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GIS-Based Spatial Nitrogen Management Model for Maize

Published online by Cambridge University Press:  01 June 2017

E. Memic ,
S. Graeff ,
W. Claupein  and
W.D. Batchelor
E. Memic*
Affiliation:
University of Hohenheim, Stuttgart, Germany
S. Graeff
Affiliation:
University of Hohenheim, Stuttgart, Germany
W. Claupein
Affiliation:
University of Hohenheim, Stuttgart, Germany
W.D. Batchelor
Affiliation:
Biosystems Engineering Department, Auburn University, Auburn, AL 36849. USA
*
†Email: [email protected] or [email protected]
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Abstract

Crop growth models including CERES-Maize and CROPGRO-Soybean have been used in the past to evaluate causes of spatial yield variability and to evaluate economic consequences of variable rate prescriptions. However, these modelling techniques have not been widely used because of an absence of user-friendly software. In this work, a nitrogen prescription model to simulate the consequences of different nitrogen prescriptions using the DSSAT crop growth models is developed. The objective is to describe a site-specific nitrogen prescription and economic optimizer program developed for computing optimum spatial nitrogen rates for maize using the CERES-Maize model. The application of the model is demonstrated on two different fields in Germany and the US. The program simulated optimum N applications that averaged 42% (McGarvey field, US) and 39% (Riech field, Germany) lower than the uniform rates actually applied in the fields. The software is written in Python and will ultimately be distributed in the public domain as a plug-in to the QGIS software.

Keywords

CERES-MaizeDSSAT crop growth modelnitrogen management
Type
Precision Nitrogen
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. 312 - 316
Copyright
© The Animal Consortium 2017 

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References

Batchelor, WD, Paz, JO and Thorp, KR 2004. Development and evaluation of a decision support system for precision agriculture. In Proceedings of the 7th International Conference on Precision Agriculture. ASA-CSSA-SSSA, Madison, WI, USA.Google Scholar
Batchelor, WD, Basso, B and Paz, JO 2002. Examples of strategies to analyze spatial and temporal yield variability using crop models. Special Edition of European Journal of Agronomy Vol. 18 (1-2), 141–158.CrossRefGoogle Scholar
FAOSTAT 2014. Statistical database of the Food and Agriculture Organization (FAO) of the United Nations, accessed November 2016.Google Scholar
Jones, JW, Hoogenboom, GJ, Porter, C, Boote, KJ, Batchelor, WD, Hunt, LA, Wilkens, PW, Singh, U, Gijsman, A and Ritchie, JT 2003. DSSAT Cropping System Model. Special Edition of European Journal of Agronomy 18, 235–265.Google Scholar
Link, EJ, Graeff, S and Batchelor, WD 2008. Evaluation of current and model-based site-specific nitrogen applications on wheat (Triticum aestivum L.) yield and environmental quality. Precision Agriculture 9, 251–267.Google Scholar
Link, EJ, Graeff, SS, Batchelor, WD and Claupein, W 2006. Evaluating the economic and environmental impact of a German compensation payment policy under uniform and variable-rate nitrogen management strategies using a crop model. Agricultural Systems 91, 135–153.Google Scholar
Miao, Y, Mulla, DJ, Batchelor, WD, Paz, JO and Robert, PC 2006. Evaluating management zone optimal N rates with a crop growth model. Agronomy Journal 98 (3), 545–553.Google Scholar
Paz, JO, Batchelor, WD, Colvin, TS, Logsdon, SD, Kaspar, TC, Karlen, DL and Babcock, BA 1999. Model-based techniques to determine variable rate nitrogen for corn. Agricultural Systems 60, 69–75.Google Scholar
Paz, JO, Batchelor, WD and Tylka, GL 2001. Method to use crop growth models to estimate potential return for variable-rate management in soybeans. Transactions of the ASAE 44 (5), 1335–1341.Google Scholar
Thorp, KR and Bronson, K 2013. A model-independent open-source geospatial tool for managing point-based environmental model simulations at multiple spatial locations. Environmental Modeling & Software 50, 25–36.Google Scholar
Thorp, KR, Batchelor, WD, Paz, JO, Steward, BL and Caragea, PC 2006. Methodology to link production and environmental risks of precision nitrogen management strategies in corn. Agricultural Systems 89 (2-3), 272–298.Google Scholar