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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (1): 159-168.doi: 10.3724/SP.J.1006.2021.03016

• TILLAGE & CULTIVATION · PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Application study of crop yield prediction based on AquaCrop model in black soil region of Northeast China

CUI Ying(), LIN Hong-Hong, XIE Yun*(), LIU Su-Hong   

  1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2020-03-13 Accepted:2020-08-19 Online:2021-01-12 Published:2020-09-15
  • Contact: XIE Yun E-mail:cuiying@mail.bnu.edu.cn;xieyun@bnu.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2017YFE0118100)

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Abstract:

Northeast black soil area is the production area of maize and soybean in China. In order to optimize the agricultural management and forecast crop yield with AquaCrop model, we use OAT (one factor at a time) method to analyze the sensitivity of the model parameters based on the experiment and field observation data, and to validate the model after calibrated the high sensitivity parameters. The results of sensitivity analysis showed that the yields of maize and soybean were both extremely sensitive to the reference harvest index (HI0) and the parameters of canopy growth and root growth. The difference was that maize was more sensitive to the canopy decline coefficient (CDC), while soybean was more sensitive to the shape factor for water stress coefficient for canopy expansion (Pexshp). Maize was more sensitive to the maximum effective rooting depth (Zx) because of its deep root, while soybean was more sensitive to the shape factor describing root zone expansion (Rexshp) because of its short roots. Maize was extremely sensitive to the crop coefficient before canopy formation and senescence (KcTr,x) and the normalized water productivity (WP*) due to the large water demand, while soybean was only generally sensitive. After calibrated the high sensitivity parameters with experiment data, the regression coefficient of simulated yield and measured yield of maize increased from 0.34 to 0.89, and the regression coefficient of simulated yield and measured yield of soybean increased from 0.80 to 0.88. Furthermore, the validation results of field observation data indicated that the determination coefficients (R2), the root mean square error (RMSE), the normalized root mean square error (NRMSE) and the model efficiency (ME) of the AquaCrop model of maize and soybean were 0.775 and 0.779, 1.076 t hm-2 and 0.299 t hm-2, 0.097 and 0.178, 0.747 and 0.730, respectively. The calibrated AquaCrop model can accurately simulate the yield of corn and soybean in the black soil area of Northeast China, and is useful for yield prediction and optimal management.

Key words: AquaCrop model, Northeastern China, sensitivity analysis, parameter calibration, maize, soybean

Table 1

Soil physical parameters of the experimental locations"

点位Location 容重
Bulk
density
(g cm-3)		 田间持水量
Field
capacity (%)		 凋萎湿度
Wilting point (%)		 有机质含量
Organic
content (%)		 氢离子浓度指数
Hydrogen ion concentration (pH)		 碱解氮含量
Available
nitrogen content
(mg kg-1)		 速效磷含量
Available
phosphorus content
(mg kg-1)		 速效钾含量
Available
potassium content
(mg kg-1)
1 1.48 30.45 15.40 3.65 6.21 142.42 6.69 44.23
2 1.43 26.85 14.48 2.46 5.86 103.13 4.19 36.19
3 1.43 28.24 16.35 2.01 5.77 69.59 3.02 46.24
4 1.54 29.39 16.12 1.38 5.91 71.98 2.24 44.23
5 1.25 45.19 18.16 2.73 5.79 117.88 2.64 36.37
6 1.36 33.86 16.94 2.15 5.79 115.15 2.82 39.36
7 1.24 38.70 17.61 2.63 5.79 88.38 3.35 44.36
8 1.37 33.53 15.78 2.53 5.67 65.65 1.47 35.37
9 1.43 28.41 11.48 0.92 5.90 38.66 2.84 25.38
10 1.45 28.10 11.11 1.40 5.68 53.83 4.67 47.35
11 1.63 18.36 10.00 0.68 6.10 30.03 1.56 14.08
12 1.51 24.75 11.89 2.36 6.19 74.65 7.60 55.35
13 1.64 17.52 8.15 1.25 6.13 46.99 12.27 34.18
14 1.37 27.65 10.94 2.39 6.38 75.56 24.97 441.73

Table 2

Introduction of parameters to be sensitive analysis in AquaCrop model"

符号
Symbol		 含义
Description
Pexp-up 限制冠层伸展的土壤水分消耗上限阈值。Soil water depletion threshold for canopy expansion-upper threshold.
Pexp-lw 限制冠层伸展的土壤水分消耗下限阈值。Soil water depletion threshold for canopy expansion-lower threshold.
Pexshp 限制冠层伸展的水分胁迫系数曲线的形状因子。Shape factor for Water stress coefficient for canopy expansion.
DeKcTr,x 因衰老、氮元素亏缺导致的作物系数下降速率。Decline of crop coefficient as a result of ageing, nitrogen deficiency.
KcTr,x 冠层形成和枯萎前的作物系数。Crop coefficient before canopy formation and senescence.
Rexshp 根区根系伸展曲线的形状因子。Shape factor describing root zone expansion.
Zmin 根系初始深度。Minimum effective rooting depth.
Zx 最大有效根深。Maximum effective rooting depth.
WP*yf 产量形成期的归一化水分生产力。
Water productivity normalized for ET0 and CO2 during yield formation (as percent WP* before yield formation).
WP* 归一化水分生产力。Water productivity normalized for ET0 and CO2.
CCx 最大冠层覆盖度。Maximum canopy cover.
CGC 冠层生长系数。Canopy growth coefficient.
CDC 冠层衰减系数。Canopy decline coefficient.
HI0 参考收获指数。Reference harvest index.

Table 3

Relative sensitivity of parameters in AquaCrop model"

模型参数
Parameter		 玉米Maize 大豆Soybean
相对敏感度RS 敏感程度Sensitivity 相对敏感度RS 敏感程度Sensitivity
Pexp-up 0.0023 低敏感Low sensitive 0.0223 低敏感Low sensitive
DeKcTr,x 0.0138 低敏感Low sensitive 0.0871 低敏感Low sensitive
Pexp-lw 0.0369 低敏感Low sensitive 0.1480 一般敏感General sensitive
Pexshp 0.0192 低敏感Low sensitive 0.9561 极敏感Extremely sensitive
Rexshp 0.0374 低敏感Low sensitive 1.9893 极敏感Extremely sensitive
Zmin 0.2321 一般敏感General sensitive 0.0871 低敏感Low sensitive
WP*yf 0.4407 一般敏感General sensitive 0.3450 一般敏感General sensitive
CCx 0.5556 一般敏感General sensitive 0.4995 一般敏感General sensitive
CGC 0.2011 一般敏感General sensitive 0.3961 一般敏感General sensitive
CDC 0.7901 极敏感Extremely sensitive 0.3745 一般敏感General sensitive
HI0 1.0185 极敏感Extremely sensitive 0.3897 一般敏感General sensitive
Zx 0.8585 极敏感Extremely sensitive 0.1197 一般敏感General sensitive
WP* 0.8509 极敏感Extremely sensitive 0.4295 一般敏感General sensitive
KcTr,x 0.7657 极敏感Extremely sensitive 0.6034 极敏感Extremely sensitive

Table 4

Calibrated parameters for simulating black soil area in Northeast China"

符号Notation 含义
Description		 单位
Unit		 默认值
Default value		 率定值Calibration value
玉米Maize 大豆Soybean
Pexp-up 限制冠层伸展的土壤水分消耗上限阈值。
Soil water depletion threshold for canopy expansion-upper
threshold.		 % of TAW 0.60 0.60 0.35
Pexp-lw 限制冠层伸展的土壤水分消耗下限阈值。
Soil water depletion threshold for canopy expansion-lower
threshold.		 % of TAW 0.25 0.14 0
Pexshp 限制冠层伸展的水分胁迫系数曲线的形状因子。
Shape factor for water stress coefficient for canopy expansion.		 — 3.0 2.9 4.0
DeKcTr,x 因衰老、氮元素亏缺导致的作物系数下降速率。
Decline of crop coefficient as a result of ageing, nitrogen
deficiency.		 % d-1 0.150 0.300 0.300
KcTr,x 冠层形成和枯萎前的作物系数。
Crop coefficient before canopy formation and senescence.		 — 1.10 1.05 1.10
Rexshp 根区根系伸展曲线的形状因子。
Shape factor describing root zone expansion.		 — 1.5 1.3 1.5
Zx 最大有效根深。
Maximum effective rooting depth.		 m 1.0 2.3 1.6
WP*yf 产量形成期的归一化水分生产力。
Water productivity normalized for ET0 and CO2 during yield
formation.		 % 100 100 51
WP* 归一化水分生产力。
Water productivity normalized for ET0 and CO2.		 g m-2 17.0 33.7 15.0
CCx 最大冠层覆盖度。Maximum canopy cover. % 80 96 98
CGC 冠层生长系数。Canopy growth coefficient. % d-1 15.0 15.8 10.0
CDC 冠层衰减系数。Canopy decline coefficient. % d-1 12.8 11.7 21.3
HI0 参考收获指数。Reference harvest index. % 50 58 43

Fig. 1

Comparison between simulated and measured yields of maize (a) and soybeans (b)"

[1] Monteith J L. The quest for balance in crop modeling. Agron J, 1996,88:695-697.
doi: 10.2134/agronj1996.00021962008800050003x
[2] Van Diepen C A, Wolf J, Van Keulen H, Rappoldt C. WOFOST: a simulation model of crop production. Soil Use Manag, 1989,5:16-24.
[3] Steduto P, Hsiao T C, Raes D, Fereres E. AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron J, 2009,101:426-437.
[4] 邢会敏, 李振海, 徐新刚, 冯海宽, 杨贵军, 陈召霞. 基于遥感和AquaCrop作物模型的多同化算法比较. 农业工程学报, 2017,33(13):183-192.
Xing H M, Li Z H, Xu X G, Feng H K, Yang G J, Chen Z X. Multi-assimilation methods based on AquaCrop model and remote sensing data. Trans CSAE, 2017,33(13):183-192 (in Chinese with English abstract).
[5] Raes D, Steduto P, Hsiao T C, Fereres E. AquaCrop—the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron J, 2009,101:438-447.
[6] 孙仕军, 张琳琳, 陈志君, 孙娟. AquaCrop作物模型应用研究进展. 中国农业科学, 2017,50:3286-3299.
Sun S J, Zhang L L, Chen Z J, Sun J. Advances in AquaCrop model research and application. Sci Agric Sin, 2017,50:3286-3299 (in Chinese with English abstract).
[7] Todorovic M, Albrizio R, Zivotic L, Abi Saab M-T, Stöckle C, Steduto P. Assessment of AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agron J, 2009,101:509-521.
[8] 秦其明, 范闻捷, 任华忠. 农田定量遥感理论、方法与应用. 北京: 科学出版社, 2018. pp 333-345.
Qin Q M, Fan W J, Ren H Z. Theory, Method and Application of Farmland Quantitative Remote Sensing. Beijing: Science Press, 2018. pp 333-345(in Chinese).
[9] 戴明宏, 陶洪斌, 廖树华, 王利纳, 王璞. 基于CERES-Maize模型的华北平原玉米生产潜力的估算与分析. 农业工程学报, 2008,24(4):30-36.
Dai M H, Tao H B, Liao S H, Wang L N, Wang P. Estimation and analysis of maize potential productivity based on CERES-Maize model in the North China Plain. Trans CSAE, 2008,24(4):30-36 (in Chinese with English abstract).
[10] Passioura J B. Simulation models: science, snake oil, education, or engineering? Agron J, 1996,88:690-694.
[11] 刘兴冉, 沈彦俊. AquaCrop模型在华北平原夏玉米水分研究中的应用. 农业现代化研究, 2014,35:371-375.
Liu X R, Shen Y J. Application of AquaCrop model for simulating the summer maize water use in North China Plain. Res Agric Modern, 2014,35:371-375 (in Chinese with English abstract).
[12] 刘琦, 龚道枝, 郝卫平, 王罕博, 高翔, 梅旭荣. 利用AquaCrop模型模拟旱作覆膜春玉米耗水和产量. 灌溉排水学报, 2015,34(6):54-61.
Liu Q, Gong D Z, Hao W P, Wang H B, Gao X, Mei X R. Simulating water use and yield of film mulched maize with AquaCrop model. J Irrig Drain, 2015,34(6):54-61 (in Chinese with English abstract).
[13] Iqbal M A, Shen Y J, Stricevic R, Pei H W, Sun H Y, Amiri E, Penas A, Rio S. Evaluation of the FAO AquaCrop model for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation. Agric Water Manage, 2014,135:61-72.
[14] Daniel C. One-at-a-Time plans. J Am Stat Assoc, 1973,68:353-360.
[15] 刘刚, 谢云, 高晓飞, 冯艳杰. ALMANAC作物模型参数的敏感性分析. 中国农业气象, 2008,29:259-263.
Liu G, Xie Y, Gao X F, Feng Y J. Sensitivity analysis on parameters of ALMANAC crop model. J Agrometeorol, 2008,29:259-263 (in Chinese with English abstract).
[16] 宋明丹, 冯浩, 李正鹏, 高建恩. 基于Morris和EFAST的CERES-Wheat模型敏感性分析. 农业机械学报, 2014,45(10):124-131.
Song M D, Feng H, Li Z P, Gao J E. Global sensitivity analyses of DSSAT-CERES-Wheat model using Morris and EFAST methods. Trans CSAM, 2014,45(10):124-131 (in Chinese with English abstract).
[17] 王玉玺, 解运杰, 王萍. 东北黑土区水土流失成因分析. 水土保持应用技术, 2002, (3):27-29.
Wang Y X, Xie Y J, Wang P. Analysis on the causes of soil erosion in the black soil area of Northeast China. Technol Soil Water Conserv, 2002, (3):27-29 (in Chinese).
[18] 程叶青, 张平宇. 中国粮食生产的区域格局变化及东北商品粮基地的响应. 地理科学, 2005,25:513-520.
Cheng Y Q, Zhang P Y. Regional patterns changes of Chinese grain production and response of commodity grain base in Northeast China. Sci Geogr Sin, 2005,25:513-520 (in Chinese with English abstract).
[19] 杨春葆. 黑土区不同灌溉量对土壤水分动态和大豆产量的影响. 东北农业大学硕士学位论文, 黑龙江哈尔滨, 2014.
Yang C B. The Effect of Irrigation Levels on Soil Water Dynamics and Soybean Yield in Black Soil Region. MS Thesis of Northeast Agricultural University, Harbin, Heilongjiang, China, 2014 (in Chinese with English abstract).
[20] 胡刚, 伍永秋, 刘宝元, 谢云. GPS和GIS进行短期沟蚀研究初探——以东北漫川漫岗黑土区为例. 水土保持学报, 2004,18(4):16-19.
Hu G, Wu Y Q, Liu B Y, Xie Y. Preliminary research on short-term channel erosion using GPS and GIS. J Soil Water Conserv, 2004,18(4):16-19 (in Chinese with English abstract).
[21] Wu Y Q, Zheng Q H, Zhang Y G, Liu B Y, Cheng H, Wang Y Z. Development of gullies and sediment production in the black soil region of northeastern China. Geomorphology, 2008,101:683-691.
[22] Doorenbos J, Kassam A H. Yield response to water. Irrig Drain Pap, 1979,33:257.
[23] Allen R G, Pereira L S, Raes D, Smith M. Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Rome: FAO, 1998. [2020-06-30]. http://www.researchgate.net/profile/Anoop_Srivastava7/post/Which_method_of_calculating_crop_evapotransppiration_is_globally_accepted/attachment/5a1e5aeeb53d2f6747c6d144/AS:565926494179334@1511938798907/download/Allen_FAO1998.pdf.
[24] Hsiao T C. The soil-plant-atmosphere continuum in relation to drought and crop production. In: O’Toole J C, eds. Drought Resistance in Crops with Emphasis on Rice. Philippines: International Rice Research Institute, 1982. pp 39-52.
[25] 邢会敏, 徐新刚, 冯海宽, 李振海, 杨福芹, 杨贵军, 贺鹏, 陈召霞. 基于AquaCrop模型的北京地区冬小麦水分利用效率. 中国农业科学, 2016,49:4507-4519.
Xing H M, Xu X G, Feng H K, Li Z H, Yang F Q, Yang G J, He P, Chen Z X. Water use efficiency of winter wheat based on AquaCrop model in Beijing. Sci Agric Sin, 2016,49:4507-4519 (in Chinese with English abstract).
[26] 金秀良. 基于AquaCrop模型与多源遥感数据的北方冬小麦水分利用效率估算. 扬州大学博士学位论文, 江苏扬州, 2015.
Jin X L. Estimation of Water Use Efficiency of Winter Wheat Based on AquaCrop Model and Multi-source Remote Sensing Data in Northern. PhD Dissertation of Yangzhou University, Yangzhou, Jiangsu, China, 2015 (in Chinese with English abstract).
[27] Hsiao T C, Heng L, Steduto P, Rojas-Lara B, Raes D, Fereres E. AquaCrop: the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agron J, 2009,101:448-459.
[28] Xie Y, Kiniry J R, Williams J R. The ALMANAC model’s sensitivity to input variables. Agric Syst, 2003,78:1-16.
[29] Heiniger R W, Vanderlip R L, Welch S M, Muchow R C. Developing guidelines for replanting grain sorghum: II. Improved methods of simulating caryopsis weight and tiller number. Agron J, 1997,89:75-83.
[30] Saltelli A. Sensitivity analysis: could better methods be used? J Geophys Res-Atmos, 1999,104:3789-3793.
[31] Vanuytrecht E, Raes D, Willems P. Global sensitivity analysis of yield output from the water productivity model. Environ Modell Software, 2014,51:323-332.
doi: 10.1016/j.envsoft.2013.10.017
[32] 邢会敏, 相诗尧, 徐新刚, 陈宜金, 冯海宽, 杨贵军, 陈召霞. 基于EFAST方法的AquaCrop作物模型参数全局敏感性分析. 中国农业科学, 2017,50:64-76.
doi: 10.3864/j.issn.0578-1752.2017.01.006
Xing H M, Xiang S Y, Xu X G, Chen Y J, Feng H K, Yang G J, Chen Z X. Global sensitivity analysis of AquaCrop crop model parameters based on EFAST method. Sci Agric Sin, 2017,50:64-76 (in Chinese with English abstract).
[33] 付驰, 李双双, 李晶, 王泳超, 芦玉双, 许为政, 魏湜. AquaCrop作物模型在松嫩平原春麦区的校正和验证. 灌溉排水学报, 2012,31(5):99-102.
Fu C, Li S S, Li J, Wang Y C, Lu Y S, Xu W Z, Wei S. Calibration and validation of AquaCrop model in spring wheat region of Songnen Plain. J Irrig Drain, 2012,31(5):99-102 (in Chinese with English abstract).
[34] Moulin S, Bondeau A, Delecolle R. Combining agricultural crop models and satellite observations: from field to regional scales. Int J Remote Sens, 1998,19:1021-1036.
[35] 黄健熙, 武思杰, 刘兴权, 马冠南, 马鸿元, 吴文斌, 邹金秋. 基于遥感信息与作物模型集合卡尔曼滤波同化的区域冬小麦产量预测. 农业工程学报, 2012,28(4):142-148.
Huang J X, Wu S J, Liu X Q, Ma G N, Ma H Y, Wu W B, Zou J Q. Regional winter wheat yield forecasting based on assimilation of remote sensing data and crop growth model with Ensemble Kalman method. Trans CSAE, 2012,28(4):142-148 (in Chinese with English abstract).
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