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Advanced Computational Procedures for the Understanding of Agricultural Processes


Affiliations
1 Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, India
2 Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh
3 Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh
4 Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
5 Bangabandhu Sheik Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
 

Growth of crops obeys certain physiological principles, which have been described, most of the times, in qualitative terms but can be quantified in response to the environment by mathematical formulae by linking the equations to each other. In process, a mathematical model is obtained that can be written as a computer program. Rapid accumulation of knowledge in the agricultural fields and increased accessibility to information technology have contributed to the development of a wide number of agricultural models. Crop simulation models can be used as a tool to assist farmer in their decisions on agronomic and management operations.
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  • Aggarwal, P. K., Kalra, N., Singh, A. K. and Sinha, S. K., Field Crops Res., 1994, 38, 73–91.
  • Hoogenboom, G. et al., Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.6, DSSAT Foundation, Prosser, Washington, USA, 2015; http://dssat.net
  • Jones, J. W. et al., Eur. J. Agron., 2003, 18, 235–265.
  • McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P. and Freebairn, D. M., Agric. Syst., 1996, 50, 255–271.
  • van Diepen, C. A., Rappoldt, C., Wolf, J. and van Keulen, H., Crop growth simulation model WOFOST version 4.1, Documentation. SOW-88-01. Centre for World Food Studies, Wageningen, The Netherlands, 1988.
  • Aggarwal, P. K., Kalra, N., Chander, S. and Pathak, H., Agric. Syst., 2006, 89, 1–25.
  • Aggarwal, P. K. et al., Agric. Syst., 2006, 89, 47–67.
  • Aggarwal, P. K. and Kalra, N., Field Crops Res., 1994, 38, 93–103.
  • Kalra, N. et al., Curr. Sci., 2008, 94(1), 82–88.
  • Kalra, N., Chakraborty, D., Ramesh Kumar, P., Jolly, M. and Sharma, P. K. Agric. Water Manage., 2007, 93, 54–64.
  • Kalra, N., Chander, S., Pathak, H., Aggarwal, P. K., Gupta, N. C., Sehgal, M. and Chakraborty, D., Outlook Agric., 2007, 36(2), 109–118.
  • Chander, S., Kalra, N. and Aggarwal, P. K., Outlook Agric., 2007, 36(1), 63–70.
  • Aggarwal, P. K. et al. (eds), Land Use Analysis and Planning for Sustainable Food Security, Indian Agricultural Research Institute, New Delhi, India, IRRI, Los Banos, Philippines and Wageningen University and Research Centre, Wageningen, The Netherlands, 2001.
  • Adhikary, P. P. et al., Aust. J. Soil Res., 2008, 46, 476–484.
  • Rattan, R. K., Indian J. Fert., 2015, 11(4), 40–61.
  • Singh, R., Kalra, N. and Mehan, K., Indian J. Fert., 2007, 3(6), 13–16; 19–26 and 54.
  • Sehgal, V. K., Sastri, C. V. S., Kalra, N. and Dadhwal, V. K., Photonirvachak, J. Indian Soc. Remote Sensing, 2005, 33(1), 1–6.
  • Das, D. K., Mishra, K. K. and Kalra, N., Int. J. Remote Sensing, 1993, 14, 3081–3092.

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  • Advanced Computational Procedures for the Understanding of Agricultural Processes

Abstract Views: 408  |  PDF Views: 144

Authors

Naveen Kalra
Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, India
J. C. Biswas
Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh
M. Maniruzzaman
Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh
A. K. Choudhury
Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
S. Akhter
Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
F. Ahmed
Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
M. A. Aziz
Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
M. M. Rahman
Bangabandhu Sheik Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
M. M. Miah
Bangabandhu Sheik Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh

Abstract


Growth of crops obeys certain physiological principles, which have been described, most of the times, in qualitative terms but can be quantified in response to the environment by mathematical formulae by linking the equations to each other. In process, a mathematical model is obtained that can be written as a computer program. Rapid accumulation of knowledge in the agricultural fields and increased accessibility to information technology have contributed to the development of a wide number of agricultural models. Crop simulation models can be used as a tool to assist farmer in their decisions on agronomic and management operations.

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DOI: https://doi.org/10.18520/cs%2Fv113%2Fi02%2F208-209