目前，基层分类单元土族、土系的研究是我国的土壤系统分类研究的重点。土系调查制图一直都是土系研究的重点。其中，土系预测制图作为一种根据已知土壤剖面点来预测区域土壤类型的方法也逐渐受到人们的重视。但是，由于平原农区这类景观缓变区地势开阔平坦，地表起伏不明显，坡度较为平缓；自然植被相对单一，景观变化较小；加之人为活动剧烈导致土地利用类型多样且易变。因此，在对国计民生影响特别重大的以水耕人为土为主要代表的景观缓变地区，土系调查制图的研究相对较少。现今，土壤肥力评价多局限于土壤表层，虽然农业土壤养分绝大多数集中在土壤表层，但是在评价土壤肥力时仅仅考虑土壤表层是远远不够的，特别在水耕人为土中，水耕氧化还原层中的潴育化过程，黏化过程和同样具有淋溶现象的潜育化过程都说明了水耕人为土剖面上具有活跃且丰富的物质交流现象，也说明了土壤剖面肥力评价的必要性与重要性。土系与生产应用结合性强，是中国土壤系统分类最基层的土壤分类单元。因此基于土系制图技术，以土系作为区域土壤肥力评价的评价单元，进而完成区域土壤肥力评价对了解土壤肥力特征具有重要的研究意义。

本文的研究目的包括：寻找一种更加方便快捷的适用于景观缓变区的土系制图方法，完成水耕人为土的土系预测制图。其研究结果可以为区域精准农业管理、生态环境建模、土壤模拟等诸多领域提供更加细致的土壤基础资料；合理划分特征土层，计算土壤单体土壤肥力指数，并结合土系预测图制作区域土壤肥力评价图。预期研究成果对于完善基于中国土壤系统分类的土壤单体肥力评价理论与方法有重要意义。最后，分析不同土系类型与土系肥力之间的关系，以期能够反映水耕人为土土壤肥力的影响因素。这为制定防止土壤肥力退化措施和土壤肥力改良方法提供了重要的理论依据和科学指导。

本研究在中国土壤系统分类理论的基础上，根据中国土壤系统分类体系与各大成土因素对水耕人为土土壤类型空间变异的影响，遴选出8种水耕人为土土系类型诊断依据。结合模糊c-均值算法（Fuzzy c-means，FCM）对28个土壤剖面样点数据进行模糊聚类分析，将其输出结果利用两种地统计学插值方法进行插值，并去模糊化得到土系预测图。利用指数法土壤肥力评价模型，选取评价指标，确定指标权重，最后计算出土壤单体肥力指数PFQI，并结合土系预测图制作区域土壤肥力评价图。进一步分析不同土系类型与土系肥力之间的关系。对研究结果进行总结和概括，得到主要结论如下：

（1）为了使最终土壤类型预测结果在土系层级甚至土族、亚类和土类的层级上都能与土壤系统分类判别的土壤类型完全匹配。本文结合中国土壤系统分类体系、水耕人为土本身的土体特征和建立土系应当与农业生产应用具有较强结合性的特点，筛选了8种土系类型诊断依据，均是在土系的检索与建立的过程中用到的土壤诊断层、诊断特性和土壤剖面性状等，在验证点参与和不参与土壤模糊连续聚类两种聚类情况下得到的聚类中心都能与手动检索建立的土系类型建立良好的对应关系。所以，选择土壤诊断层、诊断特性等作为土壤类型诊断依据，在景观缓变区基层土壤分类单元中进行土壤数值化分类及并完成土壤预测制图是可行且适用的。

（2）比较两种地统计插值方法在土系预测制图中的应用结果，相比之下，使用IDW获得的结果更加准确、合理。进一步经过混淆矩阵验证分析，Kappa系数为0.61，总分类精度达到0.65，满足土壤制图精度的基本要求，说明FCM和IDW相结合能很好的实现平原农区水耕人为土土系的预测与制图，在合理的选择土壤类型诊断依据的前提下，可以广泛的应用于平原农区景观缓变区的土系类型的空间预测与制图。

（3）将土壤单体肥力指数PFQI与水稻产量做相关分析，根据相关分析得到:PFQI与水稻产量在0.01水平（双侧）上显著相关，相关度为0.71；耕作层肥力指数与水稻产量在0.05水平（双侧）上显著相关，相关度为0.33。说明本文以土壤诊断层与诊断特性作为土壤层次划分的依据，利用主成分分析法确定评价指标与各个土层的权重，计算PFQI的评价体系具有较好的科学性与应用性，且在水耕人为土中PFQI比耕作层肥力指数与水稻产量的相关性更高。

（4）分析不同土系类型与土壤单体肥力之间的关系，得到土壤肥力主要是由土壤质地、有机质及磷钾含量等土壤养分所决定，与土层厚度和土层结构也有很大的关系。其中潜育层次的存在对土壤单体肥力会产生负面影响，降低土壤单体肥力；具有铁聚特征层的土系普遍土壤单体肥力较高。

Chinese Soil Taxonomy (CST) has made great progress at present, and it is at the stage of researching on the standard of basic taxonomic unit classification. The investigation and mapping of soil series has always been the focus of soil series research. Among them, predictive mapping of soil series as a method of predicting regional soil types based on known soil profile points has gradually attracted people's attention. The landscape slowly changing areas such as plain agricultural areas are wide and flat. It surface fluctuations are not obvious, and the slope is relatively gentle; the natural vegetation is relatively single, and the landscape changes are small; the intense human activities, the land use types are diverse and volatile, therefore, there are relatively few studies on soil surveying and mapping in areas with slowly changing landscapes, where Stagnic Anthrosols is the main representative. At present, the assessment of soil fertility is mostly limited to the soil surface. Although most of the soil nutrients in agriculture are concentrated on the soil surface. It is not enough to consider the soil surface only when evaluating the fertility, especially in Stagnic Anthrosols. The hydromorphic process, the sticking process and the gleying process with leaching phenomenon in the hydragric horizon indicate the active and abundant material exchange phenomenon on the Stagnic Anthrosols profile, as well as the fertility evaluation of the soil Pedon. Necessity and importance.

The research objectives of this article include: to find a more convenient and quick soil mapping method suitable for the slowly changing landscape area, and to complete the soil mapping prediction mapping of Stagnic Anthrosols. The research results can provide more detailed soil basic data for regional precision agriculture management, ecological environment modeling, soil simulation and other fields. The soil fertility index of Pedon was calculated by reasonably dividing characteristic soil layers, and the regional soil fertility evaluation map was made by combining with soil system prediction map. The expected research results is the theory and method of soil Pedon fertility evaluation in CST are of great significance. Finally, the variation of soil fertility in different soil layer structures and different soil types under Stagnic Anthrosols in the study area is discussed, In order to reflect the influencing factors of the soil fertility. It provides important theoretical basis and scientific guidance for formulating measures to prevent soil degradation and methods for improving soil.

Based on the CST theory and the major soil formation factors on the spatial variability of Stagnic Anthrosols types, 8 diagnosis basis of soil were selected according to the soil genetic characteristics of Stagnic Anthrosols. Fuzzy c-means (FCM) was applied to analyze the sample soil, and the output results were interpolated by two geostatistical interpolation methods. Then get the soil series prediction map by defuzzification method. Using the index method of soil evaluation model, the evaluation index is selected, the index weight is determined, and finally the PFQI is calculated. Combined with the soil series prediction map, the regional soil fertility evaluation map was made. The relationship between different soil series types and soil series fertility was further analyzed. Summarizing the research results, the main conclusions are as follows:

(1) In order to make the final soil prediction result can match the soil type discriminated by the CST at the soil series level or even at the level of the soil Family, soil subgroup and great group. This paper combines the characteristics of the CST classification system, the characteristics of the Stagnic Anthrosols, and the characteristics of establishing the soil series that should be strongly combined with agricultural production applications. 8 kinds of soil diagnosis basis. The cluster centers obtained under the two clustering cases of verification point participation and verification point non-participation in soil fuzzy continuous clustering can establish a good correspondence with the soil series established by manual retrieval. Therefore, it is feasible and applicable to select soil diagnostic layers and diagnostic characteristics as the basis for soil type diagnosis. It is feasible and applicable to carry out numerical soil classification and soil prediction mapping in the basic soil classification unit of landscape slowly changing area.

(2)Compare the application results of the two geostatistical interpolation methods in the prediction of soil series. In contrast, the results obtained using the Inverse Distance Weighted (IDW) are more accurate and reasonable. After further analysis by the confusion matrix, the Kappa coefficient is 0.61, and the total classification accuracy is 0.65, which meets the basic requirements for soil mapping accuracy. It shows that the combination of FCM algorithm and IDW can well realize the prediction and mapping of Stagnic Anthrosols in the plain agricultural area. Spatial prediction of soil systems can be widely applied to the spatial prediction and mapping of soil system types in the slowly changing landscape areas of plain agricultural areas on the premise of reasonable selection of soil diagnostic basis.

(3) Correlate the soil fertility of Pedon with rice yield: the soil fertility of Pedon and rice yield were significantly related at the 0.01 level (two sides), the correlation was 0.71. The fertility of the tillage layer is significantly correlated with the rice yield at the 0.05 level (bilateral), the correlation was 0.33. It shows that this paper uses the soil diagnosis layer and diagnosis characteristics as the basis for the division of the soil layer, uses the principal component analysis method to determine the evaluation index and the weight of each soil layer, and finally calculates the PFQI. Thr evaluation system is scientific and practical. Also shows that the fertility of Pedon in the Stagnic Anthrosols soil profile has a higher correlation with rice yield than the fertility of the cultivated layer.

(4) Analyze the relationship between different soil series types and its fertility. The fertility of the soil series is mainly determined by the soil texture, organic matter, phosphorus and potassium content and other soil nutrients, and also closely related to the thickness and structure of the soil layer. The existence of the gley horizon will have a negative impact on the fertility of the soil series and reduce the fertility level of soil fertility; the soil series with iron-accumulating characteristic layer generally has higher fertility.

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