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Kalra, Naveen
- Advanced Computational Procedures for the Understanding of Agricultural Processes
Abstract Views :276 |
PDF Views:87
Authors
Naveen Kalra
1,
J. C. Biswas
2,
M. Maniruzzaman
3,
A. K. Choudhury
4,
S. Akhter
4,
F. Ahmed
4,
M. A. Aziz
4,
M. M. Rahman
5,
M. M. Miah
5
Affiliations
1 Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, IN
2 Bangladesh Rice Research Institute, Gazipur 1701, BD
3 Bangladesh Rice Research Institute, Gazipur 1701, BD
4 Bangladesh Agricultural Research Institute, Gazipur 1701, BD
5 Bangabandhu Sheik Mujibur Rahman Agricultural University, Gazipur 1706, BD
1 Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, IN
2 Bangladesh Rice Research Institute, Gazipur 1701, BD
3 Bangladesh Rice Research Institute, Gazipur 1701, BD
4 Bangladesh Agricultural Research Institute, Gazipur 1701, BD
5 Bangabandhu Sheik Mujibur Rahman Agricultural University, Gazipur 1706, BD
Source
Current Science, Vol 113, No 02 (2017), Pagination: 208-209Abstract
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.Full Text
References
- 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.
- Assessing the Response of Forests to Environmental Variables using a Dynamic Global Vegetation Model:An Indian Perspective
Abstract Views :371 |
PDF Views:73
Authors
Affiliations
1 GIS Centre, IT&GIS Discipline, Forest Research Institute, PO: New Forest, Dehradun 248 006, IN
2 Division of Agriculture Physics, Indian Agricultural Research Institute, New Delhi 110 012, IN
3 Centre for Sustainable Technology, Indian Institute of Science, Bengaluru 560 012, IN
1 GIS Centre, IT&GIS Discipline, Forest Research Institute, PO: New Forest, Dehradun 248 006, IN
2 Division of Agriculture Physics, Indian Agricultural Research Institute, New Delhi 110 012, IN
3 Centre for Sustainable Technology, Indian Institute of Science, Bengaluru 560 012, IN
Source
Current Science, Vol 118, No 5 (2020), Pagination: 700-701Abstract
Forest ecosystems form an intricate nonlinear relationship with their surroundings. Therefore, the underlying processes are difficult to quantify. As a result, it makes the task quite challenging to evaluate the response of vegetation to their surrounding environment1. Predicting responses of vegetation dynamics requires a clear understanding of how different physiological and ecological processes are influenced by environmental drivers. A clear causality between the types and levels of stresses and corresponding responses of forests is necessary for making any rational inferences2. Significant progress in scientific understanding of plant–environment relationship, supplemented with the historical sequence of discoveries, is gradually improving the knowledge about the underlying functional relationship of plants with the environment. On the other hand, improved computational capabilities to handle multiple complex equations representing various functional relationships have made it possible to upscale the eco-physiological processes from an individual leaf to a global forest cover through computer-based programs, usually termed as a ‘Model’.Full Text
References
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- Kumar, M., Singh, H., Pandey, R., Singh, M. P., Ravindranath, N. H. and Kalra, N., Biodivers. Conserv., 2019, 28, 2163–2182.
- Peng, C., Ecol. Modell., 2000, 135, 33– 54.
- Long, S. P., In Silico Plants, 2019, 1, 1–3.
- Kumar, M., Rawat, S. P. S., Singh, H., Ravindranath, N. H. and Kalra, N., Indian J. For., 2018, 41, 1–12.
- Blanco, J. A., de Andrés, E. G., San Emeterio, L. and Lo, Y.-H., In Developments in Environmental Modelling, Elsevier, 2015, pp. 189–215.
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- Chaturvedi, R. K. et al., Mitig. Adapt. Strateg. Glob. Chang., 2011, 16, 119– 142.
- Aggarwal, P. K., Kalra, N., Chander, S. and Pathak, H., Agric. Syst., 2006, 89, 1– 25.
- Kalra, N. and Kumar, M., In Climate Change and Agriculture in India: Impact and Adaptation, 2019, pp. 21–28.
- Kumar, M. et al., Ecol. Modell., 2019, 404, 12–20.