Application of CSM-CROPGRO-Cotton model for cultivars and optimum planting dates: Evaluation in changing semi-arid climate

Article

Rahman, Muhammad Habib ur, Ahmad, Ashfaq, Wajid, Aftab, Hussain, Manzoor, Rasul, Fahd, Ishaque, Wajid, Islam, Aminul, Shelia, Vakhtang, Awais, Muhammad, Ullah, Asmat, Wahid, Abdul, Sultana, Syeda Refat, Saud, Shah, Khan, Shahbaz, Fahad, Shah, Hussain, Manzoor, Hussain, Saddam and Nasim, Wajid. 2019. "Application of CSM-CROPGRO-Cotton model for cultivars and optimum planting dates: Evaluation in changing semi-arid climate." Field Crops Research. 238, pp. 139-152. https://doi.org/10.1016/j.fcr.2017.07.007
Article Title

Application of CSM-CROPGRO-Cotton model for cultivars and optimum planting dates: Evaluation in changing semi-arid climate

ERA Journal ID5309
Article CategoryArticle
AuthorsRahman, Muhammad Habib ur, Ahmad, Ashfaq, Wajid, Aftab, Hussain, Manzoor, Rasul, Fahd, Ishaque, Wajid, Islam, Aminul, Shelia, Vakhtang, Awais, Muhammad, Ullah, Asmat, Wahid, Abdul, Sultana, Syeda Refat, Saud, Shah, Khan, Shahbaz, Fahad, Shah, Hussain, Manzoor, Hussain, Saddam and Nasim, Wajid
Journal TitleField Crops Research
Journal Citation238, pp. 139-152
Number of Pages14
Year2019
PublisherElsevier
ISSN0378-4290
1872-6852
Digital Object Identifier (DOI)https://doi.org/10.1016/j.fcr.2017.07.007
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0378429017311395
Abstract

Simulation models are widely used for making decision support especially under sub-optimal climatic conditions for yield improvement. The impact of uncertain weather conditions on cotton production can be explored with the aid of such models. Cotton, being the queen of fibers, enjoys itself a predominant position amongst all other cash crops and has the potential to narrow the gap between production and consumption of fiber and edible oil. The goal of the study was to evaluate the performance of Cropping System Model CROPGRO-Cotton for examining temporal variation in cultivars and to determine the potential impact of planting dates. The model was calibrated using a diverse range of field observation for phenology, growth and seed cotton yield (SCY) and its components of 20-April planting date, genetic coefficients were estimated using Generalized Likelihood Uncertainty Estimation (GLUE) and sensitivity analysis sub modules in CSM-DSSAT. Calibrated and evaluated results were reasonably good for phenology, growth, boll weight, SCY and yield attributes with good statistics. Simulation and observed data revealed the decreasing trend with delayed planting for all cultivars. Phenological phases were predicted well with high d-index (>0.94 and >0.85), and the dynamics of time series for LAI and biomass were predicted also well (d > 0.96 and >0.98), respectively during model evaluation for all planting dates and cultivars. Seed cotton yield was simulated with lower RMSE (137–382) at final picking, while dynamics of time series for SCY were predicted reasonably well with high values of d-index (0.95–0.99) for all cultivars during model evaluation. Planting date analyses of the years (1980–2013) agreed with observed SCY trend, showed decrease of 27% for delayed planting from 20-April to 21-June while first too early planting (10-March) had also faced 8% reduction for all cultivars. Cultivars [MNH-886, NIAB-9811 (NIAB-Kiran) and NIAB-112] outperformed with higher SCY predominantly due to longer appropriate growing season by utilizing optimal weather conditions, attained optimum growth at all key phenological phases. Cultivar NIAB-112 performed well being a short duration and attained high SCY for late planting (1-June) as well during the growing years. Cotton cultivars planting during 1-April–10-May can be suggested for the farmer’s field to avoid weather stress and to improve utilization of resources for sustainable cotton production in the region. Result demonstrated the model potential for decision support for cotton management practices in the region, including identifying optimum planting dates for cotton production.

KeywordsDSSATTemporal variationCalibrationEvaluationApplicationPakistan
ANZSRC Field of Research 2020410199. Climate change impacts and adaptation not elsewhere classified
3002. Agriculture, land and farm management
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Byline AffiliationsMuhammad Nawaz Shareef University of Agriculture, Pakistan
Washington State University, United States
University of Agriculture Faisalabad, Pakistan
Nuclear Institute for Agriculture and Biology (NIAB), Pakistan
Department of Agricultural Extension, Bangladesh
Institute for Sustainable Food Systems (ISFS), United States
Islamia University of Bahwalpur, Pakistan
Ayub Agricultural Research Institute (AARI), Pakistan
Bahauddin Zakariya University (BZU), Pakistan
COMSATS University Islamabad, Pakistan
Northeast Agricultural University Harbin, China
Huazhong Agricultural University, China
Abdul Wali Khan University, Pakistan
International Centre for Advanced Mediterranean Agronomic (CIHEAM), France
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
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