Soil properties drive microbial community structure in a large scale transect in South Eastern Australia

Soil microbial communities directly affect soil functionality through their roles in the cycling of soil nutrients and carbon storage. Microbial communities vary substantially in space and time, between soil types and under different land management. The mechanisms that control the spatial distributions of soil microbes are largely unknown as we have not been able to adequately upscale a detailed analysis of the microbiome in a few grams of soil to that of a catchment, region or continent. Here we reveal that soil microbes along a 1000 km transect have unique spatial structures that are governed mainly by soil properties. The soil microbial community assessed using Phospholipid Fatty Acids showed a strong gradient along the latitude gradient across New South Wales, Australia. We found that soil properties contributed the most to the microbial distribution, while other environmental factors (e.g., temperature, elevation) showed lesser impact. Agricultural activities reduced the variation of the microbial communities, however, its influence was local and much less than the overall influence of soil properties. The ability to predict the soil and environmental factors that control microbial distribution will allow us to predict how future soil and environmental change will affect the spatial distribution of microbes

Digital Soil Mapping

Digital soil mapping is the creation of a spatial soil information system using field and laboratory observation methods coupled with quantitative spatial prediction techniques. Digital soil mapping follows the advancement in soil and environmental observations using proximal and remote sensing. It also utilizes contemporary mathematical and statistical techniques that allow better prediction of soil properties in areas with little or no information as well as indicating the uncertainty of such predictions.JRC.H.7-Land management and natural hazard

Kerang: temukan dunia hewan berkulit keras evolusinya, ragamnya, dan habitatnya

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ANALISIS DAN PERANCANGAN SISTEM INFORMASI ADMINISTRASI KEUANGAN PADA JENJANG TK MITRA PENABUR

ANALISIS DAN PERANCANGAN SISTEM INFORMASI ADMINISTRASI KEUANGAN PADA JENJANG TK MITRA PENABUR - Analisis, Perancangan, Sistem Informasi, Administrasi keuangan

Monitoring changes in global soil organic carbon stocks from space

Soils are under threat globally, with declining soil productivity and soil health in many places. As a key indicator of soil functioning, soil organic carbon (SOC) is crucial for ensuring food, soil, water and energy security, together with biodiversity protection. While there is a global effort to map SOC stock and status, SOC is a dynamic soil property and can change rapidly as a function of land management and land use. Here, we introduce a semi-mechanistic model to monitor SOC stocks at a global scale, underpinned by one of the largest worldwide soil database to date. Our model generates a SOC stock baseline for the year 2001, which is then propagated through time by keeping track of annual landcover changes obtained from remote sensing products with loss and gain dynamics dependent on temperature and precipitation, which finally define the magnitude, rate and direction of the SOC changes. We estimated a global SOC stock in the top 30~cm of around 793 Pg with annual losses due to landcover change of 1.9 Pg SOC/yr from 2001 to 2020, 20% larger than the annual production-based emissions of the United States in 2018. The biggest losses were found in the tropic and sub-tropical regions, accounting for almost 50% of the total global loss. This is a considerable contribution to greenhouse gas emissions but it also has a direct impact on agricultural production with more than 16 million hectares per year falling below critical SOC limits. The proposed modelling framework is flexible, allowing it to be updated as more remote sensing and soil data becomes available, offering a first-of-its-kind global spatio-temporal SOC stock assessment and monitoring system

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia

Soil colour is often used as a general purpose indicator of internal soil drainage. In this study we developed a necessarily simple model of soil drainage which combines the tacit knowledge of the soil surveyor with observed matrix soil colour descriptions. From built up knowledge of the soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining → imperfectly draining → poorly draining soils generally follows the colour sequence of red → brown → yellow → grey → black soil matrix colours. For each soil profile, soil drainage is estimated somewhere on a continuous index of between 5 (very well drained) and 1 (very poorly drained) based on the proximity or similarity to reference soil colours of the soil drainage colour sequence. The estimation of drainage index at each profile incorporates the whole-profile descriptions of soil colour where necessary, and is weighted such that observation of soil colour at depth and/or dominantly observed horizons are given more preference than observations near the soil surface. The soil drainage index, by definition disregards surficial soil horizons and consolidated and semi-consolidated parent materials. With the view to understanding the spatial distribution of soil drainage we digitally mapped the index across our study area. Spatial inference of the drainage index was made using Cubist regression tree model combined with residual kriging. Environmental covariates for deterministic inference were principally terrain variables derived from a digital elevation model. Pearson’s correlation coefficients indicated the variables most strongly correlated with soil drainage were topographic wetness index (−0.34), mid-slope position (−0.29), multi-resolution valley bottom flatness index (−0.29) and vertical distance to channel network (VDCN) (0.26). From the regression tree modelling, two linear models of soil drainage were derived. The partitioning of models was based upon threshold criteria of VDCN. Validation of the regression kriging model using a withheld dataset resulted in a root mean square error of 0.90 soil drainage index units. Concordance between observations and predictions was 0.49. Given the scale of mapping, and inherent subjectivity of soil colour description, these results are acceptable. Furthermore, the spatial distribution of soil drainage predicted in our study area is attuned with our mental model developed over successive field surveys. Our approach, while exclusively calibrated for the conditions observed in our study area, can be generalised once the unique soil colour and soil drainage relationship is expertly defined for an area or region in question. With such rules established, the quantitative components of the method would remain unchanged