Growth, phenology and yield modeling for wheat-fallow cropping system in Gujarat

A K MISRA; V PANDEY; S K MISHRA; S B YADAV; H R PATEL

doi:10.56093/ijas.v89i8.92845

Authors

A K MISRA Project Scientist-D, Indian Institute of Tropical Meteorology, Pune, Maharashtra
V PANDEY Emeritus Scientist, Anand Agricultural University, Anand, Gujarat 388 110, India
S K MISHRA Assistant Agrometeorologist, Punjab Agricultural University, Faridkot, Punjab
S B YADAV Assistant Professor, Anand Agricultural University, Anand, Gujarat 388 110, India
H R PATEL Assistant Professor, Anand Agricultural University, Anand, Gujarat 388 110, India

https://doi.org/10.56093/ijas.v89i8.92845

Keywords:

CropSyst, Phenological stages, RMSE, Simulation model, Wheat-fallow

Abstract

CropSyst is a daily time step crop growth simulation model that can be used to study the effect of cropping systems management on crop productivity and environment. A total five years experimental field observations (2009-10 to 2013-14) comprising four sowing dates, viz. November 1, 15, 30 and Dec 15 for wheat-fallow crop rotations were used to simulate the growth, phenology and yield for wheat cultivar GW 322 at Anand (Gujarat). Among the five years observations, initial two years observations (2009-10 and 2010-11) were used for model parameterization and remaining yearâ€™s observations (2011-12 to 2013-14) were used for model performance evaluation. The results indicated that the model was able to predict the wheat phenology precisely in terms of mean bias error (MBE) and root mean square error (RMSE) which was less than 6 days for all the phenological stages except maturity date on Dec 15 sowing. Nov 15 sowing has resulted in maximum grain and biological yield and found to be optimum date for sowing followed by Nov 30 for this region while Dec 15 resulted in highest yield reduction. It was also observed that CropSystmodel was efficient in simulation of yield and biomass of wheat crop during various years of observations. The model has efficiently predicted grain and biological yield with Â±10% of model error for wheat crop. However, it failed to predict the leaf area index (LAI) precisely. It may also be concluded that the model error were less for early and normal sown crops but increased with the delay in sowing.

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