Guo Erjing, Yang Feiyun, Wu Lu, et al. Water-nitrogen managements for spring maize at Tuquan, Inner Mongolia based on APSIM. J Appl Meteor Sci, 2024, 35(5): 629-640. DOI:   10.11898/1001-7313.20240510.
Citation: Guo Erjing, Yang Feiyun, Wu Lu, et al. Water-nitrogen managements for spring maize at Tuquan, Inner Mongolia based on APSIM. J Appl Meteor Sci, 2024, 35(5): 629-640. DOI:   10.11898/1001-7313.20240510.

Water-nitrogen Managements for Spring Maize at Tuquan, Inner Mongolia Based on APSIM

DOI: 10.11898/1001-7313.20240510
  • Received Date: 2024-05-16
  • Rev Recd Date: 2024-07-30
  • Publish Date: 2024-09-30
  • Water and nitrogen are critical factors that constrain the sustainable production of dryland agriculture. With increasingly severe crisis of water and nitrogen resources and environment, exploring and optimizing water-nitrogen managements, and hence achieving coordinated and unified resource conservation, high and stable grain production, and high efficiency are of great significance for agricultural development. Key parameters of APSIM (agricultural production system simulator) are calibrated and validated based on spring maize phenology, yield, and field management data from Tuquan, Inner Mongolia Autonomous Region from 2013 to 2022. Combined with meteorological data from 1981 to 2022 at Tuquan, water-nitrogen management scenarios are designed under different water deficit levels. Optimal water-nitrogen managements for spring maize at Tuquan are proposed based on indicators including spring maize yield, irrigation amount, nitrogen application amount, water productivity, and agronomic efficiency of applied nitrogen. Furthermore, the suitable irrigation and nitrogen application amounts for spring maize under different precipitation year types are analyzed. Results show that normalized root mean squared errors of the simulated and observed days from emergence to flowering, days from emergence to maturity, and yield of spring maize are 1.3%, 1.2% and 2.8%, respectively. APSIM can quantitatively simulate the growth period and yield of spring maize. Based on the principle that yield, water productivity, and agronomic efficiency of applied nitrogen of spring maize do not significantly decrease compared to the maximum values of all scenarios, and irrigation and nitrogen application amounts do not significantly increase compared to the minimum values of all scenarios, four management measures with no significant differences can be selected, namely, starting automatic irrigation when water deficit reaches 40%, 50%, 60% at the depth of 0-100 cm, and when the water deficit reaches 60% at the depth of 0-60 cm. Among them, the optimal auto-irrigation management for spring maize at Tuquan is to apply irrigation when the water deficit reaches 60% at the depth of 0-100 cm. In this scenario, the irrigation amount is 171.0 mm, and the nitrogen application amount is 197.8 kg·hm-2. When the precipitation during the spring maize growing season is 200-400 mm, the appropriate irrigation amount is 233.0-283.5 mm, and the nitrogen application amount is 176.9-219.3 kg·hm-2. When the precipitation during the spring maize growing season is 401-600 mm, the appropriate irrigation amount is 110.5-148.4 mm, and the nitrogen application amount is 218.3-241.5 kg·hm-2, respectively. When the precipitation during the spring maize growing season is 601-800 mm, the suitable irrigation amount is 125.0-155.0 mm, and the nitrogen application amount is 211.8-249.9 kg·hm-2. This study provides a quantitative reference for utilizing crop mechanism models in real-time monitoring, diagnosis, and precise management of crop water and nitrogen.
  • Fig. 1  Climate resources at Tuquan Agrometeorological Observation Station of Inner Mongolia from 1981 to 2022

    Fig. 1  Climate resources at Tuquan Agrometeorological Observation Station of Inner Mongolia from 1981 to 2022

    Fig. 2  Comparison and validation of simulated and observed growth duration and yield of spring maize from 2016 to 2022

    Fig. 2  Comparison and validation of simulated and observed growth duration and yield of spring maize from 2016 to 2022

    Fig. 3  Average yield and high stability coefficient of spring maize under different scenarios

    Fig. 3  Average yield and high stability coefficient of spring maize under different scenarios

    Fig. 4  Spring maize water productivity and agronomic efficiency of applied nitrogen under different scenarios

    Fig. 4  Spring maize water productivity and agronomic efficiency of applied nitrogen under different scenarios

    Table  1  Specific values of APSIM key parameters after parameter adjustment

    描述 单位 参数值
    出苗到拔节期结束的有效积温 出苗到拔节期结束的有效积温>℃·d 出苗到拔节期结束的有效积温>210
    孕穗期到开花的有效积温 ℃·d 10
    开花到灌浆的有效积温 ℃·d 10
    开花到成熟的有效积温 ℃·d 730
    最适光周期 h 12.5
    光周期最大临界值 h 24.0
    光周期斜率 ℃·h-1 0.0
    每株最大籽粒数 650
    每平方米茎秆重 g 120
    植株高度 mm 3000
    DownLoad: Download CSV

    Table  1  Specific values of APSIM key parameters after parameter adjustment

    描述 单位 参数值
    出苗到拔节期结束的有效积温 出苗到拔节期结束的有效积温>℃·d 出苗到拔节期结束的有效积温>210
    孕穗期到开花的有效积温 ℃·d 10
    开花到灌浆的有效积温 ℃·d 10
    开花到成熟的有效积温 ℃·d 730
    最适光周期 h 12.5
    光周期最大临界值 h 24.0
    光周期斜率 ℃·h-1 0.0
    每株最大籽粒数 650
    每平方米茎秆重 g 120
    植株高度 mm 3000
    DownLoad: Download CSV

    Table  2  Variations in spring maize yield, water productivity and agronomic efficiency of applied nitrogen under different scenarios

    土壤剖面深度/cm 土壤水分亏缺程度/% 单产/ (kg·hm-2) 高稳系数 灌溉量/ mm 施氮量/ (kg·hm-2) 水分生产力/ (kg·hm-2·mm-1) 氮肥农学效率/ (kg·kg-1)
    0~200 10 13035.2 0.82 319.8 293.5 16.4 22.0
    20 12343.3 0.77 219.3* 214.1* 18.2 26.2
    30 11252.9 0.68 170.2* 189.5* 18.2 26.5
    40 9807.3* 0.54 131.2* 154.8* 17.0 26.8*
    50 8307.9* 0.39 94.1* 137.7* 15.5 24.6
    60 6661.1* 0.22 58.3* 112.4* 13.1* 23.2
    0~100 10 13230.2 0.83 358.3 317.8 15.7 19.8
    20 13044.2 0.82 279.8* 279.3 17.4 22.7
    30 12777.1 0.80 242.4* 247.1* 18.1 24.8*
    40 12458.1 0.78 214.6* 224.7* 18.6 25.3*
    50 12016.8 0.74 193.2* 217.2* 18.6 25.8*
    60 11519.0 0.69 171.0* 197.8* 18.6 24.8*
    0~60 10 13292.6 0.83 379.8 335.1 15.3 18.6
    20 13122.6 0.82 301.5* 279.4* 16.9 22.3
    30 12974.9 0.81 264.1* 262.4* 17.8 23.9*
    40 12744.2 0.79 241.0* 249.7* 18.1* 24.7*
    50 12483.6 0.77 218.8* 241.9* 18.4* 24.8*
    60 12210.3 0.75 206.1* 226.6* 18.4* 24.2*
    注:*表示与同一土壤剖面深度下土壤水分亏缺程度为10%情景的差异达到0.05显著性水平。
    DownLoad: Download CSV

    Table  2  Variations in spring maize yield, water productivity and agronomic efficiency of applied nitrogen under different scenarios

    土壤剖面深度/cm 土壤水分亏缺程度/% 单产/ (kg·hm-2) 高稳系数 灌溉量/ mm 施氮量/ (kg·hm-2) 水分生产力/ (kg·hm-2·mm-1) 氮肥农学效率/ (kg·kg-1)
    0~200 10 13035.2 0.82 319.8 293.5 16.4 22.0
    20 12343.3 0.77 219.3* 214.1* 18.2 26.2
    30 11252.9 0.68 170.2* 189.5* 18.2 26.5
    40 9807.3* 0.54 131.2* 154.8* 17.0 26.8*
    50 8307.9* 0.39 94.1* 137.7* 15.5 24.6
    60 6661.1* 0.22 58.3* 112.4* 13.1* 23.2
    0~100 10 13230.2 0.83 358.3 317.8 15.7 19.8
    20 13044.2 0.82 279.8* 279.3 17.4 22.7
    30 12777.1 0.80 242.4* 247.1* 18.1 24.8*
    40 12458.1 0.78 214.6* 224.7* 18.6 25.3*
    50 12016.8 0.74 193.2* 217.2* 18.6 25.8*
    60 11519.0 0.69 171.0* 197.8* 18.6 24.8*
    0~60 10 13292.6 0.83 379.8 335.1 15.3 18.6
    20 13122.6 0.82 301.5* 279.4* 16.9 22.3
    30 12974.9 0.81 264.1* 262.4* 17.8 23.9*
    40 12744.2 0.79 241.0* 249.7* 18.1* 24.7*
    50 12483.6 0.77 218.8* 241.9* 18.4* 24.8*
    60 12210.3 0.75 206.1* 226.6* 18.4* 24.2*
    注:*表示与同一土壤剖面深度下土壤水分亏缺程度为10%情景的差异达到0.05显著性水平。
    DownLoad: Download CSV

    Table  3  Irrigation amount of spring maize for different precipitation year types under optimal managements (unit: mm)

    降水量/mm 0~100 cm土壤剖面深度 0~60 cm土壤剖面深度
    水分亏缺程度为40% 水分亏缺程度为50% 水分亏缺程度为60% 水分亏缺程度为60%
    200~400 283.5 261.0 233.0 279.0
    401~600 148.4 126.8 110.5 137.4
    601~800 155.0 145.0 125.0 130.0
    DownLoad: Download CSV

    Table  3  Irrigation amount of spring maize for different precipitation year types under optimal managements (unit: mm)

    降水量/mm 0~100 cm土壤剖面深度 0~60 cm土壤剖面深度
    水分亏缺程度为40% 水分亏缺程度为50% 水分亏缺程度为60% 水分亏缺程度为60%
    200~400 283.5 261.0 233.0 279.0
    401~600 148.4 126.8 110.5 137.4
    601~800 155.0 145.0 125.0 130.0
    DownLoad: Download CSV

    Table  4  Nitrogen application amount of spring maize for different precipitation year types under optimal managements (unit: kg·hm-2)

    降水量/mm 0~100 cm土壤剖面深度 0~60 cm土壤剖面深度为
    水分亏缺程度40% 水分亏缺程度为50% 水分亏缺程度为60% 水分亏缺程度为60%
    200~400 219.3 198.9 176.9 210.2
    401~600 230.3 237.0 218.3 241.5
    601~800 225.1 211.7 211.8 249.9
    DownLoad: Download CSV

    Table  4  Nitrogen application amount of spring maize for different precipitation year types under optimal managements (unit: kg·hm-2)

    降水量/mm 0~100 cm土壤剖面深度 0~60 cm土壤剖面深度为
    水分亏缺程度40% 水分亏缺程度为50% 水分亏缺程度为60% 水分亏缺程度为60%
    200~400 219.3 198.9 176.9 210.2
    401~600 230.3 237.0 218.3 241.5
    601~800 225.1 211.7 211.8 249.9
    DownLoad: Download CSV
  • [1]
    Li E, Zhao J, Pullens J W M, et al. The compound effects of drought and high temperature stresses will be the main constraints on maize yield in Northeast China. Sci Total Environ, 2022, 812. DOI:  10.1016/j.scitotenv.2021.152461.
    [2]
    Chen Y Y, Wang P J, Zhang Y D, et al. Comparison of drought recognition of spring maize in Northeast China based on 3 remote sensing indices. J Appl Meteor Sci, 2022, 33(4): 466-476.
    [3]
    Huo Z G, Zhang H Y, Li C H, et al. Review on high temperature heat damage of maize in China. J Appl Meteor Sci, 2023, 34(1): 1-14.
    [4]
    Mao Z Q, Zhang Y S, Yu Z R. Water requirement and irrigation scenarios of summer maize production aided by crop growth simulation model. Acta Agron Sinica, 2003, 29(3): 419-426. doi:  10.3321/j.issn:0496-3490.2003.03.018
    [5]
    Chen D F, Luo P, Zhang F C, et al. Effects of irrigation and fertilization regulation on maize growth, water and nutrient use of drip irrigation under plastic film. Agric Res Arid Areas, 2018, 36(5): 161-168.
    [6]
    Chen S C, Liu W F, Du T S. Achieving high-yield and high-efficient management strategy based on optimized irrigation and nitrogen fertilization management and planting structure. Trans Chinese Soc Agric Eng, 2022, 38(16): 144-152.
    [7]
    Dang J Y, Pei X X, Zhang D Y, et al. Effects of integration of micro-sprinkler irrigation and nitrogen on growth and development of winter wheat and water and fertilizer use efficiency. Chinese J Appl Ecol, 2020, 31(11): 3700-3710.
    [8]
    Zhao G Q, Zhu Z X, Deng T H, et al. The influences of water and nitrogenous fertilizer on winter wheat yield and the controlling technique. J Appl Meteor Sci, 1999, 10(3): 314-320. doi:  10.3969/j.issn.1001-7313.1999.03.008
    [9]
    Liu F Q, Dou C Y, Gu G D, et al. Impacts of water and fertilizer drip irrigation on growth and yield of maize in windy sandy soil. Jiangsu Agric Sci, 2023, 51(5): 110-116.
    [10]
    Lobell D B, Hammer G L, McLean G, et al. The critical role of extreme heat for maize production in the United States. Nature Clim Change, 2013, 3(5): 497-501. doi:  10.1038/nclimate1832
    [11]
    Wang F X, Chen Y, Li Y Z. Use and management of soil water and nitrogen resources Ⅲ. The optimal management of soil water and nitrogen resources. Plant Natrition Fertil Sci, 2000, 6(1): 18-23.
    [12]
    Dong C Y, Liu Z J, Yang X G. Effects of different grade drought on grain yield of spring maize in northern China. Trans Chinese Soc Agric Eng, 2015, 31(11): 157-164.
    [13]
    Liu Z J, Yang X G, Wang J, et al. Adaptability of APSIM maize model in Northeast China. Acta Agron Sinica, 2012, 38(4): 740-746.
    [14]
    Hou Y Y, Zhang L, Wu M X, et al. Advances of modern agrometeorological service and technology in China. J Appl Meteor Sci, 2018, 29(6): 641-656.
    [15]
    Wang W J, Feng H. The progress and problems in the development of foreign crop models. Water Sav Irrig, 2012(8): 63-68.
    [16]
    Shi Y, Li Y N, Bai M J, et al. Research on the development and application of DSSAT cropping system model in water management and irrigation. China Rural Water Hydropower, 2015(1): 15-19.
    [17]
    Ran H, Kang S Z, Li F S, et al. Responses of water productivity to irrigation and N supply for hybrid maize seed production in an arid region of Northwest China. J Arid Land, 2017, 9(4): 504-514.
    [18]
    Zhao J H. Effects of Water and Nitrogen on Seed Production of Maize in Hexi Oasis Irrigation Area. Lanzhou: Gansu Agricultural University, 2016.
    [19]
    Zhou Q, Wang F X, Zhao Y, et al. Influence of water and nitrogen management and planting density on seed maize growth under drip irrigation with mulch in arid region of Northwest China. Chinese Agric Sci Bull, 2016, 32(21): 166-173.
    [20]
    Li J J, Zang W J, Li Y J, et al. Effects of different nitrogen managements on spring maize yield, water and nitrogen use efficiency under sprinkler fertigation. J Drain Irrig Mach Eng, 2020, 38(12): 1277-1283.
    [21]
    Guan K X, Guo E J, Gao J Q, et al. Climate-smart water-nitrogen managements for main patterns of double-cropping system in North China Plain. Chinese J Agrometeorol, 2023, 44(6): 453-468.
    [22]
    Cheng Y, Zhang J B, Cai Z C. Key role of matching of crop-specific N preference, soil N transformation and climate conditions in soil N nutrient management. Acta Pedol Sinica, 2019, 56(3): 507-515.
    [23]
    Guo J P, Luan Q, Wang J X, et al. Model construction of rainfall interception by maize canopy. J Appl Meteor Sci, 2020, 31(4): 397-404.
    [24]
    Zhu F L, Zhang L X, Hu X, et al. Research on precision fertilization control system based on bat optimization BP-PID algorithm. Trans Chinese Soc Agric Mach, 2023, 54(Suppl Ⅰ): 135-143.
    [25]
    Probert M E, Keating B A, Thompson J P, et al. Modelling water, nitrogen, and crop yield for a long-term fallow management experiment. Aust J Exp Agric, 1995, 35(7): 941-950.
    [26]
    Asseng S, van Keulen H, Stol W. Performance and application of the APSIM Nwheat model in the Netherlands. Eur J Agron, 2000, 12(1): 37-54.
    [27]
    Wang G C, Luo Z K, Wang E L, et al. Reducing greenhouse gas emissions while maintaining yield in the croplands of Huang-Huai-Hai Plain, China. Agric For Meteor, 2018, 260: 80-94.
    [28]
    Zhao G, Bryan B A, Song X D. Sensitivity and uncertainty analy-sis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters. Ecol Model, 2014, 279: 1-11.
    [29]
    Sun S, Wang C Y, Song Y L, et al. Distributions of high and stable yield zones for potato in the single-cropping region in northern China. J Appl Meteor Sci, 2021, 32(4): 385-396.
    [30]
    Wen Z M, Zhang Y K. Discussion on estimating high and stable yield of maize hybrids by high stability coefficient method. Acta Agron Sinica, 1994, 20(4): 508-512.
    [31]
    Jia X H, Wang J Z, Liu S M, et al. To analyse the high and stable yields of cotton new breeds by practising high and stable cofficient method. Jiangxi Cottons, 2000, 22(3): 23-25.
    [32]
    Wang X Y, Yang X G, Tao L, et al. Rice suitability zoning of alternative wetting and drying irrigation mode in three provinces of Northeast China. Trans Chinese Soc Agric Eng, 2018, 34(6): 111-120.
    [33]
    Zhang F S, Wang J Q, Zhang W F, et al. Nutrient use efficiencies of major cereal crops in China and measures for improvement. Acta Pedol Sinica, 2008, 45(5): 915-924.
    [34]
    Duncan D B. Multiple range and multiple F tests. Biometrics, 1955, 11(1): 1-42.
    [35]
    Kruskal W H, Wallis W A. Errata: Use of ranks in one-criterion variance analysis. J Am Stat Assoc, 1952, 47(260): 583-621.
    [36]
    Wang K J, Yang Q Q, Wang J, et al. Synergistic effects of water and nitrogen on photosynthetic characteristics and yield of maize. Soil Fertil Sci China, 2023(7): 48-55.
    [37]
    Zhu L, Shi H B, Wang N, et al. Crop water requirement and optimization of irrigation system of intercrop wheat and maize by ISAREG model. J Irrig Drain, 2012, 31(4): 26-31.
    [38]
    Wang X Y, Cai H J, Li L, et al. Effects of water deficit on greenhouse gas emission in wheat field in different periods. Environ Sci, 2019, 40(5): 2413-2425.
    [39]
    Li Y, Wang G F. Design and implementation of meteorological disaster risk management system. J Appl Meteor Sci, 2022, 33(5): 628-640.
    [40]
    He D, Wang E L, Wang J, et al. Uncertainty in canola phenology modelling induced by cultivar parameterization and its impact on simulated yield. Agric For Meteor, 2017, 232: 163-175.
    [41]
    Han C Y, Zhang B Z, Chen H, et al. Novel approach of upscaling the FAO AquaCrop model into regional scale by using distributed crop parameters derived from remote sensing data. Agric Water Manag, 2020, 240. DOI:  10.1016/j.agwat.2020.106288.
    [42]
    Liu B C, Wang S L, Ma Y P. A study on abroad challenges of scaling-up of crop models for regional applications. Chinese J Eco Agric, 2003, 11(4): 89-91.
    [43]
    Sun G H, Duan J Q, Li J R, et al. Agro-climatic zoning of oiltea camellia in China based on climate-land integrated impacts. J Appl Meteor Sci, 2024, 35(4): 444-455.
    [44]
    Wang J F, Zhou G S, Song Y L, et al. Effects of meteorological conditions on the yield of Lianyu No. 1 maize. J Appl Meteor Sci, 2023, 34(3): 373-378.
    [45]
    Ye P, Song C Y, Liu K W, et al. Greenhouse gas emission characteristics of different rice cropping patterns in Jianghan Plain. J Appl Meteor Sci, 2022, 33(6): 748-758.
    [46]
    Zhang H, Gao Q, Chang S T, et al. Interannual carbon exchange variability of rain-fed maize fields in Northeast China and its influencing factors. J Appl Meteor Sci, 2023, 34(2): 246-256.
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    • Received : 2024-05-16
    • Accepted : 2024-07-30
    • Published : 2024-09-30

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