Strategic targeting and tailoring of Agromet Advisory Services for Kharif sorghum in India

Authors

  • SHESHAKUMAR GOROSHI India Meteorological Department, MoES, New Delhi
  • A. P. RAMARAJ Agromet Advisory Service Division, India Meteorological Department, Ministry of Earth Sciences, New Delhi - 110 003, (India)
  • SHILPASHREE G. S. Agromet Advisory Service Division, India Meteorological Department, Ministry of Earth Sciences, New Delhi - 110 003, (India)
  • VENKADESH S. Agromet Advisory Service Division, India Meteorological Department, Ministry of Earth Sciences, New Delhi - 110 003, (India)
  • NAGARAJU DHARAVATH Agromet Advisory Service Division, India Meteorological Department, Ministry of Earth Sciences, New Delhi - 110 003, (India)
  • S. C. BHAN Agromet Advisory Service Division, India Meteorological Department, Ministry of Earth Sciences, New Delhi - 110 003, (India)
  • K. K. SINGH Agromet Advisory Service Division, India Meteorological Department, Ministry of Earth Sciences, New Delhi - 110 003, (India)

DOI:

https://doi.org/10.54386/jam.v27i3.2842

Keywords:

Agromet information, Production zones, Kharif sorghum, Cropping efficiency zones, Relative spread index (RSI), Relative yield index (RYI)

Abstract

Timely availability of reliable, location- and crop-specific weather information is critical for prioritizing actions and minimizing agricultural losses. To tailor and target advisories specific to crop and location, a nested approach was adopted. This approach was applied to Kharif sorghum in which four production zones (Primary, Secondary, Tertiary and others) and four efficiency zones (most efficient, efficient, not efficient, and inefficient) were delineated for two recent decades (2001–2010) and (2011–2020) across 168 sorghum-growing districts. Nesting these zones revealed significant transitions over time, with more than 30 districts showing a notable decline in the most efficient cropping zone (MECZ) and efficient cropping zone (ECZ), underscoring the need for focused attention and intervention. By prioritizing transition zones and tailoring advisories using integrated decision support tools and feedback mechanisms, this approach aims to build resilience and minimize losses due to climate variability and extreme weather events in the miller growing regions.

References

APEDA. (2024). Agriculture and Processed Food Products Export Development Authority

https://apeda.gov.in/milletportal/index.html (last access: 15 March 2024).

Boomiraj, K., Marimuthu, S., Suhas, P.W., Ravikumar, S., Manikandan and Tani, S. (2012). Vulnerability assessment of kharif rainfed sorghum to climate change in SAT regions of India. J. Agrometeorol., 14 (1): 2-8. https://doi.org/10.54386/jam.v14i1.1369

Chattopadhyay N, and Chandras S. (2018). Agrometeorological advisory services for sustainable development in Indian agriculture. Biodiversity Int J., 2(1): 13-18. DOI:10.15406/bij.2018.02.00036.

Ejigu, D., Pushpalatha, R., S., S., Padil, V., Gangadharan, B., Thendiyath, R., Kj, S., Upadhyay, G., and Harilal, S. (2025). Millets as a dual-purpose crop for sustainable nutritional and energy security: A comprehensive review. J. Agrometeorol., 27(2): 245–257. https://doi.org/10.54386/jam.v27i2.2892

Hansen, J.W. (2002). Applying seasonal climate prediction to agricultural production. Agric. Syst., 74(3): 305–307.

Jamidi, Muatho, M. I., Nasruddin, Ismadi, and Baidhawi. (2025). Agroclimatic suitability analysis for oil palm under projected climate in North Aceh Regency, Indonesia. J. Agrometeorol., 27(2): 177–183. https://doi.org/10.54386/jam.v27i2.2662

Kendall, M.G. (1975). Rank Correlation Methods; Griffin: London, UK. ISBN 9780852641996.

Kokilavani S and Geethalakshmi V (2013). Identification of Efficient Cropping Zone for Rice, Maize and Groundnut in Tamil Nadu. Indian J. Sci. Techn., 6 (10): 5298-5301.

Mann, H.B. (1945). Nonparametric Tests against Trend. Econometrica, 13:245.

Murthy, M.V.R., Piara Singh, Wani, S.P., Khairwal, I.S. and Srinivas, K. (2007). Yield gap analysis of sorghum and pearl millet in India using simulation modeling. Global Theme on Agroecosystems Report no. 37. International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502324, Andhra Pradesh, India, 82 pp.

Patakamuri, S.K., Muthiah, K., and Sridhar, V. (2020). Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India. Water, 12: 211.

Sandeep, V. M., B. Bapuji Rao, G. Bharathi, V U M Rao, V. P. Pramod, P. S. Chowdary, N.R. Patel, and P. Vijaya Kumar. (2017). Projecting future changes in water requirement of grain sorghum in India. J. Agrometeorol., 19(3): 217–225. https://doi.org/10.54386/jam.v19i3.630

Sandeep, V. M., V. U. M. Rao, B. Bapuji Rao, V. P. Pramod, P. Santhibhushan Chowdary, P. Vijaya Kumar, G. Bharathi, N.R. Patel, and P. Mukesh. (2018). Impact of climate change on sorghum productivity in India and its adaptation strategies. J. Agrometeorol., 20(2): 89–96. https://doi.org/10.54386/jam.v20i2.517

Sen, P.K. (1968). Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc., 63: 1379–1389.

Tabari, H., Marofi, S., Aeini, A., Talaee, P.H. and Mohammadi, K. (2011). Trend analysis of reference evapotranspiration in the western half of Iran. Agric. For. Meteorol., 151: 128–136.

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Published

01-09-2025

How to Cite

GOROSHI, S., A. P. RAMARAJ, SHILPASHREE G. S., VENKADESH S., NAGARAJU DHARAVATH, S. C. BHAN, & K. K. SINGH. (2025). Strategic targeting and tailoring of Agromet Advisory Services for Kharif sorghum in India . Journal of Agrometeorology, 27(3), 338–343. https://doi.org/10.54386/jam.v27i3.2842

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