Search

Menu
Search
in Ecology

Long-term trends and variability in sugarcane production: a five-district comparative analysis with meteorological context in Maharashtra and Karnataka, India

159 Views


Abstract

The climate-resilient planning and policy development for major cane-growing regions depends on the understanding of sugarcane production patterns which exist over long time periods and show fluctuations at the district level. The study analyzes sugarcane production data from five Indian districts by comparing the 22 years of crop-year data which includes records from 1999 to 2021 for Ahmednagar, Solapur, and Nashik in Maharashtra and Bellary and Dharwad in Karnataka. Parametric and non-parametric methods (linear regression, Mann–Kendall test, Sen’s slope) are applied to quantify trends; period-wise summaries, structural-break detection (Pettitt test), and coefficient-of-variation and extreme-year statistics are reported for all five districts. Ahmednagar and Nashik show significant positive yield trends; Solapur has the largest scale and lowest yield variability; Bellary has the highest mean yield (89.46 t/ha). Period-wise analysis identifies three regimes (1999–2006, 2007–2013, 2014–2020) with rising mean yields in most districts. Yield is relatively more stable than area and production (lowest CV in yield). For Ahmednagar, combining meteorological data to identify very good crop years shows that the drought index, moisture sufficiency, heat stress days, and yield are closely related. Extreme high and low yield years can be explained by the variations in these indices. The comparative framework and the district-level evidence enable providing distinct inputs for water-risk management, climate services, and stabilization measures in the water-limited sugarcane systems of the Deccan Plateau.

Similar content being viewed by others

Spatial and temporal patterns of agrometeorological indicators in maize producing provinces of South Africa

Article
Open access
15 July 2022

Measuring climate change’s impact on different sugarcane varieties production in the South of Goiás

Article
Open access
19 July 2023

Complex drought patterns robustly explain global yield loss for major crops

Article
Open access
06 April 2022

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Reddy, B. V. S. et al. Sweet sorghum – A potential alternate raw material for bio-ethanol and bio-energy. Int. Sorghum Millets Newsl. 46, 79–86 (2005).

    Google Scholar 

  2. Kelkar, S. M., Kulkarni, A. & Rao, K. K. Impact of climate variability and change on crop production in Maharashtra, India. Curr. Sci. 118(8), 1235–1245 (2020).

    Google Scholar 

  3. Intergovernmental Panel on Climate Change. Climate Change 2007: Impacts, Adaptation and Vulnerability, (IPCC, 2007)

  4. Guhan, V. et al. Assessing the impact of climate change on water requirement and yield of sugarcane over different agro-climatic zones of Tamil Nadu. Sci. Rep. 14, 8239. https://doi.org/10.1038/s41598-024-58771-8 (2024).

    Google Scholar 

  5. Thornton, P. K., Ericksen, P. J., Herrero, M. & Challinor, A. J. Climate variability and vulnerability to climate change: A review. Glob. Change Biol. 20(11), 3313–3328 (2014).

    Google Scholar 

  6. Kumar, A. & Jain, R. Growth and instability in agricultural productivity: A district level analysis. Agric. Econ. Res. Rev. 26, 31–42 (2013).

    Google Scholar 

  7. Murtaza, M. & Masood, T. Inter-district variations in agricultural productivity in India: Is there convergence? (2018).

  8. Indumathi, P., Deepa, S., Madhesh, C., Sengodi, R. & and and An exploratory study of sugarcane production based on agricultural climatic analysis in the Indian states. Int. J. Indian Cult. Bus. Manage. 32 (2), 209–239. https://doi.org/10.1504/IJICBM.2024.139183 (2024).

    Google Scholar 

  9. Sengupta, A. & Thangavel, M. Impact of climate change on sugarcane production in Uttar Pradesh, India: A district level study using statistical analysis and GIS mapping. Malaysian J. Sustain. Agric. 7(1), 32–37. https://doi.org/10.26480/mjsa.01.2023.32.37 (2023).

    Google Scholar 

  10. Misra, V., Gosavi, B., Rao, G. P., Viswanathan, R. & Solomon, S. AI-driven innovations in sugarcane farming and sugar industry in India: Enhancing productivity, efficiency, and sustainability. Sugar Tech https://doi.org/10.1007/s12355-025-01715-x (2026).

    Google Scholar 

  11. Kumar, S. N. et al. Impact of climate change on crop productivity in Western Ghats, coastal and northeastern regions of India. Curr. Sci. 109(3), 506–515 (2015).

    Google Scholar 

  12. Mall, R. K., Singh, R., Gupta, A., Srinivasan, G. & Rathore, L. S. Impact of climate change on Indian agriculture: A review. Clim. Change 78(2), 445–478. https://doi.org/10.1007/s10584-005-9042-x (2006).

    Google Scholar 

  13. Birthal, P. S., Khan, M. T., Negi, D. S. & Agarwal, S. Impact of climate change on yields of major food crops in India: Implications for food security. Agric. Econ. Res. Rev. 27(2), 145–155. https://doi.org/10.5958/0974-0279.2014.00019.6 (2014).

    Google Scholar 

  14. Attri, S. D. & Rathore, L. S. Simulation of impact of projected climate change on wheat in India. Int. J. Climatol. 23(6), 693–705. https://doi.org/10.1002/joc.896 (2003).

    Google Scholar 

  15. Dingre, S. K. & Gorantiwar, S. D. Determination of the water requirement and crop coefficient values of sugarcane by field water balance method in semiarid region. Agric. Water Manag. 232, 106042. https://doi.org/10.1016/j.agwat.2020.106042 (2020).

    Google Scholar 

  16. Inman-Bamber, N. G. & McGlinchey, M. G. Crop coefficients and water-use estimates for sugarcane based on long-term Bowen ratio energy balance measurements. Field Crops Res. 83(2), 125–138. https://doi.org/10.1016/S0378-4290(03)00069-8 (2003).

    Google Scholar 

  17. Wakchaure, G. C. et al. Assessment of gains in productivity and water-energy-carbon nexus with tillage, trash retention and fertigation practices in drip irrigated sugarcane. Renew. Sustain. Energy Rev. 211, 115294. https://doi.org/10.1016/j.rser.2024.115294 (2025).

    Google Scholar 

  18. Behnia, M., Ghahderijani, M., Kaab, A. & Behnia, M. Evaluation of sustainable energy use in sugarcane production: A holistic model from planting to harvest and life cycle assessment. Environ. Sustain. Indic. 26, 100617 (2025).

    Google Scholar 

  19. Tripathy, R., Nigam, R. & Bhattacharya, B. K. Agrometeorological approach for sugarcane yield estimation at regional scale using satellite remote sensing. J. Indian Soc. Remote Sens. 51(8), 1715–1728. https://doi.org/10.1007/s12524-023-01724-x (2023).

    Google Scholar 

  20. Hirsch, R. M., Slack, J. R., Smith, R. A. & and Techniques of trend analysis for monthly water quality data. Water Resour. Res. 20 (1), 107–121 (1984).

    Google Scholar 

  21. Kendall, M. G. & Gibbons, J. D. Rank Correlation Methods, (Charles Griffin, 1962).

  22. Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934 (1968).

    Google Scholar 

  23. Pettitt, A. N. A non-parametric approach to the change-point problem. Appl. Stat. 28(2), 126–135. https://doi.org/10.2307/2346729 (1979).

    Google Scholar 

  24. Bai, J. & Perron, P. Computation and analysis of multiple structural change models. J. Appl. Econom. 18 (1), 1–22 (2003).

    Google Scholar 

  25. Anderson, J. R. Risk in rural development: Challenges for managers and policy makers. Agric. Syst. 75 (2–3), 161–197 (2003).

    Google Scholar 

  26. Knapp, S. & van der Heijden, M. G. A. Statistical modeling of yield and variance instability in conventional and organic cropping systems. Agron. J. 102(2), 578–585 (2010).

    Google Scholar 

  27. Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang. 4(4), 287–291. https://doi.org/10.1038/nclimate2153 (2014).

    Google Scholar 

  28. Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989. https://doi.org/10.1038/ncomms6989 (2015).

    Google Scholar 

  29. Lobell, D. B., Schlenker, W., Costa-Roberts, J. & and Climate trends and global crop production since 1980. Science 333 (6042), 616–620 (2011).

    Google Scholar 

  30. Wheeler, T. & von Braun, J. Climate change impacts on global food security. Science 341(6145), 508–513. https://doi.org/10.1126/science.1239402 (2013).

    Google Scholar 

  31. Porter, J. R. et al. Food Security and Food Production Systems (2014).

  32. Knox, J., Hess, T., Daccache, A. & Wheeler, T. Climate change impacts on crop productivity in Africa and South Asia. Environ. Res. Lett. 7(3), 034032. https://doi.org/10.1088/1748-9326/7/3/034032 (2012).

    Google Scholar 

  33. Roudier, P., Sultan, B., Quirion, P. & Berg, A. The impact of future climate change on West African crop yields: What does the recent literature say?. Glob. Environ. Change 21(3), 1073–1083 (2011).

    Google Scholar 

  34. Naylor, R. L., Battisti, D. S., Vimont, D. J., Falcon, W. P. & Burke, M. B. Assessing risks of climate variability and climate change for Indonesian rice agriculture. Proc. Natl. Acad. Sci. U. S. A. 104(19), 7752–7757. https://doi.org/10.1073/pnas.0701825104 (2007).

    Google Scholar 

  35. Olesen, J. E. & Bindi, M. Consequences of climate change for European agricultural productivity, land use and policy. Eur. J. Agron. 16(4), 239–262. https://doi.org/10.1016/S1161-0301(02)00004-7 (2002).

    Google Scholar 

  36. Mendelsohn, R. & Dinar, A. Climate Change and Agriculture: An Economic Analysis of Global Impacts, Adaptation and Distributional Effects (Edward Elgar Publishing, 2009).

  37. Mirza, M. M. Q. Climate change and extreme weather events: Can developing countries adapt?. Clim. Policy 3(3), 233–248. https://doi.org/10.3763/cpol.2003.0330 (2003).

    Google Scholar 

  38. Lipper, L. et al. Climate-smart agriculture for food security. Nat. Clim. Chang. 4(12), 1068–1072. https://doi.org/10.1038/nclimate2437 (2014).

    Google Scholar 

  39. Venkateswarlu, B. & Shanker, A. K. Climate change and agriculture: Adaptation and mitigation strategies. Ind. J. Agron. 54 (2), 226–230 (2009).

    Google Scholar 

  40. Jat, M. L. et al. Climate change and agriculture: Adaptation strategies and mitigation opportunities for food security in South Asia and Latin America. Adv. Agron. 137, 127–235. https://doi.org/10.1016/bs.agron.2015.12.005 (2016).

    Google Scholar 

  41. Giridhar, B. J., Singh, D. R. & Jambagi, R. P. Assessing the impact of conjunctive use of rainwater and groundwater: Empirical evidence from semi-arid regions of Karnataka. Agric. Econ. Res. Rev. 38(2), 206–218. https://doi.org/10.1177/09713441251395492 (2025).

    Google Scholar 

  42. Descheemaeker, K., Zijlstra, M., Masikati, P., Crespo, O. & Homann-Kee Tui, S. Effects of climate change and adaptation on the livestock component of mixed farming systems: A modelling study from semi-arid Zimbabwe. Agric. Syst. 159, 282–295. https://doi.org/10.1016/j.agsy.2017.05.004 (2018).

    Google Scholar 

  43. IPCC. Climate Change 2021: The Physical Science Basis. (Cambridge University Press, 2021).

Download references

Acknowledgements

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/354/46. The authors acknowledge the support of data providers and agricultural departments for making production and meteorological data (IMD Pune) available for this analysis. Production and area data were obtained from the Directorate of Economics and Statistics (DES) via https://data.desagri.gov.in. Meteorological data were obtained from the India Meteorological Department, Climate Research and Services, Pune (IMD Pune) via https://www.imdpune.gov.in/.

Funding

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/354/46.

Author information

Authors and Affiliations

  1. Department of Science and Humanities, Government Polytechnic, Pune, India

    Pramod Bhalerao

  2. Department of Applied Science and Humanities, School of Computing, MIT-ADT University, Pune, India

    Krishna Kumar & Irshad Jamadar

  3. Department of Mechanical Engineering, College of Engineering, King Khalid University, PO Box 394, 61421, Abha, Saudi Arabia

    C. Ahamed Saleel

  4. Center for Engineering and Technology Innovations, King Khalid University, 61421, Abha, Saudi Arabia

    C. Ahamed Saleel

  5. Faculty of Artificial Intelligence & Engineering (FAIE), Multimedia University, Persiaran Multimedia, 63100, Cyberjaya, Selangor, Malaysia

    Shashikumar Krishnan

  6. Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University, 53100, Kuala Lumpur, Selangor, Malaysia

    Sher Afghan Khan

Authors

  1. Pramod Bhalerao
    View author publications

    Search author on:PubMed Google Scholar

  2. Krishna Kumar
    View author publications

    Search author on:PubMed Google Scholar

  3. Irshad Jamadar
    View author publications

    Search author on:PubMed Google Scholar

  4. C. Ahamed Saleel
    View author publications

    Search author on:PubMed Google Scholar

  5. Shashikumar Krishnan
    View author publications

    Search author on:PubMed Google Scholar

  6. Sher Afghan Khan
    View author publications

    Search author on:PubMed Google Scholar

Contributions

P.B.: Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization, Visualization. I.J.: Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. K.K.: Writing – review & editing, Investigation, Formal analysis. C.A.S.: Writing – review & editing, Funding acquisition, Formal analysis. S.K.: Writing – review & editing, Project administration, Supervision. S.A.K.: Writing – review & editing, Project administration, Supervision.

Corresponding authors

Correspondence to
Shashikumar Krishnan or Sher Afghan Khan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Bhalerao, P., Kumar, K., Jamadar, I. et al. Long-term trends and variability in sugarcane production: a five-district comparative analysis with meteorological context in Maharashtra and Karnataka, India.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-48029-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-026-48029-w

Keywords

  • Sugarcane
  • Production trend
  • Yield
  • Time series decomposition
  • Variability analysis

Source: Ecology - nature.com

Commensal and opportunistic bacteria resistant to fourth-generation cephalosporins or fluoroquinolones isolated from yellow-legged gulls (Larus michahellis) settled in Taranto, Southern Italy

Integrating theory and machine learning to reveal determinants of plasmid copy number

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close