基于生长模型与GIS的小麦生产力预测技术研究
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摘要
预测作物生产力、分析未来气候变化对农业生产的影响是评价粮食安全的重要内容之一。作物生长模型由于具有较强地系统综合和定量预测的功能,已成为作物生长动态预测、管理决策以及气候环境影响评估的有力工具。本研究以小麦为对象,首先运用系统分析和动态建模技术,建立了基于生理发育时间(GDD)的籽粒蛋白质组分含量模拟模型；然后基于生长模型区域化应用升尺度化技术,耦合GIS功能与小麦生长模型,构建了小麦空间生产力预测模型；进而利用情景模拟与系统分析方法,模拟与评价了生产水平和气候变化两种情景对区域小麦生产的可能影响；最后运用软构件技术,研制开发了基于生长模型和GIS的小麦生产力预测系统。研究成果为小麦生产力的区域模拟分析提供了基本框架和实验平台,也为生产潜力预测、气候影响评估和适宜对策分析奠定了数字化基础。
以生长度日为时间尺度,通过定量分析不同年份、不同品种、不同栽培条件下小麦籽粒蛋白质组分含量的变化规律,构建了基于过程的籽粒蛋白质组分含量动态模拟模型。模型采用幂函数方程描述了清蛋白含量随花后GDD的动态变化规律,对数函数方程描述了醇溶蛋白和谷蛋白含量的变化过程；并以籽粒氮素和水分因子定量了不同水氮状况对小麦籽粒蛋白质组分含量动态变化的影响。并利用独立观测资料对所构建的模型进行了初步检验。结果表明模型对不同生长条件下小麦籽粒蛋白质组分含量的变化动态具有较好的预测性。
针对生长模型区域化应用中的升尺度连接,通过耦合GIS与田块尺度的小麦生长模型(WheatGrow),建立了小麦空间生产力预测模型(Regional Wheat Model).首先借助GIS的空间插值、图层叠加等技术对气象要素、土壤特性、管理措施等模型基本输入数据进行空间栅格化预处理,通过将研究区域划分成许多视为均质的栅格,解决了区域内环境的空间变异；然后以划分好的空间栅格大小为计算单元驱动WheatGrow运行,获得栅格化模拟结果,实现了小麦区域生产力的模拟预测。利用试验观测与统计资料对WheatGrow模型和空间生产力模型进行了测试与检验。结果表明,模型具有较好的预测性与普适性,对温度与CO2变化较为敏感,可用于区域小麦产量、水分生产力、氮素生产力的模拟预测,也可以用于生产潜力分析、环境影响评估。
基于所建立的小麦空间生产力预测模型,结合情景模拟方法,构建了生产水平和气候变化两种情景分析模式。以黄淮海平原模拟分析为例,不同生产水平情景下小麦光温潜在、气候潜在及土地潜在生产力分别为9386 kg·ha-1-13907 kg·ha-1.7735 kg·ha-1-12020 kg·ha-1和3770 kg·ha-1-6140kg·ha-1之间,三个生产水平下的小麦生产力差异显著,水分对于小麦产量的限制小于氮素的限制；黄淮海平原麦区增产潜力为5534 kg'ha-1-9065 kg'ha-1,合理加大农业投入可大幅增加小麦理论产量。未来气候情景分析显示,温度升高,小麦生育期缩短,生产力形成受到影响。在不考虑CO2施肥效应时,2008年气候情景下黄淮海平原麦区雨养和灌溉小麦总体上呈现减产趋势,虽然灌溉有一定的缓解作用；考虑CO2直接施肥效应时,2080年气候情景下黄淮海平原麦区雨养和灌溉小麦均呈现出增产的趋势。据此情景分析,适当调整播期、培育新品种,可以明显提高小麦籽粒产量,减少由于温度升高而带来的小麦产量负效应。
在系统工程理论与方法的指导下,综合运用软构件技术的语言无关性、可重用性、易维护等特点,基于Visual Studio2005开发环境,采用Microsoft Visual C#为编程语言和Access数据库,通过集成小麦生长模型WheatGrow组件与SuperMap Objects GIS组件,研制开发了基于生长模型和GIS的小麦生产力预测系统,实现了不同空间尺度下小麦籽粒产量、水分生产力、氮素生产力及光温潜在、气候潜在、土地潜在生产力的动态模拟,及未来气候变化情景对小麦生产的可能性影响的评估与适宜性对策分析,为小麦区域化生产力模拟、土地生产潜力分析、气候变化影响评估、适宜性对策取向及粮食安全研究等提供了数字化支持工具。
Prediction of crop productivity and analysis on climate change impact are of great importance for evaluating future food security situation in agricultural industry. Crop growth model can be a powerful tool for predicting yield production, assisting management decision, and assessing the impacts of environment changes on agriculture. This study, taking wheat as research crop, firstly, built a simulation model on grain protein composition based on growing degree days (GDD). Secondly, a regional wheat productivity model was developed to scale up growth model from site to region levels, by integrating with Geographic Information System (GIS). Then, it was used to evaluate the influences of production levels and climate change scenarios on regional wheat production. Finally, a decision support system was constructed by combing the WheatGrow model with GIS and using component-based technology. The established system provides a digital tool for simulation of regional grain productivity, evaluation of potential productivity, assessment of climate change impacts and formulation of agricultural policy in wheat production.
Based on time-course observations on grain protein components under varied nitrogen rates and water regimes with different cultivars, the change patterns in the contents of different grain protein components with growth progress and environmental factors were characterized, and a dynamic model was developed to simulate formation processes of grain protein components in wheat grains. The dynamic content of albumin with GDD after anthesis could be described with a power model, and the contents of gliadlin and glutenin could be described with a logarithmic model. The effects of nitrogen and water conditions on grain protein components were quantified with nitrogen and water factors. The model was validated with independent experiment data. The results indicated that the present model had a good performance in predicting dynamic contents of major protein components in wheat grains.
Considering the approaches to scaling-up the crop model from plot to region level, a regional wheat model was developed by integrating the site-specific wheat model WheatGrow with GIS. To address the spatial variability, grid-based data as model input were generated in advance by interpolation and overlaying with the aid of GIS. When all the necessary input data were available, the target region was partitioned into smaller and relatively homogeneous spatial grids, and crop yields were simulated with WheatGrow for each grid cell. And then all grid results were aggregated to a regional value. Finally, with different experiment and statistical data, the model was operated at site and regional scales, respectively. The results indicated that the present model was accurate and applicable at both scales and the regional wheat model can be used to evaluate grain productivity, water productivity, nitrogen productivity, potential productivity, and climate impacts in wheat production at regional level.
By linking the established regional model and the method of scenario analysis, the wheat productivity levels in Huanghuaihai region were simulated under different production scenarios, respectively, and the effects of climate change and the adaptable strategies on wheat production were also assessed under 2080 scenario. The results shown that the simulated yields for three production scenarios were 9386 kg·ha-1-13907 kg·ha-1 for photosynthetic level (PS1),7735 kg·ha-1-12020 kg-ha-1 for photo-thermal level (PS2), and 3770 kg·ha-1-614kg·ha-1 for land level (PS3)?, respectively. The yield increasing potential defined as the gap between PSl and PS3 production situations ranged from 5534 kg·ha-1 to 9065 kg·ha-1. The wheat productivity can be enhanced by reasonable increase in agriculture inputs. As the results of climate warming, the wheat development would be accelerated and the growth duration would be shorted significantly. If the direct effect of CO2 elevation was not concerned, the rain-fed and irrigated wheat yields under 2080 scenario would decrease mostly, although irrigation might partially relieve the impacts of climate changes on wheat yield. Involving the direct CO2 effect, the trend would turn opposite. Several strategies could be proposed to offset the negative influences of climate change on wheat productivity, including changing planting date, using new variety, and enhancing agricultural input.
On the platform of Visual Studio 2005, the growth model and GIS-based wheat productivity prediction system was constructed by adopting C# language and component-based software and using Access database. With integration of growth model with GIS, the system can manage and analyze spatial data and generate fine resolution data as input for growth model, and thus could be used to forecast grain productivity levels under different production situations and at different spatial scales. By further combining with scenario simulation and strategy analysis, the present system can help to evaluate the potential impacts of climate change patterns on grain productivity and suitable management strategies in future wheat production.
以生长度日为时间尺度,通过定量分析不同年份、不同品种、不同栽培条件下小麦籽粒蛋白质组分含量的变化规律,构建了基于过程的籽粒蛋白质组分含量动态模拟模型。模型采用幂函数方程描述了清蛋白含量随花后GDD的动态变化规律,对数函数方程描述了醇溶蛋白和谷蛋白含量的变化过程；并以籽粒氮素和水分因子定量了不同水氮状况对小麦籽粒蛋白质组分含量动态变化的影响。并利用独立观测资料对所构建的模型进行了初步检验。结果表明模型对不同生长条件下小麦籽粒蛋白质组分含量的变化动态具有较好的预测性。
针对生长模型区域化应用中的升尺度连接,通过耦合GIS与田块尺度的小麦生长模型(WheatGrow),建立了小麦空间生产力预测模型(Regional Wheat Model).首先借助GIS的空间插值、图层叠加等技术对气象要素、土壤特性、管理措施等模型基本输入数据进行空间栅格化预处理,通过将研究区域划分成许多视为均质的栅格,解决了区域内环境的空间变异；然后以划分好的空间栅格大小为计算单元驱动WheatGrow运行,获得栅格化模拟结果,实现了小麦区域生产力的模拟预测。利用试验观测与统计资料对WheatGrow模型和空间生产力模型进行了测试与检验。结果表明,模型具有较好的预测性与普适性,对温度与CO2变化较为敏感,可用于区域小麦产量、水分生产力、氮素生产力的模拟预测,也可以用于生产潜力分析、环境影响评估。
基于所建立的小麦空间生产力预测模型,结合情景模拟方法,构建了生产水平和气候变化两种情景分析模式。以黄淮海平原模拟分析为例,不同生产水平情景下小麦光温潜在、气候潜在及土地潜在生产力分别为9386 kg·ha-1-13907 kg·ha-1.7735 kg·ha-1-12020 kg·ha-1和3770 kg·ha-1-6140kg·ha-1之间,三个生产水平下的小麦生产力差异显著,水分对于小麦产量的限制小于氮素的限制；黄淮海平原麦区增产潜力为5534 kg'ha-1-9065 kg'ha-1,合理加大农业投入可大幅增加小麦理论产量。未来气候情景分析显示,温度升高,小麦生育期缩短,生产力形成受到影响。在不考虑CO2施肥效应时,2008年气候情景下黄淮海平原麦区雨养和灌溉小麦总体上呈现减产趋势,虽然灌溉有一定的缓解作用；考虑CO2直接施肥效应时,2080年气候情景下黄淮海平原麦区雨养和灌溉小麦均呈现出增产的趋势。据此情景分析,适当调整播期、培育新品种,可以明显提高小麦籽粒产量,减少由于温度升高而带来的小麦产量负效应。
在系统工程理论与方法的指导下,综合运用软构件技术的语言无关性、可重用性、易维护等特点,基于Visual Studio2005开发环境,采用Microsoft Visual C#为编程语言和Access数据库,通过集成小麦生长模型WheatGrow组件与SuperMap Objects GIS组件,研制开发了基于生长模型和GIS的小麦生产力预测系统,实现了不同空间尺度下小麦籽粒产量、水分生产力、氮素生产力及光温潜在、气候潜在、土地潜在生产力的动态模拟,及未来气候变化情景对小麦生产的可能性影响的评估与适宜性对策分析,为小麦区域化生产力模拟、土地生产潜力分析、气候变化影响评估、适宜性对策取向及粮食安全研究等提供了数字化支持工具。
Prediction of crop productivity and analysis on climate change impact are of great importance for evaluating future food security situation in agricultural industry. Crop growth model can be a powerful tool for predicting yield production, assisting management decision, and assessing the impacts of environment changes on agriculture. This study, taking wheat as research crop, firstly, built a simulation model on grain protein composition based on growing degree days (GDD). Secondly, a regional wheat productivity model was developed to scale up growth model from site to region levels, by integrating with Geographic Information System (GIS). Then, it was used to evaluate the influences of production levels and climate change scenarios on regional wheat production. Finally, a decision support system was constructed by combing the WheatGrow model with GIS and using component-based technology. The established system provides a digital tool for simulation of regional grain productivity, evaluation of potential productivity, assessment of climate change impacts and formulation of agricultural policy in wheat production.
Based on time-course observations on grain protein components under varied nitrogen rates and water regimes with different cultivars, the change patterns in the contents of different grain protein components with growth progress and environmental factors were characterized, and a dynamic model was developed to simulate formation processes of grain protein components in wheat grains. The dynamic content of albumin with GDD after anthesis could be described with a power model, and the contents of gliadlin and glutenin could be described with a logarithmic model. The effects of nitrogen and water conditions on grain protein components were quantified with nitrogen and water factors. The model was validated with independent experiment data. The results indicated that the present model had a good performance in predicting dynamic contents of major protein components in wheat grains.
Considering the approaches to scaling-up the crop model from plot to region level, a regional wheat model was developed by integrating the site-specific wheat model WheatGrow with GIS. To address the spatial variability, grid-based data as model input were generated in advance by interpolation and overlaying with the aid of GIS. When all the necessary input data were available, the target region was partitioned into smaller and relatively homogeneous spatial grids, and crop yields were simulated with WheatGrow for each grid cell. And then all grid results were aggregated to a regional value. Finally, with different experiment and statistical data, the model was operated at site and regional scales, respectively. The results indicated that the present model was accurate and applicable at both scales and the regional wheat model can be used to evaluate grain productivity, water productivity, nitrogen productivity, potential productivity, and climate impacts in wheat production at regional level.
By linking the established regional model and the method of scenario analysis, the wheat productivity levels in Huanghuaihai region were simulated under different production scenarios, respectively, and the effects of climate change and the adaptable strategies on wheat production were also assessed under 2080 scenario. The results shown that the simulated yields for three production scenarios were 9386 kg·ha-1-13907 kg·ha-1 for photosynthetic level (PS1),7735 kg·ha-1-12020 kg-ha-1 for photo-thermal level (PS2), and 3770 kg·ha-1-614kg·ha-1 for land level (PS3)?, respectively. The yield increasing potential defined as the gap between PSl and PS3 production situations ranged from 5534 kg·ha-1 to 9065 kg·ha-1. The wheat productivity can be enhanced by reasonable increase in agriculture inputs. As the results of climate warming, the wheat development would be accelerated and the growth duration would be shorted significantly. If the direct effect of CO2 elevation was not concerned, the rain-fed and irrigated wheat yields under 2080 scenario would decrease mostly, although irrigation might partially relieve the impacts of climate changes on wheat yield. Involving the direct CO2 effect, the trend would turn opposite. Several strategies could be proposed to offset the negative influences of climate change on wheat productivity, including changing planting date, using new variety, and enhancing agricultural input.
On the platform of Visual Studio 2005, the growth model and GIS-based wheat productivity prediction system was constructed by adopting C# language and component-based software and using Access database. With integration of growth model with GIS, the system can manage and analyze spatial data and generate fine resolution data as input for growth model, and thus could be used to forecast grain productivity levels under different production situations and at different spatial scales. By further combining with scenario simulation and strategy analysis, the present system can help to evaluate the potential impacts of climate change patterns on grain productivity and suitable management strategies in future wheat production.
引文
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