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Nitrogen Prescription Model for DSSAT crop growth models.

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NPM - work in progress!

The NPM_v2.1 is latest version (Decembar 2023).

Nitrogen Prescription Model (NPM) version 2 for DSSAT crop growth models.

NPM tool for DSSAT crop growth models.

  1. The "NPM_v2.1.7z" file has to be unzipped (with 7-zip).

  2. Unzipped "NPM_v2.1" directory copied to the DSSAT Tools directory "C:\DSSAT48\Tools".

  3. In folder "NPM_v2" ("C:\DSSAT48\Tools\NPM_v2.1") ->NPM_v2.1.exe<- executed as Administrator.

  4. More detailed instructions can be found in user guidelines!

├── C:/DSSAT48
│   ├── Genotype
│   ├── Pest
│   ├── Maize
│   ├── Wheat
│   ├── Soil
│   ├── ...	
│   └── Tools
│       ├── GBuild
│       ├── XBuild
│       ├── ...
│       └── NPM_v2
│           ├── ...
│           └── NPM_v2.1.exe	

After executing "NPM_v2.1.exe" a working directory "NitrogenPrescriptionWorkspace" is created in "Tools" directory where optimization is conducted and optimization output files saved:

├── C:/DSSAT48
│   ├── Genotype
│   ├── Pest
│   ├── Maize
│   ├── Wheat
│   ├── Soil
│   └── Tools	
│      ├── ...	
│      └── NitrogenPrescriptionWorkspace
│          ├── *.*X (File X used for N sensitivity analysis)
│          ├── N-reatesHarvestedYield-Optimums.txt
│          ├── ...
│          ├── *_boxPlot.png	
│          └── PlantGro.OUT

NPM related publications:

Memic, E., Trenz, J., Heshmati, S., Graeff, S. 98. Evaluation of crop model-based marginal net return maximising nitrogen application retes on site-specific level in maize. In Precision Agriculture '23 Proceedings of European Conference on Precision Agriculture, Bologna, Italy, 2 July 2023; John V. Stafford, Ed.; Wageningen Academics Publishers: Wageningen, The Netherlands, 2023, pages: 781-787. https://doi.org/10.3920/978-90-8686-947-3

NPM_v2 is a new version of the NPM model published in:

Memic, E., Graeff, S., Claupein, W., & Batchelor, W. D. (2019). GIS-based spatial nitrogen management model for maize: short- and long-term marginal net return maximising nitrogen application rates. Precision Agriculture, 20(2), 295–312. https://doi.org/10.1007/s11119-018-9603-4

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