Loading...

Wheat Crop Yield Estimation using Geomatics Tools in Saharanpur District

DOI: 10.18805/IJARe.A-5331    | Article Id: A-5331 | Page : 519-526
Citation :- Wheat Crop Yield Estimation using Geomatics Tools in Saharanpur District.Indian Journal of Agricultural Research.2022.(56):519-526
Ankush Kumar Gupta, Pramod Soni pramodsoni41@gmail.com
Address : Department of Civil Engineering, M.B.M. Engineering College, Jodhpur-342 011, Rajasthan, India. 
Submitted Date : 5-07-2019
Accepted Date : 18-02-2021

Abstract

Background: For a primarily agriculture-based country like India, reliable, accurate and timely information on types of crops grown, their acreages and yield are of vital importance. In this study, attempts have been made to predict wheat crop yield in Saharanpur district of Uttar Pradesh for the year 2016-17 using various spectral indices (Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Ratio Vegetation Index and Transformed Normalized Difference Vegetation Index) and agrometeorological variables. 
Methods: The spectral indices were derived using agrometeorological data (Growing Degree Days, Temperature Difference and Helio Thermal Units) and the ground truth information was collected using the Global Positioning System in the field. Observed acreage data for the wheat crop were collected from the Saharanpur district headquarter. 
Result: The NDVI showed the highest correlation with wheat crop yield. The spectral yield model using only the NDVI showed an R2 value of 0.81 with an RMSE of 88.02 kg/ha. However, by incorporating the NDVI with temperature, the model shows much better performance with an R2 value of 0.95 and an RMSE of 49.86 kg/ha. The rainfall showed lesser correlation with the wheat crop yield as compared to the temperature, probably due to ground water dominated region.

Keywords

Crop estimation Regression Remote sensing Spectral index

References

  1. Bazgeer, S., Mahey, R.K., Sharma, P.K., Sood, A. and Sidhu, S.S. (2006). Pre-harvest wheat yield prediction using agromet-spectral-trend-yield models for Hoshiarpur and Rupnagar districts of Punjab. Journal of the Indian Society of Remote Sensing. 34(3): 269-277.
  2. Bazgeer, S., Kamali, G. and Mortazavi, A. (2007). Wheat yield prediction through agrometeorological indices for Hamedan, Iran. Desert. 12(1): 33-38.
  3. Dubey, R.P., Ajwani, N., Kalubarme, M.H., Sridhar, V.N., Navalgund, R.R., Mahey, R.K. and Na Rang, R.S. (1994). Pre-harvest wheat yield and production estimation for the Punjab, India. International Journal of Remote Sensing. 15(10): 2137-2144
  4. Iqbal, M.J., Ali, Z.U. and Ali, S.S. (2012). Agroclimatic modelling for estimation of wheat production in the Punjab Province, Pakistan. Proceedings of the Pakistan Academy of Sciences. 49(4): 241-249.
  5. Prasad, A.K., Chai, L., Singh, R.P. and Kafatos, M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation. 8(1): 26-33
  6. Rojas, O. (2007). Operational maize yield model development and validation based on remote sensing and agro meteorological data in Kenya. International Journal of Remote Sensing. 28(17): 3775-3793
  7. Sukhatme, P.V. and Panse, V.G. (1951). Crop surveys in India-II. Journal of the Indian Society of Agricultural Statistics. 3(2): 95-168.
  8. Verma, U., Dabas, D.S., Hooda, R.S., Kalubarme, M.H., Yadav, M., Grewal, M.S. and Prawasi, R. (2011). Remote sensing based wheat acreage and spectral-trend-agrometeorological Yield Forecasting: Factor Analysis Approach. Statistics and Applications. 9(1): 1-13.
  9. Verma, U., Piepho, H.P., Goyal, A., Ogutu, J.O. and Kalubarme, M.H. (2016). Role of climatic variables and crop condition term for mustard yield prediction in Haryana. Int. J. Agric. Stat. Sci. 12: 45-51.

Global Footprints