Comparison of Crop Model Validation Methods

doi:10.1016/S1671-2927(00)8656

Journal of Integrative Agriculture ›› 2012, Vol. 12 ›› Issue (8): 1274-1285.DOI: 10.1016/S1671-2927(00)8656

Comparison of Crop Model Validation Methods

CAO Hong-xin, Jim Scott Hanan, LIUYan, LIU Yong-xia, YUE Yan-bin, ZHU Da-wei, LU Jian-fei, SUN

1.Engineering Research Center for Digital Agriculture/Institute of Agricultural Economics and Information, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, P.R.China
2.The University of Queensland, Centre for Biological Information Technology, Queensland 4068, Australia
3.Agronomy College, Nanjing Agricultural University, Nanjing 210095, P.R.China
4.College of Agriculture, Yangzhou University, Yangzhou 225009, P.R.China
5.Agricultural Technological Extensive Station of Luntai County, Luntai 841600, P.R.China
6.Agronomy College, Yangtze University, Jingzhou 434025, P.R.China

收稿日期:2011-07-20 出版日期:2012-08-01 发布日期:2012-09-09
通讯作者: Correspondence CAO Hong-xin, Tel: +86-25-84391210, Fax: +86-25-84391200, E-mail: caohongxin@hotmail.com
基金资助:
This work was supported by the National High-Tech R&D Program (2006AA10Z230, 2006AA10Z219-1), the National Natural Science Foundation of China (31171455), the Jiangsu Province Agricultural Scientific Technology Innovation Fund, China (CX (10)221, CX (11)2042), the Agricultural Scientific Technology Support Program, Jiangsu Province, China (BE2008397, BE2011342), the No-Profit Industry (Meteorology) Research Program, China (GYHY201006027, GYHY201106027), and the Jiangsu Government Scholarship for Overseas Studies, China.

Comparison of Crop Model Validation Methods

CAO Hong-xin, Jim Scott Hanan, LIUYan, LIU Yong-xia, YUE Yan-bin, ZHU Da-wei, LU Jian-fei, SUN

1.Engineering Research Center for Digital Agriculture/Institute of Agricultural Economics and Information, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, P.R.China
2.The University of Queensland, Centre for Biological Information Technology, Queensland 4068, Australia
3.Agronomy College, Nanjing Agricultural University, Nanjing 210095, P.R.China
4.College of Agriculture, Yangzhou University, Yangzhou 225009, P.R.China
5.Agricultural Technological Extensive Station of Luntai County, Luntai 841600, P.R.China
6.Agronomy College, Yangtze University, Jingzhou 434025, P.R.China

Received:2011-07-20 Online:2012-08-01 Published:2012-09-09
Contact: Correspondence CAO Hong-xin, Tel: +86-25-84391210, Fax: +86-25-84391200, E-mail: caohongxin@hotmail.com
Supported by:
This work was supported by the National High-Tech R&D Program (2006AA10Z230, 2006AA10Z219-1), the National Natural Science Foundation of China (31171455), the Jiangsu Province Agricultural Scientific Technology Innovation Fund, China (CX (10)221, CX (11)2042), the Agricultural Scientific Technology Support Program, Jiangsu Province, China (BE2008397, BE2011342), the No-Profit Industry (Meteorology) Research Program, China (GYHY201006027, GYHY201106027), and the Jiangsu Government Scholarship for Overseas Studies, China.

摘要/Abstract

摘要： In this paper, the many indices used in validation of crop models, such as RMSE (root mean square errors), Sd (standard error of absolute difference), da (mean absolute difference), dap (ratio of da to the mean observation), r (correlation), and R2 (determination coefficient), are compared for the same rice architectural parameter model, and their advantages and disadvantages are analyzed. A new index for validation of crop models, dap between the observed and the simulated values, is proposed, with dap<5% as the suggested standard for precision of crop models. The different kinds of validation methods in crop models should be combined in the following aspects: (1) calculating da and dap; (2) calculating the RMSE or Sd; (3) calculating r and R2, at the same time, plotting 1:1 diagram.

关键词: crop models, validation methods, comparison

Abstract: In this paper, the many indices used in validation of crop models, such as RMSE (root mean square errors), Sd (standard error of absolute difference), da (mean absolute difference), dap (ratio of da to the mean observation), r (correlation), and R2 (determination coefficient), are compared for the same rice architectural parameter model, and their advantages and disadvantages are analyzed. A new index for validation of crop models, dap between the observed and the simulated values, is proposed, with dap<5% as the suggested standard for precision of crop models. The different kinds of validation methods in crop models should be combined in the following aspects: (1) calculating da and dap; (2) calculating the RMSE or Sd; (3) calculating r and R2, at the same time, plotting 1:1 diagram.

Key words: crop models, validation methods, comparison

CAO Hong-xin, Jim Scott Hanan, LIUYan, LIU Yong-xia, YUE Yan-bin, ZHU Da-wei, LU Jian-fei, SUN. Comparison of Crop Model Validation Methods[J]. Journal of Integrative Agriculture, 2012, 12(8): 1274-1285.

CAO Hong-xin, Jim Scott Hanan, LIUYan , LIU Yong-xia, YUE Yan-bin, ZHU Da-wei, LU Jian-fei, SUN . Comparison of Crop Model Validation Methods[J]. Journal of Integrative Agriculture, 2012, 12(8): 1274-1285.

参考文献

[1]Aber J D. 1979. A method for estimating foliage-height profiles in broad-leaved forests. Journal of Ecology, 67, 35-40.

[2]Acock B. 1989. Crop modeling in the USA. Acta Horticulturae, 248, 365-371.

[3]Basso B, Ritchie J T, Pierce F J, Braga R P, Jones J W. 2001. Spatial validation of crop models for precision agriculture. Agricultural Systems, 68, 97-112.

[4]ten Berge H F M. 1993. Building capacity for systems research at national agricultural research centers: SARP’s experience. In: Penning de Vries F W T, Teng P S, Metselaar K, eds., Systems Approaches for Agricultural Development. Kluwer Academic Publishers, Wageningen, The Netherlands. pp. 515-538.

[5]Bouma J, Jones J W. 2001. An international collaborative network for agricultural systems applications (ICASA). Agricultural Systems, 70, 355-368.

[6]Cao H X, Liu Y, Liu Y X, Hanan J S, Yue Y B, Zhu D W, Lu J F, Sun J Y, Shi C L, Ge D K, et al. 2012. Biomass-based rice (Oryza sativa L.) aboveground architectural parameter models. Journal of Integrative Agriculture. (in press) Cao H X, Zhang C L, Li G M, Zhang B J, Zhao S L, Wang B Q, Jin Z Q. 2006. Researches of simulation models of rape (Brassica napus L.) growth and development. Acta Agronomica Sinica, 32, 1530-1536. (in Chinese)

[7]Cao H X, Shi C L, Jin Z Q. 2008. Advances in researches on plant morphological structure simulation and visualization. Scientia Agrieultura Sinica, 41, 669-677. (in Chinese)

[8]Chenu K, Franck N, Lecoeur J. 2007. Simulations of virtual plants reveal a role for SERRATE in the response of leaf development to light in Arabidopsis thaliana. New Phytologist, 175, 472-481.

[9]Costes E, Smith C, Favreau R, Guédon Y, DeJong T. 2007. Linking carbon economy and architectural development of peach trees by integrating markovian models into LPEACH. [2011-07-05]. http://algorithmicbotany.org/ FSPM07/Individual/39.pdf

[10]Damour G. 2007. Models of photosynthesis need to and can be upgraded to include the effects of drought, phenological changes, sink activity and carbohydrate accumulation on the light exposure/photosynthetic capacity relationship. [2011-07-05]. http:// algorithmicbotany.org/FSPM07/15.pdf

[11]Dejong T M, Doyle J F. 1985. Seasonal relationships between leaf nitrogen content (photosynthetic capacity) and leaf canopy light exposure in peach (Prunus persica). Plant, Cell and Environment, 8, 701-706.

[12]Duncan W G, Loomis R S, Williams W A, Hanau R. 1967. A model for simulating photosynthesis in plant communities. Hilgardia, 38, 181-205.

[13]Dornbusch T, Wernecke P, Diepenbrock W. 2007. Description and visualization of graminaceous plants with an organ-based 3D architectural model, exemplified for spring barley (Hordeum vulgare L.). The Visual Computer, 23, 569-581.

[14]Easterling W E, Chen X, Hays C, Brandle J R, Zhang H. 1996. Improving the validation of model-simulated crop yield response to climate change: an application to the EPIC model. Climate Research, 6, 263-273.

[15]Evers J B, Vos J, Fournier C, Andrieu B, Chelle M, Struik P C. 2005. Towards a generic architectural model of tillering in Gramineae, as exemplified by spring wheat (Triticum aestivum). New Phytologist, 166, 801-812.

[16]Evers J B, Vos J, Fournier C, Andrieu B, Chelle M, Struik P C. 2007. An architectural model of spring wheat: Evaluation of the effects of population density and shading on model parameterization and performance. Ecological Modelling, 200, 308-320.

[17]Fox D G. 1981. Judging air quality model performance. Bulletin of the American Meteorological Society, 62, 599-609.

[18]Fourcaud T, Zhang X, Stokes A, Lambers H, Körner C. 2008. Plant growth modelling and applications: the increasing importance of plant architecture in growth models. Annals Botany, 101, 1053-1063.

[19]Gao L Z. 2001. Digital agriculture and agricultural development in China. Computer and Agriculture, 9, 1-3. (in Chinese)

[20]Gao L Z, Jin Z Q, Huang Y, Chen H, Li B B. 1992a. Rice Cultivational Simulation, Optimization and Decisionmaking System (Rcsods). Chinese Agricultural Science and Technology Press, Beijing, China. (in Chinese)

[21]Gao L Z, Jin Z Q, Huang Y, Zhang L. 1992b. Rice clock model-a computer model to simulate rice development. Agricultural Forestry Meteorology, 60, 1-16.

[22]Gao L Z, Jin Z Q, Li L. 1987. Photo-thermal models of rice growth duration for various varietal types in China. Agricultural Forestry Meteorology, 39, 205-213.

[23]Gao L Z, Jin Z Q, Zhang G S, Shi C L, Ge D K. 2000. A numerical model to simulate the incident radiation and photosynthate for rice canopies with optimum plant type. Jiangsu Journal of Agricultural Sciences, 16, 1-9. (in Chinese)

[24]Goudriaan J, Laar V H H. 1978. Calculation of daily totals of gross CO2 assimilation of leaf canopies. Netherlands Journal of Agricultural Science, 26, 373-382.

[25]Groer K C, Niemeyer O, Hemmerling R, Kurth W, Becker H, Buck-Sorlin G. 2007. A dynamic 3D model of rape (Brassica napus L.) computing yield components under variable nitrogen fertilization regimes. [2011-07-05]. http://algorithmicbotany.org/FSPM07/Individual/4.pdf

[26]Guo Y, Ma Y T, Zhan Z G, Li B G, Dingkuhn M, Luquet D, de Reffye P. 2006. Parameter optimization and field validation of the functional-structural model GREENLAB for maize. Annals of Botany, 97, 217-230.

[27]Hanan J S, Hearn A B. 2003. Linking physiological and architectural models of cotton. Agricultural Systems, 75, 47-77.

[28]Hunt L A, Pararajasingham S. 1995. CROPSIM-WHEAT: A model describing the growth and development of wheat. Canadian Journal of Plant Science, 75, 619-632.

[29]Hunt L A, Boote K J. 1998. Data for model operation, calibration and evaluation. In: Tsuji G Y, Hoogenboom G, Thornton P K, eds., Understanding Options for Agricultural Production. Kluwer Academic Publishers, Dordrecht, The Netherlands. pp. 41-54.

[30]Jin Z Q. 1996. Studies of simulation on effects of globe climate changes on food production in China. Ph D thesis, Nanjing University. (in Chinese) Jones J W. 1998. Model integration and simulation tools. Acta Horticulturae, 456, 411-417.

[31]Jones J W, Keating B A, Porter C H. 2001. Approaches to modular model development. Agricultural Systems, 70, 421-443.

[32]Lacointe A, Minchin P. 2007. Mechanistic modelling of carbon allocation among sinks: a generalised Münch model for branched architectures. [2011-07-05]. http:// algorithmicbotany.org/FSPM07/Individual/3.pdf

[33]Liu Y, Lu J F, Cao H X, Shi C L, Liu Y X, Zhu D W, Sun J Y, Yue Y B, Wei X F, Tian P P, et al. 2009. Main geometrical parameter models of rice blade based on biomass. Scientia Agricultura Sinica, 42, 4093-4099. (in Chinese)

[34]Liu Y X, Yue Y B, Liu Y, Cao H X, Ge D K, Wei X F. 2010. Quantitative research of dynamic models of the main geometric parameters of rice root system of different varieties under different nitrogen conditions. Scientia Agricultura Sinica, 43, 1782-1790. (in Chinese)

[35]Lynch J P. 2009. Structural-functional model simRoot and its applications. In: Proceedings of the 3rd International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications. Beijing, China. p. 125. Mìch R, James M, Hammel M, Hanan J, Prusinkiewicz P, Karwowski R, Lane B. 2005. CPFG Version 4.0 User’s Manual. [2011-07-05]. http://vervain.cpsc.ucalgary.ca/ redmine/projects/vlab/repository/entry/cpfg/ CPFGManual/manual.pdf. pp. 1-138.

[36]Messina C D, Jones J W, Boote K J, Vallejos C E. 2006. A gene-based model to simulate soybean development and yield responses to environment. Crop Sciences, 46, 456-466.

[37]Mo H D. 1992. Statistics for Agricultural Experiments. 2nd ed. Shanghai Scientific Technological Press, Shanghai, China. pp. 14-32, 308-460. (in Chinese)

[38]Monsi M, Saeki T. 1953. Uber den Lichtfaktor in den pflanzengesellschaften und seine bedeutung für die Stoffproduktion. Japanese Journal of Botany, 14, 22-52. (in German)

[39]Monteith J L. 1965. Light distribution and photosynthesis in field crops. Annals of Botany, 29, 17-37.

[40]Müller J, Braune H, Wernecke P, Diepenbrock W. 2007. Towards universality and modularity: a generic photosynthesis and transpiration module for functional structural plant models. [2011-07-05]. http:// algorithmicbotany.org/FSPM07/Individual/13.pdf

[41]Nakagawa H, Horie T. 1995. Modelling and prediction of developmental process in rice II. A model for simulating panicle development based on daily photoperiod and temperature. Japanese Journal of Crop Science, 64, 33-42.

[42]O’Leary G J, Connor D J. 1996. A simulation model of the wheat crop in response to water and nitrogen supply: II. Model validation. Agricultural Systems, 52, 31-55.

[43]Penning de Vries F W T, Jansen D M, ten Berge H F M, Bakema A. 1989. Simulation of Ecophysiological Process of Growth in Several Annual Crops. Simulation Monographs 29. Pudoc., Wageningen, The Netherlands. p. 271. Perttunen J, Sievänen R, Nikinmaa E, Salminen H, Vakev A J. 1996. Lignum: a tree model based oil simple structural units. Annals of Botany, 77, 87-98.

[44]Perttunen J, Sievänen R, Nikinmaa E. 1998. Lignum: a model combining the structure and the functioning of trees. Ecological Modelling, 108, 189-198.

[45]Perttunen J, Nikinmaa E, Martin J, Lechowicz, Sievänen R, Messier C. 2001. Application of the functional-structural tree model lignum to sugarmaple saplings (Acer saccharum Mmarsh) vowing in forest gaps. Annals of Botany, 88, 471-481.

[46]de Reffye P, Blaise F, Chemouny S, Jaffuel S, Fourcaud T, Houllier F. 1999. Calibration of a hydraulic architecturebased growth model on the architecture of cotton plants. Agronomie, 19, 265-280.

[47]Sadras V, Calderini D. 2009. Crop Physiology: Applications for Genetic Improvement and Agronomy. Academic Press, Amsterdam, Boston. pp. 511-529.

[48]Shi C L, Zhu Y, Cao W X. 2006. A simulation analysis on geometrical parameters of rice leaf blade. Scientia Agricultura Sinica, 39, 910-915. (in Chinese)

[49]Sievänen R, Nikinmaa E, Nygren P, Ozier-Lafontaine H, Perttunen J, Hakula H. 2000. Components of functionalstructural tree models. Annals of Forest Science, 57, 399-412.

[50]Sievänen R, Perttunen J, Nikinmaa E, Kaitaniemi P. 2008. Toward extension of a single tree functional structural model of Scots pine to stand level: effect of the canopy of randomly distributed, identical trees on development of tree structure. Functional Plant Biology, 35, 964-975.

[51]Song Y H, Guo Y, Li B G, de Reffye P. 2003. Virtual maize model I, plant morphological construting based on organ biomass accumulation. Acta Ecological Sinica, 23, 2579-2586. (in Chinese)

[52]Stockle C O, Martin S A, Campbell G S. 1994. CropSyst, a cropping systems simulation model: water/nitrogen budgets and crop yield. Agricultural Systems, 46, 335-359.

[53]Thornley J H M. 1977. Growth, maintenance and respiration: a re-interpretation. Annals of Botany, 41, 1191-1203.

[54]Uehara G, Tsuji G Y. 1993. The IBSNAT project. In: Penning de Vries F W T, Teng P S, Metselaar K, eds., Systems Approaches for Agricultural Development. Kluwer Academic Publishers, Dordrecht, The Netherlands. pp. 505-513.

[55]Watanabe T, Hanan J S, Room P M, Hasegawa T, Nakagawa H, Takahashi W. 2005. Rice morphogenesis and plant architecture: measurement, specification and the reconstruction of structural development by 3D architectural modelling. Annals of Botany, 95, 1131-1143.

[56]Wernecke P, Müller J, Dornbusch T, Wernecke A, Diepenbrock W. 2007. The virtual crop-modelling system “VICA” specified for barley. In: Vos J, Marcelis L F M, de Visser P H B, Struik P C, Evers J B, eds., Functional-Structural Plant Modelling in Crop Production. Springer, Berlin. pp. 53-64.

[57]Willmott C J. 1982. Some comments on the evaluation of model performance. Bulletin American Meteorological Society, 63, 1309-1313.

[58]Wilson J W. 1959. Analysis of the spatial distribution of foliage by two-dimensional point quadrats. New Phytologist, 58, 92-99.

[59]de Wit C T. 1958. Transpiration and Crop Yields. Instituut Voor Biologisch en Scheikundig Onderzoek van Landbouwgewassen, Versl. Landbouskd. Onderz. Wageningen, The Netherlands.

Comparison of Crop Model Validation Methods

Comparison of Crop Model Validation Methods

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics