GIS支持下豫中典型烟田土壤养分空间变异及精准管理
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摘要
土壤作为一个时空连续的变异体,具有高度的空间异质性。我国传统农业生产通常以一家一户的分散田块作为耕作单元,并且单位面积农田上的肥料施用基本没有考虑土壤属性特征,按照经验进行投入。这种方式使得施肥缺乏针对性、肥料利用率低、养分供给不平衡,而且易对水体、大气环境造成污染,也影响到优质烟叶的生产。精确的掌握田间土壤特性的空间分布状况,确定合理的土壤养分管理单元,并以此调整相应的肥料投入量,是实现烟田土壤精准管理的基础。
在平顶山烟区开展烟田土壤养分精准管理研究可为探索适合烟叶生产管理信息化模式提供参考,也为区域烟田进行土壤养分分区管理、推荐施肥提供必要的科学依据。本研究采用GPS定位,对平顶山市郏县岔河村87 hm2的烟田进行了100×100m的网格取样。综合运用GIS和地统计相结合的方法,分析了土壤养分的空间变异规律,并制作了土壤养分的空间分布格局图。运用主成分分析法进行了主成分的提取,以此为数据源进一步利用模糊聚类方法进行土壤管理分区的划分,最后利用大田试验所建立的推荐施肥模型对各个分区推荐施肥,为实现烟区土壤的差异化管理奠定了基础。主要研究结果如下:
(1)试验区土壤有机质、全氮、碱解氮的平均含量分别为11.7 g/kg,0.73g/kg和70.6 mg/kg,属偏低范围;土壤速效磷和速效钾的平均含量分别为16.1 mg/kg和105.9mg/kg,基本上属于适宜范围。土壤阳离子交换量的平均值为16.7 cmol/kg,为中等保肥供肥能力的土壤;土壤微量元素中,铁属于缺乏范围,锰、铜、锌三个元素含量适中,但变异性较大。土壤各指标存在不同程度的变异,变异范围在3.4~48.4%。土壤pH、有机质、粉粒、砂粒等变异系数较小,变异系数在3.4%~14.6%之间,表现出相对稳定性;土壤速效磷和有效态微量元素的变异系数较大,在22.0%~48.4%之间,表现出较大的空间异质性,表明有必要进行差异化管理。
(2)研究区域大部分土壤养分间均存在显著或极显著的相关关系。除速效钾外,所有的养分属性均与土壤pH存在负相关关系,表明在一定程度上降低土壤pH能够提高各养分的含量及有效性,如土壤速效磷和pH之间存在极显著的负相关关系,表明降低pH能够明显提高土壤速效磷的水平。其余养分间均存在正相关关系。土壤有机质和其它养分关系密切,和全氮、速效钾、CEC、有效铁、锰、铜之间都存在极显著正相关关系,与速效磷、有效锌之间也存在显著正相关关系,表明提高土壤有机质含量对提高其它养分的含量有促进作用。土壤养分含量和不同粒级土粒含量密切相关。土壤OM和全氮含量一般随粘粒量增加而呈增加的趋势。土壤中较高的粘粒含量对土壤溶液中的K+有相对强的胶体吸附作用,在一定程度上减少了K+的淋溶,因此,土壤速效钾含量一般随粘粒含量的增加而增加。粘粒也是土壤阳离子交换量(CEC)吸收交换点的主要来源,因而粘粒与CEC有极显著的相关性。这一点也证明了在本研究区域,土壤质地主导着土壤养分含量的空间变异。
(3)研究区域土壤属性中除有效铁含量外,其余均符合正态分布;对有效铁含量数据进行自然对数转换后呈正态分布,也满足平稳假设。地统计学分析结果表明,研究区域碱解氮的理论模型为直线模型,表现为纯块金效应,模型拟合度低。土壤阳离子交换量的最佳模型为高斯模型,其余各指标均以球状模型为最佳模型;各模型决定系数较大,表明模型的拟合度高。不同观测指标的最大相关距离差异较大,介于185 m– 1066 m之间,其中CEC相关距离最大,达到1066 m;土壤pH、OM速效磷、速效钾的空间自相关距离次之,均在300m以上;土壤全氮和有效态铁、锰、铜、锌等养分的空间自相关距离较小,在300m以下。
(4)研究区域土壤pH、全氮、有效铁、有效铜、有效锌等指标的C0/(C+C0)比值在16%~24%之间,表现出强烈的空间相关性,表明结构因素主导其空间变异;土壤有机质、速效磷、速效钾、有效锰、阳离子交换量等指标的C0/(C+C0)比值在26%~50%之间,表现出中等强度的空间相关性,表明随机因素和结构因素在决定空间变异性方面同样重要。在本研究尺度下,土壤碱解氮不存在空间相关结构,说明其空间变异主要受随机因素的控制。对于碱解氮的研究还需要进一步加大采样密度。
(5)利用地统计学方法对研究区域机械组成进行了半方差分析。结果表明,在小尺度上不同粒级土粒含量的空间变异性均存在着半方差结构,具有较强的渐变性分布规律。粉粒的半方差函数理论模型用指数模型进行拟合,砂粒和粘粒用球状模型拟合,三者的决定系数均大于0.97,表明模型的拟合度较高。不同粒级土粒含量的最大相关距离均较大,砂粒(0.02~2mm)、粉粒(0.002~0.02mm)和粘粒(<0.002mm)含量的最大相关距离分别为657,435和609m。
(6)土壤pH、有机质、全氮、速效磷、速效钾和阳离子交换量之间存在一定的相关性,各指标的信息存在一定程度重叠,通过主成分分析,提取两个特征值大于1的主成分。第一主成分(PC1)能够解释50%的总方差,主要表征有机质、全氮、速效钾和阳离子交换量;第二主成分(PC2)能够解释21%的总方差,主要表征土壤pH和速效磷。因此,PC1与有机质、全氮、速效钾和阳离子交换量的分布图有较高的相似性;同样,PC2与土壤速效磷的分布图非常相似,而与pH的分布相反。
(7)在充分了解研究区域土壤养分空间变异特征,并利用主成分分析法对原始数据进行压缩后,应用模糊-c均值聚类算法对研究区域烟田进行了精确的田间管理分区划分研究。利用两个聚类效果评价指标模糊效果指数FPI和归一化分类墒NCE,确定了最适宜的分区数。结果表明,研究区域最佳分区数为3个。分区间土壤养分差异显著性检验表明,土壤pH、有机质、速效磷、速效钾和阳离子交换量在分区间均存在显著性差异。分区1和分区2的碱解氮达到显著差异,而全氮差异不显著。总的来讲,经过分区后,分区内养分含量趋于同质性,而分区间差异显著。分区1的土壤有机质、全氮、速效钾含量最高,从CEC来看,保肥能力也最强;分区3的pH最高,土壤有机质、全氮、速效磷、速效钾含量均处于最低水平;CEC也最小,说明保肥能力相对较低。分区2速效磷含量最高,其余指标基本处于中间水平。
(8)本文以曲劳-斯坦福方程(Truog-Stanford)为基础进行烤烟推荐施肥。通过两年的大田试验初步确定了建立肥料推荐方程所需参数,包括烤烟单位产量需肥量、土壤养分校正系数、肥料利用率等,并建立了土壤碱解氮、速效磷和速效钾的土壤测定值和校正系数间的函数关系。结果表明,土壤养分测定值与校正系数之间以幂函数曲线相关最佳,经检验均达到极显著水平。在该种曲线拟合条件下,校正系数随土壤测定含量的增加而下降,符合实际情况。在初步建立的烤烟推荐施肥方程的基础上,确定了各分区推荐施肥量,为实现烟田土壤分区施肥奠定了基础。
本研究主要创新点:
(1)通过两年的田间试验,初步构建了研究区域烤烟推荐施肥模型,为烤烟精准施肥提供了理论基础;
(2)利用GPS、GIS和地统计学地手段,分析了小尺度烟田土壤养分的空间变异规律。在此基础上建立了基于主成分分析和模糊聚类相结合的划分烟田土壤管理分区的方法,为实现烟田土壤的差异化管理奠定了基础。
Soils are highly variable spatially due to the combined effects of physical, chemical, and biological processes that operate with different intensities and at different scales. Consequently, uniform management of fields often results in over-application of inputs in areas with high nutrient levels and under-application in areas with low nutrient levels. Therefore, understanding of the spatial variability of soil properties is essential in determining local fertilizer needs of tobacco. Based on classical statistics, spatial variability of soil nutrients in a tobacco-planted field was studied and spatial distribution maps of soil nutrients was generated by the combined usage of GIS and geostatistics.
The most popular approach to manage spatial variability within fields is the use of management zones (MZs), which are field subdivisions that have relatively homogeneous attributes in landscape and soil condition, and can be used to direct variable rate fertilizer application. To achieve these objectives, soil samples (0~20cm) were taken 81 points on an approximately 100-m grid in March 2007 using global positioning system to define sample locations. Soil chemical properties and texture were analyzed and their spatial variability was assessed by geostatistical techniques.
Considering that most of soil properties are often related, principal component analysis (PCA) was used to summarize soil propertied into a few meaningful components (PC), which were further used to delineate management zones by fuzzy cluster algorithms. In addition, six field experiments were conducted to establish models for nitrogen, phosphorus and potassium fertilizer recommendation in Jia County, Pingdingshan, during 2007and 2008. This provides a basis of information for site-specific fertilizer management in tobacco-planted field. The main conclusions were as follows:
(1) Coefficients of variation ranged from approximately 3.4% for pH to almost 48% for available Zn. Soil OM, TN, AN, AK, CEC and texture had medium CV (11.5%~23.4%). Soil AP and available microelement had higher CV, indicating the heterogeneity of soil properties. Thus, differentiated management may be necessary to achieve maximum economic and environmental benefits. Distributions of all the variables, except Fe, were only slightly skewed (skewness < 1), and their medians were close to their means. The Kolmogorov-Smirnov test revealed that all variables were normally distributed (P > 0.05) and did not require transformation. By log-transformation, soil available Fe showed normal distribution, thereby providing a basis for further structural analysis.
(2) The results of semivariogram analyses are shown that coefficients of determination (R2) for all variables, except soil AN and available ZN, were greater than 0.87, indicating good fits. Soil pH, OM, TN, AP, AK,sand, clay and microelements were modeled best with spherical models, whereas CEC and silt with Gaussian and exponential models. Soil AN showed only pure-nugget effect fitted by linear model at sampling interval of 100-m, i.e., nearly horizontal linear semivariogram, indicating spatial independence. This suggested that a higher sampling density might be recommended for AN in this region. Ranges of spatial dependence varied from 274m (TN) to 1066m (CEC).
(3) The ratio of nugget variance to sill variance can be used to classify spatial dependence of soil properties. A ratio<25% indicated strong spatial dependence, between 25% and75% indicated moderate spatial dependence, and >75% indicated weak spatial dependence. Soil properties with strong and moderate spatial dependence will be more readily managed and an accurate site-specific fertilization scheme for precision farming more easily developed. In this study, the geostatistical analysis suggested that most soil properties showed moderate to strong spatial dependence. Soil pH, TN, Fe, Cu, Zn, sand and clay were strongly spatially dependent with nugget/sill ratios between 16% and 24%, respectively. Soil OM, AP, AK and CEC were moderately spatially dependent with nugget/sill ratios ranging from 30% to 50%. Soil AN showed no spatial dependence. Soil AP and AK had significant correlation with intrinsic properties, for example, pH, OM, and clay, indicating the low extrinsic component of variability and could be used as the basis for differentiated fertilizer application in this region.
(4) PCA and fuzzy cluster algorithm were then performed to delineate MZs. The first two PCs with eigenvalues greater than 1 were considered for clustering analysis, which was performed in MZA procedure using fuzzy c-means cluster algorithm. Performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimum cluster number. Results showed that the optimum number of MZs for this study area was three and analysis of variance indicated the heterogeneity of soil fertility among different MZs, which also showed the combined usage of PCA and fuzzy cluster algorithm is an effective method to characterize spatial variability of soil properties within MZs. The defined MZs provide a basis of information for site-specific fertilizer management in the tobacco-planted field.
(5) By determining the parameters correlative with Truog-Stanford equation building, the models for nitrogen, phosphorus and potassium fertilizers recommendation were established based on two years field experiments. Using the three equations, the fertilizer application for three management zones were recommended respectively.
The innovation points for this study are as follows:
(1) Using GIS and geostatistical techniques, spatial variability was assessed and contour maps generated for soil nutrients in a tobacco field. Establishing a procedure to delineate management zones with the combined usage of PCA and fuzzy cluster algorithm, this provides a basis for defining management zones for tobacco field.
(2) Determining correlative model parameters and initially establishing fertilizers recommendation equations of nitrogen, phosphorus and potassium for flue-cured tobacco in this region.
在平顶山烟区开展烟田土壤养分精准管理研究可为探索适合烟叶生产管理信息化模式提供参考,也为区域烟田进行土壤养分分区管理、推荐施肥提供必要的科学依据。本研究采用GPS定位,对平顶山市郏县岔河村87 hm2的烟田进行了100×100m的网格取样。综合运用GIS和地统计相结合的方法,分析了土壤养分的空间变异规律,并制作了土壤养分的空间分布格局图。运用主成分分析法进行了主成分的提取,以此为数据源进一步利用模糊聚类方法进行土壤管理分区的划分,最后利用大田试验所建立的推荐施肥模型对各个分区推荐施肥,为实现烟区土壤的差异化管理奠定了基础。主要研究结果如下:
(1)试验区土壤有机质、全氮、碱解氮的平均含量分别为11.7 g/kg,0.73g/kg和70.6 mg/kg,属偏低范围;土壤速效磷和速效钾的平均含量分别为16.1 mg/kg和105.9mg/kg,基本上属于适宜范围。土壤阳离子交换量的平均值为16.7 cmol/kg,为中等保肥供肥能力的土壤;土壤微量元素中,铁属于缺乏范围,锰、铜、锌三个元素含量适中,但变异性较大。土壤各指标存在不同程度的变异,变异范围在3.4~48.4%。土壤pH、有机质、粉粒、砂粒等变异系数较小,变异系数在3.4%~14.6%之间,表现出相对稳定性;土壤速效磷和有效态微量元素的变异系数较大,在22.0%~48.4%之间,表现出较大的空间异质性,表明有必要进行差异化管理。
(2)研究区域大部分土壤养分间均存在显著或极显著的相关关系。除速效钾外,所有的养分属性均与土壤pH存在负相关关系,表明在一定程度上降低土壤pH能够提高各养分的含量及有效性,如土壤速效磷和pH之间存在极显著的负相关关系,表明降低pH能够明显提高土壤速效磷的水平。其余养分间均存在正相关关系。土壤有机质和其它养分关系密切,和全氮、速效钾、CEC、有效铁、锰、铜之间都存在极显著正相关关系,与速效磷、有效锌之间也存在显著正相关关系,表明提高土壤有机质含量对提高其它养分的含量有促进作用。土壤养分含量和不同粒级土粒含量密切相关。土壤OM和全氮含量一般随粘粒量增加而呈增加的趋势。土壤中较高的粘粒含量对土壤溶液中的K+有相对强的胶体吸附作用,在一定程度上减少了K+的淋溶,因此,土壤速效钾含量一般随粘粒含量的增加而增加。粘粒也是土壤阳离子交换量(CEC)吸收交换点的主要来源,因而粘粒与CEC有极显著的相关性。这一点也证明了在本研究区域,土壤质地主导着土壤养分含量的空间变异。
(3)研究区域土壤属性中除有效铁含量外,其余均符合正态分布;对有效铁含量数据进行自然对数转换后呈正态分布,也满足平稳假设。地统计学分析结果表明,研究区域碱解氮的理论模型为直线模型,表现为纯块金效应,模型拟合度低。土壤阳离子交换量的最佳模型为高斯模型,其余各指标均以球状模型为最佳模型;各模型决定系数较大,表明模型的拟合度高。不同观测指标的最大相关距离差异较大,介于185 m– 1066 m之间,其中CEC相关距离最大,达到1066 m;土壤pH、OM速效磷、速效钾的空间自相关距离次之,均在300m以上;土壤全氮和有效态铁、锰、铜、锌等养分的空间自相关距离较小,在300m以下。
(4)研究区域土壤pH、全氮、有效铁、有效铜、有效锌等指标的C0/(C+C0)比值在16%~24%之间,表现出强烈的空间相关性,表明结构因素主导其空间变异;土壤有机质、速效磷、速效钾、有效锰、阳离子交换量等指标的C0/(C+C0)比值在26%~50%之间,表现出中等强度的空间相关性,表明随机因素和结构因素在决定空间变异性方面同样重要。在本研究尺度下,土壤碱解氮不存在空间相关结构,说明其空间变异主要受随机因素的控制。对于碱解氮的研究还需要进一步加大采样密度。
(5)利用地统计学方法对研究区域机械组成进行了半方差分析。结果表明,在小尺度上不同粒级土粒含量的空间变异性均存在着半方差结构,具有较强的渐变性分布规律。粉粒的半方差函数理论模型用指数模型进行拟合,砂粒和粘粒用球状模型拟合,三者的决定系数均大于0.97,表明模型的拟合度较高。不同粒级土粒含量的最大相关距离均较大,砂粒(0.02~2mm)、粉粒(0.002~0.02mm)和粘粒(<0.002mm)含量的最大相关距离分别为657,435和609m。
(6)土壤pH、有机质、全氮、速效磷、速效钾和阳离子交换量之间存在一定的相关性,各指标的信息存在一定程度重叠,通过主成分分析,提取两个特征值大于1的主成分。第一主成分(PC1)能够解释50%的总方差,主要表征有机质、全氮、速效钾和阳离子交换量;第二主成分(PC2)能够解释21%的总方差,主要表征土壤pH和速效磷。因此,PC1与有机质、全氮、速效钾和阳离子交换量的分布图有较高的相似性;同样,PC2与土壤速效磷的分布图非常相似,而与pH的分布相反。
(7)在充分了解研究区域土壤养分空间变异特征,并利用主成分分析法对原始数据进行压缩后,应用模糊-c均值聚类算法对研究区域烟田进行了精确的田间管理分区划分研究。利用两个聚类效果评价指标模糊效果指数FPI和归一化分类墒NCE,确定了最适宜的分区数。结果表明,研究区域最佳分区数为3个。分区间土壤养分差异显著性检验表明,土壤pH、有机质、速效磷、速效钾和阳离子交换量在分区间均存在显著性差异。分区1和分区2的碱解氮达到显著差异,而全氮差异不显著。总的来讲,经过分区后,分区内养分含量趋于同质性,而分区间差异显著。分区1的土壤有机质、全氮、速效钾含量最高,从CEC来看,保肥能力也最强;分区3的pH最高,土壤有机质、全氮、速效磷、速效钾含量均处于最低水平;CEC也最小,说明保肥能力相对较低。分区2速效磷含量最高,其余指标基本处于中间水平。
(8)本文以曲劳-斯坦福方程(Truog-Stanford)为基础进行烤烟推荐施肥。通过两年的大田试验初步确定了建立肥料推荐方程所需参数,包括烤烟单位产量需肥量、土壤养分校正系数、肥料利用率等,并建立了土壤碱解氮、速效磷和速效钾的土壤测定值和校正系数间的函数关系。结果表明,土壤养分测定值与校正系数之间以幂函数曲线相关最佳,经检验均达到极显著水平。在该种曲线拟合条件下,校正系数随土壤测定含量的增加而下降,符合实际情况。在初步建立的烤烟推荐施肥方程的基础上,确定了各分区推荐施肥量,为实现烟田土壤分区施肥奠定了基础。
本研究主要创新点:
(1)通过两年的田间试验,初步构建了研究区域烤烟推荐施肥模型,为烤烟精准施肥提供了理论基础;
(2)利用GPS、GIS和地统计学地手段,分析了小尺度烟田土壤养分的空间变异规律。在此基础上建立了基于主成分分析和模糊聚类相结合的划分烟田土壤管理分区的方法,为实现烟田土壤的差异化管理奠定了基础。
Soils are highly variable spatially due to the combined effects of physical, chemical, and biological processes that operate with different intensities and at different scales. Consequently, uniform management of fields often results in over-application of inputs in areas with high nutrient levels and under-application in areas with low nutrient levels. Therefore, understanding of the spatial variability of soil properties is essential in determining local fertilizer needs of tobacco. Based on classical statistics, spatial variability of soil nutrients in a tobacco-planted field was studied and spatial distribution maps of soil nutrients was generated by the combined usage of GIS and geostatistics.
The most popular approach to manage spatial variability within fields is the use of management zones (MZs), which are field subdivisions that have relatively homogeneous attributes in landscape and soil condition, and can be used to direct variable rate fertilizer application. To achieve these objectives, soil samples (0~20cm) were taken 81 points on an approximately 100-m grid in March 2007 using global positioning system to define sample locations. Soil chemical properties and texture were analyzed and their spatial variability was assessed by geostatistical techniques.
Considering that most of soil properties are often related, principal component analysis (PCA) was used to summarize soil propertied into a few meaningful components (PC), which were further used to delineate management zones by fuzzy cluster algorithms. In addition, six field experiments were conducted to establish models for nitrogen, phosphorus and potassium fertilizer recommendation in Jia County, Pingdingshan, during 2007and 2008. This provides a basis of information for site-specific fertilizer management in tobacco-planted field. The main conclusions were as follows:
(1) Coefficients of variation ranged from approximately 3.4% for pH to almost 48% for available Zn. Soil OM, TN, AN, AK, CEC and texture had medium CV (11.5%~23.4%). Soil AP and available microelement had higher CV, indicating the heterogeneity of soil properties. Thus, differentiated management may be necessary to achieve maximum economic and environmental benefits. Distributions of all the variables, except Fe, were only slightly skewed (skewness < 1), and their medians were close to their means. The Kolmogorov-Smirnov test revealed that all variables were normally distributed (P > 0.05) and did not require transformation. By log-transformation, soil available Fe showed normal distribution, thereby providing a basis for further structural analysis.
(2) The results of semivariogram analyses are shown that coefficients of determination (R2) for all variables, except soil AN and available ZN, were greater than 0.87, indicating good fits. Soil pH, OM, TN, AP, AK,sand, clay and microelements were modeled best with spherical models, whereas CEC and silt with Gaussian and exponential models. Soil AN showed only pure-nugget effect fitted by linear model at sampling interval of 100-m, i.e., nearly horizontal linear semivariogram, indicating spatial independence. This suggested that a higher sampling density might be recommended for AN in this region. Ranges of spatial dependence varied from 274m (TN) to 1066m (CEC).
(3) The ratio of nugget variance to sill variance can be used to classify spatial dependence of soil properties. A ratio<25% indicated strong spatial dependence, between 25% and75% indicated moderate spatial dependence, and >75% indicated weak spatial dependence. Soil properties with strong and moderate spatial dependence will be more readily managed and an accurate site-specific fertilization scheme for precision farming more easily developed. In this study, the geostatistical analysis suggested that most soil properties showed moderate to strong spatial dependence. Soil pH, TN, Fe, Cu, Zn, sand and clay were strongly spatially dependent with nugget/sill ratios between 16% and 24%, respectively. Soil OM, AP, AK and CEC were moderately spatially dependent with nugget/sill ratios ranging from 30% to 50%. Soil AN showed no spatial dependence. Soil AP and AK had significant correlation with intrinsic properties, for example, pH, OM, and clay, indicating the low extrinsic component of variability and could be used as the basis for differentiated fertilizer application in this region.
(4) PCA and fuzzy cluster algorithm were then performed to delineate MZs. The first two PCs with eigenvalues greater than 1 were considered for clustering analysis, which was performed in MZA procedure using fuzzy c-means cluster algorithm. Performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimum cluster number. Results showed that the optimum number of MZs for this study area was three and analysis of variance indicated the heterogeneity of soil fertility among different MZs, which also showed the combined usage of PCA and fuzzy cluster algorithm is an effective method to characterize spatial variability of soil properties within MZs. The defined MZs provide a basis of information for site-specific fertilizer management in the tobacco-planted field.
(5) By determining the parameters correlative with Truog-Stanford equation building, the models for nitrogen, phosphorus and potassium fertilizers recommendation were established based on two years field experiments. Using the three equations, the fertilizer application for three management zones were recommended respectively.
The innovation points for this study are as follows:
(1) Using GIS and geostatistical techniques, spatial variability was assessed and contour maps generated for soil nutrients in a tobacco field. Establishing a procedure to delineate management zones with the combined usage of PCA and fuzzy cluster algorithm, this provides a basis for defining management zones for tobacco field.
(2) Determining correlative model parameters and initially establishing fertilizers recommendation equations of nitrogen, phosphorus and potassium for flue-cured tobacco in this region.
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