| Analysis and optimization of the sensitivity of parameters affecting spring wheat yield under different nitrogen application gradients |
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| DOI：10.7606/j.issn.1000-7601.2025.02.25 |
| Key Words: spring wheat nitrogen application gradient yield\|related parameters sensitivity analysis APSIM model Gansu Province |
| Author Name | Affiliation | | LIU Jiahui | College of Information Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China | | LIU Qiang | College of Information Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China | | LI Guang | College of Forestry, Gansu Agricultural University, Lanzhou, Gansu 730070, China |
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| Abstract: |
| The study area is located in Anjiapo Village, Fengxiang Town, Anding District, Dingxi City, Gansu Province. Utilizing local meteorological data from 2005 to 2022, measured data on dryland spring wheat from 2015 to 2018, and regional statistical yearbook data from 2005 to 2022, the APSIM model was employed to simulate 25 model parameters under different nitrogen application gradients during the wheat sowing period (0, 21, 42, 63, 84, 105 kg·hm-2, and 126 kg·hm-2). The EFAST method was applied to analyze the sensitivity of these parameters. The results indicated that under different nitrogen application gradients, 11 parameters had a significant impact on spring wheat yield: accumulated temperature during the grain filling period, accumulated temperature from grain filling to maturity, accumulated temperature from stem elongation to flowering, maximum grain mass per plant, crop water demand, thermal time from emergence to jointing, number of grains per gram of stem, maximum leaf area index reduction due to shading, plant weight per plant, daily potential grain filling rate from flowering to grain filling, and the extinction coefficient. Among these, the extinction coefficient and the number of grains per gram of stem had the most significant impact on yield, while the sensitivity ranking of other parameters to spring wheat yield varied under different nitrogen application gradients. Subsequently, the Markov Chain Monte Carlo algorithm was used to optimize these 11 parameters. The optimized parameters significantly improved the accuracy of APSIM yield simulations. The root mean square error (RMSE) between the measured and simulated yield decreased from 194.6 kg·hm-2 to 25.75 kg·hm-2, while the relative RMSE dropped from 34.10% to 4.53%. Additionally, the model’s coefficient of determination (R2) improved from 0.778 to 0.943. This study employed the EFAST sensitivity analysis method in conjunction with the Markov Chain Monte Carlo algorithm to identify and optimize key parameters influencing spring wheat yield under varying nitrogen application gradients. This approach enhances the accuracy and reliability of the APSIM model in simulating spring wheat yield. |
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