Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-25T05:22:10.364Z Has data issue: false hasContentIssue false
  • English
  • Français

A tool based on remotely sensed LAI, yield maps and a crop model to recommend variable rate nitrogen fertilization for wheat

Published online by Cambridge University Press:  01 June 2017

F. Bourdin ,
F.J. Morell ,
D. Combemale ,
P. Clastre ,
M. Guérif  and
A. Chanzy
F. Bourdin*
Affiliation:
UMR 1114 Emmah INRA-UAPV, 84914 Avignon, cedex 9 - France
F.J. Morell
Affiliation:
UMR 1114 Emmah INRA-UAPV, 84914 Avignon, cedex 9 - France present address : Pioneer Hi-Bred Spain S.L., 41012 Sevilla, Spain
D. Combemale
Affiliation:
UMR 1114 Emmah INRA-UAPV, 84914 Avignon, cedex 9 - France
P. Clastre
Affiliation:
UMR 1114 Emmah INRA-UAPV, 84914 Avignon, cedex 9 - France
M. Guérif
Affiliation:
UMR 1114 Emmah INRA-UAPV, 84914 Avignon, cedex 9 - France
A. Chanzy
Affiliation:
UMR 1114 Emmah INRA-UAPV, 84914 Avignon, cedex 9 - France
*
†Email: [email protected]
Get access

Abstract

Inversing the STICS crop model with remote-sensing-derived leaf area index (LAI) and yield data from the previous crop is used to retrieve some soil permanent properties and crop emergence parameters. Spatialized nitrogen (N) fertilization recommendations are provided to farmers, for the second and third N applications, following the screening of eleven N application rates under a range of possible forthcoming climates, with the objective to maximize of the gross margin while respecting some environmental constraints. As a first field validation, we show (1) the improvement brought by the assimilation of LAI and yield into STICS to simulate crop and soil variables and (2) the interest of site specific application to maximize both the gross margin and the agro-environmental criterion.

Keywords

durum wheatcrop modelremote sensingvariable rate nitrogen fertilization
Type
Spatial Crop Models
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 672 - 677
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Booltink, HWG, Van Alphen, BJ, Batchelor, WD, Paz, JO, Stoorvogel, JJ and Vargas, R 2001. Tools for optimizing management of spatially-variable fields. Agricultural Systems 70, 445–476.CrossRefGoogle Scholar
Brisson, N, Gary, C, Justes, E, Roche, R, Mary, B, Ripoche, D, et al. 2003. An overview of the crop model STICS. European Journal of Agronomy 18, 309–332.CrossRefGoogle Scholar
Comifer 2013. Calcul de la fertilisation azotée : Guide méthodologique pour l’établissement des prescriptions locales. Cultures annuelles et prairies (Nitrogen fertilization calculation: methodological guide book for the establishment of local recommendations. Annual crops and grasslands). Editions COMIFER. Retrieved from : http://www.comifer.asso.fr/images/publications/brochures.Google Scholar
Coucheney, E, Buis, S, Launay, M, Constantin, J, Mary, B, García de Cortázar-Atauri, I et al. 2015. Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France. Environmental Modelling and Software 64, 177–190.CrossRefGoogle Scholar
Duclos, G 1994. Atlas des sols de la région Provence-Alpes-Côte d’Azur (Soil atlas for the Provence-Alpes-Côte d’Azur area). Ministere de l’agriculture et de la peche / Societe du canal de Provence, pp 531–541.Google Scholar
Duveiller, G, Weiss, M, Baret, F and Defourny, P 2011. Retrieving wheat Green Area Index during the growing season from optical time series measurements based on neural network radiative transfer inversion. Remote Sensing Environment 115, 887–896.CrossRefGoogle Scholar
Engel, T 1997. Use of nitrogen simulation models for site-specific nitrogen fertilization. Precision Agriculture, BIOS Scientific Publishers Ltd, pp 361–369.Google Scholar
Guillaume, S, Bergez, JE, Wallach, D and Justes, E 2011. Methodological comparison of calibration procedures for durum wheat parameters in the STICS model. European Journal of Agronomy 35, 115–126.CrossRefGoogle Scholar
Guérif, M, Houlès, V, Makowski, D and Lauvernet, C 2007. Data assimilation and parameter estimation for precision agriculture using the crop model STICS. In: D Wallach, D Makowski and JW Jones. Working with Dynamic Crop Models, pp 395-402.Google Scholar
Houlès, V, Mary, B, Guérif, M, Makowski, D and Justes, E 2004. Evaluation of the ability of the crop model STICS to recommend nitrogen fertilisation rates according to agro-environmental criteria. Agronomie 24, 339–349.CrossRefGoogle Scholar
Houlès, V, Guérif, M, Mary, B, Machet, JM, Moulin, S and Beaudoin, N 2005. A tool devoted to recommend spatialized nitrogen rates at the field scale, based on a crop model and remote sensing observations assimilation. In Precision Agriculture ‘05; Proceedings of the 5th European Conference on Precision Agriculture, J Stafford (ed) June 9-12, Uppsala, Sweden.Google Scholar
Pringle, MJ, McBratney, AB, Whelan, BM and Taylor, JA 2003. A preliminary approach to assessing the opportunity for site-specific crop management in a field, using yield monitor data. Agricultural Systems 76, 273–292.CrossRefGoogle Scholar
Varella, H, Guérif, M and Buis, S 2010a. Global sensitivity analysis measures the quality of parameter estimation: The case of soil parameters and a crop model. Environmental Modelling and Software 25, 310–319.CrossRefGoogle Scholar
Varella, H, Guérif, M, Buis, S and Beaudouin, N 2010b. Soil properties estimation by inversion of a crop model and observations on crops improves the prediction of agro-environmental variables. European Journal of Agronomy 33, 139–147.CrossRefGoogle Scholar
Verger, A, Vigneau, N, Chéron, C, Gilliot, JM, Comar, A and Baret, F 2014. Green area index from an unmanned aerial system over wheat and rapeseed crops. Remote Sensing of Environment 152, 654–664.CrossRefGoogle Scholar
Wösten, JMH, Lilly, A, Nemes, A and Lebas, C 1999. Development and use of a database of hydraulic properties of European soils. Geoderma 90, 169–185.CrossRefGoogle Scholar