Open AccessDevelopment of a rice yield prediction system over Bhubaneswar, India: combination of extended range forecast and CERES ‐rice model
Author(s) -
Ghosh K.,
Singh Ankita,
Mohanty U. C.,
Acharya Nachiketa,
Pal R. K.,
Singh K. K.,
Pasupalak S.
Publication year - 2015
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1483
Subject(s) - yield (engineering) , environmental science , crop , forecast skill , global forecast system , meteorology , range (aeronautics) , benchmark (surveying) , crop yield , climatology , crop simulation model , agricultural engineering , numerical weather prediction , agronomy , geography , engineering , aerospace engineering , biology , materials science , geodesy , geology , metallurgy
Use of seasonal and sub‐seasonal forecast products of experimental extended range forecast system ( ERFS ) in crop models is investigated for improving the rice grain yield prediction skill for the ensuing monsoon season in the experimental station at Bhubaneswar, India. A stochastic disaggregation is used to downscale seasonal and monthly forecast products in daily weather sequences. These weather series are taken as input in Crop Estimation through Resource and Environment Synthesis (CERES)‐rice crop simulation model for the crop yield prediction at different stages of forecast: June–September (4 month forecast), July–September (3 month forecast), August–September (2 month forecast) and monthly forecast for September (1 month forecast). To avoid a technological trend in historical yield data, yields simulated with observed weather data have been used as the benchmark (observed rice yield) to evaluate the yields simulated using experimental ERFS forecasts. The findings recommend the efficiency of forecast products to capture year‐to‐year variability in observed rice yield at experimental stations. A significant enhancement in the prediction skill is noticed as the season advances due to incorporation of observed weather data, reducing uncertainty of yield prediction. The outcomes are useful for taking decisions well in advance for transplanting of rice as well as for other input management and farm activities during different stages of the crop growing season.