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Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (10): 2854-2860.doi: 10.3724/SP.J.1006.2023.23064

• RESEARCH NOTES • Previous Articles    

Yield simulation from APSIM-Maize by using the leave-one-out cross validation approach

YANG Xue-Ning1,2(), ZHANG Yong-Qiang1(), ZHANG Xuan-Ze1, MA Ning1, ZHANG Jun-Mei3   

  1. 1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100101, China
    3Dalat Banner Agricultural and Animal Husbandry Technology Extension Center, Dalat 014300, Inner Mongolia, China
  • Received:2023-09-27 Accepted:2023-04-18 Online:2023-10-12 Published:2023-04-27
  • Contact: E-mail: zhangyq@igsnrr.ac.cn
  • Supported by:
    “Science for a Better Development of Inner Mongolia” Program (Integrated Demonstration of Comprehensive Management and Effective Utilization of Water Resources in the Ten Kongduis)(KJXM-EEDS-2020005);Bureau of Science and Technology of the Inner Mongolia Autonomous Region, and the Ordos Science & Technology Plan (Calculation and Evaluation of Carbon Storage and Carbon Sink in Terrestrial Ecosystem in Ordos)(2022EEDSKJZDZX016)

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Abstract:

The Agricultural Production Systems Simulator (APSIM) has been extensively used in crop yield estimation and agricultural management. Leave-one-out cross validation is a key way to improve the model simulation capability with the limited filed data. In this study, we evaluate the parameters sensitivity to maize yield and evaluate the reliability of parameters with the leave-one-out cross validation based on the sensitivity rank of parameters. These results showed that: (1) The sensitive parameters for maize yield were ranked in descending order as: transpiration efficiency coefficient, radiation use efficiency, thermal time from flower to maturity, thermal time from emergence to end of juvenile phase, thermal time from flowering to start grain-filling, potential grain growth rate, photoperiod slope, and potential grains per head. (2) The coefficients of variation of parameters fluctuated from 1.06% to 23.32%, which indicates that the variation of parameter was small and the reliability of parameters was high. (3) The adjusted APSIM-Maize model performed well in simulating spring maize yield (R2 = 0.72; RMSE = 401.5 kg hm-2), which indicates that the model had great adaptability in estimating spring maize yield in the Ten Kongduis. Our study provides an insight to improve the reliability of parameters with limited field data.

Key words: APSIM-Maize model, cross validation, spring maize

Table 1

Spring maize yield and field management data from 2012 to 2019"

年份Year 产量Yield (kg hm-2) 种植密度Plant density (plant m-3)
2012 11,801.41 6.30-6.75
2013 12,141.89 6.30-6.75
2014 11,967.90 6.75-7.50
2015 11,237.44 6.75-7.50
2016 11,606.40 6.75-7.50
2017 11,675.40 7.50-8.25
2018 11,859.90 7.50-8.25
2019 12,429.90 7.50-8.25

Fig. 1

Sketch map of the leave-one-out cross validation RMSEi and R2i represent root mean square error and the determination coefficient at the ith cross validation (i = 1, 2, …, 8), respectively. RMSE_Calj and RMSE_Valj represent the jth year's root mean square of calibration and validation at cross validation (j = 2012, 2013, …, 2019), respectively."

Fig. 2

Ranking parameters based on the total sensitivity index in descending order transp_eff_cf: transpiration efficiency coefficient; rue: radiation use efficiency; tt_flower_to_maturity: thermal time from flower to maturity; tt_emerg_to_endjuv: thermal time from emergence to end of juvenile phase; tt_flower_to_start_grain: thermal time from flowering to start grain-filling; grain_gth_rate: potential grain growth rate; photoperiod_slope: photoperiod slope; head_grain_no_ max: potential grains per head."

Fig. 3

Cross validation and calibration of APSIM-Maize model (a): the comparison of the root mean squared error (RMSE) between cross validation and calibration, among them, RMSE_Cal is the RMSE of calibration at cross validation, RMSE_Val is the RMSE of validation at cross validation, and RMSE is the RMSE at calibration. (b): the comparison of simulated and observed yield at cross validation (b1-b8) and calibration (b9)."

Table 2

Cross validation and calibration of parameter sets"

参数名称
Parameter name		 交叉验证 Cross validation CV
(%)		 率定 Calibration
1 2 3 4 5 6 7 8
transp_eff_cf 0.012 0.013 0.012 0.013 0.012 0.012 0.012 0.012 3.53 0.012
rue 1.84 1.73 1.81 1.81 1.82 1.81 1.8 1.9 2.42 1.82
tt_flower_to_maturity 740 729 733 737 723 740 726 748 1.06 723
tt_emerg_to_endjuv 180 174 187 170 200 192 186 177 5.07 180
tt_flower_to_start_grain 246 142 222 238 225 244 225 268 15.39 225
grain_gth_rate 16.81 9.06 11.37 15.56 11.39 15.55 17.02 19.95 23.32 11.41
photoperiod_slope 14 12.07 14.5 21.67 13.48 13.96 16.06 14.06 18.24 20.08

Fig. 4

Relationship between the coefficient of variation and sensitivity ranking of parameters"

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