基于测土配方施肥数据的水稻土养分分析

采用测土配方施肥系统整理和统计水稻土养分有效测试数据,结合第二次全国土壤普查,比较分析水稻土不同分类、种植模式、区域和时间养分变化趋势,旨在了解盈江县水稻土养分变化、及耕地地力保护与提升薄弱环节,促使耕地质量保护与提升的重视程度逐渐提高与加强。

中国东部沿海季风区土壤全氮格局及其与水热条件关系

采用全国测土配方施肥项目土壤基础养分数据集,研究了年平均温度和年平均降水量对土壤全氮含量的影响。按照年平均温度<5、5～10、10～15、15～20、>20℃以及年平均降水量0～200、200～400、400～600、600～800、800～1000、1000～1200、1200～1400、1400～1 600mm和>1600mm等区间,分别研究了年平均温度和年平均降水量对土壤全氮含量的影响。结果表明:(1)在年平均温度低于10℃的区域内,土壤全氮含量与年平均温度的相关性较好,年平均温度高于20℃的区域内,土壤全氮含量与年平均降水量和年平均温度的相关性都很差;(2)在年平均降水量0～200mm的区域内,土壤全氮含量与年平均降水量有显著的相关性,而与年平均温度的相关性很差;(3)在年平均降水量200～400、400～600、600～800mm的区域内,土壤全氮含量与年平均温度的相关性要好于其与年平均降水量的相关性,尤其在400～800mm年降水量区间内的半湿润地区,土壤全氮含量与年平均温度呈现极显著的相关关系,在年平均降水量大于800mm后,年平均温度和平均降水量对土壤全氮含量的影响并不明显。

粮食规模经营户生产经营行为与耕地质量变化特征

规模经营户作为我国粮食生产主力军,研究其在不同区域、不同利用方式下耕地质量变化的规律特征,对新时期下提升农业综合生产能力具有十分重要的意义。对全国4个典型省份313个粮食规模户2015年和2018年2轮跟踪测土和生产经营数据进行了分析,结果表明:耕地质量总体稳中向好,其中土壤有机质、全氮保持平稳,土壤速效磷、速效钾显著增加;区域间变化差异明显,黑龙江、浙江和河南省耕地质量稳中向好,而四川省土壤肥力下降趋势明显;土壤有机质、速效磷和速效钾变化量与经营规模呈倒U形关系;全氮变化量与经营规模之间呈正相关。为此,建议引导粮食规模户开展适度规模经营,聚焦短板、因地制宜,加强耕地质量建设,推广保护性耕作措施。

Back analysis for soil slope based on measuring inclination data

Based on the analysis of several objective functions,a new method was proposed.Firstly,the feature of the inclination curve was analyzed.On this basis,the soil could be divided into several blocks with different displacements and deformations.Then,the method of the soil division was presented,and the characteristic of single soil block was studied.The displacement of the block had two components:sliding and deformation.Moreover,a new objective function was constructed according to the deformation of the soil block.Finally,the sensitivities of the objective functions by traditional method and the new method were calculated,respectively.The result shows that the new objective function is more sensitive to mechanical parameters and the inversion result is close to that obtained by the large direct shear apparatus.So,this method can be used in slope back analysis and its effectiveness is proved.

Development of a GIS-based Decision Support System for Assessing Land Use Status

The objectives of this study are to assess land s ui tability and to predict the spatial and temporal changes in land use types (LUTs ) by using GIS-based land use management decision support system. A GIS databas e with data on climate, topography, soil characteristic, irrigation condition, f ertilizer application, and special socioeconomic activities has been developed a nd used for the evaluation of land productivity for different crops by integrati ng with a crop growth model-the erosion productivity impact calculator (EPIC). International food policy simulation model (IFPSIM) is also embedded into GIS fo r the predictions of how crop demands and crop market prices will change under a lternative policy scenarios. An inference engine (IE) including land use choice model is developed to illustrate land use choice behavior based on logit models , which allows to analyze how diversified factors ranging from climate changes, crop price changes to land management changes can affect the distribution of agr icultural land use. A test for integrated simulation is taken in each 0.1° by 0.1° grid cell to predict the change of agricultural land use types at globa l level. Global land use changes are simulated from 1992 to 2050.

Prediction of Soil Depth from Digital Terrain Data by Integrating Statistical and Visual Approaches

Information about the spatial distribution of soil attributes is indispensable for many land resource management applications; however, the ability of soil maps to supply such information for modern modeling tools is questionable. The objectives of this study were to investigate the possibility of predicting soil depth using some terrain attributes derived from digital elevation models （DEMs） with geographic information systems （GIS） and to suggest an approach to predict other soil attributes. Soil depth was determined at 652 field observations over the A1-Muwaqqar Watershed （70 km2） in Jordan. Terrain attributes derived from 30-m resolution DEMs were utilized to predict soil depth. The results indicated that the use of multiple linear regression models within small watershed subdivisions enabled the prediction of soil depth with a difference of 50 cm for 77% of the field observations. The spatial distribution of the predicted soil depth was visually coincided and had good correlations with the spatial distribution of the classes amalgamating three terrain attributes, slope steepness, slope shape, and compound topographic index. These suggested that the modeling of soil-landscape relationships within small watershed subdivisions using the three terrain attributes was a promising approach to predict other soil attributes.

Development and Prospect of Study on Soil Nonlinear Dynamic Characteristics under Strong-Motion

Ground motions are significantly influenced by dynamic characteristics of overburden soil layers near ground surface,as thick and soft soil layers would obviously amplify the ground motion strength. The conventional research method on soil nonlinear dynamic characteristics under strong motions is based on experiments in laboratories for the deficiency of observation data,but it is difficult to reliably simulate the complex factors of soils in actual earthquake durations,including loading paths,boundary conditions,and drainage conditions. The incremental data of the vertical downhole observation array,which is comprised of at least one observation point on ground surface and one observation point in a downhole rock base, makes it possible to study soil nonlinear dynamics according to in situ observation data,and provides new basic data and development opportunities to soil nonlinear dynamics studies.

Influence of stress anisotropy on the cylindrical cavity expansion in undrained elastic-perfectly plastic soil

This work presents an analysis of the influence of stress anisotropy on cylindrical cavity expansions in an undrained elastic-perfectly plastic soil. This problem was formulated by assuming a large strain in both the elastic and plastic zones around the cavity and a plain strain condition during the cavity expansion process. The solutions for the limit pressure, stress, and excess pore pressure were obtained by introducing the anisotropic initial stress coefficient K_0 into the conventional cylindrical cavity expansion method.The proposed solutions were then used to interpret the piezocone penetration test, and the suitability of the solutions was verified by comparing the prediction with the piezocone penetration test data. Subsequently, parametric studies were carried out to investigate the influence of stress anisotropy on the stress, excess pores pressure distributions around an expanding cylindrical cavity, and limit pressure. The results show that the proposed cylindrical cavity expansion method under stress anisotropy is suitable and can be used to investigate the piezocone cone test. The present work improves upon the conventional theoretical framework of cavity expansion and can be applied to the determination of the stresses around axially loaded piles and around in-situ testing devices such as penetrometers.

Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data

Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer （MODIS） products （MOD09A1 and MODllA2）, covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index （TVDI）. Three LST-VI spaces were constructed by normalized difference vegetation index （NDVI）, enhanced vegetation index （EVI）, and modified soil-adjusted vegetation index （MSAVI）, respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space （TVDIE） revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.

Estimation of Soil Carbon Input in France: An Inverse Modelling Approach

Development of a quantitative understanding of soil organic carbon （SOC） dynamics is vital for management of soil to sequester carbon （C） and maintain fertility, thereby contributing to food security and climate change mitigation. There are well-established process-based models that can be used to simulate SOC stock evolution; however, there are few plant residue C input values and those that exist represent a limited range of environments. This limitation in a fundamental model component （i.e., C input） constrains the reliability of current SOC stock simulations. This study aimed to estimate crop-specific and environment-specific plant-derived soil C input values for agricultural sites in France based on data from 700 sites selected from a recently established French soil monitoring network （the RMQS database）. Measured SOC stock values from this large scale soil database were used to constrain an inverse RothC modelling approach to derive estimated C input values consistent with the stocks. This approach allowed us to estimate significant crop-specific C input values （P 〈 0.05） for 14 out of 17 crop types in the range from 1.84 =h 0.69 t C ha-1 year-1 （silage corn） to 5.15 =k 0.12 t C ha-1 year-1 （grassland/pasture）. Furthermore, the incorporation of climate variables improved the predictions. C input of 4 crop types could be predicted as a function of temperature and 8 as a function of precipitation. This study offered an approach to meet the urgent need for crop-specific and environment-specific C input values in order to improve the reliability of SOC stock prediction.