《土地法学》课程教学改革的研究与实践

教学研究与教学改革对于提高教学质量和专业建设水平具有重要的意义。从课程教学内容组织、教材等资源选取、教学方法与教学手段等方面对《土地法学》课程教学开展了研究与实践,提出了课堂讲授结合案例教学、角色扮演模拟教学、网络互动教学以及规范化课程考核等具体教学手段,并进行了实践应用。结果表明,上述教学改革对于激发学生的学习兴趣、保持教学内容的现势性、提高教学质量具有积极的推动作用。

《土地法学》课程教学改革的思考

本文结合贵州大学土地资源管理专业的办学特点,对《土地法学》课程的教学内容进行了优化和调整,创新设计了课程的考核体系;从理论讲授和实践环节两方面,提出具体的教学改革方法与模式,如案例、讨论、情景模拟及案例听审教学等,并提议建立网络教学平台增进教学交流,以提高课程的教学效果,培养"实用、管理、创新"型的土地资源管理专业人才。

Method for Evaluating the Degrees of Land Use Sustainability of Mountainous County and its Application in Yunnan Province,China

The evaluation of sustainable land use is the key issue in the field of studying the sustainable land utilization. In general analysis, the sustainable land use is evaluated respectively from its ecological sustainability, economic sustainability and social sustainability in China and other countries in recent years. Although this evaluation is an important work, it seems insufficient and hard to comprehensively reflect the whole degree of land use sustainability. Thus, to make up this deficiency, this paper brings forward the evaluation indexes, which make it possible to quantitatively reflect the whole degree of land use sustainability, namely, the concept of ＂degrees of overall land use sustainability＂ （Dos）, and research and measurement development of the method of and calculation in Dos. Taking the evaluation of the degree of land use sustainability in county regions of Yunnan Province as the actual example for analysis, results are basically as follows： 1） The degree of land use sustainability （Dos） is the ration index to organically and systematically integrate the degree of ecological friendliness （DeF）, the degree of economic viability （Dev） and the degree of social acceptability （Dsa）, able to comprehensively reflect the whole sustainability degree of regional land use 2） Based on the value of Dos, the grading system and standard for the sustainability of land use may be established and totally divided into five grades, namely, the high-degree sustainability, middle-degree sustainability, low-degree sustainability, conditional sustainability and non-sustainability. Meanwhile, the standard for distinguishing sustainability grades has also been confirmed so as to determine the nature of sustainability degrees in different grades. This makes the possibility for the combination of nature determination with ration in research result and provides with the scientific guideline and decision-making gist for better implementation of sustainable land use strategy. 3） The practice in evaluation of sustainability degree in county regional land use in Yunnan shows that the value of the degree of land use sustainability （Dos） of whole Yunnan Province is only 58.39, belonging to the grade of low-degree sustainability. Two thirds of counties in the whole province represent the grade of ＂conditional sustainability＂ and ＂non-sustainability＂ in the sustainability of land use. Among these counties, 11.11 % shows ＂non- sustainability＇. The lowest degree of land use sustainability appears especially in the middle plateau mountain region of Northeast Yunnan, where the value of Dos in most counties （districts） is below 40 %, belonging to the grade of ＂non-sustainability＂. The sustainability degree in the karst mountainous region in lower-middle plateau mountain region in Southeast Yunnan is generally low and the value of sustainability degree （Dos） in most of the counties （cities and districts） is below 55. The value of sustainability degree （Dos） in most of the counties （cities and districts） in the north, west, northwest and southwest parts of Yunnan is below 55. This article also analyzes the reasons of low degree of sustainability in land use in Yunnan and puts forward the countermeasures to increase the degree of sustainability in land use in the whole province.

Spatial Variability of Soil Organic Carbon in Different Hillslope Positions in Toshan Area, Golestan Province, Iran: Geostatistical Approaches

Accessibility to organic carbon(OC) budget is required for sustainable agricultural development and ecosystem preservation and restoration. Using geostatistical models to describe and demonstrate the spatial variability of soil organic carbon(SOC) will lead to a greater understanding of this dynamics. The aim of this paper is to present the relationships between the spatial variability of SOC and the topographic features by using geostatistical methods on a loess mountain-slope in Toshan region, Golestan Province, northern Iran. Hence, 234 soil samples were collected in a regular grid that covered different parts of the slope. The results showed that such factors as silt, clay, saturated moisture content, mean weighted diameter(MWD) and bulk density were all correlated to the OC content in different slope positions, and the spatial variability of SOC more to slope positions and elevations. The coefficient of variation(CV) indicated that the variability of SOC was moderate in different slope positions and for the mountain-slope as a whole. However, the higher variability of SOC(CV = 45.6%) was shown in the back-slope positions. Also, the ordinary cokriging method for clay as covariant gave better results in evaluating SOC for the whole slope with the RMSE value 0.2552 in comparison with the kriging and the inverse distance weighted(IDW) methods. The interpolation map of OC for the slope under investigation showed lowering SOC concentrations versus increasing elevation and slope gradient. The spatial correlation ratio was different between various slope positions and related to the topographic texture.

Spatial Variability of Soil Organic Carbon and Related Factors in Jiangsu Province,China

Soil organic carbon (SOC) plays a key role in the global carbon cycle.In this study,we used statistical and geostatistical methods to characterize and compare the spatial heterogeneity of SOC in soils of Jiangsu Province,China,and investigate the factors that influence it,such as topography,soil type,and land use.Our study was based on 24 186 soil samples obtained from the surface soil layer (0-0.2 m) and covering the entire area of the province.Interpolated values of SOC density in the surface layer,obtained by kriging based on a spherical model,ranged between 3.25 and 32.43 kg m 3.The highest SOC densities tended to occur in the Taihu Plain,Lixia River Plain,along the Yangtze River,and in high-elevation hilly areas such as those in northern and southwest Jiangsu,while the lowest values were found in the coastal plain.Elevation,slope,soil type,and land use type significantly affected SOC densities.Steeper slope tended to result in SOC decline.Correlation between elevation and SOC densities was positive in the hill areas but negative in the low plain areas,probably due to the effect of different land cover types,temperature,and soil fertility.High SOC densities were usually found in limestone and paddy soils and low densities in coastal saline soils and alluvial soils,indicating that high clay and silt contents in the soils could lead to an increase,and high sand content to a decrease in the accumulation of SOC.SOC densities were sensitive to land use and usually increased in towns,woodland,paddy land,and shallow water areas,which were strongly affected by industrial and human activities,covered with highly productive vegetation,or subject to long-term use of organic fertilizers or flooding conditions.

Decision support for integrated river basin management—Scientific research challenges

Integrated River Basin Management(IRBM)has been a long discussed way to sustainably manage water and land resources;yet,very few examples of effective IRBM are found because there is a lack of sufficient scientific support,as well as institutional accommodation,to successfully implement it.This paper overviews the major challenges with IRBM,the promising scientific approaches for the implementation of IRBM,and the areas of needed research,with considerable issues and experiences from China.It is expected that novel research will draw together disparate disciplines into an integrated scientific framework,upon which better modeling tools,stakeholder involvement,and decision-making support can be built.Cutting-edge new technologies will bring ideas of IRBM forward to theory and finally to practice.The paper will prompt scientists to undertake research to fill in the gaps in the current IRBM knowledge base and provide practitioners guidance on how to incorporate scientifically based information within the IRBM decision process.

An Insight into Machine Learning Algorithms to Map the Occurrence of the Soil Mattic Horizon in the Northeastern Qinghai-Tibetan Plateau

Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree(CART), random forest(RF), boosted regression trees(BRT), and support vector machine(SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies(measured based on area under the receiver operator characteristic curve score(AUC)) and produced more stable results for the BRT(AUC of 0.921 ± 0.012, mean ± standard deviation) and RF(0.908 ± 0.013) algorithms compared to the CART algorithm(0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value(0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely time-consuming.Therefore, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.