基于GIS与模型的小麦籽粒品质生态区划研究
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摘要
进行作物生态条件及其生态反应相似性或差异性的空间分析和区划研究,有助于充分利用与挖掘自然资源和品种遗传潜力,设计优质、高产和高效的作物生产模式,推动作物生产的区域化、产业化发展,对指导区域作物生产及粮食安全保障具有重要意义。地统计学理论是研究空间变量分布及变异规律的有效方法之一,但前人甚少将其引入进行作物品质的定量化空间分析与生态区划研究。本研究以小麦为对象,在建立模型输入参数空间数据源的基础上,首先研究了小麦籽粒品质预测模型的空间升尺度方法；其次,基于地统计学理论,探讨了基于网格点的小麦籽粒品质空间变异特征的定量化分析方法；然后,基于非监督分类算法初步提出了小麦籽粒品质生态区划方案；最后,通过有效耦合与集成基础数据、小麦模型与WebGIS平台等,建立了具有综合功能的数字麦作支持系统。研究成果有助于促进作物模型的尺度化和区域化应用,确立小麦品质的空间变异规律和生态区划,为数字麦作及现代农业的发展奠定技术基础。
首先基于小麦籽粒品质预测模型的参数需求和区域性气候因子的变化特征,通过比较3种空间插值法的模拟精度,确定普通克里格法为适宜的气象数据空间插值法。以此对江苏省和我国主要冬麦区内气象台站实际观测的逐日最高气温、最低气温、日照时数和降雨量4个主要气候要素进行了空间推算,生成了2个研究区域逐日4个气候要素5km×5km空间分辨率栅格图以及区域内6个冬小麦品种开花至成熟期的日均温、日较差、总日照时数和总降雨量4个气象因子5km×5km空间分辨率栅格图,为小麦籽粒品质预测模型的升尺度研究、品质空间变异分析和生态区划提供了区域化基础数据平台。
通过解析小麦籽粒品质预测模型的机理及模型升尺度的技术路径,基于构建的区域化基础数据平台,研究探讨了小麦籽粒品质预测模型的空间升尺度方法。首先采用“先计算后插值”和“先插值后计算”两种升尺度方法,模拟了小麦籽粒品质预测模型的区域表现,进而确定“先插值后计算”为小麦籽粒品质预测模型的适宜升尺度方法。然后,在不同气象环境下对模型进行了区域应用,所得结果与点尺度下的情景基本吻合,表明小麦籽粒品质预测模型“先插值后计算”的升尺度方法具有较好的区域适用性。
基于地统计学理论和品质空间变异模式,运用半方差函数理论和模型升尺度方法,探索了定量小麦籽粒品质空间变异规律的方法。首先对小麦籽粒品质预测模型的区域模拟结果进行了半方差函数拟合;其次,基于拟合函数的特征参数进行了籽粒品质的空间相关性、最大相关范围、不同方向空间变异性的定量化分析。结果显示,基于地统计学和模型升尺度方法,可有效融合基于网格点的品质指标与地理空间坐标,从而进行区域内籽粒品质指标空间变异的定量化分析,且精细的栅格图可直观、定量地描述区域内品质空间分布和变异趋势。
运用小麦籽粒品质预测模型的升尺度方法和籽粒品质空间变异的定量化方法,分析提出了我国主要冬麦区的小麦品质生态区划。首先,对我国主要冬麦区的气候环境和升尺度品质预测结果进行了定量分析。然后,以区域模拟结果为数据源,建立了空间计算模型,并对各品质指标数据进行了基于网格点的融合处理,进而以非监督分类法对各网格单元进行了基于主要品质指标的空间聚类。最后,根据国家小麦籽粒品质标准,分析评价归属于同一空间区域的主要品质指标统计值,提出了数字化小麦籽粒品质生态区划方法。根据研究结果,初步将我国主要冬麦区划分为强筋、中强筋、中筋、中弱筋和弱筋5个冬小麦籽粒品质生态区,并以精细的栅格图实现了直观、定量的空间表达。
在上述工作的基础上,通过探索小麦品质预测模型、管理知识模型和生长模拟模型与WebGIS技术的集成机制,提出了“XML技术+自定义地图引擎”的技术思路,设计了合理的系统体系结构,建立了基于模型和WebGIS的数字麦作支持系统(MGDWFSS).系统实现了网络环境下小麦适宜栽培方案设计、生长动态模拟、产量品质预测、品质生态区划及生产管理决策等综合功能。从而为作物模型的升尺度应用提供了量化方法与关键技术,为区域小麦品质分析评价与管理决策提供了数字化平台。
Spatial analysis and zoning on the similarities and differences of crop ecological factors and responses are of vital significance for regional crop production and food security in China. It helps to design high-quality, high-yield and high-efficiency crop production patterns, drive its regionalization and industrialization, as well as make full use of natural resources and excavate species' genetic potentials. Geostatistic theory serves as one of effective means to study spatial distribution and variation of production factors; however, it has rarely been used in quantified spatial analysis and zoning of crop quality. In this research, firstly, spatial data sources were built as input variables of a wheat grain quality estimation model, spatial scaling-up methods for model prediction were studied, and spatial variability of grain quality in wheat was quantified to the granularity of grid based on the geostatistical theory. Then, a preliminary digital ecological zoning scheme of wheat grain quality was proposed based on the unsupervised classification algorithm. Finally, a comprehensive digital wheat farming support system is established through effective coupling and integration of database, wheat models and WebGIS platform. The results will help promote applications of crop models at regional scale, evaluate the spatial variation of wheat quality and make ecological zoning, and provide the technical basis for development of digital wheat farming and modern agriculture.
First, based on the parameter demand of the wheat grain quality estimation model and the change characteristics of regional climate, the method of Ordinary Kriging was determined as the appropriate spatial weather data interpolation method by comparing the precision of three different spatial interpolation methods. Then, the actual daily maximum temperature, minimum temperature, rainfall and sunlight hours, obtained from different weather stations within Jiangsu Province and across main winter wheat growing regions in China, were interpolated using this method at the 5km×5km resolution to generate the spatial grid map for two study areas of these four climate elements on a daily basis, as well as the spatial grid map of the four climatic factors (average daily temperature, difference of diurnal temperatures, total sunlight hours, and total rainfall) for six winter wheat varieties from anthesis to maturity. The results provide the basis regional data platform with high spatial-temporal resolution for scaling-up of wheat grain quality estimation model, and for analyzing spatial quality variation and making spatial ecology zoning.
By analyzing the estimation mechanism of wheat grain quality model and technical approach of model scaling-up, the scaling-up method was explored based on the regional data platform. First, the regional performance of the wheat grain quality estimation model was simulated in two different ways. One way was to run the model at each location and then interpolate its results on a grid (i.e., "calculate first, interpolate later", denoted by "CI"). The other way was to interpolate the model inputs on a grid firstly, and then run the model at each grid nodes (i.e., "interpolate first, calculate later", denoted by "IC"). The IC technology turns out to be suitable for scaling up the wheat grain quality estimation model. Then, the model was applied at regional scale under various climate conditions, and the results were largely consistent with the results obtained by applying wheat grain quality estimation model under point scale scenarios.
Based on the geostatistical theory and the spatial pattern of grain quality variation, quantitative methods to explore the characteristics of wheat grain quality variation were studied with semi-variance function theory and the suitable model scale-up method. First, regional simulation results were analyzed with semi-variance function; secondly, based on the characteristic parameters of the fitting function, the grain quality was quantitatively analyzed for spatial correlations, the maximum correlation scope, as well as spatial variability in different directions. The results show that based on geostatistics and scale-up model, quality indices based on grid can be effectively integrated with geo-spatial coordinates. The spatial variation of regional grain quality indices can be quantified, and the fine mapping based on grid can intuitively quantify the spatial distribution and variation trend of regional grain quality.
The digital wheat quality zoning in China's main winter wheat growing regions was established based on the up-scaling wheat grain quality estimation model and the quantitative analysis of spatial variability. First, the climate environment of China's main winter wheat growing areas and the predicted quality results were quantitatively analyzed. Then, the quality indices simulated through up-scaling model were integrated with grid and an established spatial calculation model. The main quality indices were clustered spatially based on the non-supervision classification algorithm. Finally, according to the national standards of wheat grain quality, the statistical characteristics of main quality indices from the same region were analyzed and evaluated, resulting in a digital wheat grain quality ecological zoning method. Based on the above method, China's main winter wheat growing regions were divided into five primary ecological zones, with a fine mapping based on grid for a visual, quantitative and spatial expression.
Based on the above-mentioned results, the mechanism and methodology for integrating the GIS technology with wheat grain quality estimation model, management knowledge model and growth simulation model were further explored. By designing a reasonable framework of system architecture with the XML technology plus a custom map engine, a model and WebGIS-based digital wheat farming support system (MGDWFSS) was developed. The system realized the comprehensive functions as suitable cultivation plan design, dynamic growth simulation, yield and quality forecasting, quality ecological zoning, and management decision-making in a network environment. Overall, the present study has provided quantitative methods and key technologies for up-scaling crop system models. It has also constructed a digital platform for regional productivity analysis and evaluation as well as management decision-making in wheat production.
首先基于小麦籽粒品质预测模型的参数需求和区域性气候因子的变化特征,通过比较3种空间插值法的模拟精度,确定普通克里格法为适宜的气象数据空间插值法。以此对江苏省和我国主要冬麦区内气象台站实际观测的逐日最高气温、最低气温、日照时数和降雨量4个主要气候要素进行了空间推算,生成了2个研究区域逐日4个气候要素5km×5km空间分辨率栅格图以及区域内6个冬小麦品种开花至成熟期的日均温、日较差、总日照时数和总降雨量4个气象因子5km×5km空间分辨率栅格图,为小麦籽粒品质预测模型的升尺度研究、品质空间变异分析和生态区划提供了区域化基础数据平台。
通过解析小麦籽粒品质预测模型的机理及模型升尺度的技术路径,基于构建的区域化基础数据平台,研究探讨了小麦籽粒品质预测模型的空间升尺度方法。首先采用“先计算后插值”和“先插值后计算”两种升尺度方法,模拟了小麦籽粒品质预测模型的区域表现,进而确定“先插值后计算”为小麦籽粒品质预测模型的适宜升尺度方法。然后,在不同气象环境下对模型进行了区域应用,所得结果与点尺度下的情景基本吻合,表明小麦籽粒品质预测模型“先插值后计算”的升尺度方法具有较好的区域适用性。
基于地统计学理论和品质空间变异模式,运用半方差函数理论和模型升尺度方法,探索了定量小麦籽粒品质空间变异规律的方法。首先对小麦籽粒品质预测模型的区域模拟结果进行了半方差函数拟合;其次,基于拟合函数的特征参数进行了籽粒品质的空间相关性、最大相关范围、不同方向空间变异性的定量化分析。结果显示,基于地统计学和模型升尺度方法,可有效融合基于网格点的品质指标与地理空间坐标,从而进行区域内籽粒品质指标空间变异的定量化分析,且精细的栅格图可直观、定量地描述区域内品质空间分布和变异趋势。
运用小麦籽粒品质预测模型的升尺度方法和籽粒品质空间变异的定量化方法,分析提出了我国主要冬麦区的小麦品质生态区划。首先,对我国主要冬麦区的气候环境和升尺度品质预测结果进行了定量分析。然后,以区域模拟结果为数据源,建立了空间计算模型,并对各品质指标数据进行了基于网格点的融合处理,进而以非监督分类法对各网格单元进行了基于主要品质指标的空间聚类。最后,根据国家小麦籽粒品质标准,分析评价归属于同一空间区域的主要品质指标统计值,提出了数字化小麦籽粒品质生态区划方法。根据研究结果,初步将我国主要冬麦区划分为强筋、中强筋、中筋、中弱筋和弱筋5个冬小麦籽粒品质生态区,并以精细的栅格图实现了直观、定量的空间表达。
在上述工作的基础上,通过探索小麦品质预测模型、管理知识模型和生长模拟模型与WebGIS技术的集成机制,提出了“XML技术+自定义地图引擎”的技术思路,设计了合理的系统体系结构,建立了基于模型和WebGIS的数字麦作支持系统(MGDWFSS).系统实现了网络环境下小麦适宜栽培方案设计、生长动态模拟、产量品质预测、品质生态区划及生产管理决策等综合功能。从而为作物模型的升尺度应用提供了量化方法与关键技术,为区域小麦品质分析评价与管理决策提供了数字化平台。
Spatial analysis and zoning on the similarities and differences of crop ecological factors and responses are of vital significance for regional crop production and food security in China. It helps to design high-quality, high-yield and high-efficiency crop production patterns, drive its regionalization and industrialization, as well as make full use of natural resources and excavate species' genetic potentials. Geostatistic theory serves as one of effective means to study spatial distribution and variation of production factors; however, it has rarely been used in quantified spatial analysis and zoning of crop quality. In this research, firstly, spatial data sources were built as input variables of a wheat grain quality estimation model, spatial scaling-up methods for model prediction were studied, and spatial variability of grain quality in wheat was quantified to the granularity of grid based on the geostatistical theory. Then, a preliminary digital ecological zoning scheme of wheat grain quality was proposed based on the unsupervised classification algorithm. Finally, a comprehensive digital wheat farming support system is established through effective coupling and integration of database, wheat models and WebGIS platform. The results will help promote applications of crop models at regional scale, evaluate the spatial variation of wheat quality and make ecological zoning, and provide the technical basis for development of digital wheat farming and modern agriculture.
First, based on the parameter demand of the wheat grain quality estimation model and the change characteristics of regional climate, the method of Ordinary Kriging was determined as the appropriate spatial weather data interpolation method by comparing the precision of three different spatial interpolation methods. Then, the actual daily maximum temperature, minimum temperature, rainfall and sunlight hours, obtained from different weather stations within Jiangsu Province and across main winter wheat growing regions in China, were interpolated using this method at the 5km×5km resolution to generate the spatial grid map for two study areas of these four climate elements on a daily basis, as well as the spatial grid map of the four climatic factors (average daily temperature, difference of diurnal temperatures, total sunlight hours, and total rainfall) for six winter wheat varieties from anthesis to maturity. The results provide the basis regional data platform with high spatial-temporal resolution for scaling-up of wheat grain quality estimation model, and for analyzing spatial quality variation and making spatial ecology zoning.
By analyzing the estimation mechanism of wheat grain quality model and technical approach of model scaling-up, the scaling-up method was explored based on the regional data platform. First, the regional performance of the wheat grain quality estimation model was simulated in two different ways. One way was to run the model at each location and then interpolate its results on a grid (i.e., "calculate first, interpolate later", denoted by "CI"). The other way was to interpolate the model inputs on a grid firstly, and then run the model at each grid nodes (i.e., "interpolate first, calculate later", denoted by "IC"). The IC technology turns out to be suitable for scaling up the wheat grain quality estimation model. Then, the model was applied at regional scale under various climate conditions, and the results were largely consistent with the results obtained by applying wheat grain quality estimation model under point scale scenarios.
Based on the geostatistical theory and the spatial pattern of grain quality variation, quantitative methods to explore the characteristics of wheat grain quality variation were studied with semi-variance function theory and the suitable model scale-up method. First, regional simulation results were analyzed with semi-variance function; secondly, based on the characteristic parameters of the fitting function, the grain quality was quantitatively analyzed for spatial correlations, the maximum correlation scope, as well as spatial variability in different directions. The results show that based on geostatistics and scale-up model, quality indices based on grid can be effectively integrated with geo-spatial coordinates. The spatial variation of regional grain quality indices can be quantified, and the fine mapping based on grid can intuitively quantify the spatial distribution and variation trend of regional grain quality.
The digital wheat quality zoning in China's main winter wheat growing regions was established based on the up-scaling wheat grain quality estimation model and the quantitative analysis of spatial variability. First, the climate environment of China's main winter wheat growing areas and the predicted quality results were quantitatively analyzed. Then, the quality indices simulated through up-scaling model were integrated with grid and an established spatial calculation model. The main quality indices were clustered spatially based on the non-supervision classification algorithm. Finally, according to the national standards of wheat grain quality, the statistical characteristics of main quality indices from the same region were analyzed and evaluated, resulting in a digital wheat grain quality ecological zoning method. Based on the above method, China's main winter wheat growing regions were divided into five primary ecological zones, with a fine mapping based on grid for a visual, quantitative and spatial expression.
Based on the above-mentioned results, the mechanism and methodology for integrating the GIS technology with wheat grain quality estimation model, management knowledge model and growth simulation model were further explored. By designing a reasonable framework of system architecture with the XML technology plus a custom map engine, a model and WebGIS-based digital wheat farming support system (MGDWFSS) was developed. The system realized the comprehensive functions as suitable cultivation plan design, dynamic growth simulation, yield and quality forecasting, quality ecological zoning, and management decision-making in a network environment. Overall, the present study has provided quantitative methods and key technologies for up-scaling crop system models. It has also constructed a digital platform for regional productivity analysis and evaluation as well as management decision-making in wheat production.
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