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Research Article

volume 43 issue 4 (august 2020) : 530-538,   Doi: 10.18805/LR-4153

Decision Support System (DSS) on Pulses in India

K
K.P. Vishwajith
P
P.K. Sahu
B
B.S. Dhekale
P
P. Mishra
C
Chellai Fatih
1Department of Agricultural Statistics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur- 741 252, Nadia, West Bengal, India.

  • Submitted16-04-2019|

  • Accepted06-01-2020|

  • First Online 17-03-2020|

  • doi 10.18805/LR-4153

Cite article:- Vishwajith K.P., Sahu P.K., Dhekale B.S., Mishra P., Fatih Chellai (2020). Decision Support System (DSS) on Pulses in India. Legume Research. 43(4): 530-538. doi: 10.18805/LR-4153.

ABSTRACT

Pulses are popularly known as poor man’s meat. Pulses mainly constituted of gram, arhar, mung, urad and lentil etc. The energy content of most pulses have been found to be between 300 and 540 Kcal/100g. India  is  the  largest  producer  and  consumer  of  pulses  in  the  world contributing around 14.36% of the total global production. Decision support system (DSS) has been designed and developed using a visual basic platform for major pulse crops grown in India. The main objective is to provide decision support regarding recommended water, weed, pest and diseases management, state wise varieties and nutrient management based on the soil test value to the farmer, agriculture workers, students, research workers, extension workers and others, even in absence of the experts. The system is console application, menu driven and user friendly. A sincere effort has been made with the available data to develop the decision support system to bridge the gap between the farmers and the experts using visual basic platform.

REFERENCES

  1. Carisse, O. (Ed.) (2010). Fungicides. BoD–Books on Demand.
  2. Daniel, A.R. (2011). Crop nutrient management decision support system for black gram and paddy in Tamil Nadu. Available at: http://www.ifad.org/operations/projects/regions. Last accessed on 7th July 2015. 
  3. Debeljak, M., Trajanov, A., Kuzmanovski, V., Schröder, J., Sandén, T., Spiegel, H., et al. (2019). A field-scale decision support system for assessment and management of soil functions.
  4. Frontiers in Environmental Science, 7, p.115.
  5. Ganesan, V. (2007). Decision Support System “Crop-9-DSS” for Identified Crops. In Proceedings of 10th International.
  6. GOI (2015). India in business, Investment and technology promotion division, Ministry of External Affairs, GOI. Available at http://indiainbusiness.nic.in. Last accessed on 9th December 2015.
  7. Gorry, G.A. and Scott Morton, M.S (1971). A framework for MIS. Sloan Management Review 13: 55-70.
  8. http://faostat.fao.org, last accessed on 7-July-14)
  9. Kannaiyan, S. (1999). Bio-resources Technology for Sustainable Agriculture. Associated Publishing Company, New Delhi. P. 422.
  10. Larson, J.A., Roberts, R.K., English, B.C., Cochran, R.L. and Wilson, B.S. (2005). A computer decision aid for the cotton yield monitor investment Decision. Comp. Elect. Agril. 48: 216-34.
  11. Nagendra. R.T. and Kumar R.D. (2006). Nutrient management decision support system for appropriate use of fertilizer and amendments in agriculture. In Pro: AFTIA 2006 fifth Conference of Asian Federation for Information Technology in Agriculture.
  12. Neeser, C., Dille, J.A., Krishnan, G., Mortensen, D.A., Rawlinson, J.T., Martin, A.R. and Bills, L.B. (2004). WeedSOFT®: a weed management decision support system. Weed Science. 52(1): 115-122.
  13. Pal, S., Sethi, I.C and Alka, A. (2007). Decision support system for nutrient management in crops. J. Indian Soc. Agril. Sci. 61: 389-99.
  14. Parsons, D.J., Benjamin, L.R., Clarke, J., Ginsburg, D., Mayes, A., Milne, A.E. and Wilkinson, D.J. (2008). Weed Manager- a model-based decision support system for weed manage-ment in arable crops. Computers and Electronics in Agriculture. 65(2): 155-167.
  15. Ramamoorthy, B., Narasimham, R.L., Dinesh, R.S., (1967). Fertilizer application for specific yield target of sonara-64 wheat. Indian Farming. 17: 43–45.
  16. Rao K H V D and Satish Kumar D (2004). Spatial Decision support system for watershed management. Water Resources Management. 18: 407–423.
  17. Singh, A.K., Singh, S.S., Prakesh, V., Santhosh, K. and Dwivedi, S.K. (2015). Pulses production in India: present status, bottleneck and way forward. J Agri. Search. 2: 75-83.
  18. Singh, N. and Gupta, N. (2016). ICT based decision support systems for Integrated Pest Management (IPM) in India: A review. Agricultural Reviews. 37(4): 309-316.
  19. Vishwajith, K.P., Sahu, P. K., Mishra, P., Devi, M., Dubey, A., Singh, R.B., Dhekale, B.S., Fatih, C. and Suman. (2019). Modelling and forecasting of mung production in India. Current Journal of Applied Science and Technology. 34(1): 1-19.
  20. Vishwajith, K.P, A.R.S. Bhat., K.V. Ashalatha and P.K. Sahu. (2014). Decision support system for fertilizer recommendation-    A case study. Indian J. Agron. 59: 344-49.
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    Research Article

    volume 43 issue 4 (august 2020) : 530-538,   Doi: 10.18805/LR-4153

    Decision Support System (DSS) on Pulses in India

    K
    K.P. Vishwajith
    P
    P.K. Sahu
    B
    B.S. Dhekale
    P
    P. Mishra
    C
    Chellai Fatih
    1Department of Agricultural Statistics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur- 741 252, Nadia, West Bengal, India.

    • Submitted16-04-2019|

    • Accepted06-01-2020|

    • First Online 17-03-2020|

    • doi 10.18805/LR-4153

    Cite article:- Vishwajith K.P., Sahu P.K., Dhekale B.S., Mishra P., Fatih Chellai (2020). Decision Support System (DSS) on Pulses in India. Legume Research. 43(4): 530-538. doi: 10.18805/LR-4153.

    ABSTRACT

    Pulses are popularly known as poor man’s meat. Pulses mainly constituted of gram, arhar, mung, urad and lentil etc. The energy content of most pulses have been found to be between 300 and 540 Kcal/100g. India  is  the  largest  producer  and  consumer  of  pulses  in  the  world contributing around 14.36% of the total global production. Decision support system (DSS) has been designed and developed using a visual basic platform for major pulse crops grown in India. The main objective is to provide decision support regarding recommended water, weed, pest and diseases management, state wise varieties and nutrient management based on the soil test value to the farmer, agriculture workers, students, research workers, extension workers and others, even in absence of the experts. The system is console application, menu driven and user friendly. A sincere effort has been made with the available data to develop the decision support system to bridge the gap between the farmers and the experts using visual basic platform.

    REFERENCES

    1. Carisse, O. (Ed.) (2010). Fungicides. BoD–Books on Demand.
    2. Daniel, A.R. (2011). Crop nutrient management decision support system for black gram and paddy in Tamil Nadu. Available at: http://www.ifad.org/operations/projects/regions. Last accessed on 7th July 2015. 
    3. Debeljak, M., Trajanov, A., Kuzmanovski, V., Schröder, J., Sandén, T., Spiegel, H., et al. (2019). A field-scale decision support system for assessment and management of soil functions.
    4. Frontiers in Environmental Science, 7, p.115.
    5. Ganesan, V. (2007). Decision Support System “Crop-9-DSS” for Identified Crops. In Proceedings of 10th International.
    6. GOI (2015). India in business, Investment and technology promotion division, Ministry of External Affairs, GOI. Available at http://indiainbusiness.nic.in. Last accessed on 9th December 2015.
    7. Gorry, G.A. and Scott Morton, M.S (1971). A framework for MIS. Sloan Management Review 13: 55-70.
    8. http://faostat.fao.org, last accessed on 7-July-14)
    9. Kannaiyan, S. (1999). Bio-resources Technology for Sustainable Agriculture. Associated Publishing Company, New Delhi. P. 422.
    10. Larson, J.A., Roberts, R.K., English, B.C., Cochran, R.L. and Wilson, B.S. (2005). A computer decision aid for the cotton yield monitor investment Decision. Comp. Elect. Agril. 48: 216-34.
    11. Nagendra. R.T. and Kumar R.D. (2006). Nutrient management decision support system for appropriate use of fertilizer and amendments in agriculture. In Pro: AFTIA 2006 fifth Conference of Asian Federation for Information Technology in Agriculture.
    12. Neeser, C., Dille, J.A., Krishnan, G., Mortensen, D.A., Rawlinson, J.T., Martin, A.R. and Bills, L.B. (2004). WeedSOFT®: a weed management decision support system. Weed Science. 52(1): 115-122.
    13. Pal, S., Sethi, I.C and Alka, A. (2007). Decision support system for nutrient management in crops. J. Indian Soc. Agril. Sci. 61: 389-99.
    14. Parsons, D.J., Benjamin, L.R., Clarke, J., Ginsburg, D., Mayes, A., Milne, A.E. and Wilkinson, D.J. (2008). Weed Manager- a model-based decision support system for weed manage-ment in arable crops. Computers and Electronics in Agriculture. 65(2): 155-167.
    15. Ramamoorthy, B., Narasimham, R.L., Dinesh, R.S., (1967). Fertilizer application for specific yield target of sonara-64 wheat. Indian Farming. 17: 43–45.
    16. Rao K H V D and Satish Kumar D (2004). Spatial Decision support system for watershed management. Water Resources Management. 18: 407–423.
    17. Singh, A.K., Singh, S.S., Prakesh, V., Santhosh, K. and Dwivedi, S.K. (2015). Pulses production in India: present status, bottleneck and way forward. J Agri. Search. 2: 75-83.
    18. Singh, N. and Gupta, N. (2016). ICT based decision support systems for Integrated Pest Management (IPM) in India: A review. Agricultural Reviews. 37(4): 309-316.
    19. Vishwajith, K.P., Sahu, P. K., Mishra, P., Devi, M., Dubey, A., Singh, R.B., Dhekale, B.S., Fatih, C. and Suman. (2019). Modelling and forecasting of mung production in India. Current Journal of Applied Science and Technology. 34(1): 1-19.
    20. Vishwajith, K.P, A.R.S. Bhat., K.V. Ashalatha and P.K. Sahu. (2014). Decision support system for fertilizer recommendation-    A case study. Indian J. Agron. 59: 344-49.
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