从群体到个体尺度——基于数据的DSSAT和GreenLab作物模型连接探索

doi:10.12133/j.smartag.2021.3.2.202103-SA006

Smart Agriculture ›› 2021, Vol. 3 ›› Issue (2): 77-87.doi: 10.12133/j.smartag.2021.3.2.202103-SA006

• 专题--作物模型与可视化 • 上一篇下一篇

从群体到个体尺度——基于数据的DSSAT和GreenLab作物模型连接探索

王秀娟^1,²(), 康孟珍^1,³(), 华净^1,⁴, DE REFFYE Philippe⁵

^1.中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
^2.中国科学院大学人工智能学院，北京 100049
^3.北京市智能化技术与系统工程技术研究中心，北京 100190
^4.青岛中科慧农科技有限公司，山东青岛，266000
^5.法国农业发展研究中心（CIRAD）植物学与植物构筑模型联合实验室（UMR AMAP），蒙彼利埃F -34398，法国

收稿日期:2021-03-16 修回日期:2021-05-20 出版日期:2021-06-30
基金项目:
国家自然科学基金项目(62076239);中国科学院与泰国科技发展署合作研究资助项目(GJHZ2076)
作者简介:王秀娟（1982－），女，博士，助理研究员，研究方向为作物模型。E-mail：xiujuan.wang@ia.ac.cn。
通信作者: 康孟珍（1975－），女，博士，副研究员，研究方向为计算植物和智慧农业。电话：010-82544502。E-mail：mengzhen.kang@ ia.ac.cn

From Stand to Organ Level—A Trial of Connecting DSSAT and GreenLab Crop Model through Data

WANG Xiujuan^1,²(), KANG Mengzhen^1,³(), HUA Jing^1,⁴, DE REFFYE Philippe⁵

^1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
^2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
^3.Beijing Engineering Research Center of Intelligent Systems and Technology, Beijing 100190, China
^4.Qingdao Agri Tech Co. , Ltd. , Qingdao 266000, China
^5.CIRAD, UMR AMAP, Montpellier F-34398, France

Received:2021-03-16 Revised:2021-05-20 Online:2021-06-30
Foundation items:National Natural Science Foundation of China(62076239);Collaborative Research Project between the Chinese Academy of Sciences and the Thailand Science and Technology Development Agency(GJHZ2076)
About author:WANG Xiujuan, E-mail：xiujuan.wang@ia.ac.cn
Corresponding author:KANG Mengzhen, E-mail：mengzhen.kang@ ia.ac.cn

摘要/Abstract

摘要：

作物模型的研究涉及作物生长发育的复杂过程，空间上从分子到细胞、组织、器官、个体、群体等不同尺度，时间尺度上可以从秒到年。基于不同的研究需求，切换作物模型尺度，可使得作物模型的适用性更广泛灵活。其中，如何从群体尺度的作物模型转入个体尺度的作物模型是本研究的内容。本研究基于四个玉米品种的两个处理（灌溉和雨养）的已有的实验数据和基于这些数据的DSSAT系统的模拟数据，校准功能结构模型GreenLab的参数，以计算结果一致为指标，探索不同空间尺度模型建立接口的方法，比较不同模型的特点。结果表明，GreenLab模型可以复现DSSAT系统的模拟数据和实际测量数据，进一步可以反演出各种器官之间生物量的分配并进行三维可视化展示。最后讨论了不同空间尺度模型结合的优势及应用领域。

关键词: 作物模型, 不同尺度模型, 功能结构模型, 模型连接, DSSAT, GreenLab, 参数估计

Abstract:

Crop models involve complex plant processes, which can be built in different scales of space and time, from molecule, cell, organ, tissue, individual to stand in space and from second to year in time. Based on different research requirements, switching the model scales can make the applicability of the model more extensive and flexible. How to switch the crop model from stand level to organ level is the content of this research. The DSSAT software (stand level) and functional-structural plant model 'GreenLab' (organ level) were chosen to explore the possibility to switch the crop model from stand to organ level. The DSSAT can simulate the growth and development processes of crops in detail according to the growth period by taking the data of weather, soil, crop management, and observational data as input. The GreenLab can simulate the growth and development and their interaction of crops by considering plant structure, and the model parameters can be estimated according to the measurements. In this study, the experimental data contains two parts: the measurements of four maize cultivars with two treatments (irrigated and rainfed) in DSSAT, and the simulations including the weights of leaves, internodes and fruits per day using DSSAT based on the measurements. The simulation results of DSSAT were used to calibrate the parameters of the environmental (E), sink strength (P_o), and remobilization (k_b and k_i) in GreenLab, and to compute the weights of leaves, internodes and fruits for each phytomer. The simulation results of the GreenLab model were compared and analyzed with the experimental data and the simulations of DSSAT. The consistency of calculation results could be an indicator to explore the method of building an interface between different-scale crop models, and to compare the characteristics of different models. The results showed that the GreenLab model could reproduce the simulation data of the DSSAT and the measurement data, including the leaf area index (LAI) and the total weight of the plants, and further could compute the biomass for each organ (leaf, internode and fruit), and the biomass distribution among organs, the biomass production (Q), the demand (D) and the ratio between Q and D during the growth. Therefore, the detailed information of organ growth and development could be reproduced and the 3D structures of plant could be given. Finally, the advantages and application fields of different-scale model integration were discussed.

Key words: crop model, different model scale, functional-structural plant model, model integration, DSSAT, GreenLab, parameter estimation

中图分类号:

S184

王秀娟, 康孟珍, 华净, DE REFFYE Philippe. 从群体到个体尺度——基于数据的DSSAT和GreenLab作物模型连接探索[J]. 智慧农业(中英文), 2021, 3(2): 77-87.

WANG Xiujuan, KANG Mengzhen, HUA Jing, DE REFFYE Philippe. From Stand to Organ Level—A Trial of Connecting DSSAT and GreenLab Crop Model through Data[J]. Smart Agriculture, 2021, 3(2): 77-87.

参考文献

1	MARCELIS L F M, HEUVELINK E, GOUDRIAAN J. Modelling biomass production and yield of horticultural crops: A review[J]. Scientia Horticulturae, 1998, 74(1): 83-111.
2	刘海龙, 诸叶平, 李世娟, 等. DSSAT作物系统模型的发展与应用[J]. 农业网络信息, 2011 (11): 5-12.
2	LIU H, ZHU Y, LI S, et al. Development and application of DSSAT cropping system model[J]. Agriculture Network Information, 2011, (11): 5-12.
3	BOUMAN B A M, KEULEN H V, LAAR H H V, et al. The 'School of de Wit' crop growth simulation models: A pedigree and historical overview[J]. Agricultural Systems, 1996, 52(2): 171-198.
4	王天铎, 于强. 植物生理学中的数学模型[M]// 陈晓亚, 汤章城. 植物生理学与分子生物学(第三版). 北京: 高等教育出版社, 2007.
4	WANG T, YU Q. Mathematical models in plant physiology [M]// CHEN X, TANG Z. Plant physiolgoy and melocular biology (3rd). Beijing: Higher Education Press, 2007.
5	OSMOND C B, BJ?RKMAN O, ANDERSON D J. Physiological processes in plant ecology[M]. Berlin, Heidelberg: Springer, 1980.
6	杨靖民, 杨靖一, 姜旭, 等. 作物模型研究进展[J]. 吉林农业大学学报, 2012, 34(5): 553-561.
6	YANG J, YANG J, JIANG X, et al. Progress of crop model research[J]. Journal of Jilin Agricultural University, 2012, 34(5): 553-561.
7	JONES C A, KINIRY J R. CERES-maize: A simulation model of maize growth and development[M]. College Station, USA: Texas A & M University Press, 1986.
8	HEUVELINK E. Dry matter partitioning in tomato: Validation of a dynamic simulation model[J]. Annals of Botany, 1996, 77(1): 71-80.
9	BRISSON N, MARY B, RIPOCHE D, et al. STICS [Simulateur multidisciplinaire pour les Cultures Standards]: A generic model for the simulation of crops and their water and nitrogen balances. 1. Theory and parameterization applied to wheat and corn[J]. Agronomie (France), 1998, 18(5-6): 311-346.
10	KEATING B A, CARBERRY P S, HAMMER G L, et al. An overview of APSIM, a model designed for farming systems simulation[J]. European Journal of Agronomy, 2003, 18(3-4): 267-288.
11	康孟珍. 植物功能结构模型研究的回顾与展望[J]. 系统仿真学报, 2012, 24(10): 5-14.
11	KANG M. Review and perspectives on research about functional-structural plant models[J]. Journal of System Simulation, 2012, 24(10): 5-14.
12	YAN H P, KANG M Z, DE REFFYE P, et al. A dynamic, architectural plant model simulating resource-dependent growth[J]. Annals of Botany, 2004, 93(5): 591-602.
13	MA Y, WEN M, GUO Y, et al. Parameter optimization and field validation of the functional-structural model GREENLAB for maize at different population densities[J]. Annals of Botany, 2008, 101(8): 1185-1194.
14	KANG M Z, EVERS J B, VOS J, et al. The derivation of sink functions of wheat organs using the GREENLAB model[J]. Annals of Botany, 2008, 101 (8): 1099-1108.
15	王秀娟, 李冬, 林宝刚, 等. 油菜分枝拓扑结构随机模拟[J]. 中国科学: 生命科学, 2019, 49(1): 67-76.
15	WANG X, LI D, LIN B, et al. Stochastic simulation of branch morphological structure in oilseed rape[J]. Scientia Sinica Vitae, 2019, 49(1): 67-76.
16	WANG X, KANG M, FAN X, et al. What are the differences in yield formation among two cucumber (Cucumis sativus L.) cultivars and their F1 hybrid?[J]. Journal of Integrative Agriculture, 2020, 19(7): 1789-1801.
17	FAN X, KANG M, HEUVELINK E, et al. A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth[J]. Ecological Modelling, 2015, 312: 363-373.
18	KANG M, WANG F. From parallel plants to smart plants: Intelligent control and management for plant growth[J]. IEEE/CAA Journal of Automatica Sinica, 2017, 4(2): 161-166.
19	YANG W, FENG H, FENG X, et al. Crop phenomics and high-throughput phenotyping: Past decades,current challenges, and future perspectives[J]. Molecular Plant, 2020, 13(2): 187-214.
20	SOLER C M T, HOOGENBOOM G, SENTELHAS P C, et al. Impact of water stress on maize grown off‐Season in a subtropical environment[J]. Journal of Agronomy and Crop Science, 2007, 193: 247-261.
21	SOLER C M T, HOOGENBOOM G, SENTELHAS P C. Thermal time for phenological development of four maize hybrids grown off-season in a subtropical environment[J]. Journal of Agricultural Science, 2005, 143: 169-182.
22	SOLER C M T, SENTELHAS P C, HOOGENBOOM G. Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment[J]. European Journal of Agronomy, 2007, 27(2-4): 165-177.
23	HEUVELINK E. Dry matter partitioning in a tomato plant-one common assimilate pool[J]. Journal of Experimental Botany, 1995, 46 (289): 1025-1033.
24	LEMAIRE S, MAUPAS F, COURNEDE PH, et al. Analysis of the density effects on the source-sink dynamics in sugar-beet growth[C]// The 2009 Plant Growth Modeling, Simulation, Visualization, and Applications (PMA '09.
24	Washington D.C., USA: IEEE Computer Society, 2009: 285-292.
25	FENG L, MAILHOL J C, REY H, et al. Comparing an empirical crop model with a functional structural plant model to account for individual variability[J]. European Journal of Agronomy, 2014, 53: 16-27.
26	BAEY C, DIDIER A, LEMAIRE S, et al. Parametrization of five classical plant growth models applied to sugar beet and comparison of their predictive capacity on root yield and total biomass[J]. Ecological Modelling, 2014, 290: 11-20.
27	DE REFFYE P, HU B, KANG M, et al. Two decades of research with the greenlab model in agronomy[J]. Annals of Botany, 2020, 3(127): 281-295.
28	LETORT V, MAHE P, COURNèDE P H, et al. Quantitative genetics and functional-structural plant growth models: Simulation of quantitative trait loci detection for model parameters and application to potential yield optimization[J]. Annals of Botany, 2008, 101(8): 1243-1254.
29	WANG H, KANG MZ, HUA J. Simulating plant plasticity under light environment: A source-sink approach[C]// 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications. Piscataway, New York, USA: IEEE, 2012.
30	ZHU X G, LYNCH J P, LEBAUER D S, et al. Plants in silico: Why, why now and what?—An integrative platform for plant systems biology research[J]. Plant Cell & Environment, 2016, 39(5): 1049-1057.
31	CHEW Y H, WENDEN B, FLIS A, et al. Multiscale digital arabidopsis predicts individual organ and whole-organism growth[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(39): E4127-E4136.
32	PENG B, GUAN K, TANG J, et al. Towards a multiscale crop modelling framework for climate change adaptation assessment[J]. Nature Plants, 2020, 6(4): 338-348.

从群体到个体尺度——基于数据的DSSAT和GreenLab作物模型连接探索

From Stand to Organ Level—A Trial of Connecting DSSAT and GreenLab Crop Model through Data

在线阅读

知网下载

本地下载

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价