Skip to main content

Advertisement

Log in

Conjugation of AMUL and ISRO: Development of Feed and Fodder for Dairy Industries

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Feed and fodder comprises about 65% of the cost of milk production of a dairy industry. It is a crucial input for enhancing the milk production. To address the issue of fodder availability at first, its assessment is required. Thus, we have implemented remote sensing technique for fodder crop assessment at state level to create a baseline for fodder crop availability for dairy managers to plan for its procurement during deficit and for better management purposes during its excess. We have devised a technique for remote sensing-based fodder crop assessment based on spectral pattern of growth, i.e. normalised difference vegetation index profile and land surface wetness index profile of series of IRS LISS-III satellite data taken during the crop growth cycle for a hybrid method of crop classification. Second objective to address the issue of mitigating the deficit of fodder crops, we have demonstrated the satellite derived intersection of probable high soil wetness area and available current fallows during a crop growing season which can be utilised for growing fodder crops. For macro-level planning in a state for developing new fodder-growing areas, we have demonstrated the availability of soil wetness factor from SMAP data. Fallow land available between two cropping seasons can be identified through remote sensing for growing short duration fast growing fodder crops. This project has been a demonstration project for AMUL in Gujarat to implement it subsequently at national level.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 1, 1–24.

    Article  Google Scholar 

  • Di Bella, C., Faivre, R., Ruget, F., Seguin, B., Guerif, M., Combal, B., et al. (2004). Remote sensing capabilities to estimate pasture production in France. International Journal of Remote Sensing, 25, 5359–5372.

    Article  Google Scholar 

  • Dutta, S., Patel, N. K., Medhavy, T. T., Srivastava, S. K., Mishra, N., & Singh, K. R. P. (1998). Wheat crop classification using multidate IRS LISS-I data. Journal of Indian Society of Remote Sensing, 26, 7–14.

    Article  Google Scholar 

  • FASAL. http://www.ncfc.gov.in/fasal.html. Retrieved April 28, 2017.

  • Guo, X., Wilmshurst, J. F., & Li, Z. (2010). Comparison of laboratory and field remote sensing methods to measure forage quality. International Journal of Environment Research Public Health, 7, 3513–3530.

    Article  Google Scholar 

  • ICAR. (2013). Forage crops and grasses. In Handbook of Agriculture (6th ed., pp. 1353–1417).

  • Jensen, J. R. (1997). Introductory digital image processing: A remote sensing perspective (1st ed.). Upper Saddle River: Pearson Prentice Hall.

    Google Scholar 

  • Jensen, J. R. (2007). Remote sensing of the environment: An earth resource perspective (2nd ed.). Upper Saddle River: Prentice-Hall.

    Google Scholar 

  • Kumar, S., Krishnan, R., & Nigam, S. (2008). Contribution of livestock in Indian scenario. Agricultural Situation of India, 16(1), 25–28.

    Google Scholar 

  • Roumiguie, A., Jacquin, A., Sigel, G., Poilve, H., Hagolle, O., & Dayde, J. (2015). Validation of a Forage Production Index (FPI) derived from MODIS fCover time-series using high-resolution satellite imagery: Methodology, results and opportunities. Remote Sensing, 7, 11525–11550.

    Article  Google Scholar 

  • Rouse, J. W., Haas, Jr., R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS, NASA SP-351. In Third ERTS-1 symposium, NASA, Washington (Vol. 1, pp. 309–317).

  • Simonetti, E., Simonetti, D., & Preatoni, D. (2014). Phenology-based land cover classification using Landsat 8 time series (pp. 1–57). Ispra: Joint Res. Centre, EUR.

    Google Scholar 

Download references

Acknowledgements

Authors are grateful to Director, Space Applications Centre (ISRO), Ahmedabad, and Dr. Raj Kumar, Deputy Director, SAC, for sponsoring this project and their encouragement and support to carry out this work. Support provided by Dr. Dharmendar K. Pandey for providing valuable soil wetness map is acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sujay Dutta.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dutta, S., Dwivedi, S., Bhattacharya, B.K. et al. Conjugation of AMUL and ISRO: Development of Feed and Fodder for Dairy Industries. J Indian Soc Remote Sens 50, 409–416 (2022). https://doi.org/10.1007/s12524-020-01172-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-020-01172-x

Keywords

Navigation