油菜生长模拟与决策支持系统研究
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摘要
作物生长模型与决策支持系统的研究和广泛应用对于农业生产的信息化和数字化有重要的理论意义和应用价值。本研究借鉴国际上先进的建模理论和本实验室形成的作物模拟方法体系和基本框架，运用系统分析原理和动态建模技术，通过不同类型品种的播期和氮素试验对油菜生长发育的基本规律及其与环境因子之间的关系予以定量解析和综合，构建了一个基于生理生态过程的油菜生长模拟模型；采用面向对象技术、软构件技术及决策支持技术，构建了符合COM标准的油菜生长模型组件，进一步研制了基于生长模型的油菜管理决策支持系统(包括单机版、网络版)，实现了在网络和单机环境下油菜生长动态模拟预测和管理决策支持功能，为数字化油作系统的构建奠定了基础。
以油菜生理生态过程为基础，通过定量分析油菜对温度(包括春化作用)和光周期的反应，构建了以生理发育时间为尺度的油菜生育期模拟模型。每日生理发育时间由每日生理效应累积形成，每日热效应、春化效应、光周期效应与品种灌浆速率互作共同决定每日生理效应大小。模型通过引入温度敏感性、生理春化时间、光周期敏感性和基本灌浆因子4个参数反应不同油菜品种的遗传差异，从而使得每个油菜品种达到特定生育时期所需的生理发育时间恒定。
建立油菜叶面积指数模拟模型综合基于库或源限制下的叶面积增长模式，其中库限制下叶面积指数的增长呈指数方程，且受到温度、水分和氮素因子的影响；源限制下叶面积指数增长用比叶面积法来模拟。油菜角果面积指数由比角果面积和角果干物重来决定。比叶面积和比角果面积均为生理发育时间的函数。
针对油菜冠层生态结构的特点，构建了油菜光合作用和干物质积累的模拟模型。采用“三层模型”法，即通过对花、角果、叶三层分别计算光能截获和光合作用，利用高斯积分法计算每层的光合量而得出每日的冠层总同化量。模型充分量化了油菜生理年龄、温度、氮素、水分等因子对最大光合速率的影响，同时考虑了维持呼吸与生长呼吸的消耗，使模型具有较强的机理性和通用性。
利用油菜器官生育进程及环境因子之间的定量关系，构建了基于分配指数的油菜地上部器官干物质分配动态模拟模型。各器官干物质分配指数随着生理发育时间而动态变化，基因型、播期、氮素及水分水平对各器官干物质分配指数的模式没有显著影响，但影响各器官干物质分配指数的大小。其中，氮素营养水平对绿色叶片干物重分配影响最大，氮素营养水平越高，绿色叶片分配指数越大；播期影响角果分配指数，晚播的角果分配指数高于早播。模型引入氮素营养指数、水分影响因子及播期影响因子来定量油菜各器官在实际生产条件下的分配强度，同时考虑了品种遗传特性的影响。
进一步结合实验室已有的水分平衡模型和养分平衡模型，集成建立了综合性油菜生长模拟模型(RapeGrow)，包括阶段发育、器官生长、光合作用与光合作用与物质生产，物质分配与产量形成，水分平衡和养分平衡等。利用不同品种的播期试验和氮肥试验的资料对建立的模型进行了初步的校正和核实，结果表明，模型能较好的预测油菜的油菜主要生育期、绿色面积指数增长和消亡动态、干物质积累和分配动态以及籽粒产量。模型总体上具有较好的机理性、预测性和适应性。
通过定量分析冬油菜绿色面积指数动态与品种类型、生态环境和生产技术水平之间的关系，运用系统工程与动态建模的方法，以基于生理发育时间的动态生长度日为主线，建立了定量化和广适性的冬油菜适宜绿色面积指数动态设计的知识模型，包括群体叶面积指数动态和角果面积指数动态。利用不同生态点的常年逐日气象资料以及不同品种类型、产量目标资料对所建知识模型进行了实例分析，表明知识模型对不同条件下冬油菜适宜绿色面积指数的设计具有较好的可靠性和适用性。
在气象因子、土壤条件、品种特征和栽培管理数据库的支持下，利用作物生长模拟技术、决策支持技术、计算机软构件技术，构建了基于生长模型的油菜管理决策支持系统(GMDSSRSM)，包括单机版(GMDSSRSM~A)和网络版(GMDSSRSM~W)。其中单机版系统利用VB和VC++语言编程实现，网络版系统在．net平台上用C#语言开发系统，分别具有数据管理、模拟预测、方案评估、时空分析、敏感性分析、专家咨询、系统帮助等功能。本研究所建立的油菜生长模型及决策支持系统为进一步构建数字化油作系统奠定了技术基础。
The research and application of crop growth model and decision support system for rapeseed management would be important for facilitating development of informational and igital agriculture. In the present study, the relationships of growth and development to environment factors were analyzed and integrated by using the field experiments data with different genotypes, sowing dates and nitrogen application levels. By adopting studying advanced modeling technology abroad and the methodology of crop growth model developed by our lab, a physiological process-based rapeseed simulation model was developed through the system analysis and mathematical modeling. By using object-oriented framework structure and component design, a component based rapeseed growth model (RapeGrow) was developed with COM standard. Then, a growth model-based decision support system for rapeseed management (GMDSSRSM) was established by integrating decision support techniques, which realized the functions of dynamic simulation, growth prediction, decision-making in rapeseed production under the environments of stand-alone and network, respectively. The present study should be useful for prediction of growth performance under different conditions and construction of rapeseed digital management system in rapeseed crop.
Based on the eco-physiological processes of rapeseed development, a simulation model for predicting phenological stages of oilseed rape (brassica napus L.) was developed with the physiological development time (PDT) as time scale and by quantifying the effects of temperature (including vernalization) and photoperiod on oilseed rape. The interaction among daily thermal effectiveness, photoperiod effectiveness, vernalization effectiveness and filling rate determined the daily physiological effectiveness, which was accumulated to obtain physiological development time. Four specific genetic parameters as temperature sensitivity, physiological vernalization time, photoperiod sensitivity and basic filling duration factor were added to adjust the genotypic differences in rapeseed development so that all different varieties could reach the same physiological development time at a given development stage.
Dynamic leaf area expansion in rapeseed was simulated through the relationship between LAI and source or sink limited dry matter. The daily leaf expansion rate increased exponentially under the source limitation, as influenced by the factors of temperature, water and nitrogen deficits, whereas under the sink limitation, leaf area expansion was quantified on the basis of specific leaf area (SLA). Pod area was calculated from specific pod area (SPA) and pod dry matter. The specific leaf area and specific pod area were determined from physiological development time (PDT) and genotype.
Based on the characters of canopy structure, a simulation model for photosynthetic production and dry matter accumulation was established for rapeseed crop. The model used a "3 layer model", calculating the radiation interception and photosynthesis on the layers of flowers, pods and leaves, respectively. Gaussian integration method was used to calculate the photosynthesis of each layer, and the daily total canopy photosynthesis was the sum of the each layer's photosynthesis. The effects of physiological age, temperature, nitrogen and water deficit factors on maximum photosynthesis were quantified respectively. The model also considered maintenance and growth respiration in determining net photosynthetic production.
Based on eco-physiological processes, a simulation model on dry matter partitioning and yield formation in rapeseed was developed through the relationships of partitioning index to development progress and environment factors. Partitioning indices of green leaf, stem and pod changed with physiological development time, as influenced by genotype, sowing date, nitrogen level and water status. Green leaf dry weight was regulated by nitrogen nutrition index (NNI), the higher nitrogen nutrition index and the higher partitioning index of green leaf. The factor of sowing date was introduced to regulate partitioning index of pod, which increased with sowing date delayed.
By integrating the above submodels with existing wate and nutrient sub-models, a comprehensimve growth simulation model (RapeGrow) was constructed for rapeseed crop. The RapeGrow includes six sub models for simulating phasic development, organ formation, biomass production, yield and quality formation, soil-crop water relations, and nutrient (N, P, K) balance. Validation of these individual sub-models with the field experiments data of different genotypes, sowing dates, and nitrogen levels showed that the models could accurately predict main development stages, green area index, dry matter accumulation and partitioning to different organs under different conditions. The present model appears to have good explanation, reliable prediction and wide adaptation.
Based on the analysis and extraction of research data on cultural theories and technologies in winter rapeseed, a quantitive and general knowledge model was developed for present design of suitable green area index dynamics in winter rapeseed. The model was driven by physiological development time-based growing degree days and by quantitative relationships of growth characters to variety traits, eco-environments and production levels, and can be used for designing the suitable time-course dynamics of leaf area index and pod area index under different conditions. Case studies with the data sets of normal climatic year, different variety types and yield levels at different eco-sites indicate a good performance of the model system in guidance and wide applicability.
Driven by soil, variety, weather and management databases, a growth model-based decision system for rapeseed management (GMDSSRSM) was developed by integrating growth simulation technology and component-based software technology, including the stand-alone edition (GMDSSRSM~A) and web-based edition (GMDSSRSM~W). The GMDSSRSM~A was established on the platforms of VC++ and VB by adopting the characteristics of object-oriented and component-based software and coupling the growth prediction function and decision-making function for cultural management. Then, GMDSSRSM~W was further developed on the platform of. net with the language of C# for web users. The implemented system can be used for evaluating individual and comprehensive management strategies based on the results of rapeseed growth simulation under various environments and different genotypes. The GMDSSRSM should quantitivelay a technical foundation for construction and application of digital farming system in modern rapeseed production.
以油菜生理生态过程为基础，通过定量分析油菜对温度(包括春化作用)和光周期的反应，构建了以生理发育时间为尺度的油菜生育期模拟模型。每日生理发育时间由每日生理效应累积形成，每日热效应、春化效应、光周期效应与品种灌浆速率互作共同决定每日生理效应大小。模型通过引入温度敏感性、生理春化时间、光周期敏感性和基本灌浆因子4个参数反应不同油菜品种的遗传差异，从而使得每个油菜品种达到特定生育时期所需的生理发育时间恒定。
建立油菜叶面积指数模拟模型综合基于库或源限制下的叶面积增长模式，其中库限制下叶面积指数的增长呈指数方程，且受到温度、水分和氮素因子的影响；源限制下叶面积指数增长用比叶面积法来模拟。油菜角果面积指数由比角果面积和角果干物重来决定。比叶面积和比角果面积均为生理发育时间的函数。
针对油菜冠层生态结构的特点，构建了油菜光合作用和干物质积累的模拟模型。采用“三层模型”法，即通过对花、角果、叶三层分别计算光能截获和光合作用，利用高斯积分法计算每层的光合量而得出每日的冠层总同化量。模型充分量化了油菜生理年龄、温度、氮素、水分等因子对最大光合速率的影响，同时考虑了维持呼吸与生长呼吸的消耗，使模型具有较强的机理性和通用性。
利用油菜器官生育进程及环境因子之间的定量关系，构建了基于分配指数的油菜地上部器官干物质分配动态模拟模型。各器官干物质分配指数随着生理发育时间而动态变化，基因型、播期、氮素及水分水平对各器官干物质分配指数的模式没有显著影响，但影响各器官干物质分配指数的大小。其中，氮素营养水平对绿色叶片干物重分配影响最大，氮素营养水平越高，绿色叶片分配指数越大；播期影响角果分配指数，晚播的角果分配指数高于早播。模型引入氮素营养指数、水分影响因子及播期影响因子来定量油菜各器官在实际生产条件下的分配强度，同时考虑了品种遗传特性的影响。
进一步结合实验室已有的水分平衡模型和养分平衡模型，集成建立了综合性油菜生长模拟模型(RapeGrow)，包括阶段发育、器官生长、光合作用与光合作用与物质生产，物质分配与产量形成，水分平衡和养分平衡等。利用不同品种的播期试验和氮肥试验的资料对建立的模型进行了初步的校正和核实，结果表明，模型能较好的预测油菜的油菜主要生育期、绿色面积指数增长和消亡动态、干物质积累和分配动态以及籽粒产量。模型总体上具有较好的机理性、预测性和适应性。
通过定量分析冬油菜绿色面积指数动态与品种类型、生态环境和生产技术水平之间的关系，运用系统工程与动态建模的方法，以基于生理发育时间的动态生长度日为主线，建立了定量化和广适性的冬油菜适宜绿色面积指数动态设计的知识模型，包括群体叶面积指数动态和角果面积指数动态。利用不同生态点的常年逐日气象资料以及不同品种类型、产量目标资料对所建知识模型进行了实例分析，表明知识模型对不同条件下冬油菜适宜绿色面积指数的设计具有较好的可靠性和适用性。
在气象因子、土壤条件、品种特征和栽培管理数据库的支持下，利用作物生长模拟技术、决策支持技术、计算机软构件技术，构建了基于生长模型的油菜管理决策支持系统(GMDSSRSM)，包括单机版(GMDSSRSM~A)和网络版(GMDSSRSM~W)。其中单机版系统利用VB和VC++语言编程实现，网络版系统在．net平台上用C#语言开发系统，分别具有数据管理、模拟预测、方案评估、时空分析、敏感性分析、专家咨询、系统帮助等功能。本研究所建立的油菜生长模型及决策支持系统为进一步构建数字化油作系统奠定了技术基础。
The research and application of crop growth model and decision support system for rapeseed management would be important for facilitating development of informational and igital agriculture. In the present study, the relationships of growth and development to environment factors were analyzed and integrated by using the field experiments data with different genotypes, sowing dates and nitrogen application levels. By adopting studying advanced modeling technology abroad and the methodology of crop growth model developed by our lab, a physiological process-based rapeseed simulation model was developed through the system analysis and mathematical modeling. By using object-oriented framework structure and component design, a component based rapeseed growth model (RapeGrow) was developed with COM standard. Then, a growth model-based decision support system for rapeseed management (GMDSSRSM) was established by integrating decision support techniques, which realized the functions of dynamic simulation, growth prediction, decision-making in rapeseed production under the environments of stand-alone and network, respectively. The present study should be useful for prediction of growth performance under different conditions and construction of rapeseed digital management system in rapeseed crop.
Based on the eco-physiological processes of rapeseed development, a simulation model for predicting phenological stages of oilseed rape (brassica napus L.) was developed with the physiological development time (PDT) as time scale and by quantifying the effects of temperature (including vernalization) and photoperiod on oilseed rape. The interaction among daily thermal effectiveness, photoperiod effectiveness, vernalization effectiveness and filling rate determined the daily physiological effectiveness, which was accumulated to obtain physiological development time. Four specific genetic parameters as temperature sensitivity, physiological vernalization time, photoperiod sensitivity and basic filling duration factor were added to adjust the genotypic differences in rapeseed development so that all different varieties could reach the same physiological development time at a given development stage.
Dynamic leaf area expansion in rapeseed was simulated through the relationship between LAI and source or sink limited dry matter. The daily leaf expansion rate increased exponentially under the source limitation, as influenced by the factors of temperature, water and nitrogen deficits, whereas under the sink limitation, leaf area expansion was quantified on the basis of specific leaf area (SLA). Pod area was calculated from specific pod area (SPA) and pod dry matter. The specific leaf area and specific pod area were determined from physiological development time (PDT) and genotype.
Based on the characters of canopy structure, a simulation model for photosynthetic production and dry matter accumulation was established for rapeseed crop. The model used a "3 layer model", calculating the radiation interception and photosynthesis on the layers of flowers, pods and leaves, respectively. Gaussian integration method was used to calculate the photosynthesis of each layer, and the daily total canopy photosynthesis was the sum of the each layer's photosynthesis. The effects of physiological age, temperature, nitrogen and water deficit factors on maximum photosynthesis were quantified respectively. The model also considered maintenance and growth respiration in determining net photosynthetic production.
Based on eco-physiological processes, a simulation model on dry matter partitioning and yield formation in rapeseed was developed through the relationships of partitioning index to development progress and environment factors. Partitioning indices of green leaf, stem and pod changed with physiological development time, as influenced by genotype, sowing date, nitrogen level and water status. Green leaf dry weight was regulated by nitrogen nutrition index (NNI), the higher nitrogen nutrition index and the higher partitioning index of green leaf. The factor of sowing date was introduced to regulate partitioning index of pod, which increased with sowing date delayed.
By integrating the above submodels with existing wate and nutrient sub-models, a comprehensimve growth simulation model (RapeGrow) was constructed for rapeseed crop. The RapeGrow includes six sub models for simulating phasic development, organ formation, biomass production, yield and quality formation, soil-crop water relations, and nutrient (N, P, K) balance. Validation of these individual sub-models with the field experiments data of different genotypes, sowing dates, and nitrogen levels showed that the models could accurately predict main development stages, green area index, dry matter accumulation and partitioning to different organs under different conditions. The present model appears to have good explanation, reliable prediction and wide adaptation.
Based on the analysis and extraction of research data on cultural theories and technologies in winter rapeseed, a quantitive and general knowledge model was developed for present design of suitable green area index dynamics in winter rapeseed. The model was driven by physiological development time-based growing degree days and by quantitative relationships of growth characters to variety traits, eco-environments and production levels, and can be used for designing the suitable time-course dynamics of leaf area index and pod area index under different conditions. Case studies with the data sets of normal climatic year, different variety types and yield levels at different eco-sites indicate a good performance of the model system in guidance and wide applicability.
Driven by soil, variety, weather and management databases, a growth model-based decision system for rapeseed management (GMDSSRSM) was developed by integrating growth simulation technology and component-based software technology, including the stand-alone edition (GMDSSRSM~A) and web-based edition (GMDSSRSM~W). The GMDSSRSM~A was established on the platforms of VC++ and VB by adopting the characteristics of object-oriented and component-based software and coupling the growth prediction function and decision-making function for cultural management. Then, GMDSSRSM~W was further developed on the platform of. net with the language of C# for web users. The implemented system can be used for evaluating individual and comprehensive management strategies based on the results of rapeseed growth simulation under various environments and different genotypes. The GMDSSRSM should quantitivelay a technical foundation for construction and application of digital farming system in modern rapeseed production.
引文
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