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作物学报 ›› 2012, Vol. 38 ›› Issue (04): 740-746.doi: 10.3724/SP.J.1006.2012.00740

• 研究简报 • 上一篇    下一篇

APSIM玉米模型在东北地区的适应性

刘志娟1,杨晓光1,*,王静1,吕硕1,李克南1,荀欣1,王恩利2   

  1. 1中国农业大学资源与环境学院,北京 100193; 2澳大利亚联邦科工组织土地与水研究所,澳大利亚堪培拉GPO Box 1666
  • 收稿日期:2011-07-29 修回日期:2011-12-19 出版日期:2012-04-12 网络出版日期:2012-02-13
  • 通讯作者: 杨晓光, E-mail: yangxg@cau.edu.cn
  • 基金资助:

    本研究由国家重点基础研究发展计划(973计划)项目(2009CB118608)和引进国际先进农业科学技术计划(948计划)重点项目(2011-G9)资助。

Adaptability of APSIM Maize Model in Northeast China

LIU Zhi-Juan1,YANG Xiao-Guang1,*,WANG Jing1,LÜ Shuo1,LI Ke-Nan1,XUN Xin1,WANG En-Li2   

  1. 1 College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; 2 The Commonwealth Scientific and Industrial Research Organisation Land and Water, GPO Box 1666, Black Mountain, Canberra, ACT 2601, Australia
  • Received:2011-07-29 Revised:2011-12-19 Published:2012-04-12 Published online:2012-02-13
  • Contact: 杨晓光, E-mail: yangxg@cau.edu.cn

摘要: 利用东北地区6个典型农业气象试验站的玉米田间试验数据和同期逐日气象数据对APSIM模型(农业生产系统模型)在东北玉米产区的适应性进行了初步研究。先依据各站第一组玉米试验数据对模型相关参数进行调试、确定,再利用另一组试验数据检验模型模拟玉米生育期、叶面积指数、地上部总生物量和产量的可靠性。结果表明,APSIM模型模拟的播种至出苗、开花和成熟各阶段天数与实测天数有较好的一致性,其误差分别为0~2.0、0.7~2.0和0.7~2.3 d;哈尔滨地区模拟的叶面积指数和地上部总生物量相对均方根差分别为33%和11%,模拟效果较好;黑龙江哈尔滨、海伦、泰来,吉林桦甸、通化和辽宁本溪的模拟产量与实际产量的NRMSE分别为18%、13%、4%、4%、5%和2%。说明APSIM模型对东北地区玉米生育期、叶面积指数动态变化过程、地上部总生物量动态变化过程和最终产量具有较好的模拟结果,验证后的APSIM模型在东北地区具有较好的适应性。以上结果为今后在东北地区深入开展玉米生产潜力以及解析产量形成的限制因素等研究提供了技术平台与支撑。

关键词: 东北, 玉米, APSIM, 参数调试, 适应性

Abstract: The APSIM (Agricultural Production Systems Simulator) model was introduced to simulate maize growth, development and yields in the northeast China using the field experimental data and climate data collected from six typical agricultural meteorological stations in the studied area. APSIM was calibrated using the first part of data to determine the varietal parameters, then simulated the growing periods, leaf area index (LAI), total above-ground biomass and yields using the second part of data at each site. The results showed that there was a good agreement between the simulated and observed values in growing periods. The difference of growing periods from sowing to emergence, form sowing to flowering and form sowing to maturity between simulated and observed data was 02.0, 0.72.0, and 0.72.3 d, respectively. The Normalized Root Mean Square Error(NRMSE) values for measured and simulated LAI and total above-ground biomass in Harbin station were 33% and 11%, respectively. NRMSE values for measured and simulated yield in Harbin, Hailun, Tailai, Huadian, Tonghua, and Benxi station were 18%, 13%, 4%, 4%, 5%, and 2%, respectively. These results indicated that APSIM model has good ability to simulate the growing periods, dynamic process of LAI, dynamic process of above-ground biomass and yield of maize in Northeast of China. This research supports the model application in Northeast of China, such as simulating maize potential yield, or prescribing yield limiting factors.

Key words: the Northeast China, Maize, APSIM, Calibration, Adaptability

[1]Yang X, Lin E D, Ma S M, Ju H, Guo L P, Xiong W, Li Y, Xu Y L. Adaptation of agriculture to warming in Northeast China. Clim Change, 2007, 84: 45–58

[2]Cheng Y-Q(程叶青), Zhang P-Y(张平宇). Regional patterns changes of Chinese grain production and response of commodity grain base in Northeast China. Sci Geogr Sin (地理科学), 2005, 25(5): 513–520 (in Chinese with English abstract)

[3]Ma S-Q(马树庆), Wang Q(王琪), Wang C-Y(王春乙), Huo Z-G(霍治国). The risk division on climate and economic loss of maize chilling damage in Northeast China. Geogr Res (地理研究), 2008, 27(5): 1169–1177 (in Chinese with English abstract)

[4]Asseng S, Keulen H V, Stol W. Performance and application of the APSIM wheat model in the Netherlands. Eur J Agron, 2000, 12: 37–54

[5]Probert M E, Keating B A, Thompson J P, Parton W J. Modelling water, nitrogen, and crop yield for a long-term fallow management experiment. Aust J Exp Agric, 1995, 35: 941–950

[6]Asseng S, Fillery I R P, Dunin F X, Keating B A, Meinke H. Potential deep drainage under wheat crops in a Mediterranean climate: I. Temporal and spatial variability. Aust J Agric Res, 2001, 52: 45–56

[7]Asseng S, Anderson G C, Dunin F X, Fillery I R P, Dolling P J, Keating B A. Use of the APSIM wheat model to predict yield, drainage, and NO3-leaching for a deep sand. Aust J Agric Res, 1997, 49: 363–378

[8]Wu D R, Yu Q, Lu C H, Hengsdijk H. Quantifying production potentials of winter wheat in the North China Plain. Eur J Agron, 2006, 24: 226–235

[9]Wang E L, Cresswell H, Paydar Z, Gallant J. Opportunities for manipulating catchment water balance by changing vegetation type on a topographic sequence: a simulation study. Hydrol Process, 2008, 22: 736–749

[10]Wang E L, Xu J X, Smith C J. Value of historical climate knowledge, SOI based seasonal climate forecasting and stored soil moisture at sowing in crop nitrogen management in south eastern Australia. Agric Forest Meteor, 2008, 148: 1743–1753

[11]Wang E L, Yu Q, Wu D R, Xia J. Climate, agricultural production and hydrological balance in the North China Plain. Int J Climatol, 2008, doi:10.1002/joc.1677

[12]Keating B A, Carberry P S, Hammer G L, Probert M E, Robertson M J, Holzworth D, Huth N I, Hargreaves J N G, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes J P, Silburn M, Wang E, Brown S, Bristow K L, Asseng S, Chapman S, McCown R L, Freebairn D M, Smith C J. An overview of APSIM, a model designed for farming systems simulation. Eur J Agron, 2003, 18: 267–288

[13]Asseng S, Keating B A, Fillery I R P, Gregory P J, Bowden J W, Turner N C, Palta J A, Abrecht D G. Performance of the APSIM-wheat model in Western Australia. Field Crops Res, 1998, 57: 163–179

[14]Robertson M J, Carberry P S, Huth N I, Turpin J E, Probert M E, Poulton P L, Bell M, Wright G C, Yeates S J, Brinsmead R B. Simulation of growth and development of diverse legume species in APSIM. Aust J Agric Res, 2002, 53: 429–446

[15]Bassu S, Asseng S, Motzo R, Giunta F. Optimising sowing date of durum wheat in a variable Mediterranean environment. Field Crops Res, 2009, 111: 109–118

[16]Assenga S, Jamieson P D, Kimball B, Pinter P, Sayre K, Bowden J W, Howden S M. Simulated wheat growth affected by rising temperature, increased water deficit and elevated atmospheric CO2. Field Crops Res, 2004, 85: 85–102

[17]Peake A S, Robertson M J, Bidstrup R J. Optimising maize plant population and irrigation strategies on the Darling Downs using the APSIM crop simulation model. Aust J Exp Agric, 2008, 48: 313–325

[18]Chen C, Wang E, Yu Q. Modelling the effects of climate variability and water management on crop water productivity and water balance in the North China Plain. Agric Water Manage, 2010, 97: 1175–1184

[19]Wang L(王琳), Zheng Y-F(郑有飞), Yu Q(于强), Wang E-L(王恩利). Applicability of agricultural production systems simulator (APSIM) in simulating the production and water use of wheat-maize continuous cropping system in North China Plain. Chin J Appl Ecol (应用生态学报), 2007, 18(11): 2480–2486 (in Chinese with English abstract)

[20]Li Y(李艳), Xue C-Y(薛昌颖), Liu Y(刘园), Yang X-G(杨晓光). Adoptability of APSIM model simulating growth of winter wheat in Beijing and Yucheng. Meteorology (气象), 2008, 34(special issue): 271–279 (in Chinese)

[21]Sun N(孙宁), Feng L-P(冯利平). Assessing the climatic risk to crop yield of winter wheat using crop growth models. Trans CSAE (农业工程学报), 2005, 21(2): 106–110 (in Chinese with English abstract)

[22]Liu Z-J(刘志娟), Yang X-G(杨晓光), Wang W-F(王文峰), Li K-N(李克南), Zhang X-Y(张晓煜). Characteristic of agricultural climate resource in the context of global climate change in three provinces of Northeast China. Chin J Appl Ecol (应用生态学报), 2009, 20(9): 2199–2206 (in Chinese with English abstract)

[23]Allen R G, Pereira L S, Raes D, Smith M. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage. Rome: Food and Agriculture Organization of the United Nations, 1998. p 56

[24]Wallach D, Goffinet B. Mean squared error of prediction in models for studying ecological and agronomic systems. Biometrics, 1987, 43: 561–573

[25]Willmott C J. Some comments on the evaluation of model performance. Bull Am Meteor Soc, 1982, 63: 1309–1313
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