Harinarayanan M.N Manivannan. V Ga Dheebakaran Guna. M


Extended Range of Forecast Service (ERFS) is highly useful for planning of cropping season and midterm correction at the farm level. The medium-range and long-range forecast validation have many studies, whereas ERF has less that needs to be studied. Maize is an important field crop in India after rice and wheat.  Therefore, the prediction of maize yield has significant importance. In the present study, ERFS data were validated by correlation analysis using monthly observed rainfall frequency and intensity. This data was imported to DSSAT (Decision Support System for Agro-technology Transfer) to simulate maize yield of Erode district of Tamil Nadu. The model output and actual yield data from Erode were compared. Forecasted monthly total rainfall was correlated at a rate of 0.97r value with that observed. Yield simulation of maize was done using DSSAT by integrating ERFS data and the observed monthly data. Mean per cent deviation among the yields of observed weather and the disaggregated one tended to be -15.7 %. The average deviation between the yields of ERF forecasted weather data and actual yield was very high ( -29.7 % ) for Erode. Mean % deviation between the yields of observed weather and the actual yield was -14.7 %. Downscaled and accurate weather forecasts could be facilitated for yield prediction of crops by DSSAT model. Yield prediction by the model under observed weather was convenient and usable. Model under-predicted the yields when using ERF data. Both model and ERF forecast need to be improved further for higher resolution.


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ERFS, DSSAT, Erode district, Rainfall, Maize, Yield prediction

Anonymous (2019). Report, https://www.statista.com/
Anonymous (2014). India Maize Summit 14. edited by Proceedings of Maize in India. FICCI, Federation House, New Delhi, March 20–21, 2014.
Attri, S. D. & Tyagi, A. (2010). Climate profile of India. Environment Monitoring and Research Center, India Meteorology Department: New Delhi, India.
Chattopadhyay, N., Rao, K.V., Sahai, A.K., Balasubramanian, R., Pai, D.S., Pattanaik, D.R., Chandras, S.V. & Khedikar, S. (2018). Usability of extended range and seasonal weather forecast in Indian agriculture. Mausam, 69(1), pp.29-44.
Dhekale, B. S., M. M. Nageswararao, Archana Nair, U. C. Mohanty, D. K. Swain, K. K. Singh & T. Arunbabu (2018). Prediction of kharif rice yield at Kharagpur using disaggregated extended range rainfall forecasts. Theoretical and Applied Climatology 133 (3-4), 1075-1091.
District diagnostic report – Erode (2020), Department of Rural Development & Panchayat Raj Government of Tamil Nadu retrieved from https://tnrtp.org
Ghosh, K., Ankita Singh, U. C. Mohanty, Nachiketa Acharya, R. K. Pal, K. K. Singh, and S. Pasupalak (2015). Development of a rice yield prediction system over Bhubaneswar, India: combination of extended range forecast and CERES‐rice model. Meteorological Applications 22 (3):525-533. https://doi.org/10.1002/met.1483
He, J., Dukes, M. D., Jones, J. W., Graham, W. D. & Judge, J. (2009). Applying GLUE for estimating CERES-Maize genetic and soil parameters for sweet corn production. Transactions of the ASABE, 52(6), 1907-1921. (doi: 10.13031/2013.29218).
Kaur, N. and Singh, M.J. (2019). Verification of medium range weather forecast for the Kandi region of Punjab. MAUSAM, 70(4), pp.825-832.
Kumar, Arvind, K. K. Singh, R. Balasubramaniyan, A. K. Baxla, P. Tripathi & B. N. Mishra (2010). Validation of CERES-Maize model for growth, yield attributes and yield of kharif maize for NEPZ of eastern UP. Journal of Agrometeorology 12 (1):118-120.
Ma, H., Malone, R.W., Jiang, T., Yao, N., Chen, S., Song, L., Feng, H., Yu, Q. & He, J., (2020). Estimating crop genetic parameters for DSSAT with modified PEST software. European Journal of Agronomy, 115, p.126017. https://doi.org/10.1016/j.eja.2020.126017
Pattanaik, D. R. (2022). A Report on Numerical Weather Prediction Products For Sectoral Applications. Retrieved from https://researchgate.net
Pattanaik, D. R. & Das, A.K. (2015). Prospect of application of extended range forecast in water resource management: a case study over the Mahanadi River basin. Natural Hazards 77 (2):575-595 https://doi.org/10.1007/s11069-015-1610-4.
Raleigh, C., Jordan, L. & Salehyan, I. (2008, March). Assessing the impact of climate change on migration and conflict. In paper commissioned by the World Bank Group for the Social Dimensions of Climate Change workshop, Washington, DC (pp. 5-6).
Rugira, P., Ma, J., Zheng, L., Wu, C. & Liu, E. (2021). Application of DSSAT CERES-maize to identify the optimum irrigation management and sowing dates on improving maize yield in Northern China. Agronomy, 11(4), p.674. https://doi.org/10.3390/agronomy11040674
Sofi, K. G. I. L. A., Chandran, M. S., Rani, S., Dekhle, B. & PAU, R. P. (2015). Development of Climatic Risk Management Tools in Agriculture Using ERFS. Flow Chart of Disaggregation Method. IMD text book, School of Earth, Ocean and Climatic Sciences, IIT Bhubaneswar (Odhisa) and IMD (New Delhi) 1- 5
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M.N, H., V , M., Dheebakaran , G., & M , G. (2022). Usability of monthly ERFS (Extended Range Forecast System) to predict maize yield using DSSAT (Decision Support System for Agro-technology Transfer) model over Erode District of Tamil Nadu . Journal of Applied and Natural Science, 14(SI), 244–250. https://doi.org/10.31018/jans.v14iSI.3709
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