Rice Yield Prediction of Chengalpattu District in Tamilnadu Using Crop Simulation Model (Ceres-Rice Model)

Topics: Rice, International Rice Research Institute, Regression analysis Pages: 14 (3599 words) Published: October 30, 2010
Proceedings of International Conference on Emerging Scenarios in Space Technology and Applications (ESSTA2008), Sathyabama University, Jeppiaar Nagar, Chennai, India.

Rice Yield Prediction of Chengalpattu District in Tamilnadu using Crop Simulation Model (CERES-Rice Model) Nethaji Mariappan.V.E1, Manoharan.N2, Ravichandran.M3 & Dadhwal.V.K4 1, 2

Centre for Remote Sensing and Geoinformatics, Sathyabama University,Chennai -600 119 3 Faculty of Agriculture, Annamalai University, Annamalai Nagar-680 002 4 Head, CMD/ARG/RESA, Space Applications Centre, (ISRO), Ahmedabad – 380 015 and genotype of the crop. Among these variables, weather plays a significant role in rice growth, development and ultimately the yield. A numerable work on the role of agro metrological parameters in rice yield was done by Huke and Sardido 1980; Sreenivasan 1980. A number predictions ranging from agro meteorological to crop simulation model on rice yield were developed by Angus and Zandstra 1980; Horie et al., 1992; Kropff et al., 1994; McMennamy 1980; Stansel and Fries 1980; Yao and LeDue 1980. These approaches are limited by site specificness of the model as well as availability of short historical data series. Alocilja and Ritchie 1988 developed an upland rice growth model for exclusively for DSSAT (Decision Support System for Agrotechnology Transfer), followed by Hoogenboom et al., 1999 for upland I.INTRODUCTION and lowland rice that was included in the CERES-Rice model.Currently simulated model were evaluated for regional yield prediction (Saseendran et al., 1998a; Saseendran et al., 1998b; Saseendran et al., 1999; Mohandass et al., 1995) as well as the impact of climate change on rice (Lal et al., 1998; Lal et al., 1999; Rathore et al., 2001). In this study, CERES (Crop Estimation through Resources and Environment Synthesis)-Rice developed by Hoogenboom et al., 1999 model has been used to simulate crop growth and yield over a period of 19 years from 1979 to 1998 (except 1992, due to non availability of weather data) for Chengalpattu district. An approach relating observed yield deviation from its technological trend and simulated yield deviation from its average in conjunction with technology trend model was used for predicted the rice (1979-1998). Then, rice yield was also forecasted for the period 1999 to 2001.

Abstract:- Rice yields was evaluated during the period (19791998) using CERES-Rice model to simulate seasonal yield variability and forecast rice yield prediction (1999 - 2001) of Chengalpattu district in Tamil Nadu. Yield deviations (Ydobs) from single linear technology trends (Ŷobs) (Period 1979-1998) were used along with year-wise deviations in simulated yields (Ydsim) from its nineteen-year period (Ŷsim) (1979-1998) used for yield prediction. The deviations of simulated to the deviation of trend rice yields during 1979-1999 and its relationship signified the importance in predicting the trend yield with RMSE of 269.43, absolute per cent error 10.20 and further explain the direction and magnitude of predicted yield for the period (19992001). Key words: CERES-Rice, DSSAT3.5, Simulation model, Chengalpattu

Rice plant is highly adaptable to its environment. Rice can be grown in many different locations under variety of climates. Production statistics reveal that Asia is not only home area of O. sativa but also a major rice-growing area of the world. India is one of the major growing areas in Asia, where rice is the staple food for 65% of the total population, constituting about 43% of the total food grain production and 47% of total cereal production. In India, Tamil Nadu forms the major rice growing area with non-limited availability of irrigation. Rice in Tamil Nadu is principally grown as an irrigated crop in all the seasons, and 78% of the rice area is planted to a first crop (Sreenivasan 1980). Productivity of the crop is determined by its environmental variables viz., weather, soil, farm inputs, technological advancement...
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