Measuring and modelling the actual energy involved in aggregate breakdown

This work investigates the distribution of soil aggregates for Vertisols and Ferrasols as a function of the actual energy involved in dispersion, known as the dispersive energy. For Vertisols showing an aggregate hierarchy, the breakdown of aggregates 2–50 μm is modelled using the aggregate liberation and dispersion characteristic curve indicating a stepwise breakdown of soil aggregates. Meanwhile, for Ferrasols, the breakdown of 2–50 μm aggregates increases monotonically with increasing dispersive energy, which is indicative of the direct release of silt and clay from the disruption of aggregates. For soils displaying an aggregate hierarchy, the relative rate constant of aggregate liberation is much larger than the relative rate constant of the aggregate dispersion. Furthermore, the redistribution of aggregates for a number of size ranges within the 2–50 μm fraction illustrates a number of different pathways in aggregate breakdown, and the assumption that aggregates follow an exponential decay may not detect the presence of a weakly expressed aggregate hierarchy. The exponential decrease in ultrasonic power over time for the Ferrasols, as opposed to the prominent drop or steps in the curves for the Vertisols, confirms the lack of a prominent aggregate hierarchy.     

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

Apparent electrical conductivity of soil (ECa) is a property frequently used as a diagnostic tool in precision agriculture, and is measured using vehicle‐mounted proximal sensors. Crop‐yield data, which is measured by harvester‐mounted sensors, is usually collected at a higher spatial density compared to ECa. ECa and crop‐yield maps frequently exhibit similar spatial patterns because ECa is primarily controlled by the soil clay content and the interrelated soil moisture content, which are often significant contributors to crop‐yield potential. By quantifying the spatial relationship between soil ECa and crop yield, it is possible to estimate the value of ECa at the spatial resolution of the crop‐yield data. This is achieved through the use of a local regression kriging approach which uses the higher‐resolution crop‐yield data as a covariate to predict ECa at a higher spatial resolution than would be prudent with the original ECa data alone. The accuracy of the local regression kriging (LRK) method is evaluated against local kriging (LK) and local regression (LR) to predict ECa. The results indicate that the performance of LRK is dependent on the performance of the inherent local regression. Over a range of ECa transect survey densities, LRK provides greater accuracy than LK and LR, except at very low density. Maps of the regression coefficients demonstrated that the relationship between ECa and crop yield varies from year to year, and across a field. The application of LRK to commercial scale ECa survey data, using crop yield as a covariate, should improve the accuracy of the resultant maps. This has implications for employing the maps in crop‐management decisions and building more robust calibrations between field‐gathered soil ECa and primary soil properties such as clay content.     

Enrichment of gene-coding sequences in maize by genome filtration

Approximately 80% of the maize genome comprises highly repetitive sequences interspersed with single-copy, gene-rich sequences, and standard genome sequencing strategies are not readily adaptable to this type of genome. Methodologies that enrich for genic sequences might more rapidly generate useful results from complex genomes. Equivalent numbers of clones from maize selected by techniques called methylation filtering and High C0t selection were sequenced to generate approximately 200,000 reads (approximately 132 megabases), which were assembled into contigs. Combination of the two techniques resulted in a sixfold reduction in the effective genome size and a fourfold increase in the gene identification rate in comparison to a nonenriched library.     

Fundamental study on the flow speed distribution around small traps

ABSTRACT:   This study measured the flow speed distribution using actual small traps to investigate the effects of their materials and shapes by analyzing the characteristics of the water flow patterns around them. Five types of trap (netted semi-cylinder shaped, wire semi-cylinder shaped, heart shaped, box shaped, and a cylinder shaped traps) constructed from different materials were used in the experiment. The flow speed distribution around each trap was measured in a flume tank. Flow speeds in the flume tank were 0.1, 0.2, 0.3, and 0.4 m/s. The webbing of the traps was different in shape and had various diameters of twine. The diameters of twine ranged from 0.55 to 1.00 mm. The relationship between the solidity ratio of the twine and flow distribution of the traps was also analyzed. For the netted semi-cylinder shaped and cylinder shaped traps, the area of reduced flow speed extended along the downstream area and its width slightly increased with distance from the trap's entrance. The heart shaped trap is shown to have the largest solidity ratio and this resulted in a high reduction of the flow speed downstream from the trap.     

Comparing temperature correction models for soil electrical conductivity measurement

There are various factors that affect soil electrical conductivity (EC) measurements, including soil texture, soil water content, cation exchange capacity (CEC) and others. Temperature is an important environmental variable, and different models can be used to correct for its effect on EC measurements and standardize the measurements to 25°C. It is relevant to analyze these models and to determine whether they are consistent with each other. Some models were wrongly cited. We found that the exponential model of Sheets and Hendrickx as corrected by Corwin and Lesch in 2005 performs the best. The ratio model also performs well between 3°C and 47°C.     

多土壤信息测量传感器的硬件系统集成设计（英）

土壤在作物的生长过程中起到了重要的作用,所以对于基于决策的作物生产管理,土壤信息是必需的。传统的土壤取样获取土壤信息技术耗时且成本高,尤其是对于大规模农田土壤信息测量。目前一些近地面的可连续测量的土壤信息传感器技术能够提供高精度的数字土壤信息地图,然而这些商业化的技术成熟的传感器通常需要单独使用。该文提出了将γ射线光谱仪GR320、利用电磁感应原理的EM38和EM31以及Veris 3100和Veris pH这些可在农田近地面连续测量的土壤特性测试传感仪器集成在一起同时使用的方案,介绍了此集成系统的硬件设备和相关特性参数以及今后需要继续研究解决的问题。利用该系统可一次获得不同的土壤特性参数数据,如土壤矿物质含量,不同深度的土壤电导率值和土壤pH值等,可避免多次测量车辆行走对土壤的压实。多传感器数据之间的互相补充可以进一步提高且更有利于精确农业中基于土壤信息的决策规划。该系统适用于大面积农田土壤特性测量。