CSM-CROPGRO model to simulate safflower phenological development and yield

Obaid Afzal; Mukhtar Ahmed; Fayyaz-Ul-Hassan; Ghulam Shabbir; Shakeel Ahmed; Gerrit Hoogenboom

doi:10.1007/s00484-024-02662-0

CSM-CROPGRO model to simulate safflower phenological development and yield

Int J Biometeorol. 2024 Mar 27. doi: 10.1007/s00484-024-02662-0. Online ahead of print.

Authors

Obaid Afzal¹, Mukhtar Ahmed², Fayyaz-Ul-Hassan¹, Ghulam Shabbir³, Shakeel Ahmed⁴, Gerrit Hoogenboom⁵

Affiliations

¹ Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, 46300, Pakistan.
² Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, 46300, Pakistan. ahmadmukhtar@uaar.edu.pk.
³ Department of Plant Breeding and Genetics, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, 46300, Pakistan.
⁴ Institute of Agronomy, Bahauddin Zakariya University, Multan, 60800, Pakistan.
⁵ Global Food Systems Institute & Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, 32608, USA.

PMID: 38538982
DOI: 10.1007/s00484-024-02662-0

Abstract

Crop simulation models are valuable tools for decision making regarding evaluation and crop improvement under different field conditions. CSM-CROPGRO model integrates genotype, environment and crop management portfolios to simulate growth, development and yield. Modeling the safflower response to varied climate regimes are needed to strengthen its productivity dynamics. The main objective of the study was to evaluate the performance of DSSAT-CSM-CROPGRO-Safflower (Version 4.8.2) under diverse climatic conditions. The model was calibrated using the field observations for phenology, biomass and safflower grain yield (SGY) of the year 2016-17. Estimation of genetic coefficients was performed using GLUE (Genetic Likelihood Uncertainty Estimation) program. Simulated results for days to flowering, maturity, biomass at flowering and maturity and SGY were predicted reasonably with good statistical indices. Model evaluation results elucidate phenological events with low root mean square error (6.32 and 6.52) and high d-index (0.95 and 0.96) for days to flowering and maturity respectively for all genotypes and climate conditions. Fair prediction of safflower biomass at flowering and maturity showed low RMSE (887.3 and 564.3 kg ha^-1) and high d-index (0.67 and 0.93) for the studied genotypes across the environments. RMSE for validated safflower grain yield (101.8 kg ha^-1) and d-index (0.95) depicted that model outperformed for all genotypes and growing conditions. Longer appropriate growing conditions at NARC-Islamabad took optimal duration to assimilate photosynthetic products lead to higher grain yield. Safflower resilience to different environments showed that it can be used as an alternate crop for different agroecological regions. Furthermore, CROPGRO-Safflower model can be used as tool to further evaluate inclusion of safflower in the existing cropping systems of studied regions.

Keywords: CSM-DSSAT-CROPGRO; Calibration; GLUE; Genetic coefficients; Safflower.