Modeling the interplay of cultivars, irrigation regimes, and planting dates for optimizing cotton productivity in core and non-core cotton areas of Pakistan

• CSM-CROPGRO-Cotton model fairly simulated cotton phenology, biomass, and yield. • Long-term simulations showed higher cotton productivity in core areas compared to non-core areas. • Irrigation at 70 % and 90 % plant available water resulted in maximum biomass and yield. • Medium-duration cultivars achieved the highest biomass, seed cotton yield, and WUE. • Planting dates had minimal impact on cotton yield. Cotton, the world’s most widely cultivated fiber crop, faces increasing challenges from climate change across many production regions. Optimizing cotton production requires aligning cultivar selection, irrigation, and planting dates, yet evidence and decision-support tools are lacking. The study aimed to evaluate the DSSAT-CSM-CROPGRO-Cotton to identify promising combinations of cultivars, irrigation regimes, and planting dates for core and non-core cotton growing areas of Pakistan. The model was calibrated and validated using an extensive dataset from twelve (short, medium, and long duration) cultivars and eleven sites across core and non-core cotton growing areas of Pakistan over two seasons. The model was then used to assess cotton production for 30 years on these sites, under three planting dates (25-Apr, 10-May, and 25-May) and four irrigation regimes (30 %, 50 %, 70 %, and 90 % plant available water). Calibration results showed satisfactory model performance for simulating phenology, biomass, and yield across cultivars and sites, with normalized root mean square error remaining below 20 %. Long-term simulations demonstrated superior performance with the highest biomass, yield, harvest index, water use efficiency (WUE), and WUE irrigation (WUE_irri) in core areas. Long-duration cultivars excelled in the harvest index, whereas medium-duration cultivars showed the maximum biomass, yield, WUE, and WUE_irri. 70 % and 90 % irrigation produced similar maximum biomass and yield, and WUE was highest at 50 % irrigation. Planting dates had no significant impact on yield. The findings offer useful insights to guide cotton growers to make informed decisions and improve productivity across Pakistan.

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Publication Details

Journal
Computers and Electronics in Agriculture
Published
2025-11-29
DOI
https://doi.org/10.1016/j.compag.2025.111257
Primary Topic
Climate change impacts on agriculture
Type
article
Field-Weighted Citation Impact
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article

Modeling the interplay of cultivars, irrigation regimes, and planting dates for optimizing cotton productivity in core and non-core cotton areas of Pakistan

Raheel Osman, Shenglei Fu, Til Feike, Lin Wang et al.
Computers and Electronics in Agriculture
Climate change impacts on agriculture
article

Modeling the interplay of cultivars, irrigation regimes, and planting dates for optimizing cotton productivity in core and non-core cotton areas of Pakistan

Raheel Osman, Shenglei Fu, Til Feike, Lin Wang, Zareen Sarfraz, Muhammad Shahid Iqbal, Syed Tahir Ata-Ul-Karim, Abid Mahmood
article en

Abstract

• CSM-CROPGRO-Cotton model fairly simulated cotton phenology, biomass, and yield. • Long-term simulations showed higher cotton productivity in core areas compared to non-core areas. • Irrigation at 70 % and 90 % plant available water resulted in maximum biomass and yield. • Medium-duration cultivars achieved the highest biomass, seed cotton yield, and WUE. • Planting dates had minimal impact on cotton yield. Cotton, the world’s most widely cultivated fiber crop, faces increasing challenges from climate change across many production regions. Optimizing cotton production requires aligning cultivar selection, irrigation, and planting dates, yet evidence and decision-support tools are lacking. The study aimed to evaluate the DSSAT-CSM-CROPGRO-Cotton to identify promising combinations of cultivars, irrigation regimes, and planting dates for core and non-core cotton growing areas of Pakistan. The model was calibrated and validated using an extensive dataset from twelve (short, medium, and long duration) cultivars and eleven sites across core and non-core cotton growing areas of Pakistan over two seasons. The model was then used to assess cotton production for 30 years on these sites, under three planting dates (25-Apr, 10-May, and 25-May) and four irrigation regimes (30 %, 50 %, 70 %, and 90 % plant available water). Calibration results showed satisfactory model performance for simulating phenology, biomass, and yield across cultivars and sites, with normalized root mean square error remaining below 20 %. Long-term simulations demonstrated superior performance with the highest biomass, yield, harvest index, water use efficiency (WUE), and WUE irrigation (WUE_irri) in core areas. Long-duration cultivars excelled in the harvest index, whereas medium-duration cultivars showed the maximum biomass, yield, WUE, and WUE_irri. 70 % and 90 % irrigation produced similar maximum biomass and yield, and WUE was highest at 50 % irrigation. Planting dates had no significant impact on yield. The findings offer useful insights to guide cotton growers to make informed decisions and improve productivity across Pakistan.

Computers and Electronics in AgricultureVol. 241
Julius Kühn-Institut (DE), Henan University (CN), Aarhus University (DK), Pakistan Agricultural Research Council (PK), Agricultural Genomics Institute at Shenzhen (CN), Ayub Agriculture Research Institute (PK), Chinese Academy of Agricultural Sciences (CN)
Openalex Percentile: Top 72%
Climate change impacts on agriculture
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