Alfred E. Hartemink

Abstract Being critical to achieving Sustainable Development Goals (SDGs) of the United Nations, strengthening understanding of the properties and processes of soil at national and regional scales is imperative. The necessity to realize SDGs by 2030 also inspires a greater sense of responsibility and care for soils. Sustainable management of soil health is important to achieving several SDGs. Pertinent SDGs intricately connected with soil health include SDG 1 (End Poverty), 2 (Zero Hunger), 3 (Good Health and Wellbeing), 5 (Gender Equality), 6 (Clean Water and Sanitation), 7 (Affordable and Clean Energy), 9 (Industry Innovation and Infrastructure), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), 13 (Climate Action), and 15 (Life on Land). Some of these SDGs rely considerably on plant production and others depend on soil processes. Pertinent among soil processes are water movement, heat transfer, sorption and physical filtration, ion exchange, and biochemical and biophysical transformations. In terms of specific accomplishments, 130 countries have aligned with the Zero Hunger Challenge, the globally available fresh water has decreased to 71% of needs, Technosols (soils whose formation is influenced by anthropogenic based materials) are used in urban ecosystems, food wastes are composted, specific targets of Land Degradation Neutrality have been signed by several countries, and soil C sequestration targets are widely implemented through initiatives such as the 4 Per Thousand (4P1000) initiative, Platform on Climate Action in Americas (PLACA), Adapting African Agriculture (AAA), Living Soils of the Americas (LiSAM), etc. In addition, policy and regulatory frameworks being widely promoted by several U.N. agencies (e.g., U.N. SDGs,limiting global warming to 1.5°C or 2 °C) can be supported by innovations in soil science including forensic soil science, remote sensing and other innovations. Soil health is becoming a central element of the research and innovation program of the EU, aiming to reach a 75% of healthy soils by 2030. In addition, the importance of soil health to human health and environmental issues is being widely promoted through educational books on soil science and secondary schools, as well as the revision of curricula. With continuous progress in movement into the digital world, transfer and communication of knowledge of the soil sciences can improve for the end users, policymakers, and the general public but additional efforts are needed. Soil science knowledge and research forms a significant contribution to specific aspects of food and nutritional security, human wellbeing, nature conservancy, and global peace and harmony. Achieving critical SDGs by 2030 can be facilitated by soil restoration and sustainable management.

The COVID-19 pandemic has disrupted the global food supply chain and exacerbated the problem of food and nutritional insecurity. Here we outline soil strategies to strengthen local food production systems, enhance their resilience, and create a circular economy focused on soil restoration through carbon sequestration, on-farm cycling of nutrients, minimizing environmental pollution, and contamination of food. Smart web-based geospatial decision support systems (S-DSSs) for land use planning and management is a useful tool for sustainable development. Forensic soil science can also contribute to cold case investigations, both in providing intelligence and evidence in court and in ascertaining the provenance and safety of food products. Soil can be used for the safe disposal of medical waste, but increased understanding is needed on the transfer of virus through pedosphere processes. Strengthening communication between soil scientists and policy makers and improving distance learning ...

Soil carbon storage is affected by particle-size fractions and Fe oxides. We assessed soil carbon concentrations in different particle-size fractions, determined the soil chemical composition of the soil, and weathering and mineralogy of sandy soils of the Wisconsin Central Sands, USA. Three land uses were studied (agriculture, forest, and prairie). The soils contained a minimum of 830 g sand kg−1 up to 190 cm soil depth. Approximately 46% of the sand was in the 250–500 μm fraction, and 5% was <125 μm. Soil carbon ranged from 5 to 13 g kg−1 in the topsoil, and decreased with depth. The <45 μm fraction tended to have high concentrations of carbon, ranging from 19 to 43 g kg−1 in the topsoil. Silicon content was over 191 g Si kg−1, and was lowest in the Bt horizons (191–224 g Si kg−1). Up to 29 g Fe kg−1 and 39 g Al kg−1 were present in the soil, and were highest in the Bt horizons. These soils were mostly quartz, and diopside was found throughout the soil profiles. Weathering i...

The role of soil organic carbon (SOC) in maintaining soil conditions and its resulting services is well established. Currently, over 1,000 articles per year are being published in peer-reviewed journals, and increasing at about 10% per year. It was not until the 1980s when the relation between soils and climate change was noted, and it was realized that soils play a key role as a sink and source of greenhouse gases (GHGs) ( Bouwman 1990, Scharpenseel, Ayoub and Schomaker 1990, Jenny 1980). A large number of research projects have been initiated globally in which soil C is a key component, and there have been some excellent reviews ( Lal 2004, Stockmann et al. 2013, Melillo et al. 2011). Yet, there is a lack of focus in soil C research in relation to current environmental challenges. Here we recommend research priorities to advance the knowledge base and use of soil C in relation to global human and environmental challenges: food and fiber production, water scarcity and purification,...

Soil variation was investigated in a Mollisol soil profile wall in south central Wisconsin, USA. The soil was classified as a fine-loamy, mixed, superactive, mesic Pachic Argiudolls. Data were collected from a 1 × 1 m soil profile wall that was divided into a 10 × 10 cm raster. The following measurements were made: volumetric moisture content, soil pH, soil organic carbon (SOC) concentration, and elemental analysis of Al, Ca, Fe, Mn, P, Si, Ti, and Zr by portable X-ray fluorescence (pXRF). Spatial variation of soil properties was analyzed and mapped. All the soil properties demonstrated horizontal variation within the soil profile. The extent of horizontal variation changed with depth. The magnitude and direction of these changes showed no general pattern, differing between the soil properties. The SOC concentration showed constant horizontal variation at all depths except 70–80 cm. The soil pH demonstrated the lowest horizontal variation in the top 30 cm of the profile. The horizontal variation of Fe concentration tended to increase with depth. Soil property depth functions showed considerable variation between vertical transects. Only the SOC concentration and the soil pH demonstrated fairly consistent responses to changes in depth. The soil showed spatial variation within soil horizons. The soil pH and the Fe concentration showed low within-horizon variation in all soil horizons. SOC concentration showed moderate within-horizon variation in the Ap1 horizon and high within-horizon variation in the Bt horizon. Overall, the Bt horizon contained the greatest spatial variation. All soil horizons contained high within-horizon variation of at least one soil property. These results have some implications for sampling pedons.

Soil organic carbon (SOC) concentration differs by depth, soils, and distinct land uses. Different methods have been used to calculate SOC stocks, and here, we used data from 10 pedons from Southern Brazil to compare four methods: horizon values with discrete data, exponential function, equal-area exponential function, and equal-area quadratic spline function. SOC stocks were calculated up to 30 cm and 100 cm depth from (i) the original data, (ii) the standardized data based on equal mass, (iii) the standardized data based on equal mass minus coarse fragments (gravels). Results were compared calculating SOC stocks up to 30 and 100 cm depth. Discrete values by horizon produced mean SOC stocks for 30 and 100 cm depth of 6.9 and 14.6 kg/m2 for original values, 6.5 and 14.1 kg/m2 for standardized values by mass, and 6.3 and 13.5 kg/m2 for standardized values by mass minus gravels. Negative exponential functions produced mean values of 6.1 and 14.1 kg/m2 for original values, 5.6 and 13.3 kg/m2 for standardized values by equal mass, and 5.4 and 12.9 kg/m2 for standardized values by equal mass minus gravels. Equal-area exponential function had mean values of 7.1 and 14.5 kg/m2 for original values, 6.6 and 13.9 kg/m2 for standardized values by equal mass, and 6.4 and 13.5 kg/m2 for standardized values by equal mass minus gravels. Equal-area spline produced SOC averages of 6.8 and 14.7 kg/m2 for original values, 6.3 and 14.2 kg/m2 for standardized values by equal mass, and 6.1 and 13.7 kg/m2 for standardized values by equal mass minus gravels. From the comparison, we found that negative exponential functions produced lower SOC stocks than horizons in the upper layers and higher stocks than horizons in the lower layers; equal-area exponential produced SOC stocks that are statistically similar to horizon values; equal-area spline function produced values up to 30 cm depth statistically similar to horizon values and statistically different up to 100 cm depth. We can conclude that different methods for calculating SOC stocks by depth produce significantly different results and values derived from equal-area exponential and equal-area splines are more similar to those of the horizons.

There is a growing need for spatially continuous and quantitative soil information for environmental modeling and management, especially at the national scale. This study was aimed at predicting soil particle‐size fractions (PSF) for Nigeria using random forest model (RFM). Equal‐area quadratic splines were fitted to Nigerian legacy soil profile data to estimate PSFs at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) using the GlobalSoilMap project specification. We applied an additive log‐ratio (ALR) transformation of the PSFs. There was a better prediction performance (based on 33% model validation) in the upper depth intervals than the lower depth intervals (e.g., R2 of 0.53; RMSE of 13.59 g kg−1 for clay at 0–5 cm and R2 of 0.16; RMSE of 15.60 g kg−1 at 100–200 cm). Overall, the PSFs show marked variations across the entire Nigeria region with a higher sand content compared with silt and clay contents and increasing clay content with soil depth. The va...

Agriculture is the cornerstone of Rwanda's economy. The authors review how the sector has changed and specifically what soil management practices are now being implemented to enhance coffee production. Coffee covers around 2.3% of total cultivated arable land, and is grown mainly by smallholder farmers on plots of less than one hectare. Rwanda produces high-quality speciality or fully washed coffee, which is intercropped with annual crops due to land scarcity to enable farmers to achieve a better combination of food and cash crops. Most of the agricultural soils have a pH of < 5.2 and are highly deficient in phosphorus. Reduced land fragmentation, increased organic and inorganic fertilizer applications and mulching are all needed to boost yields. These practices will also help to improve the soils' chemical and physical properties and control erosion on the steep cultivated slopes.

In this book, research on soil C is presented from different parts of the world, and from the fundamental aspects all the way to its management at a range of temporal and spatial scales. Here we summarize a set of priorities and these are loosely grouped in: Research priorities in soil C in space and time; Research priorities in soil C properties and processes, and research priorities in Soil C use and management. A list is presented that focuses on monitoring, assessment and upscaling as well as the biochemistry and role of soil structure in the sequestration of soil C. The effect of fire, black carbon, deep carbon and hydrophobicity as well as depth distribution are important research priorities. The relation between soil C and agronomic productivity are discussed in the context of sustainable intensification for advancing global food security. Lastly, some considerations are discussed how linkages with other disciplines might enhance the impact of the soil science community in soil C research.

... Investigate how soil fertility decline differs ... Most studies focusing on the subject of soil fertility decline contain few data ... by critically analysing historical and published information in a quantitative manner supple-mented with detailed case studies, so as to improve our understand ...

Plantation agriculture is more than 400 years old and contributes to the regional and national economies in many tropical countries. This paper reviews some of the main environmental issues related to plantation agriculture with perennial crops, including soil erosion, soil fertility decline, pollution, carbon sequestration and biodiversity. Soil erosion and soil fertility decline are of concern in some areas, but in most plantations these are being checked by cover crops and inorganic fertilizer applications. Few studies have been conducted on the issue of carbon sequestration under perennial plantation cropping. Reductions in deforestation yield much greater benefits for a reduction in CO2emissions than expanding plantation agriculture. The biggest threat to biodiversity is the loss of habitat through expansion of the plantation area. Despite the environmental problems and concerns, this review has shown that crop yields of most perennial crops have increased over time due to impr...

No information is available on the decomposition and nutrient release pattern of Piper aduncum and Imperata cylindrica despite their importance in shifting cultivation systems of Papua New Guinea and other tropical regions. We conducted a litter bag study (24 weeks) on a Typic Eutropepts in the humid lowlands to assess the rate of decomposition of Piper aduncum, Imperata cylindrica and

A B S T R A C T We investigated four sampling designs for soil organic carbon (SOC) stock assessment of soil profiles: (i) sampling by horizons, (ii) vertical transect sampling, (iii) depth-based stratified random sampling, (iv) fuzzy c-means sampling in which we explored the use of vis-NIR spectroscopy, image analysis and color models. An Alfisol and Mollisol profile wall (1 × 1 m) was divided into a 10 × 10 cm raster and 100 samples (about 200 g each) were collected at the centers of grid cells for SOC analysis. Bulk density samples were collected from each 10-cm depth interval along a single vertical transect and the SOC stock was calculated using 100 points in the profile wall. Horizon-based sampling for the Mollisol (5 horizons) ranged from 231 to 262 Mg C ha − 1 , whereas it ranged from 69 to 99 Mg C ha − 1 in the Alfisol (3 horizons). The SOC stocks obtained by 1 to 7 vertical transects ranged from 68 to 81 Mg C ha − 1 in the Alfisol, and 239 to 246 Mg C ha − 1 in the Mollisol. Depth-based stratified random sampling resulted in the SOC stocks ranging from 77 to 82 Mg C ha − 1 in the Alfisol and 234 to 257 Mg C ha − 1 in the Mollisol, and the standard errors decreased with increasing sample size from 10 to 70. Fuzzy c-means clustering created clusters similar to the field delineated horizons. A sample size of 7 in both profiles was sufficient to estimate the mean profile SOC stock by fuzzy c-means sampling. The CIE L*a*b* color model resulted in more accurate estimation in the Alfisol, but the vis-NIR spectra resulted in more accurate estimation in the Mollisol. Soil depth improved the performance of vis-NIR spectra. It is concluded that in these soils, at least two or three vertical transects are required to capture the horizontal variation for estimating profile SOC stock. Depth-wise stratified random sampling reduces the number of samples and is suitable when horizontal variation is high. Fuzzy c-means sampling is useful to determine the minimum sample size for profile SOC stock assessment but requires ancillary data and processing before sampling the soil profile.

—Here we report on the variation of a soilscape in south central Wisconsin, USA. The variation in soil properties and soil features results in four soil order (Entisols, Inceptisols, Alfisols and Mollisols). Observations were made along a 200 m transect in a field that was cultivated since 1870. Slopes ranged from 7.5% on the back slope to 0% in the lower part. The soilscape had a total relief difference of 7.0 m. The soils were studied by 41 soil pits (60 cm), 6 soil pits (125 cm), 15 soil augers (100 cm), and ground-penetrating radar imagery. The summit and shoulder consist of coarse glacial outwash (loamy sands) over limestone whereas the lower part is lacustrine sediments over coarse outwash (loams, silty loams). The A-horizon thickness ranged from 14 to 52 cm with thick A horizons at the toeslope that also had the lowest soil pH. The soil organic carbon (SOC) contents of the A horizons ranged from 11.6 to 46.9 g C kg –1 , and the higher contents are in the lower part of the soilscape. SOC stocks (0–20 cm depth) ranged from 50 to 70 Mg C ha –1 on the summit and backslope, but were 80 to 95 Mg C ha –1 in the flat part of the soilscape. The lowest soybean yields (1.6 Mg ha –1) were found at the summit and the highest yield (6.3 Mg ha –1) at the lower end of the backslope. Soybean yields were correlated to the thickness of the A horizon, and every 10 cm increase in A horizon thickness yielded an extra 0.6 Mg soybeans ha –1. Analysis of spherical magnetic particles was used to estimate soil erosion rates that were highest on the backslope (16.2 Mg ha –1 yr –1) and rates of soil deposition in the lowest part of the soilscape was 18.8 Mg ha P1 yr –1. It seems that there is no net soil and SOC loss within this soils-cape. All in all, we found 4 soil taxonomic orders within 200 m. The variation in this soilscape was substantial and probably enhanced by 140 years of cultivation.

A B S T R A C T Krotovinas, burrows of small mammals, occur widely in the soils of the steppes. The distribution and effect of krotovinas on soil organic carbon (SOC) stocks were studied in a deep profile (450 cm) at an archaeological site of the Palaeolithic period at Shenyang Agricultural University of China. It was found krotovinas occurred at all depths, and there was variation in their distribution and size. The density of krotovinas increased with depth, and was the highest (10/m 2) at 250 cm depth. The average area of krotovinas' sections in most part of the profile ranged from 100 to 160 cm 2. Krotovinas covered an area of 7 to 17% of the profile wall. Average SOC concentration in the krotovinas was 5.3 g/kg, whereas it was 4.5 g/kg in the surrounding soil matrix. SOC stocks at 100 to 450 cm accounted for 72% of total SOC stocks at 0 to 450 cm depth. Total SOC stock in the whole profile (0 to 450 cm) was 295 t/ha considering the presence of krotovinas, whereas it was 274 t/ha when the krotovinas were excluded. This study shows how krotovinas can be quantified, and that krotovinas can affect SOC concentration and change patterns of SOC distribution. The amount of SOC stored in deep soils should be taken into consideration for evaluating SOC stocks.

Digital soil mapping has largely expanded across the globe since the early 2000s. Numerous national, continental and global digital soil mapping products have been produced. In this paper, we review some major developments and highlight areas where progress is needed, including refinements in uncertainty assessments and increasing soil data collection. There are several useful top-down approaches, but we sense that digital soil mapping should be conducted at a regional or local level to be consistent with its use and application, and to ensure efficient soil data collection, and involvement of end users. Despite the numerous mapping activities, a concerted action is needed for enhanced investment in soil mapping, including capacity building and training the next generation of soil surveyors who are grounded in pedology as well as digital soil mapping.

A B S T R A C T Soil organic carbon (SOC) plays a crucial role in maintaining fertility and productivity in sandy soils. This study mapped the spatial variability of SOC concentration, A-horizon thickness, and SOC stocks from the Central Sands in Wisconsin. Soil samples were collected from three different areas (area A, B, and C) that were sampled through grid sampling (GS, n = 100), conditioned latin hypercube sampling (cLHS, n = 100), and random sampling (RS, n = 150) schemes. Average SOC concentration of the A-horizon from soil sampling area A, B, and C were 6.1, 7.1, 8.3 g kg − 1 , respectively. The mean A-horizon thickness for agricultural soils was 28 cm compared to 15 cm under adjacent grassland. Regression kriging was selected as prediction model where EC a and local topographic information (i.e., slope gradient, slope aspect, elevation, wetness index, altitude above channel network etc.) were used as predictors. We observed an increased SOC content, SOC stock, and A-horizon thickness with EC a and wetness index. SOC from area B had the strongest spatial dependency (NSR = 0.64) followed by area A (NSR = 0.72), whereas that from area C was the weakest (NSR = 0.78). Compared to SOC content and A-horizon thickness prediction, SOC stocks prediction had the maximum uncertainty. Predicted SOC stock (t ha − 1) ranged from 28 for sampling area A to 40 for B, and 59 for area C. These high SOC stocks are the result of decade long intensive agriculture with high amount of nitrogen input and irrigation. It has resulted in deep A-horizon and high SOC stocks. This study found that SOC stocks in the Central Sands could be estimated from A-horizon thickness (R 2 ~0.5).

A B S T R A C T We investigated four sampling designs for soil organic carbon (SOC) stock assessment of soil profiles: (i) sampling by horizons, (ii) vertical transect sampling, (iii) depth-based stratified random sampling, (iv) fuzzy c-means sampling in which we explored the use of vis-NIR spectroscopy, image analysis and color models. An Alfisol and Mollisol profile wall (1 × 1 m) was divided into a 10 × 10 cm raster and 100 samples (about 200 g each) were collected at the centers of grid cells for SOC analysis. Bulk density samples were collected from each 10-cm depth interval along a single vertical transect and the SOC stock was calculated using 100 points in the profile wall. Horizon-based sampling for the Mollisol (5 horizons) ranged from 231 to 262 Mg C ha − 1 , whereas it ranged from 69 to 99 Mg C ha − 1 in the Alfisol (3 horizons). The SOC stocks obtained by 1 to 7 vertical transects ranged from 68 to 81 Mg C ha − 1 in the Alfisol, and 239 to 246 Mg C ha − 1 in the Mollisol. Depth-based stratified random sampling resulted in the SOC stocks ranging from 77 to 82 Mg C ha − 1 in the Alfisol and 234 to 257 Mg C ha − 1 in the Mollisol, and the standard errors decreased with increasing sample size from 10 to 70. Fuzzy c-means clustering created clusters similar to the field delineated horizons. A sample size of 7 in both profiles was sufficient to estimate the mean profile SOC stock by fuzzy c-means sampling. The CIE L*a*b* color model resulted in more accurate estimation in the Alfisol, but the vis-NIR spectra resulted in more accurate estimation in the Mollisol. Soil depth improved the performance of vis-NIR spectra. It is concluded that in these soils, at least two or three vertical transects are required to capture the horizontal variation for estimating profile SOC stock. Depth-wise stratified random sampling reduces the number of samples and is suitable when horizontal variation is high. Fuzzy c-means sampling is useful to determine the minimum sample size for profile SOC stock assessment but requires ancillary data and processing before sampling the soil profile.

We measured and mapped the spatial distribution of Al, Si, Fe, Mn, Ca, pH, soil moisture content (θ), and color of a soil profile wall of a Typic Udipsamments. A 10 × 10 cm grid was laid on the soil profile wall, and 70 soil samples were collected from the grid centers. The spatial distribution of these properties was mapped with block kriging. The kriged values of the elements and red color were used in k-means clustering to identify soil horizons. Variation in the profile was considerable, but we observed that Fe, Mn, Ca, pH, and θ decreased with soil depth, while red color increased. The concentration of Al and Si increased at depth between 30 and 60 cm from the soil surface. The k-means clustering was able to locate three soil horizons in the profile, which was comparable to the standard soil profile description. We found that pXRF and soil color index coupled with clustering could be useful in digital soil morphometrics for the identification of soil horizons.

Digital soil morphometrics is defined as the application of instruments and techniques for measuring and mapping soil profile properties and deriving continuous depth functions. Here, we discuss some of the main results that were presented at the " Inaugural Global Workshop on Digital Soil Morphometrics " that was held in June 2015. We focus on instruments and techniques that have been used to measure soil properties in the field as well as in the laboratory, on modelling of soil depth functions, on the mapping of the soil profile (soil profile imaging) and on the use and applications of digital soil morphometrics. There have been considerable advancements in sampling and analysing soil profile properties in the field. Some instruments are restricted to dry soil, whereas others are affected by ambient light conditions. The mapping of the soil profile has yielded several methods to derive soil horizons and can deal with the variation within soil horizons. There are a certain number of soil depth functions that can be used for most soil properties and soil types. The use and application of digital soil morphometrics is mostly confined to enhanced pedological insight including soil classification, but with time, we envision that it can transform the way we observe, analyse and understand soils.