Weather based thumb rule models for formulating the crop insurance schemes for wheat in Punjab

Sakshi Mahajan, Prabhjyot Kaur, S. S. Sandhu

Article ID: 2522
Vol 5, Issue 1, 2024
VIEWS - 100 (Abstract)


The Wundanyi sub-catchment of Taita Hills is experiencing a high rate of deforestation due to the conversion of all its original forestland to agriculture and settlement during the last century. The landscape dynamics coupled with rainfall fluctuations in these critical ecosystems may significantly affect water resource distribution and food security in Taita Taveta County and its environs. This study aimed to establish the trends of selected hydroclimatic variables in the Wundanyi sub-catchment from 1970 to 2030 and their specific and combined effects on surface runoff and streamflow during the same period. The analysis was based on statistical trend analysis and dynamic landscape modeling using both historical and primary hydroclimatic data from Wundanyi and Voi weather stations. Results show highly variable mean seasonal and annual values of temperature, rainfall, runoff, and discharge in both Wundanyi and Voi weather stations. Increasing mean temperatures and rainfall were observed during the long dry season (JJAS) while decreasing seasonal discharges were observed during both the JJAS dry season and the OND short rainy season. These anomalies were pronounced in 1980–1981, 1986–1987, and 1992–1993, probably due to both global and local environmental changes affecting Taita Hills in general and the Wundanyi sub-catchment in particular. The predicted effects of rainfall fluctuation were supported by declining surface runoff of 1.3% during JJAS, and an increase of 0.8% during the OND, with similar effects on river discharges. The combined effects of climate variability and land use and cover changes (LUCC) on surface runoff were estimated to increase by 200 mm during JJAS and 370 mm during OND, while river discharges increased by 2.37 m3/s and 1.93 m3/s during JJAS and OND, respectively. Consequently, natural forest covers have significant control effects on surface runoff and can boost river discharges amid diverse agricultural cropping practices. Hence, crop diversification, agroforestry, and soil and water conservation structures are recommended to maintain effective control of LUCC on hydrological processes going on in the Wundanyi sub-catchment.


catchment management; climate variability; climate change; LUCC; river flow; seasonality; Taita hills

Full Text:



1. Kurukulasuriya P, Rosenthal S. Climate Change and Agriculture: A Review of impacts and adaptations. Available online: (accessed on 8 March 2023).

2. Zobeidi T, Yazdanpanah M, Bakhshi A. Climate change risk perception among agriculture students: the role of knowledge, environmental attitude, and belief in happening. J Agr Sci Tech. 2020; 22(1): 43-55.

3. Sun Y. Enhanced Weather-Based Index Insurance Design for Hedging Crop Yield Risk. Frontiers in Plant Science. 2022; 13. doi: 10.3389/fpls.2022.895183

4. Kumar PV, Rao VUM, Bhavani O, et al. Sensitive growth stages and temperature thresholds in wheat (Triticum aestivum L.) for index-based crop insurance in the Indo-Gangetic Plains of India. The Journal of Agricultural Science. 2015; 154(2): 321-333. doi: 10.1017/s0021859615000209

5. Anonymous. Weather Based Agro Advisory Services. Available online: (accessed on 8 March 2023).

6. Nakka S, Jugulam M, Peterson D, et al. Herbicide resistance: Development of wheat production systems and current status of resistant weeds in wheat cropping systems. The Crop Journal. 2019; 7(6): 750-760. doi: 10.1016/j.cj.2019.09.004

7. Akter N, Rafiqul Islam M. Heat stress effects and management in wheat. A review. Agronomy for Sustainable Development. 2017; 37(5). doi: 10.1007/s13593-017-0443-9

8. Shiferaw B, Smale M, Braun HJ, et al. Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Security. 2013; 5(3): 291-317. doi: 10.1007/s12571-013-0263-y

9. Anonymous. India: wheat production volume in rabi season 2023. Available online: wheat-during-rabi-season/ (accessed on 8 March 2023).

10. Anonymous. Package of practice of Rabi crops. Punjab Agricultural University, Ludhiana. 2023. pp. 1-21.

11. Ahmad Dar E, Brar AS, Dar SA, et al. Quantitative response of wheat to sowing dates and irrigation regimes using CERES-Wheat model. Saudi Journal of Biological Sciences. 2021; 28(11): 6198-6208. doi: 10.1016/j.sjbs.2021.06.074

12. Chenu K, Porter JR, Martre P, et al. Contribution of Crop Models to Adaptation in Wheat. Trends in Plant Science. 2017; 22(6): 472-490. doi: 10.1016/j.tplants.2017.02.003

13. Prabhjyot-Kaur, Singh H, Rao VUM, et al. Agrometeorology of wheat in Punjab state of India. Published online 2015. doi: 10.13140/RG.2.1.5105.6721

14. Gahlot S, Lin TS, Jain AK, et al. Impact of environmental changes and land management practices on wheat production in India. Earth System Dynamics. 2020; 11(3): 641-652. doi: 10.5194/esd-11-641-2020

15. Tyagi SK, Singh R, Krishnan P, et al. Variations in meteorological conditions resulted decline in wheat yield in North-West Indo-Gangetic plains. J Agric Physics. 2013; 13: 175-181.

16. Rao BB, Chowdary PS, Sandeep VM, et al. Spatial analysis of the sensitivity of wheat yields to temperature in India. Agricultural and Forest Meteorology. 2015; 200: 192-202. doi: 10.1016/j.agrformet.2014.09.023

17. Faghih H, Behmanesh J, Rezaie H, et al. Climate and rainfed wheat yield. Theoretical and Applied Climatology. 2021; 144(1-2): 13-24. doi: 10.1007/s00704-020-03478-9

18. Lee BH, Kenkel P, Brorsen BW. Pre-harvest forecasting of county wheat yield and wheat quality using weather information. Agricultural and Forest Meteorology. 2013; 168: 26-35. doi: 10.1016/j.agrformet.2012.08.010

19. Prabhjyot-Kaur, Sandhu SS, Singh S, et al. Climate Change- Punjab Scenario. Published online 2013. doi: 10.13140/RG.2.1.1368.4960

20. Prabhjyot-Kaur, Sandhu SS, Dhir A. Mitigation and risk management of climate change in crop cultivation through the adoption of Agromet Advisory Bulletin (AAB) in NICRA adopted villages in Punjab. MAUSAM. 2023; 75(1): 249-256. doi: 10.54302/mausam.v75i1.6140

21. Chattopadhyay N, Chandras S. Agrometeorological Advisory Services for Sustainable Development in Indian Agriculture. Biodiversity International Journal. 2018; 2(1). doi: 10.15406/bij.2018.02.00036

22. Vashisth A, Singh R, Das D K, et al. Weather based agromet advisories for enhancing the production and income of the farmers under changing climate scenario Int J Agric Sci Food Technol. 2013; 4: 847-50.

23. Kumar PV, Rao AVM, Chandran MA, et al. Micro-level Agromet advisory services using block level weather forecast—a new concept-based approach. Curr Sci. 2017; 112: 227–28.

24. Vishnoi L, Kumar A, Kumar S, et al. Weather based crop insurance for risk management in agriculture. J Agrometeorol 2020; 22: 101-108. doi: 10.54386/jam.v22i2.149

25. Aditya KS, Kishore A, Khan MD. Exploring farmers’ willingness to pay for crop insurance products: A case of weather-based crop insurance in Punjab, India. Agric Econ Res Rev. 2020; 33: 135-46.

26. Bala A, Prabhjyot-Kaur. Formulation of weather based “Weekly Thumb Rule Models” for prediction of potential productivity of wheat in Punjab. Int J Agric Plant Sci. 2013; 1: 17-32.

27. Gill KK, Sandhu SS, Divya, et al. Pre-harvest wheat yield prediction using CERES-wheat model for Ludhiana district, Punjab, India. Journal of Agrometeorology. 2018; 20(4): 319-321. doi: 10.54386/jam.v20i4.574

28. Sandhu SS, Kaur P, Gill KK, et al. The effect of recent climate shifts on optimal sowing windows for wheat in Punjab, India. Journal of Water and Climate Change. 2019; 11(4): 1177-1190. doi: 10.2166/wcc.2019.241

29. Sandhu SS, Prabhjyot-Kaur, Tripathu P, et al. Effect of intra-seasonal temperature on wheat at different locations of India: A study using CERES-Wheat model. Journal of Agrometeorology. 2016; 18(2): 222-233. doi: 10.54386/jam.v18i2.939

30. Praveen KM, Kamath SKV, Ranjeetha K, et al. Analyzing the impact of weather based agro-advisory services of GKMS project among arecanut growers of Udupi district of Karnataka. J Pharm Innov. 2022; 11: 07-11.

31. Kumar PV, Bal SK, Dhakar R, et al. Algorithms for weather-based management decisions in major rainfed crops of India: Validation using data from multi-location field experiments. Agron J. 2021; 113: 1816–1830. doi: 10.54386/jam.v20i4.574

32. Dupdal R, Patil BL. Economic analysis of farmer’s awareness and perception about weather based crop insurance as tool of mitigation against climate variability in north Karnataka. International Journal of Commerce and Business Management. 2017; 10(2): 77-82. doi: 10.15740/has/ijcbm/10.2/77-82

33. Dupdal R, Patil BL, Patil SL, et al. Weather based crop insurance scheme: opportunities and challenges. Int Res J Social Sci. 2020; 9: 56-59.

34. Farooq M, Bramley H, Palta JA, et al. Heat Stress in Wheat during Reproductive and Grain-Filling Phases. Critical Reviews in Plant Sciences. 2011; 30(6): 491-507. doi: 10.1080/07352689.2011.615687

35. Craufurd PQ, Vadez V, Jagadish SVK, et al. Crop science experiments designed to inform crop modeling. Agricultural and Forest Meteorology. 2013; 170: 8-18. doi: 10.1016/j.agrformet.2011.09.003


  • There are currently no refbacks.

Copyright (c) 2024 Sakshi Mahajan, Prabhjyot-Kaur, S. S. Sandhu

License URL:

This site is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).