基于带权值的模糊关联规则的土壤质量评价

Fuzzy association rules (FARs) can be powerful in assessing regional soil quality, a critical step prior to land planning and utilization; however, traditional FARs mined from soil quality database, ignoring the importance variability of the rules, can be redundant and far from optimal. In this study, we developed a method applying different weights to traditional FARs to improve accuracy of soil quality assessment. After the FARs for soil quality assessment were mined, redundant rules were eliminated according to whether the rules were significant or not in reducing the complexity of the soil quality assessment models and in improving the comprehensibility of FARs. The global weights, each representing the importance of a FAR in soil quality assessment, were then introduced and refined using a gradient descent optimization method. This method was applied to the assessment of soil resources conditions in Guangdong Province, China. The new approach had an accuracy of 87%, when 15 rules were mined, as compared with 76% from the traditional approach. The accuracy increased to 96% when 32 rules were mined, in contrast to 88% from the traditional approach. These results demonstrated an improved comprehensibility of FARs and a high accuracy of the proposed method.     

湿地土壤质量退化的模糊综合评价

Wetland soil quality degradation caused by large-scale agricultural reclamation on the Sanjiang Plain of Northeast China has been widely reported. A relative soil quality evaluation (RSQE) model and a projection pursuit evaluation (PPE) model based on real-coded accelerating genetic algorithm were introduced to evaluate quality variations in top layers of the main wetland soils on the Sanjiang Plain in 1999 and 2003, respectively. As soil quality degradation boundaries were vague, this study established two fuzzy synthetic evaluation (FSE) models based on the original data and criteria used in the RSQE and PPE models. The outputs of the two FSE models were obtained by choosing two fuzzy composite operators M (∧,∨) and M (?,⊕). Statistical analysis showed that the results of the FSE, RSQE, and PPE models were correlated. In particular, outputs of the FSE model using M (?,⊕) were significantly correlated with those of the RSQE model with r = 0.989 at P < 0.01. Compared with RSQE and PPE models, the FSE model may be more objective in showing soil quality variations and was closer to the natural situation, showing the feasibility and applicability of the FSE model in evaluating soil quality degradation. However, the choice of composite operator was of critical importance. The study of wetland soil quality degradation on the Sanjiang Plain was of scientific and practical significance for protection and management of soils and for sustainable development of agriculture in this area in the future.     

基于模糊神经网络集成的土壤资源评价性能的改进

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts＇ experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network （BPNN）, a fuzzy radial basis function neural network （RBFNN）, a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators＇ experiences was considerably reduced.     

Sustainable agriculture evaluation model for red soil hill region of southeast China

Agricultural sustainability for economic development is important and a complex issue throughout the world; however,it is difficult to synthetically evaluate its use in the policy making process. The objective of this study was to evaluate sustainable agriculture in the red soil hill region of Southeast China through a newly proposed method combining four separate sub-systems: regional population (P), resource (R), environmental (E), and socio-economic (S). This new index system was proposed to appraise synthetically the agricultural sustainability of the red soil hill region from 1988 to 1996 with a two-step method assessing: a) the agricultural sustainability in each province independently and b) the relative sustainability of each province to the whole region. The first step only provided a development trend for each province based on its original situation, while the second step provided additional information on the comparative status of each province in agricultural development to the region as a whole. Higher index scores were found for the economy and resource categories denoting improvement. However, lower scores in the environment category indicated the improvement was achieved at the cost of deteriorating ecological surroundings due to an increasing population that demanded more from the agro-ecosystem and put heavier pressures on it. Results also showed that water and soil losses in this region were the major obstacles encountered in sustainable agriculture development. The assessment results were verified when compared with results from another method. This suggested that the new assessment system was reliable and credible in evaluating agricultural sustainability on a regional scale.     

应用基于加速基因的算法的真实代码的目标追踪评估模型对三江平原水田质量变化进行评价

A new technique of dimension reduction named projection pursuit is applied to model and evaluate wetland soil quality variations in the Sanjiang Plain, Helongjiang Province, China. By adopting the im-proved real-coded accelerating genetic algorithm (RAGA), the projection direction is optimiT~d and multi-dimensional indexes are converted into low-dimensional space. Classification of wetland soils and evaluation of wetland soil quality variations are realized by pursuing optimum projection direction and projection func-tion value. Therefore, by adopting this new method, any poesible human interference can be avoided and sound results can be achieved in researching quality changes and classitlcation of wetland soils.     

基于GIS技术的红壤资源划分与评估

A small scale red soil resources information system(RSRIS) with applied mathematical models was developed and applied in red soil resources(RSR) classification and evaluation,taking Zhejiang Province,a typical distribution area of red soil,as the study area.Computer-aided overlay was conductied to classifty RSR types.The evaluation was carried out by using three methods,i.e.,index summation,square root of index multiplication and fuzzy comprehensive assessment,with almost identical results,The result of index summation could represent the basic qualitatie condition of RSR,that of square root of index miltiplication reflected the real condition of RSR qualitative rank,while fuzzy comprehensive assessment could satisfactorily handle the relationship between the evaluation factors and the qualitative rank of RSR,and therefore it is a feasible method for RSR evaluation.     

自动土壤图基于知识的分类

A machine-learning approach was developed for automated building of knowledge bases for soil resources mapping by using a classification tree to generate knowledge from training data. With this method, building a knowledge base for automated soil mapping was easier than using the conventional knowledge acquisition approach. The knowledge base built by classification tree was used by the knowledge classifier to perform the soil type classification of Longyou County, Zhejiang Province, China using Landsat TM bi-temporal images and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on a field survey. The accuracy assessment mad maalysis of the resultant soil maps suggested that the knowledge bases built by the machine-learning method was of good quality for mapping distribution model of soll classes over the study area.     

不同种植模式和土壤类型条件下土壤健康的定量评价

Soil health assessment is an important step toward understanding the potential effects of agricultural practices on crop yield, quality and human health. The objectives of this study were to select a minimum data set for soil health evaluation from the physical, chemical and biological properties and environmental pollution characteristics of agricultural soil and to develop a soil health diagnosis model for determining the soil health status under different planting patterns and soil types in Chongming Island of Shanghai, China. The results showed that the majority of the farmland soils in Chongming Island were in poor soil health condition, accounting for 48.9% of the survey samples, followed by the medium healthy soil, accounting for 32.2% of the survey samples and mainly distributed in the central and mid-eastern regions of the island. The indicators of pH, total organic carbon, microbial biomass carbon and Cd exerted less influence on soil health, while the soil salinization and nitrate accumulation under a greenhouse cropping pattern and phosphate fertilizer shortage in the paddy field had limited the development of soil health. Dichlorodiphenyltrichloroethanes, hexachlorocyclohexanes and Hg contributed less to soil health index (SHI) and showed no significant difference among paddy field, greenhouse and open-air vegetable/watermelon fields. The difference of the SHI of the three soil types was significant at P = 0.05. The paddy soil had the highest SHI values, followed by the gray alluvial soil, and the coastal saline soil was in a poor soil health condition, indicating a need to plant some salt-tolerant crops to effectively improve soil quality.     

The frontier of soil science: Soil health

<正>Soils are a valuable resource with life activity in terrestrial ecosystem, and soil health and its sustainable management are becoming a major focus of global concern. A healthy soil is a “harmonious social system”, which should have good structure, functional state,and buffering performance to maintain the dynamic balance of soil ecosystem. Soil health has become the frontier of soil science. The development of theoretical and practical approaches for soil health evaluation and managem...     

基于DNA编码的人工免疫模型在土壤质量评价中的应用

针对目前土壤质量评价方法中存在的不足之处,该文通过分析人工免疫模型中二进制编码所存在的问题,提出采用DNA编码对其进行改进,构造一种基于DNA编码的人工免疫模型进行土壤质量评价。利用该模型对东莞赤红壤现代农业试验区进行土壤质量评价,将试验区土壤质量分为4等,根据实地抽样对照评价的结果,结果表明采用基于DNA编码的人工免疫模型进行土壤质量评价时与实际相符,并具有稳定、结果可靠等特点,能较好地解决在进行土壤质量评价时,对于具有空间特性、模糊性、不确定性以及多指标的对象难以评价等问题。     

农业灌溉水质评价的投影寻踪模型

针对农业灌溉用水水质综合评价过程中存在的评价结果不相容性和难以客观反映水质的真实属性等问题，提出了一种基于数据探索的投影寻踪综合评价模型，并采用实数编码的免疫遗传算法进行模型优化求解。结果说明：与传统水质评价方法相比，该方法数学概念清晰，评价结果更精确合理，较好地解决了系统综合评价中的片面性和不相容性问题。该投影寻踪模型在农业系统工程预测及评价中具有较强适用性。     

聚类支持下决策树模型在耕地评价中的应用

为了挑选合理的学习样本，增强决策树模型在耕地评价应用的有效性，运用聚类方法挑选学习样本，用改进C5．0决策树算法建立耕地评价模型，提出一种新的评价思路。运用此方法以广东省龙川县耕地为研究对象，以试验法挑选出6种聚类结果的学习样本，确定4000个样本作为最终的学习样本；利用决策代价权重来改进决策树评价模型，最终建立的评价模型的预测精度达到94．92％，满足了实际情况的需要。试验结果表明综合运用聚类和决策树模型进行耕地评价是可行的，其建立的评价模型具有精度高、鲁棒性和易理解性等特点。     

基于GIS和PSR模型的农用地资源评价研究

利用Arc／Info和“压力-状态-响应”(PSR)模型，选取土壤污染综合指数作为一个压力指标，对江汉平原后湖地区农用地进行了环境质量评价，从而为该地区合理的土地利用规划和环境保护提供依据。引入PSR模型和特尔菲法确定样区评价因子，层次分析法计算相应因子的权系数；利用Arc／Info矢量化相关图层和进行缓冲区分析后建立空间数据库；然后根据各评价因子的属性确定其分值函数，通过对样点数据进行量化后采用多因子综合指数和法计算环境质量指数，划分级别；应用Arc／Info实现相关结果的输出。结果表明：后湖地区农用地环境质量中等偏上，其中二、三级地分别占47．27％和35．83％；通过对土壤污染状况和农用地环境质量等级进行分析，发现二者存在明显的正相关性。     

基于熵权法的城市水资源脆弱性研究——以广东省为例

[目的]研究城市水资源的脆弱性,客观反映水资源的可持续发展能力,为建设可持续发展城市提供参考。[方法]本文把广东省21个行政市及佛山市顺德区水资源列为实证对象,从自然因素、人为因素、承载力因素3个角度建立评价指标体系;在熵权法计算影响权重的基础上利用线性加权求和法构建水资源脆弱性评价模型。[结果]广东省水资源脆弱性总体呈现较脆弱,深圳市水资源脆弱性呈现不脆弱,在22个研究对象中水资源脆弱性表现最稳定。[结论]根据广东省水资源脆弱性评价结果结合智慧城市中可持续发展的目标可知,由于产业结构、水资源利用效率较低等问题导致广东省水资源脆弱性较高,可以通过产业结构改革,建设智慧水务和利用科技水平提高水资源利用效率等方法来降低水资源脆弱性。     

耕地资源管理信息系统的建立及应用——以永济市为例

以山西省永济市为例 ,运用GIS技术建立了耕地资源管理信息系统。该系统采用MapGis管理空间数据库和属性数据库 ,FoxPro管理数据录入、应用模型分析与统计数据库 ;叠加土地利用现状图、基本农田保护区规划图和土壤图 ,形成 6 782个图斑 ,组成空间数据库的基本管理与评价单元 ,使得每个评价单元上土地利用方式一致、土壤类型一致。采用VisualC 语言开发了层次分析、模糊综合评价等应用模型。针对永济市的实际情况 ,经过专家组研究从 6 4个耕地地力调查与质量评价指标中筛选出 15项指标进行评价。对 6 782个评价单元中的每一个评价单元 ,用 15项耕地质量评价指标计算其综合评价指数 ,并以该指数作为耕地质量评价依据。评价结果反映出该市耕地中低产田面积大、施肥结构不合理、忽视盐碱地改造和垆土地改良利用等一系列问题 ,并针对耕地的不同等级所存在的障碍因素提出了相应的建议。该系统可掌握全市耕地地力及质量状况 ,为耕地地力建设与土壤改良利用提供了依据。     

非农建设占用耕地耕层土壤剥离潜力评价方法

在开发建设项目日益扩大增长的今天,剥离被非农建设占用的耕地,是缓解人地矛盾,保护耕地资源的有效措施。该文在Arc GIS的技术支持下,以重庆市江津区为例,从耕层土壤质量与耕地质量2个角度,提供了一种定量评价了非农建设占用耕地可剥离的耕层土壤剥离潜力的方法。将研究区耕层土壤质量等级图层与非农建设占用的耕地质量等级图层叠加,得出江津区非农建设占用耕地可剥离耕层土壤资源潜力,可剥离的耕层土壤总面积达到了30.18 km2,占非农建设占用耕地总面积的76.39%,主要集中在江津区经济发达、非农建设发展迅速,地势低平、集中连片、交通便利、增值潜力巨大的中北部地区。根据江津区耕层土壤质量等级以及江津区非农建设占用的耕地质量等级,将可剥离的耕层土壤划分为3个等级,以便确定非农建设占用的耕层土壤剥离的优先秩序以及后备土壤资源;可剥离的优质耕层土壤资源稀缺,一级可剥离耕层土壤面积仅为0.32 km2,占可剥离耕层土壤总量的1.03%,二级可剥离耕层土壤面积为4.64 km2,占可剥离耕层土壤总量的15.37%。因此在丘陵山地开发建设的过程中耕地保护任务重大,更需对被占用优质的耕地进行耕层土壤剥离。该文为区域开发建设、耕地资源保护提供了科学依据,对后续的耕层土壤利用规划乃至西南丘陵山地区的非农建设规划布局具有一定借鉴意义。     

基于GIS的江淮丘陵地区典型县域土壤质量评价

以安徽省江淮丘陵地区的定远县为例,自1401个土壤样本点获取土壤有机质、全氮、速效磷、速效钾、土壤侵蚀程度、耕层厚度共六项指标,利用GIS空间插值方法,使用隶属度函数,构建土壤质量综合评价模型,对该县土壤质量进行评价。研究结果表明:（1）研究区平均土壤质量指数为0.56,采用等间距法将土壤质量划分为5级,其中三等地占总面积的59.54%,二等地占31.4%,一等地和四等地分别为1.53%、7.52%,研究区内没有五等地;（2）土壤质量在空间分布上呈现出东南方向至西北方向下降的趋势,平原地区的土壤养分含量高于丘陵岗地、质量相对较好;（3）不同土地利用类型土壤中水田土壤质量略高于旱地、草地和林地。土壤质量的评价结果对于研究区改善土壤质量、合理配置农业土地资源有一定参考价值。     

粤北岩溶山区农业水土环境问题及对策

粤北岩溶山区是广东省主要的贫困地区之一,自然环境先天脆弱,水土流失严重、水资源短缺、旱涝灾害频繁等一系列生态问题已成为制约当地社会经济可持续发展的关键因素。在广泛、深入调查的基础之上,分析了粤北岩溶山区农业水土环境问题的现状及主要成因,提出了改善农业生态环境,促进农业水土资源可持续发展的针对性措施。     

农用地土壤重金属超标评价与安全利用分区

土地资源是大自然馈赠给人类的独特珍品,承载万物。开展区域农用地土壤重金属超标评价与安全利用分区、分类利用管控,促进区域土地资源合理、高效利用是新常态下耕地保护的基本要求。该文分不同p H值区间、8种不同地类,基于现行土壤环境质量评价和食品安全评价的众多标准集成土壤环境质量现状评价标准,以江苏省某市为例,运用单因子指数法、内梅罗综合指数法和潜在生态危害指数法开展土壤重金属超标评价,进而提出了安全利用等级划分方案和分区、分类利用与管理的管控策略。结果表明:将研究区农用地分为安全、基本安全、低风险、中风险和高风险5种综合分区,分别采取优先保护、综合监控、预警防控和限制利用等措施,以精细化管理思想为出发点,从源头控制、农艺措施调控、种植作物调整、地类转换以及土壤修复、综合整治等方面提出了各综合分区的安全利用管控策略。因此,开展区域农用地土壤重金属超标评价与安全利用分区研究为确保土地资源合理利用保护耕地提供了重要思路。