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Rice-Fallow Targeting for Cropping Intensification through Geospatial Technologies in the Rice Belt of Northeast India

Author

Listed:
  • Amit Kumar Srivastava

    (International Rice Research Institute (IRRI), South Asia Regional Centre (SARC), NSRTC Campus, G. T. Road, Collectory Farm, P.O. Industrial Estate, Varanasi 221006, India)

  • Suranjana Bhaswati Borah

    (International Rice Research Institute (IRRI), 5th Floor, Nayantara Building, G.S. Road, Sixmile, Guwahati 781011, India)

  • Payel Ghosh Dastidar

    (International Rice Research Institute (IRRI), South Asia Regional Centre (SARC), NSRTC Campus, G. T. Road, Collectory Farm, P.O. Industrial Estate, Varanasi 221006, India)

  • Archita Sharma

    (Directorate of Research (Agri.), Assam Agricultural University (AAU), Barbheta, Jorhat 785013, India)

  • Debabrat Gogoi

    (Directorate of Research (Agri.), Assam Agricultural University (AAU), Barbheta, Jorhat 785013, India)

  • Priyanuz Goswami

    (Directorate of Research (Agri.), Assam Agricultural University (AAU), Barbheta, Jorhat 785013, India)

  • Giti Deka

    (Directorate of Research (Agri.), Assam Agricultural University (AAU), Barbheta, Jorhat 785013, India)

  • Suryakanta Khandai

    (International Rice Research Institute (IRRI), 5th Floor, Nayantara Building, G.S. Road, Sixmile, Guwahati 781011, India)

  • Rupam Borgohain

    (Directorate of Research (Agri.), Assam Agricultural University (AAU), Barbheta, Jorhat 785013, India)

  • Sudhanshu Singh

    (International Rice Research Institute (IRRI), South Asia Regional Centre (SARC), NSRTC Campus, G. T. Road, Collectory Farm, P.O. Industrial Estate, Varanasi 221006, India)

  • Ashok Bhattacharyya

    (Directorate of Research (Agri.), Assam Agricultural University (AAU), Barbheta, Jorhat 785013, India)

Abstract

Rice-fallow areas have significant potential to sustainably increase agricultural intensification to address growing global food demands while simultaneously increasing farmers’ income by harnessing the residual soil moisture in rainfed ecologies. Assam is the largest rice-growing belt in northeast India during kharif ; however, for the next rabi season, an average of 58% of the rice areas remain uncultivated and are described as rice-fallow ( Kharif , rabi and zaid are the crop seasons in the study area. The kharif season refers to the monsoon/rainy season and corresponds to the major crop season in the region extending from June to October. The rabi season refers to the winter season extending from November to April, and the zaid season is the summer crop season from April to June). Unutilized rice-fallow areas with optimum soil moisture for a second crop were identified over three consecutive years using multiple satellite data (optical and radar) for the state of Assam and an average accuracy of 92.6%. The reasons governing the existence of rice-fallow areas were analyzed, and an average of 0.88 million ha of suitable rice-fallow areas, based on soil moisture availability, were identified. Targeted interventions were carried out in selected locations to demonstrate the potential of sustainable cropping intensification. Maize, with best management practices, and a yield between 5.5 and 6 t/ha, was demonstrated as a successful second crop during the rabi season in selected areas with optimum residual soil moisture after the kharif paddy harvest. This study highlights the significance of geospatial technology to effectively identify and target suitable rice-fallow areas for cropping intensification and to enhance productivity and profitability.

Suggested Citation

  • Amit Kumar Srivastava & Suranjana Bhaswati Borah & Payel Ghosh Dastidar & Archita Sharma & Debabrat Gogoi & Priyanuz Goswami & Giti Deka & Suryakanta Khandai & Rupam Borgohain & Sudhanshu Singh & Asho, 2023. "Rice-Fallow Targeting for Cropping Intensification through Geospatial Technologies in the Rice Belt of Northeast India," Agriculture, MDPI, vol. 13(8), pages 1, July.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1509-:d:1204568
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    References listed on IDEAS

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    1. Kar, Gouranga & Kumar, Ashwani, 2009. "Evaluation of post-rainy season crops with residual soil moisture and different tillage methods in rice fallow of eastern India," Agricultural Water Management, Elsevier, vol. 96(6), pages 931-938, June.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Sushanta Kumar Naik & Santosh Sambhaji Mali & Bal Krishna Jha & Rakesh Kumar & Surajit Mondal & Janki Sharan Mishra & Arun Kumar Singh & Ashis Kumar Biswas & Arbind Kumar Choudhary & Jaipal Singh Chou, 2023. "Intensification of Rice-Fallow Agroecosystem of South Asia with Oilseeds and Pulses: Impacts on System Productivity, Soil Carbon Dynamics and Energetics," Sustainability, MDPI, vol. 15(2), pages 1-27, January.
    4. Saud, Jayanta & Bezbaruah, M.P., 2020. "Open grazing and cropping intensity in the Brahmaputra Valley," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 33(1), June.
    5. Wu, Wenbin & Yu, Qiangyi & You, Liangzhi & Chen, Kevin & Tang, Huajun & Liu, Jianguo, 2018. "Global cropping intensity gaps: Increasing food production without cropland expansion," Land Use Policy, Elsevier, vol. 76(C), pages 515-525.
    6. Aditi Bhattacharyya & Raju Mandal, 2016. "A Generalized Stochastic Production Frontier Analysis of Technical Efficiency of Rice Farming: A Case Study from Assam, India," Working Papers 1603, Sam Houston State University, Department of Economics and International Business.
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