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Calibration of Yield-Formation Parameters in the APSIM-Wheat Model Based on Chaos Particle Swarm Optimization Algorithm
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Zhengqiang ZHAO, Qiang LIU, Rui MA
Journal of Triticeae Crops | 2026, 46(4) : 550 - 558
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Journal of Triticeae Crops | 2026, 46(4): 550-558
• Physiology, Ecology and Cultivation •
Calibration of Yield-Formation Parameters in the APSIM-Wheat Model Based on Chaos Particle Swarm Optimization Algorithm
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Zhengqiang ZHAO, Qiang LIU, Rui MA
Affiliations
  • College of Information Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
Published: 2026-04-15 doi: 10.7606/j.issn.1009-1041.2026.04.14
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To address the challenges of time consumption, low accuracy, and inefficiency in the localization calibration of yield formation parameters for spring wheat in the APSIM model under dryland conditions, a Chaos Particle Swarm Optimization (CPSO) algorithm was employed. Based on meteorological data from Dingxi City, Gansu Province, spanning 1971 to 2023, as well as yield data extracted from the Dingxi Statistical Yearbook for the periods 1971—2013 and 2022—2023, along with field-measured data collected from Mazichuan Village, Anding District, Dingxi City, between 2014 and 2021, key parameters influencing spring wheat yield were calibrated using the CPSO algorithm. The results showed that after parameter optimization with CPSO, the root mean square error (RMSE) decreased from 39.21 kg·hm-2 to 24.64 kg·hm-2; the normalized RMSE (NRMSE) dropped from 2.32% to 1.65%; and the modeling efficiency (ME) increased from 0.965 to 0.991. Therefore, through parameter optimization with CPSO, the fitting degree of APSIM model to spring wheat yield was significantly improved, and the model had better adaptation to Dingxi City, Gansu Province.

Dryland spring wheat  /  APSIM model  /  Yield formation  /  Chaos particle swarm optimization  /  Parameter optimization.
Zhengqiang ZHAO, Qiang LIU, Rui MA. Calibration of Yield-Formation Parameters in the APSIM-Wheat Model Based on Chaos Particle Swarm Optimization Algorithm[J]. Journal of Triticeae Crops, 2026 , 46 (4) : 550 -558 . DOI: 10.7606/j.issn.1009-1041.2026.04.14
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doi: 10.7606/j.issn.1009-1041.2026.04.14
  • Receive Date：2025-06-20
  • Online Date：2026-09-11
  • Published：2026-04-15
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  • Received：2025-06-20
  • Revised：2025-06-30
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    College of Information Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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