The Soil and Landscape Grid of Australia (SLGA) is the first continental version of the GlobalSoilMap concept and the first nationally consistent, fine spatial resolution set of continuous soil ...attributes with Australia-wide coverage. The SLGA relies on digital soil mapping methods and integrates historical soil data, new measurement with spectroscopic sensors, novel spatial modelling and a web-service delivery architecture. The SLGA provides soil, regolith and landscape estimates at the centre point of 3 arcsecond grid cells (~90×90m) across Australia. At each point, there are estimates of 11 soil attributes and confidence intervals for each estimate to a depth of 2m or less, depth of regolith and a set of terrain descriptors. The information system also includes a library of mid-infrared spectra, an inference engine that allows estimation of additional soil parameters and an information model that enables users to access the system via web services. The explicit mapping of depth, bulk density and coarse fragments allows estimation of material stores and fluxes on a volumetric basis. The SLGA therefore has immediate applications in carbon, nitrogen and water process modelling. The map of regolith depth will find immediate application to studies of vadose zone processes, including solute transport, groundwater and nutrient fluxes beyond the root zone. Landscape attributes at 1 and 3 arcseconds are useful for a wide spectrum of ecological, hydrological and broader environmental applications. The SLGA can be accessed at no cost from www.csiro.au/soil-and-landscape-grid. It is managed and delivered as part of the Australian Soil Resource Information System (ASRIS).
•Soil spectroscopy adoption is limited by a lack of spectral calibration libraries.•We propose a global soil spectral calibration library & estimation service.•Spectral and reference analyses are ...performed in one central gold standard lab.•Users will be able to upload spectra and obtain soil property estimates.•Calibrations are continuously improved by adding sample outliers.
There is growing global interest in the potential for soil reflectance spectroscopy to fill an urgent need for more data on soil properties for improved decision-making on soil security at local to global scales. This is driven by the capability of soil spectroscopy to estimate a wide range of soil properties from a rapid, inexpensive, and highly reproducible measurement using only light. However, several obstacles are preventing wider adoption of soil spectroscopy. The biggest obstacles are the large variation in the soil analytical methods and operating procedures used in different laboratories, poor reproducibility of analyses within and amongst laboratories and a lack of soil physical archives. In addition, adoption is hindered by the expense and complexity of building soil spectral libraries and calibration models. The Global Soil Spectral Calibration Library and Estimation Service is proposed to overcome these obstacles by providing a freely available estimation service based on an open, high quality and diverse spectral calibration library and the extensive soil archives of the Kellogg Soil Survey Laboratory (KSSL) of the Natural Resources Conservation Service of the United States Department of Agriculture (USDA). The initiative is supported by the Global Soil Laboratory Network (GLOSOLAN) of the Global Soil Partnership and the Soil Spectroscopy for Global Good network, which provide additional support through dissemination of standards, capacity development and research. This service is a global public good which stands to benefit soil assessments globally, but especially developing countries where soil data and resources for conventional soil analyses are most limited.
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This study assessed the perception and use of digital applications for soil fertility management and conservation strategies among small-scale crop farmers in southwest Nigeria. A total of 376 ...farmers were randomly selected across the six southwest states. The data collected were analyzed using descriptive statistics. The majority of the farmers relied on perception and other non-scientific approaches such as the appearance of weeds and performance of crops in the previous season to assess soil fertility. Only 1.1% and 0.3% of the farmers assessed soil fertility through soil tests and digital applications, respectively. Most farmers adopted bush fallowing and the use of inorganic fertilizers to improve soil fertility. Although 4.8% of the farmers indicated that they had digital applications on their mobile phones, only 2.9% claimed to have used these. More than half (56.4%) of the farmers stated that a lack of awareness of the existence of digital applications and internet-enabled telephones were the reasons they have not been able to use digital applications. The majority of the farmers (97.3%) indicated their willingness to embrace the use of new farm decision digital applications which could provide more information, especially on soil fertility, if introduced. More extensive services focusing on older, less literate farmers and farmers who hitherto did not belong to any farmers’ association are advocated for in order to encourage the use of digital applications and soil fertility management and conservation practices.
Soil data form the basis of soil information systems across the globe. Soil information needs, and the questions posed by users, are likely to evolve in response to advances in technology in this era ...of Big Data. This poses a challenge to the pedological community which is already experiencing a decline in soil knowledge and expertise. With a decrease in soil data collection by governments, it is timely to reconsider how and what soil information should be provided to future users. A public–private partnership is advocated to deliver timely and accessible soil information to users. Two public–private provisioning programs are presented, and advantages and considerations for sharing soil data and information amongst industry, government, research organizations, service providers and land managers for these are discussed. Interoperable, open‐source and agreed soil community standards are used to present soil data and information through spatial web portals with tools for interpretation of soil data for public and private beneficiaries.
The 4M crop model was used to investigate the prospective effects of climate change on the agro-ecological characteristics of Hungary. The model was coupled with a detailed meteorological database ...and spatial soil information systems covering the whole territory of Hungary. Plant-specific model parameters were determined by inverse modeling. Future meteorological data were produced from the present meteorological data by combining a climate change scenario and a stochastic weather generator. Using the available and the generated data, the present and the prospective agro-ecological characteristics of Hungary were determined. According to the simulation results, average yields will decrease considerably (~30%) due to climate change. The rate of nitrate leaching will prospectively decrease as well. The fluctuations of both the yields and the annual nitrate leaching rates will most likely increase approaching the end of the twenty-first century. On the basis of the simulation results, the role of autumn crops is likely to become more significant in Hungary. The achieved results can be generalized for more extended regions based on the concept of spatial (geographical) analogy.
The article presents an overview and brief characteristics of the selected soil information systems in the Czech Republic. It suggests synchronisation of their development, particularly some ...convergence of the Land Evaluation Information System and Soil and Terrain Digital Database. In the pilot area of Litoměřice district, it demonstrates the application of the SOTER methodology for the construction of middle- and detail-scale soil maps, using the data from the General survey of agricultural soils. It not only shows the variety of the district soil conditions, but it also supplements them with the data gathered in the 2006 soil survey.
The article presents an overview and brief characteristics of the selected soil information systems in the Czech Republic. It suggests synchronisation of their development, particularly some ...convergence of the Land Evaluation Information System and Soil and Terrain Digital Database. In the pilot area of Litomerice district, it demonstrates the application of the SOTER methodology for the construction of middle- and detail-scale soil maps, using the data from the General survey of agricultural soils. It not only shows the variety of the district soil conditions, but it also supplements them with the data gathered in the 2006 soil survey.
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•Ball-milled and < 2 mm soils showed similar accuracy in MIR and NIR spectral models.•Grinding soils did not improve the accuracy in both MIR and NIR models.•MIR/NIR spectroscopy can ...distinguish first horizon from subsurface horizon soils.•Chemical information is the same in ball-milled and < 2 mm spectra models.•MIR/NIR spectroscopy can be used to determine a range of soil attributes.
This study evaluated the influence on predicted physical and chemical parameters of soil particle sizes commonly used in the infrared spectra acquisition, < 0.100 mm (ball-milled) and < 2 mm for MIR and NIR ranges, respectively. The influences were evaluated through the accuracy (RMSEP and RPIQ) results and the chemical information extracted by multivariate classification and regression models. For this a national population of soils containing 888 samples from 225 modal soil profiles, each with the reference values of sand, silt, clay, pH(CaCl2), pH(Water), total carbon, organic carbon (OC), cation exchange capacity, nitrogen, aluminium and bulk density, was used. Spectra were collected in MIR and NIR ranges using samples with both particle sizes. For each soil attribute, 29 random calibration and validation sets were generated and SVM, PLS and Cubist regression models were built. This same strategy was used to classify the soil samples according to their respective horizons (1 or 2-7) using SVM, PLS-DA and random forest algorithms. Results obtained by the randomised calibration and validation set did not present positive or negative bias on the RMSEP and RPIQ values based on soil particle sizes. In general, random variations of the RMSEP and RPIQ values were observed for all soil attributes. In addition, ball-milled and < 2 mm spectral models did not present large differences in both accuracy parameters simultaneously. The median Matthews correlation coefficient values calculated by the classification models showed minor variations of 2.61% and 0.65% for samples from both particle sizes in MIR and NIR ranges, respectively. The ‘Variable Importance in Projection’ or VIP scores, calculated by PLS and PLS-DA models, did not show any large variation in the chemical information extracted from MIR and NIR spectra for models built using samples from both particle sizes. The results from this study show that scanning ball-milled or < 2 mm sieved soil samples will result in spectra models in MIR and NIR ranges with the same accuracy and same chemical information. This suggests there is a big potential to eliminate the ball-milling sample step in soil laboratories that use MIR and NIR vibrational spectroscopy techniques to predict soil attributes, thereby reducing the time and costs associated with soil analysis.
Core Ideas
Ensemble machine learning methods were used to obtain gridded soil property and class maps.
Final predictions were generated for six soil properties and two soil classes at 100‐m ...resolution.
Soil data are easier to integrate with spatially explicit models compared with multicomponent map units.
Soil property maps are available at seven standard depths.
With growing concern for the depletion of soil resources, conventional soil maps need to be updated and provided at finer and finer resolutions to be able to support spatially explicit human–landscape models. Three US soil point datasets—the National Cooperative Soil Survey Characterization Database, the National Soil Information System, and the Rapid Carbon Assessment dataset—were combined with a stack of over 200 environmental datasets and gSSURGO polygon maps to generate complete coverage gridded predictions at 100‐m spatial resolution of six soil properties (percentage of organic C, total N, bulk density, pH, and percentage of sand and clay) and two US soil taxonomic classes (291 great groups GGs and 78 modified particle size classes mPSCs) for the conterminous United States. Models were built using parallelized random forest and gradient boosting algorithms as implemented in the ranger and xgboost packages for R. Soil property predictions were generated at seven standard soil depths (0, 5, 15, 30, 60, 100, and 200 cm). Prediction probability maps for US soil taxonomic classifications were also generated. Cross validation results indicated an out‐of‐bag classification accuracy of 60% for GGs and 66% for mPSCs; for soil properties, RMSE for leave‐location‐out cross‐validation was 0.74 (R2 = 0.68), 17.8 wt% (R2 = 0.57), 12 wt% (R2 = 0.46), 3.63 wt% (R2 = 0.41), 0.2 g cm−3 (R2 = 0.42), and 0.27 wt% (R2 = 0.39) for pH, percent sand and clay, weight percentage of organic C, bulk density, and weight percentage of total N, respectively. Nine independent validation datasets were used to assess prediction accuracies for soil class models, and results ranged between 24 and 58% and between 24 and 93% for GG and mPSC prediction accuracies, respectively. Although mapping accuracies were variable and likely lower than gSSURGO in some areas, this modeling approach can enable easier integration of soil information with spatially explicit models compared with multicomponent map units.
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