IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v24y2022i3d10.1007_s10668-021-01578-8.html
   My bibliography  Save this article

Weather variability trends in Gangetic plains of Uttar Pradesh, India: influence on cropping systems and adaptation strategies

Author

Listed:
  • Tapendra Kumar Srivastava

    (ICAR-Indian Institute of Sugarcane Research)

  • Pushpa Singh

    (ICAR-Indian Institute of Sugarcane Research)

  • Ram Ratan Verma

    (ICAR-Indian Institute of Sugarcane Research)

Abstract

Weather variability over the long run exhibits the trends of change in climate and forewarns for development and deployment of adaptation measures. Gangetic plain of Uttar Pradesh in India is an agriculturally important geographical region of South East Asia. The region is vulnerable to weather variability led glacier melting, climate change impacts and increased competition for land. In addition, changes in rainfall, groundwater and weather patterns are deteriorating the agricultural and water systems that are bound to affect the food production and throw the poor populace into chaotic conditions. As weather variability trends are being increasingly used for sustaining the food production in climate-sensitive regions, the present study was taken up in Lucknow district of Uttar Pradesh. Daily meteorological datasets of temperature, rainfall, rainy days, evaporation, wind speed, relative humidity and bright sunshine hours during the past 63 years (1956–2018) were analysed for long-term trends. The study indicated conspicuous long-term trends of reduction in annual rainfall (− 28.97 mm decade−1), rising level of daily Tmin (0.09 °C a decade) and RH (1.08% decade−1) coupled with significant declining trends in evaporation (− 0.31 mm day−1), wind speed (− 0.29 km h−1) and bright sunshine hours (− 0.19 h day−1), that poignantly elucidates a clear warming trend over the period in the region. Multi-pronged adaptation strategies comprising of development of water efficient crop varieties, cropping system diversification with less water requiring crops, adoption of water efficient irrigation techniques, surface water harvesting and copious ground water recharge have been proposed for coping up.

Suggested Citation

  • Tapendra Kumar Srivastava & Pushpa Singh & Ram Ratan Verma, 2022. "Weather variability trends in Gangetic plains of Uttar Pradesh, India: influence on cropping systems and adaptation strategies," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3588-3618, March.
    • Handle: RePEc:spr:endesu:v:24:y:2022:i:3:d:10.1007_s10668-021-01578-8
    DOI: 10.1007/s10668-021-01578-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-021-01578-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-021-01578-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Aggarwal, P.K. & Banerjee, B. & Daryaei, M.G. & Bhatia, A. & Bala, A. & Rani, S. & Chander, S. & Pathak, H. & Kalra, N., 2006. "InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. II. Performance of the model," Agricultural Systems, Elsevier, vol. 89(1), pages 47-67, July.
    2. Claudia Tebaldi & David Lobell, 2018. "Estimated impacts of emission reductions on wheat and maize crops," Climatic Change, Springer, vol. 146(3), pages 533-545, February.
    3. Xin Lu & David J. Wrathall & Pål Roe Sundsøy & Md. Nadiruzzaman & Erik Wetter & Asif Iqbal & Taimur Qureshi & Andrew J. Tatem & Geoffrey S. Canright & Kenth Engø-Monsen & Linus Bengtsson, 2016. "Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen," Climatic Change, Springer, vol. 138(3), pages 505-519, October.
    4. Chand, Ramesh & Kumar, Praduman & Kumar, Sant, 2012. "Total Factor Productivity and Returns to Public Investment on Agricultural Research in India," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 25(2).
    5. Aggarwal, P.K. & Kalra, N. & Chander, S. & Pathak, H., 2006. "InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description," Agricultural Systems, Elsevier, vol. 89(1), pages 1-25, July.
    6. K, Sudarkodi & K, Sathyabama, 2011. "The Impact Of Climate Change On Agriculture," MPRA Paper 29784, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. M. Sujithra & Subhash Chander, 2013. "Simulation of rice brown planthopper, Nilaparvata lugens (Stal.) population and crop-pest interactions to assess climate change impact," Climatic Change, Springer, vol. 121(2), pages 331-347, November.
    2. Paresh B. Shirsath & Vinay Kumar Sehgal & Pramod K. Aggarwal, 2020. "Downscaling Regional Crop Yields to Local Scale Using Remote Sensing," Agriculture, MDPI, vol. 10(3), pages 1-14, March.
    3. Kattarkandi Byjesh & Soora Kumar & Pramod Aggarwal, 2010. "Simulating impacts, potential adaptation and vulnerability of maize to climate change in India," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 15(5), pages 413-431, June.
    4. Fargue-Lelièvre, A. & Le Cœur, D. & Baudry, J., 2011. "Integrating farming techniques in an ecological matrix model: Implementation on the primrose (Primula vulgaris)," Ecological Modelling, Elsevier, vol. 222(4), pages 1002-1015.
    5. Trnka, M. & Muška, F. & Semerádová, D. & Dubrovský, M. & Kocmánková, E. & Žalud, Z., 2007. "European Corn Borer life stage model: Regional estimates of pest development and spatial distribution under present and future climate," Ecological Modelling, Elsevier, vol. 207(2), pages 61-84.
    6. Sulav Paudel & Lalit P. Sah & Mukti Devkota & Vijaya Poudyal & P.V. Vara Prasad & Manuel R. Reyes, 2020. "Conservation Agriculture and Integrated Pest Management Practices Improve Yield and Income while Reducing Labor, Pests, Diseases and Chemical Pesticide Use in Smallholder Vegetable Farms in Nepal," Sustainability, MDPI, vol. 12(16), pages 1-16, August.
    7. Singh, P. & Aggarwal, P. K. & Bhatia, V. S. & Murty, M. V. R. & Pala, M. & Oweis, T. & Benli, B. & Rao, K. P. C. & Wani, S. P., 2009. "Yield gap analysis: modelling of achievable yields at farm level," IWMI Books, Reports H041995, International Water Management Institute.
    8. Selvaraj Krishnan & Subhash Chander, 2015. "Simulation of climatic change impact on crop-pest interactions: a case study of rice pink stem borer Sesamia inferens (Walker)," Climatic Change, Springer, vol. 131(2), pages 259-272, July.
    9. K. Viswanath & P. Sinha & S. Naresh Kumar & Taru Sharma & Shalini Saxena & Shweta Panjwani & H. Pathak & Shalu Mishra Shukla, 2017. "Simulation of leaf blast infection in tropical rice agro-ecology under climate change scenario," Climatic Change, Springer, vol. 142(1), pages 155-167, May.
    10. A. Mukherjee & A. K. S. Huda, 2018. "Assessment of climate variability and trend on wheat productivity in West Bengal, India: crop growth simulation approach," Climatic Change, Springer, vol. 147(1), pages 235-252, March.
    11. Kalra, Naveen & Chakraborty, Debashis & Ramesh Kumar, P. & Jolly, Monica & Sharma, P.K., 2007. "An approach to bridging yield gaps, combining response to water and other resource inputs for wheat in northern India, using research trials and farmers' fields data," Agricultural Water Management, Elsevier, vol. 93(1-2), pages 54-64, October.
    12. Faramarzi, Monireh & Yang, Hong & Schulin, Rainer & Abbaspour, Karim C., 2010. "Modeling wheat yield and crop water productivity in Iran: Implications of agricultural water management for wheat production," Agricultural Water Management, Elsevier, vol. 97(11), pages 1861-1875, November.
    13. K. Hebbar & M. Venugopalan & A. Prakash & P. Aggarwal, 2013. "Simulating the impacts of climate change on cotton production in India," Climatic Change, Springer, vol. 118(3), pages 701-713, June.
    14. Mohammed Khalil Mellal & Rassim Khelifa & Abdelmadjid Chelli & Naima Djouadi & Khodir Madani, 2023. "Combined Effects of Climate and Pests on Fig ( Ficus carica L.) Yield in a Mediterranean Region: Implications for Sustainable Agricultural Strategies," Sustainability, MDPI, vol. 15(7), pages 1-12, March.
    15. Vayssières, Jonathan & Guerrin, François & Paillat, Jean-Marie & Lecomte, Philippe, 2009. "GAMEDE: A global activity model for evaluating the sustainability of dairy enterprises Part I - Whole-farm dynamic model," Agricultural Systems, Elsevier, vol. 101(3), pages 128-138, July.
    16. Saon Banerjee & Subharanjan Das & Asis Mukherjee & Apurba Mukherjee & B. Saikia, 2016. "Adaptation strategies to combat climate change effect on rice and mustard in Eastern India," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 21(2), pages 249-261, February.
    17. Dhakar, Rajkumar & Sehgal, Vinay Kumar & Chakraborty, Debasish & Sahoo, Rabi Narayan & Mukherjee, Joydeep & Ines, Amor V.M. & Kumar, Soora Naresh & Shirsath, Paresh B. & Roy, Somnath Baidya, 2022. "Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing," Agricultural Systems, Elsevier, vol. 195(C).
    18. Pathak, H. & Wassmann, R., 2007. "Introducing greenhouse gas mitigation as a development objective in rice-based agriculture: I. Generation of technical coefficients," Agricultural Systems, Elsevier, vol. 94(3), pages 807-825, June.
    19. Siad, Si Mokrane & Iacobellis, Vito & Zdruli, Pandi & Gioia, Andrea & Stavi, Ilan & Hoogenboom, Gerrit, 2019. "A review of coupled hydrologic and crop growth models," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    20. Verma, Amit Kumar & Garg, Pradeep Kumar & Prasad, K.S. Hari & Dadhwal, Vinay Kumar, 2023. "Variety-specific sugarcane yield simulations and climate change impacts on sugarcane yield using DSSAT-CSM-CANEGRO model," Agricultural Water Management, Elsevier, vol. 275(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:24:y:2022:i:3:d:10.1007_s10668-021-01578-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.