Spatio-Temporal Transformation of Cropping Patterns in the Western Haryana Plains, India: A Five-Decade Analysis (1974-2022)
20 / 9
Keywords:
Cropping pattern, paddy, wheat, western Haryana plains, geospatial dataAbstract
The increase in crop productivity resulting from the Green Revolution has had both positive and negative consequences for the natural environment, particularly with respect to water resources. Changes in cropping patterns have also increased the environmental risks associated with agricultural activities. The present study examines the long-term spatio-temporal changes in cropping patterns in the Western Haryana Plains of northern India during 1974 to 2022, with special emphasis on the expansion of wheat and paddy cultivation using geospatial data. Satellite data were used to analyse two major crops, wheat and paddy, grown during the rabi and kharif seasons, respectively. The expansion of irrigation facilities in the Western Haryana Plains has resulted in significant changes in crop distribution. The area under paddy increased from 0.74% in 1974 to 21.4% in 2022, while the area under wheat expanded from 9.5% to 55.1% of the total cultivable land during the same period. These changes in cropping patterns are largely attributable to improved irrigation infrastructure, which has facilitated the adoption of modern agricultural technologies. Furthermore, government policies introduced following the Green Revolution, aimed at achieving food security, have particularly promoted the cultivation of paddy and wheat. Consequently, large areas of land have been brought under the rice-wheat cropping system.
Downloads
References
Ahmed, O.S., Shemrock. A., Chabot, D., Dillon, C., Williams. G., Wasson, R. and Franklin, S. E. 2017. Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle. International Journal of Remote Sensing 38: 2037-2052.
Arias, M., Campo-Bescós, M.Á. and Álvarez-Mozos, J. 2020. Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain. Remote Sensing 12: 278.
doi:10.3390/rs12020278
Arvind, Hooda, R.S., Sheoran, H.S., Kumar, D., Satyawan, A. and Bhardwaj, S. 2020. RS based regional crop identification and mapping: A case study of Barwala sub-branch of Western Yamuna canal in Haryana (India). Indian Journal of Traditional Knowledge 19(1): 182-186.
Atzberger, C. 2013. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing 5: 949-981.
doi:10.3390/rs5020949
Bhalla, S. 1989. Technological change and women workers, evidence from the expansionary phase in Haryana agriculture. Economic and Political Weekly 24(43): 67-78.
Casa, R., Rossi, M., Sappa, G. and Trotta, A. 2009. Assessing crop water demand by remote sensing and GIS for the Pontina Plain, Central Italy. Water Resource Management 23(9): 1685-1712.
Dadhwal, V.K., Sehgal, V.K., Singh, R.P. and Rajak, D. R. 2005. Wheat yield modeling using satellite remote sensing with weather data: Recent Indian experience. Mausam 54: 253-262.
Doraiswamy, P., Moulin, S., Cook, P. and Stern, A. 2003. Crop yield assessment from Remote Sensing. Photogrammetric Engineering and Remote Sensing 69: 665-674.
Duro, D.C., Franklin, S.E. and Dubé, M. G. 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing Environment 118: 259-272.
Garg, N.K., and Ali, A. 1998. Two-level optimization model for Lower Indus Basin. Agricultural Water Management 36(1): 1-21.
Gao, F. J., Masek, M., Schwaller., Hall, F. 2006. On the Blending of the Landsat and MODIS Surface Reflectance: Predicting Daily Landsat Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing 44(8): 2207-2218.
doi:10.1109/TGRS.2006.872081.
Ghosh, B.K. 2011. Determinants of the changes in cropping pattern in India: 1970-71 to 2006-07. Bangladesh Development Studies 34(2): 109-120.
Hassan, M.I. and Inderjeet 2000. Canal irrigation and land degradation in Haryana. Transactions of the Institute of Indian Geographers 22(1): 51-61.
Khan, A., Hansen, M.C. and Potapov, P. 2016. Landsat-based wheat mapping in the heterogeneous cropping system of Punjab, Pakistan. International Journal Remote Sensing 37: 1391-1410.
Kumar, S. and Kumar, P. 2019. Area estimation of paddy crop in Haryana by different sensing platforms. Journal of Emerging Technologies and Innovative Research (JETIR) 6(3): 140-148.
Latif, M.A. 2019. Multi-crop recognition using UAV-based high-resolution NDVI time-series. Journal of Unmanned Vehicle Systems 7: 207-218.
Li, Q., Wang, C., Zhang, B. and Lu, L. 2015. Object-based crop classification with Landsat-MODIS enhanced time-series data. Remote Sensing 7: 16091-16107.
Matuschke, I., Mishra, R.R. and Qaim, M. 2007. Adoption and impact of hybrid wheat in India. World Development 35(8): 1422-1435.
Nelson, E., Ravichandran, A.R.L. and Antony, U. 2019. The impact of the green revolution on indigenous crops of India. Journal of Ethnic Foods 6(1):1-10.
Nuarsa, W.I., Nishio, F. and Hongo, C. 2011. Spectral characteristics and mapping of rice plants using multi-temporal Landsat data. Journal of Agricultural Science 3(1): 54-67.
Patel, N.R., Bhattacharjee, B., Mohammed, A.J., Tanupriya, B. and Saha, S.K. 2006. Remote sensing of regional yield assessment of wheat in Haryana, India. International Journal of Remote Sensing 19(10): 4071-4090.
Rajput, J., Kushwaha, N. L., Sikka, A., Faiz Alam, M., Mahapatra, S., Sena, D. R., ... and Mani, I. 2024. Water accounting of groundwater over exploited districts in Haryana and Punjab states to analyse impacts of water conservation measures on water availability. Water Supply 24(9): 3093-3117.
Rawat, S.D. and Bala, S. 2021. Changing cropping pattern in Haryana: A spatio-temporal analysis of major food crops. International Journal of All Research Education and Scientific Methods (IJARESM) 9(3): 212-217.
Sangwan, B. and Monika 2017. Spatio-Temporal Trends in the Development of Irrigation in Haryana (1980-81 to 2010-11). Amar: An Inter- Disciplinary Research Journal 3(3): 80-88.
Sarkar, A., Sen. S. and Kumar, A. 2009. Rice-wheat cropping cycle in Punjab: A comparative analysis of sustainability status in different irrigation systems. Environment, Development and Sustainability 11(4): 751-763.
Singh, P., Saharan, J.P., Sharma, K. and Saharan, S. 2010. Physio-chemical and EDXRF Analysis of groundwater of Ambala, Haryana, India. Researcher 2(1): 68-75.
Singh, P. and Benbi, D. K. 2021. Physical and chemical stabilization of soil organic matter in cropland ecosystems under rice-wheat, maize-wheat and cotton-wheat cropping systems in northwestern India. Carbon Management 12(6): 603-621.
Sushma, B., Prawasi, R., Hooda, R.S., Yadav, M., and S. 2019. Mapping area under major rabi crops in Jhajjar district of Haryana using Geoinformatics. International Journal of Current Microbiology Applied Science 8(11): 1134-1140.
Taha, M. F., Mao, H., Zhang, Z., Elmasry, G., Awad, M. A., Abdalla, A., Mousa, S., Elwakeel, A. E., and Elsherbiny, O. 2025. Emerging Technologies for Precision Crop Management Towards Agriculture 5.0: A Comprehensive Overview. Agriculture 15(6): 582.
Tariq, A., Jianguo, Y., Alexandre, S.G., Khan, M.R. and Mumtaz, F. 2023 Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-spatial Information Science 26(3): 302-320.
doi:10.1080/10095020.2022.2100287.
Thenkabail, P.S. 2010. Global croplands and their importance for water and food security in the twenty-first century: Towards an ever green revolution that combines a second Green Revolution with a Blue Revolution. Remote. Sensing 2: 2305-2312.
doi:10.3390/rs2092305
Verma, U., Dabas, D.S., Hooda, R.S., Kalubarme, M.H., Yadav, M., Grewal, M.S., Sharma, M.P. and Prawasi, R. 2011. Remote sensing based wheat acreage and spectral-trend agro-meteorological yield forecasting: factor analysis approach. Statistics and Applications 9(1-2): 1-13.
Viskovic, L., Kosovic, I.N., Mastelic, T. 2019. Crop classification using multi-spectral and multitemporal satellite imagery with machine learning Inter pp. 1-5. National Conference on Software, Telecommunications and Computer Networks (SoftCOM), 1-5.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 K. Nageswara Rao, Vinod Kumar, P. Swarna Latha

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.





