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Adapting DSSAT Model for Simulation of Cotton Yield for Nitrogen Levels and Planting Dates
Author(s) -
Arshad Muhammad Naveed,
Ahmad Ashfaq,
Wajid Syed Aftab,
Cheema Muhammad Jehanzeb Masud,
Schwartz Mark W.
Publication year - 2017
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2017.04.0233
Subject(s) - dssat , crop simulation model , anthesis , sowing , dry matter , agronomy , yield (engineering) , mathematics , cultivar , phenology , crop , simulation modeling , environmental science , biology , materials science , mathematical economics , metallurgy
Experiment was conducted to test and validate DSSAT model for dynamic simulation of growth, development, and seed‐cotton yield. Simulated values of days to anthesis, maturity, dry matter, and yield by model were reliable with recorded data. Seasonal analysis showed 23.0, 4.2, and 14.9% yield reduction at Faisalabad, Sahiwal, and Multan, respectively under future changing climate. Strategy analysis showed that May sown of cultivar FH‐142 at Faisalabad and Sahiwal and MNH‐886 at Multan with 200 kg N ha −1 can be a viable option to get maximum yield.The DSSAT module for Cotton Crop Modeling has been widely evaluated as a tool to predict the effect of climate change on productivity. A 2‐yr multifactorial experiment was conducted at three locations of Pakistan (Faisalabad, Sahiwal, and Multan) to test and validate this model for dynamic simulation of growth, development, and seed‐cotton ( Gossypium hirsutum L.) yield of cultivars (3) at varying N increments (three levels) sown at two different timings (1 May and 1 June). The model was first calibrated with field data collected during 2014 based on the best performing treatment (May sown and 200 kg N ha −1 ). Data of year 2015 was then used for further validation. Modeled values of various phenological attributes (e.g., days to anthesis and maturity) by model were reliable with recorded data, having root mean square error (RMSE) less than 2 d during both years. The RMSE values for total dry matter and seed‐cotton yield were reasonably good (278–573 kg ha −1 and 237–422 kg ha −1 , respectively). Applying 1980 to 2015 climate histories for the three regions, we found Faisalabad to be vulnerable up to 23.0% reduction of yield followed by Multan (14.9%), whereas the Sahiwal region is modeled as much more resilient, with less than 5% predicted reductions in yield. Finally, we found that strategic cultivar choice and timing of planting can alleviate many of the adverse impacts of changing climates on cotton yield. We conclude that the DSSAT model can be effective as a tool to make strategic cotton planting choices under changing climates.