately doubled with applications based on only canopy
sensor information and again this is approximately doubled with applications based on both soil and
canopy sensor information. The results may guide developers to decide on what type of information
should be included in their decision support systems.