scholarly journals Development of a rice yield prediction system over Bhubaneswar, India: combination of extended range forecast and CERES-rice model

2014 ◽  
Vol 22 (3) ◽  
pp. 525-533 ◽  
Author(s):  
K. Ghosh ◽  
Ankita Singh ◽  
U. C. Mohanty ◽  
Nachiketa Acharya ◽  
R. K. Pal ◽  
...  
Keyword(s):  
Rice Yield ◽  
Yield Prediction ◽  
Prediction System ◽  
Extended Range

Kharif rice yield prediction over Gangetic West Bengal using IITM-IMD extended range forecast products

2021 ◽  
Author(s):  
Javed Akhter ◽  
Raju Mandal ◽  
Rajib Chattopadhyay ◽  
Susmitha Joseph ◽  
Avijit Dey ◽  
...  
Keyword(s):  
West Bengal ◽  
Rice Yield ◽  
Yield Prediction ◽  
Extended Range

Assessment of rice yield prediction models based on big data analytics for better supply chain decision-making in Thailand

2021 ◽  
Vol 12 (3) ◽  
pp. 221
Author(s):  
John K.M. Kuwornu ◽  
Chutiporn Anutariya ◽  
Attaphongse Taparugssanagorn ◽  
Sumanya Ngandee
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Prediction Models ◽  
Rice Yield ◽  
Big Data Analytics ◽  
Yield Prediction

scholarly journals Comparison Statistical Rice Yield Prediction with Multiple Weather Parameters

2021 ◽  
pp. 31-38
Author(s):  
T. Thurkkaivel ◽  
G. A. Dheebakaran ◽  
V. Geethalakshmi ◽  
S. G. Patil ◽  
K. Bhuvaneshwari
Keyword(s):  
Bayesian Model ◽  
Tamil Nadu ◽  
Rice Yield ◽  
Model Performance ◽  
Research Centre ◽  
Yield Prediction ◽  
Sunshine Hours ◽  
Tamil Nadu State ◽  
Weather Parameters ◽  
Essential Food

Advance knowledge of harvestable products, especially essential food crops such as rice, wheat, maize, and pulses, would allow policymakers and traders to plan procurement, processing, pricing, marketing, and related infrastructure and procedures. There are many statistical models are being used for the yield prediction with different weather parameter combinations. The performance of these models are dependent on the location’s weather input and its accuracy. In this context, a study was conducted at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore during Kharif (2020) season to compare the performance of four multivariate weather-based models viz., SMLR, LASSO, ENET and Bayesian models for the rice yield prediction at Tanjore district of Tamil Nadu State with Tmax, Tmin, Mean RH, WS, SSH, EVP and RF.  The results indicated that the R2, RMSE, and nRMSE values of the above models were ranged between 0.54 to 0.79 per cent, 149 to 398 kg/ha, 4.0 to 10.6 per cent, respectively. The study concluded that the Bayesian model was found to be more reliable followed by LASSO and ENET. In addition, it was found that the Bayesian model could perform better even with limited weather parameters and detention of wind speed, sunshine hours and evaporation data would not affect the model performance. It is concluded that Bayesian model may be a better option for rice yield forecasting in Thanjavur districts of Tamil Nadu.

MJO Prediction Skill Using IITM Extended Range Prediction System and Comparison with ECMWF S2S

2020 ◽  
Vol 177 (10) ◽  
pp. 5067-5079
Author(s):  
Avijit Dey ◽  
R. Chattopadhyay ◽  
A. K. Sahai ◽  
R. Mandal ◽  
S. Joseph ◽  
...  
Keyword(s):  
Prediction Skill ◽  
Prediction System ◽  
Extended Range

scholarly journals Dynamical Extended-Range Prediction of Early Monsoon Rainfall over India

2009 ◽  
Vol 137 (4) ◽  
pp. 1480-1492 ◽  
Author(s):  
Frédéric Vitart ◽  
Franco Molteni
Keyword(s):  
Lead Time ◽  
Monsoon Rainfall ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Variable Resolution ◽  
Sensitivity Experiments ◽  
Extended Range ◽  
Forecasting System

Abstract The 15-member ensembles of 46-day dynamical forecasts starting on each 15 May from 1991 to 2007 have been produced, using the ECMWF Variable Resolution Ensemble Prediction System monthly forecasting system (VarEPS-monthy). The dynamical model simulates a realistic interannual variability of Indian precipitation averaged over the month of June. It also displays some skill to predict Indian precipitation averaged over pentads up to a lead time of about 30 days. This skill exceeds the skill of the ECMWF seasonal forecasting System 3 starting on 1 June. Sensitivity experiments indicate that this is likely due to the higher horizontal resolution of VarEPS-monthly. Another series of sensitivity experiments suggests that the ocean–atmosphere coupling has an important impact on the skill of the monthly forecasting system to predict June rainfall over India.

Prediction of kharif rice yield at Kharagpur using disaggregated extended range rainfall forecasts

2017 ◽  
Vol 133 (3-4) ◽  
pp. 1075-1091 ◽  
Author(s):  
B. S. Dhekale ◽  
M. M. Nageswararao ◽  
Archana Nair ◽  
U. C. Mohanty ◽  
D. K. Swain ◽  
...  
Keyword(s):  
Rice Yield ◽  
Extended Range
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