Open AccessPerformance of SUBSTOR model on growth and yield of potato varieties under different planting dates in a sub-tropical environment
Author(s) -
Yogesh Kumar,
R. P. Singh,
Anil Kumar
Publication year - 2021
Publication title -
journal of agrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 11
eISSN - 2583-2980
pISSN - 0972-1665
DOI - 10.54386/jam.v23i2.71
Subject(s) - dssat , phenology , crop simulation model , sowing , mathematics , mean squared error , yield (engineering) , coefficient of determination , crop , growing season , field experiment , agronomy , statistics , biology , materials science , metallurgy
A field experiment was conducted during Rabi season of two consecutive years 2016-17 and 2017-18 at research farm, Department of Agricultural Meteorology, CCSHAU, Hisar under sub-tropical environment of Haryana. DSSAT (v4.7) family of SUBSTOR module was employed to simulate the comparison of observed values with simulated values under field conditions with a view to a view to assess the performance of model. The model was calibrated for (2016-17) and derived their genetic coefficient and further outputs were validated for second year (2017-18) experiments. Calibration and validation were done on crop grown under four planting dates viz. 8th Oct. (D1), 22th Oct. (D2), 5th Nov. (D3) and 23rd Nov (D4) in main plot treatment and sub-plots treatments consisted of three varieties Kufri Bahar (V1), Kufri Pushkar (V2) and Kufri Surya (V3) were tested in split plot design with four replications. The results affirms that model overestimated the phenology (days to tuber initiation and physiological maturity) and growth and yield parameters like accumulation of maximum LAI, tuber and biological yield. The model’s simulation performance was found satisfactory, and the model overestimated with fair agreement (±10). Performance of model tested with help of Mean absolute error (MAE), Mean bias error (MBE), Root mean square error (RMSE), r (correlation) and PE (Percent error). The model had capability for optimum potato crop management, phenology prediction and future yield estimation.