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作物学报 ›› 2024, Vol. 50 ›› Issue (2): 464-477.doi: 10.3724/SP.J.1006.2024.31018

• 耕作栽培·生理生化 • 上一篇    下一篇

温度升高下APSIM模型春小麦籽粒生长参数敏感性分析及优化

张康1(), 聂志刚1,*(), 王钧1, 李广2   

  1. 1甘肃农业大学信息科学技术学院, 甘肃兰州 730070
    2甘肃农业大学林学院, 甘肃兰州 730070
  • 收稿日期:2023-03-12 接受日期:2023-09-13 出版日期:2024-02-12 网络出版日期:2023-10-11
  • 通讯作者: *聂志刚, E-mail: niezg@gsau.edu.cn
  • 作者简介:E-mail: 2942138300@qq.com
  • 基金资助:
    国家自然科学基金项目(32160416);甘肃省教育厅产业支撑计划项目(2021CYZC-15);甘肃省教育厅产业支撑计划项目(2022CYZC-41);甘肃省优秀研究生“创新之星”项目(2022CXZXS-026)

Sensitivity analysis and optimization of spring wheat grain growth parameters under APSIM model with the increase of temperature

ZHANG Kang1(), NIE Zhi-Gang1,*(), WANG Jun1, LI Guang2   

  1. 1College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, Gansu, China
    2College of Forestry, Gansu Agricultural University, Lanzhou 730070, Gansu, China
  • Received:2023-03-12 Accepted:2023-09-13 Published:2024-02-12 Published online:2023-10-11
  • Contact: *E-mail: niezg@gsau.edu.cn
  • Supported by:
    National Natural Science Foundation of China(32160416);Industrial Support Program of Gansu Provincial Education Department(2021CYZC-15);Industrial Support Program of Gansu Provincial Education Department(2022CYZC-41);“Innovation Star” Project of Gansu Province Outstanding Postgraduate Students(2022CXZXS-026)

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摘要：

为有效识别基于APSIM模型籽粒生长参数中春小麦产量敏感性参数, 快速并准确的估算当地模型参数。使用甘肃省定西市安定区凤翔镇安家沟村1971—2018年的气象数据和2000—2018年旱地春小麦大田试验数据, 并利用EFAST方法对进行了5个增温梯度(0℃、0.5℃、1.0℃、1.5℃和2.0℃)下32个模型参数进行敏感性分析。粒子群算法对各个增温条件下均敏感的参数进行优化验证。结果表明: 不同温度变化梯度下, 对旱地春小麦产量影响最大的籽粒生长模型参数有9个, 分别为消光系数、每克茎籽粒数量、穗粒数、单株最大籽粒质量、灌浆到成熟积温、出苗到拔节积温、株高、最大比叶面积和光合叶片老化的水分胁迫斜率。并且对产量敏感性强度有着显著的差异, 其中消光系数和每克茎籽粒数量是对春小麦产量影响最大的参数, 其他参数在不同温度下对春小麦产量的敏感性顺序存在差异。利用粒子群算法针对这9个参数进行优化, 相较于优化前, 优化后的春小麦产量、开花期和灌浆期籽粒干物质的均方根误差、归一化均方根误差和模型有效性指数均得到了显著改善, 参数优化后开花期、灌浆期、成熟期产量的均方根误差平均值分别由13.50 kg hm-2减小到5.99 kg hm-2、183.17 kg hm-2减小到69.44 kg hm-2、141.69 kg hm-2减小到48.51 kg hm-2, 归一化均方根误差平均值分别由4.94%减小到2.19%、10.92%减小到4.65%、8.39%减小到2.87%, 模型有效性指数平均值分别由0.894提高到0.979、0.893提高到0.981、0.898提高到0.988。优化后的参数有效地提高了模型的预测精度。此研究为APSIM模型在当地应用和模型参数校准提供了科学依据。

关键词: 参数, 敏感性分析, APSIM模型, 粒子群算法, 春小麦

Abstract:

In order to effectively identify spring wheat yield sensitivity parameters in grain growth parameters based on APSIM model, the local model parameters were quickly and accurately estimated. Using the meteorological data of Anjiagou Village, Fengxiang Town, Anding District, Dingxi City, Gansu Province from 1971 to 2018 and the field test data of dryland spring wheat from 2000 to 2018, the sensitivity analysis of 32 model parameters under five temperature gradients (0℃, 0.5℃, 1.0℃, 1.5℃, and 2℃) was conducted by EFAST method. The particle swarm optimization algorithm is used to optimize and verify the parameters sensitive at all temperatures. The results showed that, under different temperature gradients, there were nine grain growth model parameters that had the greatest influence on the yield of spring wheat in dry land, which were extinction coefficient, the number of grains per gram of stem, the number of grains per ear, the maximum grain mass per plant, the accumulated temperature from filling to maturity, the accumulated temperature from emergence to jointing, plant height, the maximum specific leaf area, and water stress slope of photosynthetic leaf aging. The sensitivity intensity of spring wheat yield was significantly different, among which extinction coefficient and the number of seeds per gram of stem were the most influential parameters in spring wheat yield, and the sensitivity sequence of other parameters was different under different temperatures. Particle swarm optimization algorithm was used to optimize the nine parameters. Compared with before optimization, the optimized spring wheat yield, root mean square error of grain dry matter, normalized root mean square error and model validity index were significantly improved. After parameter optimization, the optimized spring wheat yield, root mean square error of grain dry matter and model validity index were significantly improved. The mean square error of yield at maturity stage decreased by 13.50-5.99 kg hm-2, 183.17-69.44 kg hm-2, and 141.69-48.51 kg hm-2, respectively. The normalized root-mean-square error decreases by 4.94%-2.19%, 10.92%-4.65%, 8.39%-2.87%, and the average model validity index increases by 0.894-0.979, 0.893-0.981, and 0.898-0.988, respectively. The optimized parameters can effectively improve the prediction accuracy of the model. This study provides a scientific basis for the local application of APSIM model and the calibration of model parameters.

Key words: parameter, sensitivity analysis, APSIM model, particle swarm optimization, spring wheat

表1

APSIM模型模拟研究区的主要土壤属性参数"

土层
Soil layer
(cm)		 田间最大持水量
Drained upper limit
(mm mm-1)		 小麦有效水分下限Wheat low limit
(mm mm-1)		 容重
Bulk density
(g cm-3)		 铵态氮
NH4-N
(mg kg-1)		 硝态氮
NO3-N
(mg kg-1)
>0-5 0.27 0.09 1.29 6.30 19.10
>5-10 0.27 0.09 1.23 5.20 15.20
>10-30 0.27 0.09 1.32 5.10 23.10
>30-50 0.27 0.09 1.20 4.90 16.60
>50-80 0.26 0.09 1.14 4.60 16.80
>80-110 0.27 0.10 1.14 4.80 18.20
>110-140 0.27 0.11 1.13 4.80 16.40
>140-170 0.27 0.13 1.12 5.80 13.70
>170-200 0.27 0.15 1.11 4.10 15.40

图1

模型结构图"

图2

敏感性分析流程图"

图3

利用粒子群算法校准APSIM模型参数流程图"

表2

选择参数的上下限及分布和模型输出"

参数名称
Parameter		 下限值
Lower bound		 上限值
Upper bound
每克茎籽粒数量Grains per gram stem (GGS) (grain g-1) 10 40
灌浆期籽粒日潜在灌浆速率Potential grain filling rate (PGFR) (g grain-1 d-1) 0.001 0.004
开花到灌浆期籽粒日潜在灌浆速率Potential grain growth rate (PGR) (g grain-1 d-1) 0.0005 0.0015
日潜在籽粒氮积累速率Potential grain n filling rate (PGNFR) (g grain-1 d-1) 0.000,027,5 0.000,082,5
籽粒氮日积累速率下限Minimum grain n filling rate (MFR) (g grain-1 d-1) 0.000,007,5 0.000,022,5
谷粒氮限制灌浆因子n fact grain (NFG) (-) 0 1
最小叶氮浓度y n conc min leaf (YML) (g g-1) 0 0.02
临界叶氮浓度y n conc crit leaf (YCL) (g g-1) 0 0.06
最小茎氮浓度YMS (YMS YMS) (g g-1) 0 0.02
临界茎氮浓度y n conc crit stem (YCS) (g g-1) 0 0.05
作物水分需求eo crop factor default (EFD) (-) 0.75 2.25
单株最大籽粒质量MGS (Max grain size) (g) 0.02 0.06
分蘖重Dm tiller max (DTM) (g) 0.6 1.8
单株重X stem wt (XSW) (g) 2 8
株高Y height (YH) (mm) 500 1500
穗粒数Grain num coeff (GNC) (-) 10 50
出苗到拔节积温tt end of juvenile (TOJ) (℃ d) 200 600
拔节到开花积温tt floral initiation (TFI) (℃ d) 250 800
开花到灌浆积温tt flowering (TF) (℃ d) 60 180
灌浆到成熟积温tt start grain fill (TGF) (℃ d) 200 900
作物春化敏感性指数Vern sens (VS) (-) 0 5
作物光周期敏感性指数Photop sens (PS) (-) 0 5
辐射利用效率Rue (y rue) (RYR) (g MJ-1) 1.1160 1.3640
消光系数K (y extinct coef) (K) (-) 0 1
日平均温度影响灌浆速率x temp grainfill (XTG) (-) 0 1
缺氮对光合作用的影响倍数N fact photo (NFPO) (-) 0.75 2.25
物候的氮限制因子N fact pheno (NFPE) (-) 50 150
光合叶片老化的水分胁迫斜率sen rate water (SRW) (-) 0.05 0.15
遮阴导致的叶面积老化敏感性参数sen light slope (SLS) (-) 0.001 0.003
遮阴导致老化的最大叶面积指数lai sen light (LSL) (m2 m-2) 3.5 10.5
植物开始的叶面积initial tpla (IT) (mm2) 100 300
最大比叶面积y sla max (YSM) (mm2 g-1) 22,000 45,000

图4

0℃下产量参数敏感性系数 GGS: 每克茎籽粒数量; PGFR: 灌浆期籽粒日潜在灌浆速率; PGR: 开花到灌浆期籽粒日潜在灌浆速率; PGNFR: 日潜在籽粒氮积累速率; MFR: 籽粒氮日积累速率下限; NFG: 谷粒氮限制灌浆因子; YML: 最小叶氮浓度; YCL: 临界叶氮浓度; YMS: 最小茎氮浓度; YCS: 临界茎氮浓度; EFD: 作物水分需求; MGS: 单株最大籽粒质量; DTM: 分蘖重; XSW: 单株重; YH: 株高; GNC: 穗粒数; TOJ: 出苗到拔节积温; TFI: 拔节到开花积温; TF: 开花到灌浆积温; TGF: 灌浆到成熟积温; VS: 作物春化敏感性指数; PS: 作物光周期敏感性指数; RYR: 辐射利用效率; K: 消光系数; XTG: 日平均温度影响灌浆速率; NFPO: 缺氮对光合作用的影响倍数; NFPE: 物候的氮限制因子; SRW: 光合叶片老化的水分胁迫斜率; SLS: 遮阴导致的叶面积老化敏感性参数; LSL: 遮阴导致老化的最大叶面积指数; IT: 植物开始的叶面积; YSM: 最大比叶面积。"

图5

0.5℃产量参数敏感性系数 缩写同图4。"

图6

1℃下产量参数敏感性系数 缩写同图4。"

图7

1.5℃下产量参数敏感性系数 缩写同图4。"

图8

2℃下产量参数敏感性系数 缩写同图4。"

表3

APSIM模型春小麦参量相关参数的初值"

参数
Parameter		 初值
Initial value		 优化值
Optimized value
每克茎籽粒数量Grains per gram stem (GGS) (grain g-1) 24 26
单株最大籽粒质量Max. grain size (MGS) (g) 0.04 0.046
株高Y height (YH) (mm) 1000 1013.81
穗粒数Grain num coeff (GNC) (-) 30 28
出苗到拔节积温tt end of juvenile (TOJ) (℃ d) 400 360.44
灌浆到成熟积温tt start grain fill (TGF) (℃ d) 550 589.91
消光系数K y extinct coef (K) (-) 0.50 0.45
光合叶片老化的水分胁迫斜率sen rate water (SRW) (-) 0.10 0.11
最大比叶面积y sla max (YSM) (mm2 g-1) 26,000 24,317.50

图9

春小麦产量模拟值、优化值与实测值线性拟合"

图10

生育期春小麦干物质模拟检验结果"

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