José Padarian

Using Google's cloud-based platform for digital soil mapping

A digital soil mapping exercise over a large extent and at a high resolution is a computationally expensive procedure. It may take days or weeks to obtain the final maps and to visually evaluate the prediction models when using a desktop workstation. To increase the speed of time-consuming procedures, the use of supercomputers is a common practice. GoogleTM has developed a product specifically designed for mapping purposes (Earth Engine), allowing users to take advantage of its computing power and the mobility of a cloud-based solution. In this work, we explore the feasibility of using this platform for digital soil mapping by presenting two soil mapping examples over the contiguous United States. We also discuss the advantages and limitations of this platform at its current development stage, and potential improvements towards a fully functional cloud-based soil mapping platform. HighlightsIt is possible to include the platform as part of a digital soil mapping workflow.Map generation is 40-100 times faster compared with traditional digital soil mapping.To be a complete solution, implementation of geostatistical methodologies is needed.We encourage researches to participate during the beta-testing period to improve it.

Using genetic programming to transform from Australian to USDA/FAO soil particle-size classification system

The difference between the International (adopted by Australia) and the USDA/FAO particle-size classification systems is the limit between silt and sand fractions (20 μm for the International and 50 µm for the USDA/FAO). In order to work with pedotransfer functions generated under the USDA/FAO system with Australian soil survey data, a conversion should be attempted. The aim of this work is to improve prior models using larger datasets and a genetic programming technique, in the form of a symbolic regression. The 2–50 µm fraction was predicted using a USDA dataset which included both particle-size classification systems. The presented model reduced the root mean square error (%) by 14.96 and 23.62% (IGBP-DIS dataset and Australian dataset, respectively), compared with the previous model.

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

Soil, through its various functions, plays a vital role in the Earths ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration and organic carbon content, root density and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale.

The location- and scale- specific correlation between temperature and soil carbon sequestration across the globe

Much research has been conducted to understand the spatial distribution of soil carbon stock and its temporal dynamics. However, an agreement has not been reached on whether increasing global temperature has a positive or negative feedback on soil carbon stocks. By analysing global maps of soil organic carbon (SOC) using a spherical wavelet analysis, it was found that the correlation between SOC and soil temperature at the regional scale was negative between 52° N and 40° S parallels and positive beyond this region. This was consistent with a few previous studies and it was assumed that the effect was most likely due to the temperature-dependent SOC formation (photosynthesis) and decomposition (microbial activities and substrate decomposability) processes. The results also suggested that the large SOC stocks distributed in the low-temperature areas might increase under global warming while the small SOC stocks found in the high-temperature areas might decrease accordingly. Although it remains unknown whether the potential increasing soil carbon stocks in the low-temperature areas can offset the loss of carbon stocks in the high-temperature areas, the location- and scale- specific correlations between SOC and temperature should be taken into account for modeling SOC dynamics and SOC sequestration management.

Preferential flow paths in two alluvial soils with long-term additions of pig slurry in the Mediterranean zone of Chile

Spatial variability of soil hydraulic properties helps to understand the complexity and dynamic patterns of solute distributions in soils. This study assessed the effects of slurry additions and soil physical properties on preferential flow and nitrate concentrations in two Mediterranean soils of central Chile under conventional agriculture management with long-term slurry additions. Two alluvial basins continuously cropped with maize (Zea mays) were selected, Pichidegua (Mollic Xerofluvent) and San Pedro (Fluventic Humixerept). Soils that had been amended with pig slurry were compared with unamended controls. Soil texture, bulk density, particle density, organic matter, porosity and hydraulic conductivity (Ks) were measured. Soil nitrate concentrations at different depths were determined every 2 weeks during the study, and a dye tracer test using Brilliant Blue FCF was conducted on each soil. Digital picture analysis was then performed to classify flow types in the soil profiles through the distribution of stained path width. Stained path width allowed classification of soil flow types. Preferential flow showed no evidence of being affected by pig slurry; the stained patterns were mainly explained by tillage and soil physical properties, such as texture and Ks. Matrix flow-types in the first horizon grading to macropore flow-types at depth characterised all profiles, except San Pedro amended soil, where homogeneous matrix flow dominated through the entire profile. Concentrations of nitrate showed temporal variation during the measurement season, mainly due to the mineralisation–immobilisation budget and leaching, both triggered by Mediterranean climate conditions. Physical properties helped to explain nitrate distribution in the studied soils.

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1km in 2014, followed by an update at a resolution of 250m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.

Grassland Carbon Sequestration Ability in China: A New Perspective from Terrestrial Aridity Zones ☆

ABSTRACT Current climate change (e.g., temperature and precipitation variations) profoundly influences terrestrial vegetation growth and production, ecosystem respiration, and nutrient circulation. Grasslands are sensitive to climate change, and the carbon sequestration ability is closely related to water availability. However, how the terrestrial water budget influences regional carbon sequestration by the grassland ecosystem is still unclear. In this study, we modified a terrestrial biogeochemical model to investigate net ecosystem productivity (NEP) of Chinese grasslands under different aridity index (AI) levels from 1982 to 2008. The results showed that Chinese grasslands acted as a carbon sink of 33.7 TgC. yr-1, with a clear decrease in the spatial distribution from the humid end (near-forest) to the arid end (near-desert). During these 27 years, gross primary productivity (GPP) and net primary productivity (NPP) significantly increased with regional warming over the entire range of the AI, but no significant tendency was found for NEP. Meanwhile, only NPP in the arid zone (AR) and the semiarid zone (SAR) were significantly correlated with mean annual precipitation (MAP), and no significant correlation was found between heterotrophic respiration (Rh and MAP; NPP and Rh were both positively correlated with mean annual temperature (MAT) in all AI zones except for NPP in AR; no significant correlation between NEP and MAP or MAT was found. These results revealed that the grasslands with different AI levels keep different response patterns to temperature and precipitation variations. On the basis of these results, we predicted that the gap of carbon sequestration ability between humid and arid grassland will expand. The total carbon sink in Chinese grasslands will continue to fluctuate, but there is a danger that it might shrink in the future because of a combination of climatic and human factors, although CO2 fertilization and N deposition might partly mitigate this reduction.

Quantitative assessments of water-use efficiency in Temperate Eurasian Steppe along an aridity gradient.

Water-use efficiency (WUE), defined as the ratio of net primary productivity (NPP) to evapotranspiration (ET), is an important indicator to represent the trade-off pattern between vegetation productivity and water consumption. Its dynamics under climate change are important to ecohydrology and ecosystem management, especially in the drylands. In this study, we modified and used a late version of Boreal Ecosystem Productivity Simulator (BEPS), to quantify the WUE in the typical dryland ecosystems, Temperate Eurasian Steppe (TES). The Aridity Index (AI) was used to specify the terrestrial water availability condition. The regional results showed that during the period of 1999–2008, the WUE has a clear decreasing trend in the spatial distribution from arid to humid areas. The highest annual average WUE was in dry and semi-humid sub-region (DSH) with 0.88 gC mm-1 and the lowest was in arid sub-region (AR) with 0.22 gC mm-1. A two-stage pattern of WUE was found in TES. That is, WUE would enhance with lower aridity stress, but decline under the humid environment. Over 65% of the region exhibited increasing WUE. This enhancement, however, could not indicate that the grasslands were getting better because the NPP even slightly decreased. It was mainly attributed to the reduction of ET over 70% of the region, which is closely related to the rainfall decrease. The results also suggested a similar negative spatial correlation between the WUE and the mean annual precipitation (MAP) at the driest and the most humid ends. This regional pattern reflected the different roles of water in regulating the terrestrial ecosystems under different aridity levels. This study could facilitate the understanding of the interactions between terrestrial carbon and water cycles, and thus contribute to a sustainable management of nature resources in the dryland ecosystems.

Pedotransfer Functions and Soil Inference Systems

The term pedotransfer function (PTF) was coined by Bouma (1989) as ‘translating data we have into what we need’. Pedotransfer functions are regression functions used to predict soil properties that would be otherwise infeasible to obtain. Typical reasons for this infeasibility include, but are not limited to, the cost, time, difficulty or hazard involved in procuring direct measurements. Each PTF is developed around some insight into a soil’s physical, chemical or biological properties that relates a set of input parameters (predictor properties) to an output parameter (a predicted property).