Soil properties in organic olive groves compared with that in natural areas in a mountainous landscape in southern Spain

This study evaluates soil properties in organically managed olive groves and natural zones in a mountainous area of Andalusia, Spain. Two soil types (Eutric Regosol and Eutric Cambisol) and the most common soil management methods (tillage and two intensities of grazing) were studied. Both soil types in the groves had values not much lower than those in the natural areas. Average (±SE) values in the groves were 1.58 ± 0.71% for organic carbon, 323 ± 98 g kg^?1 for macroaggregate stability, 1.11 ± 0.16 g cm^?3 for bulk density, 3.5 ± 1.6 mm h^?1 for saturated hydraulic conductivity and 1209 ± 716 mg CO₂ kg^?1 for soil respiration. Overall, these values tended to be lower in the tilled compared with that in the grazed groves. The average phosphorus soil content (5.83 ± 5.22 mg kg^?1) was low for olive production and within adequate ranges for N (0.12 ± 0.05%) and K (142 ± 81 mg kg^?1). Soil erosion was high in the tilled groves (35.5 ± 18.2 t ha^?1 year^?1) with soil loss correlating with indicators of soil degradation such as organic carbon content and water stable macroaggregates. In the grazed groves, soil loss was moderate with no clear indications of soil degradation. Overall, there was significant farm‐to‐farm variability within the same soil and land management systems. Olive production had a moderate effect on soil degradation compared with natural areas and olive cultivation could be sustained in future if cover crop soil management replaced tillage, especially in the most sloping areas.     

Short-term gully retreat rates over rolling hill areas in black soil of Northeast China

Soil loss has become one severe problem in black soil areas of Northeast China after several decades of cultivation. Gully erosion is one of its main components. In this study, short-term gully retreat was monitored from 5 active gullies selected in representative black soil area during April 2002 to June 2004, using differential global positioning system (GPS). With the support of geographic information system (GIS), multitemporal digital elevation models (DEM) were constructed from the data collected by GPS and then used for further analysis. This presents a new method to compute the retreat rate of gully heads and the rate of soil losses caused by gully erosion. The results indicate that the average volumetric retreat rate was 729.1 m³ year^− 1, corresponding with an average linear retreat rate of 6.2 m year^− 1 in gully head and planimetric changes of 323.6 m² year^− 1 during the two monitored years, but more erosion took place during the second and third monitored period compared to the first. The erosion by freeze thawing and snowmelt accounts for a large percent. And this will be emphasized when rainfall is added in spring. If only considering the third monitored period, the conservatively estimated retreat rate by freeze thawing and snowmelt (i.e. before rainy season) may even reach 8.6 m year^− 1 in gully head, with a volumetric rate of 120.9 m³ year^− 1 and planimetric changes of 173.6 m² year^− 1. These results reveal that gully erosion is a great threat in the study area and conservation measures are urgently needed. Based on the analysis of multi-temporal DEM, one conceptual model for gully developing in black soil of Northeast China is proposed, which is supported by the data.     

Integration of multiple soil parameters to evaluate soil quality: a field example

Development of a method to assess and monitor soil quality is critical to soil resource management and policy formation. To be useful, a method for assessing soil quality must be able to integrate many different kinds of data, allow evaluation of soil quality based on alternative uses or definitions and estimate soil quality for unsampled locations. In the present study we used one such method, based on non-parametric geostatistics. We evaluated soil quality from the integration of six soil variables measured at 220 locations in an agricultural field in southeastern Washington State. We converted the continous data values for each soil variable at each location to a binary variable indicator transform based on thresholds. We then combined indicator transformed data for individual soil variables into a single integrative indicator of soil quality termed a multiple variable indicator transform (MVIT). We observed that soil chemical variables, pools of soil resources, populations of microorgansims, and soil enzymes covaried spatially across the landscape. These ensembles of soil variables were not randomly distributed, but rather were systematically patterned. Soil quality maps calculated by kriging showed that the joint probabilities of meeting specific MVIT selection were influenced by the critical threshold values used to transform each individual soil quality variable and the MVIT selection criteria. If MVIT criteria adequately reflect soil quality then the kriging can produce maps of the probabilty of a soil being of good or poor quality.     

约束非线性混合离散变量优化设计的一个方法

本文对机械设计中离散变量优化问题进行了讨论,提出了一个用于全离散变量或混合离散变量优化设计的方法——模式与随机搜索法。文中阐述了算法的原理,给出了迭代公式与框图。考核结果表明,本算法比通常的优化方法具有更好的可靠性与有效性,所得到的离散最优解更合理,并且计算时间与迭代次数明显减少。     

Farm and Operator Characteristics Affecting the Awareness and Adoption of Precision Agriculture Technologies in the US

Precision agriculture (PA) technologies have been commercially available since the early 1990s. However, not only has the pace of adoption in the US been relatively modest but a surprisingly large number of producers are not familiar with these technologies. Using farm level survey data, this study quantifies the role that awareness plays in the decision to adopt PA technology and allows us to explore the potential for public or private information programs to affect the diffusion of PA. PA adoption and awareness are modeled as jointly determined dichotomous variables and their determinants are estimated using a two-stage (i.e. instrumental variable) logistic specification. The first-stage logit model indicated that operator education and computer literacy, full-time farming, and farm size positively affected the probability of PA awareness while the effect of age was negative. Grain and oilseed farms (i.e. corn, soybean, and small grains) and specialty crop farms (i.e. fruits, vegetables, and nuts) as well as farms located in the Heartland and Northern Great Plains regions were most likely to be aware of PA technologies. The second-stage PA adoption logit model, which included an instrumental variable to account for the endogeneity of awareness, revealed that farm size, full-time farming, and computer literacy positively influenced the likelihood of PA adoption. Grain and oilseed farms were the most likely types of farms to adopt PA as were farms in the Heartland region. Awareness, as defined in this study, was not found to be limiting the adoption of PA, suggesting that farmers for whom the technology is profitable are already aware of the technology and that a sector-wide public or private initiative to disseminate PA information would not likely have a major impact on PA diffusion.     

数字地形模型在泥石流发生发育特征研究中的应用

 泥石流是发生在山区的一种易对人民的生产、生活条件和生存发展空间造成严重破坏的自然现象,其发生、发展受诸多因素影响,但其中地形地貌条件是影响泥石流发生发育的主要因素。随着计算机技术和3S技术的发展,在GIS平台上,利用地形图、遥感影像等建立数字地形模型（DTM）,模拟地面形状,提取地形信息,对泥石流现象进行研究,将是一种新的思路和方法。本文以MAPGIS地理信息系统为工具,以地形图为数据源,建立DTM,结合土地利用相关资料,分析了山东省下港乡泥石流发生发育原因及特征,为这一研究进行了有益的探索。     

山区高分辨率遥感影像地形辐射校正方法研究

高空间分辨率的卫星影像和航摄影像越来越广泛地应用于山区农、林业资源调查和山区生态环境监测等领域.但是由于山区地形起伏和侧视成像等原因,使得影像存在一定的阴影,从而严重影响这些高分辨率影像的应用.针对这一问题,本文借鉴在遥感影像条带噪声去除中非常有效的矩匹配方法基本原理,提出了基于矩匹配的山区高分辨率遥感影像辐射校正方法,同时对被验证TM影像地形辐射校正最佳的C校正方法作了相应的改进.并以北京市西部山区的1 m分辨率的航空影像为例,分别用矩匹配方法和C校正改进方法进行了地形辐射校正,并对处理结果进行了对比分析.结果表明,对于地形复杂的山区,图像较大的高分辨率影像,矩匹配方法优于C校正改进方法.     

Interpreting Soil Electrical Conductivity and Terrain Attribute Variability with Soil Surveys

Utilizing soil electrical conductivity (EC) measurements and terrain attributes for precision management will require secondary soil information for adequate interpretation. The objective of this study was to determine whether readily available second-order soil surveys were of adequate quality to aid with interpreting soil EC and terrain data. For three locations in Kentucky, USA, first-order soil surveys were created, second-order surveys reports were obtained, elevation was measured and used to calculate terrain attributes (slope, aspect, plan curvature, profile curvature), and bulk soil electrical conductivity was measured. Three analytical methods (an ordinary least squares analysis and two random field analyses), visual map assessment, and examination of least-squares means were used to assess the relationships between soil EC measurements, terrain attributes and first- and second-order soil surveys. The OLS and random field analyses were problematic. However, the ranking of the OLS F-statistics appeared to reflect the general relationship between landscape variables and first-order soil surveys. The landscape variables related particularly well with soil properties that had been impacted by past soil erosion. Unfortunately, however, second-order soil surveys in this study were not created at suitable scales to adequately interpret EC and terrain data regarding erosion history or other attributes. While these surveys may provide some useful information, field measurements, sampling, and observations will likely be required to develop high quality interpretations of soil EC and terrain attribute data.     

具有连续变量的差分方程振动的充分条件

利用Ｌｅｂｅｓｇｕｅ控制收敛定理研究具有连续变量的差分方程的振动性，得到了方程振动的一个充分条件。     

Comparative evaluation of the contributions of soil physicochemical properties to variations in the yields of four major staple food crops in eastern Nigeria

The contributions of soil variables to the variations in the yields of cassava (Manihot esculenta), yam (Dioscorea rotundata), maize (Zea mays) and pigeon pea (Cajanus cajan) were evaluated over 2 years in this study. The data were from three replicates of two randomized complete block design experiments sited in a newly cleared forest and on previously cultivated land both in Nsukka, eastern Nigeria. The 28 soil physicochemical properties and six crop yield parameters measured were partitioned between location and year before applying a stepwise regression procedure to analyze them.

The study showed that soil variables accounted for >70% of the variation in cassava root yields and harvest index. Both soil physical and chemical properties contributed but the former (particularly macroporosity, microporosity, total porosity and bulk density) contributed most. Selected soil variables also accounted for >70% of the variation in yam tuber yield and shape index of tubers especially in 1998. In both crop years chemical properties appeared to dominate over the physical ones. Soil variables accounted for between 51 and 99% of the variation in maize grain and stover yields. The only exception was the figure of 44% obtained at the forest location in 1998. Soil pH, total exchangeable acidity and microporosity were particularly important contributors to the variations in both maize yield parameters. The contributions of soil variables to pigeon pea yield parameters were low (<50%) except in 1999 at the forest location where seven soil variables accounted for over 85% variation in seed yield.

It was obvious generally from the study that soil variables were important determinants of yield variations in the four crops. It was also shown that physical properties should always be included in this kind of analysis. Also the number of soil variables which were of significance generally increased when the level of soil properties was low, as was the case with the cultivated site versus forest site, and 1999 versus 1998 analysis. Thus increasing the number of soil variables used and partitioning them into more homogeneous units helped to improve the results obtained using the procedure.