TY - JOUR

T1 - A simulation of variable rate nitrogen application in winter wheat with soil and sensor information

T2 - An economic feasibility study

AU - Pedersen, Michael Friis

AU - Gyldengren, Jacob Glerup

AU - Pedersen, Søren Marcus

AU - Diamantopoulos, Efstathios

AU - Gislum, René

AU - Styczen, Merete Elisabeth

N1 - Funding Information: This research has been partially supported by Innovation Fund Denmark (InnovationsFonden), under the Future Cropping partnership, grant number 5107-00002B . We highly appreciate the many valuable inputs from the editor and reviewers. Publisher Copyright: © 2021 Elsevier Ltd

PY - 2021

Y1 - 2021

N2 - CONTEXT: Variable rate nitrogen (N) management strategies are often based on information about soil texture or information 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 to this is to use deterministic crop growth models to generate a wider range of weather x treatment combinations. OBJECTIVE: This study examines if ‘static’ soil profile information or ‘dynamic’ canopy sensor type information provide a better basis for decision making concerning N-application at the subfield level. METHODS: The DAISY model was used to simulate crop growth in a five-year crop rotation on six soil profiles found in a heterogeneous sandy loam field. A range of management descriptions and simulations were made using 5 × 500 years of synthetic weather data with each crop in a five-year rotation set at the first year of the five parallel simulations. Simulated growth variables were used as proxies for a ‘dynamic’ canopy sensor information system. The differential gross margin 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. From regressions and backward induction analysis, the N application that maximizes the expected grain revenue minus fertilizer expenditure was estimated for four information cases; Case 1) Uniform application, assuming no prior information, Case 2) application based on soil type information, Case 3) application based on canopy sensor information and Case 4) application based on combined soil and canopy sensor information. RESULTS AND CONCLUSIONS: Findings from this study indicated that decisions with soil information alone provide an annual differential gross margin of variable rate application (without considering cost of information and technology) between 3.88 and 13.30 € ha−1 across price and soil variation. This margin approximately doubled with applications based on canopy sensor information and further doubled again with applications based on both soil and canopy sensor information. SIGNIFICANCE: Thus, knowledge of the soil has the potential to improve interpretation of sensor signals for fertilization planning. The results may guide developers to decide on what type of information should be included in their decision support systems.

AB - CONTEXT: Variable rate nitrogen (N) management strategies are often based on information about soil texture or information 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 to this is to use deterministic crop growth models to generate a wider range of weather x treatment combinations. OBJECTIVE: This study examines if ‘static’ soil profile information or ‘dynamic’ canopy sensor type information provide a better basis for decision making concerning N-application at the subfield level. METHODS: The DAISY model was used to simulate crop growth in a five-year crop rotation on six soil profiles found in a heterogeneous sandy loam field. A range of management descriptions and simulations were made using 5 × 500 years of synthetic weather data with each crop in a five-year rotation set at the first year of the five parallel simulations. Simulated growth variables were used as proxies for a ‘dynamic’ canopy sensor information system. The differential gross margin 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. From regressions and backward induction analysis, the N application that maximizes the expected grain revenue minus fertilizer expenditure was estimated for four information cases; Case 1) Uniform application, assuming no prior information, Case 2) application based on soil type information, Case 3) application based on canopy sensor information and Case 4) application based on combined soil and canopy sensor information. RESULTS AND CONCLUSIONS: Findings from this study indicated that decisions with soil information alone provide an annual differential gross margin of variable rate application (without considering cost of information and technology) between 3.88 and 13.30 € ha−1 across price and soil variation. This margin approximately doubled with applications based on canopy sensor information and further doubled again with applications based on both soil and canopy sensor information. SIGNIFICANCE: Thus, knowledge of the soil has the potential to improve interpretation of sensor signals for fertilization planning. The results may guide developers to decide on what type of information should be included in their decision support systems.

KW - Canopy sensors

KW - Crop modelling

KW - Differential gross margin

KW - Precision agriculture

KW - Soil profiles

KW - Variable rate N application

U2 - 10.1016/j.agsy.2021.103147

DO - 10.1016/j.agsy.2021.103147

M3 - Journal article

AN - SCOPUS:85107600026

VL - 192

JO - Agricultural Systems

JF - Agricultural Systems

SN - 0308-521X

M1 - 103147

ER -