Open AccessEvaluation of DSSAT-CANEGRO model for phenology and yield attributes of sugarcane grown in different agroclimatic zones of Punjab, India
Author(s) -
Jagir Singh,
Shruti Mishra,
P. K. Kingra,
K. S. Singh,
Barun Biswas,
Vikrant Singh
Publication year - 2018
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.v20i4.566
Subject(s) - dssat , phenology , cane , cultivar , crop , mathematics , forensic science , veterinary medicine , agronomy , biology , medicine , biochemistry , sugar
DSSAT-CANEGRO model was calibrated and validated for four sugarcane cultivars planted at three dates in two agroclimatic zones of Indian Punjab. For calibration two years (2015-16 and 2016-17) data on phenological stages, growth and yield attributes of sugarcane were recorded from the field experiments conducted under All India Coordinated Research Project (AICRP) on sugarcane at Faridkotwhereas, for validation field experiments were conducted during 2017-18 at Regional Research Station (RRS) of Punjab. Faridkot representing western plain zone and Gurdaspur representing undulating plain zone. The genetic coefficients were derived separately for each cultivar. The results revealed that at Faridkot and Gurdaspur, the observed fresh cane yield was 89.8 and 98.6 (t ha-1), whereas simulated was 90.3 and 105.6 t ha-1 respectively. The mean observed days to reach physiological maturity were 297.9±14.2 at Faridkot and 298±16.5 days at Gurdaspur. Whereas, CANEGRO model simulated 305.4±17.1 and 304.3±17.4 days, respectively. The mean percent error for simulation of aerial dry biomass was 7.02 per cent at Faridkot and 11.5 per cent at Gurdaspur. For different phenological stages, growth as well as yield attributes, the maximum RMSE remained below 8.65 which confirmed the strength of the model. Different statistical procedures adopted for validation of the model proved the efficiency of the DSSAT-CANEGRO model for simulation of the crop growth and production with fair degree of accuracy.