The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W6
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 251–256, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W6-251-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 251–256, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W6-251-2019

  26 Jul 2019

26 Jul 2019

AUTOMATION OF THE DSSAT CROP GROWTH SIMULATION MODEL

G. Sachin1, J. Mohammed Ahamed2, K. Nagajothi2, M. Rana3, and B. S. Murugan1 G. Sachin et al.
  • 1Kalasalingam Academy of Research and Education, Krishnankovil - 626126, Tamil Nadu, India
  • 2Regional Remote Sensing Centre-South, NRSC, ISRO, Bangalore - 560037, Karnataka, India
  • 3Centre of Excellence for Energy and Environment Studies (CEEES), DCRUST, Murthal, Sonipat, Haryana, India

Keywords: Crop simulation, Crop modeling, Decision aid, Geospatial Decision Support System, Climate Variability, Climate Change

Abstract. Crop Simulation Models (CSM) simulate the growth, development, and yield of crops using various inputs such as soil water, carbon and nitrogen processes, and management practices. DSSAT (Decision Support System for Agrotechnology Transfer) is a software program that comprises dynamic crop growth simulation models for over 42 crops. It incorporates modules for crop, soil, and weather to simulate long-term outcomes of crop management strategies. DSSAT-CSM requires various data for model operation. This includes data on the site where the model is to be operated, on the daily weather during the growth cycle, on the characteristics of the soil at the beginning of the growing cycle or crop sequence, and on the management of the crop. Acquisition of the data and providing the data to the DSSAT model is tedious and time-consuming as each individual value has to be manually entered. Additionally, crop simulation models can only be run for specific points and not for entire locations. Sometimes site-specific data especially weather data cannot be obtained. The output thus produced is difficult to analyze spatially at a large scale. The main purpose of this paper is to take the required dataset directly from spatial data. This is done by dividing locations into grids and taking the data from each grid. Python scripts are then used to convert this data into crop model format which is then run through DSSAT on an individual basis. The output thus obtained is be entered back into their respective grids as spatial data.