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The effects of pH on nutrient availability are not solely caused by to the effects on reaction with soils but are an interaction between these effects and the effects on rate of uptake by plants. Some effects are specific to particular ions, but an important aspect is that plant roots and soil particles both have variable charge surfaces. This influences availability, but in opposite directions. Sulfate is an example of this interplay. Its sorption by soil decreases markedly with increasing pH and thus “soil availability” increases. However, plant uptake also decreases with increasing pH thus “plant availability” decreases. For phosphate, the plant effect is stronger than the soil effect and uptake decreases with increasing pH. In contrast, effects of increasing pH on molybdate adsorption are so large that they dominate the overall effect. Sorption of cations, such as zinc or copper, increases with increasing pH but uptake rate also increases. The net effect is a small decrease in availability with increasing pH. Boron is an exception; there are small effects of pH on sorption; and it is the uncharged boric acid molecules that are taken up by plant roots. Their uptake is not affected by charge and uptake is proportional to the concentration of uncharged boric acid molecules. We argue that emphasis on the effects of pH on reactions with soil has led to a distorted picture of the effects of pH on nutrient availability.

The Burdur Lake is located in the southwest of Turkey, and its area has decreased by 40% from 211 km2 in 1975 to 126 km2 in 2019. In this study, we investigated how the soil has changed in the lacustrine material. Three soil profiles were sampled from the former lakebed (chronosequence profiles: P1, 2007; P2, 1994; and P3, 1975), and three soil profiles under different land use types (biosequence profiles: P4, native forest vegetation; P5, agriculture; and P6, lakebed) were sampled. The chronosequence and biosequence soil profiles represented various distances from the Burdur Lake and showed different stages of lacustrine evolution. Soil electrical conductivity (EC; 18.1 to 0.4 dS m–1), exchangeable Na+ (34.7 to 1.4 cmol kg–1) and K+ (0.61 to 0.56 cmol kg–1), and water-soluble Cl– (70.3 to 2.1 cmol L–1) and SO24– (275.9 to 25.0 cmol L–1) decreased with increasing distance from the Burdur Lake, whereas the A horizon thickness (10 to 48 cm), structure formation (0 to 48 cm), gleization-oxidation depth (0 to 79 cm), and montmorillonite and organic matter (OM; 25.9 to 46.0 g kg–1) contents increased in the chronosequence soil profiles. The formation of P3 in the chronosequence and P5 in the biosequence soil profiles increased due to longer exposure to pedogenic processes (time, land use, vegetation, etc.). Changes in EC, exchangeable cation (Na+ and K+) and water-soluble anion (Cl– and SO24–) concentrations of the salt-enriched horizon, OM, gleization-oxidation depth, A horizon thickness, and structure formation of the chronosequence and biosequence soil profiles (especially the topsoil horizon) were highly related to the distance from the Burdur Lake, time, and land use.

Soil depth is a critical attribute of any soil, and determines rooting, moisture and nutrient storage, mineral reserve, anchorage, and a range of conditions that affect plant growth. We reviewed papers from four primary soil science journals and extracted how deep the soils were studied in those papers. Soil depth was obtained over a 30-years period (1989–2019) from papers in: European Journal of Soil Science, Geoderma, Plant and Soil, and Soil Biology and Biochemistry. In total, 1146 papers were reviewed, and 37% (420 papers) included information on how deep the soil was studied. The number of papers that included soil depth increased from 31% in 1989 to 47% in 2019. The average soil depth studied was 27 cm, but it was 53 cm between 1989 and 1999, and 24 cm between 2004 and 2019. Most of the studies were from Europe, and 41% of the papers contained soil classification. Research that focused on soil mineralogy and technology tended to study soils to a greater depth (average 74 cm), whereas the depth in soil biology research was on average 18 cm. Over 80% of the soils were sampled by fixed depth and not by soil horizon. Soil depth is lacking from about half of the papers in these four journals. The depth of the soil studied has halved in the past 30 years. Soil processes, soil properties, and microbial communities are depth-dependent, and for a more complete understanding, soils should be studied to a greater depth.

Different combinations of organic mulch were applied in smallholder coffee farming systems to assess their effects on soil nutrient contents and coffee yield at three sites in different agro-ecological zones in Rwanda. Mulching systems consisted of Cymbopogon spp. (T1), Panicum spp. (T2), Cymbopogon spp. and Panicum spp. (T3), Eucalyptus spp. and Cymbopogon spp. (T4), mixed residues (T5) and un-mulched coffee used as control (T6). Mulch had significant and specific effects at each site (p < 0.001). T3 reduced soil pH value and exchangeable acidity at Kibirizi, while at Karongi and Ruli, these effects were observed with T4 and T5. T4 and T5 significantly increased the content of soil carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg). The amount of nutrients released was regulated by the amount and type of mulch applied, the agro-ecological conditions and the soil properties at each site. The increased soil nutrient levels led to improved soil fertility conditions and increased coffee yields. The coffee yields were significantly increased with T1 at Karongi (p < 0.05) by up to 1.9 t ha−1. T2 and T3 had significantly higher yields at Kibirizi. Yields at Kibirizi were 48% lower compared to yields at Karongi; at this site, T1, T2, T3, T4 and T5 increased yields by 57%, 26%, 31%, 20% and 28%, respectively, when compared to the no mulching treatment (T6). However, coffee yields over 1.9 t ha−1 can only be obtained with additional applications of inorganic fertilizer at different rates depending on the agro-ecological zone and soil type.

We tested one front‐end data fusion method to combine visible near‐infrared (vis–NIR) and portable X‐ray fluorescence (PXRF) spectra for predicting different soil properties and investigated the contribution of different sensor data. A total of 197 soil samples were collected from 25 Alfisols and Mollisols in south‐central Wisconsin, USA. Soils were analysed in the laboratory for clay, sand, silt content, total carbon (TC), total nitrogen (TN) and pH. Air‐dried soil samples were scanned with vis–NIR and PXRF spectrometers. A principal component analysis (PCA) was applied to each of the vis–NIR and PXRF spectra to extract the first 10 principal components (PCs), which were used in the Cubist model. Five types of input data were compared for constructing the models using a front‐end data fusion approach, including: (a) 10 PCs from vis–NIR spectra, (b) 10 PCs from spectra of PXRF beam 1 (XRF40), (c) 10 PCs from spectra of PXRF beam 2 (XRF10), (d) concatenating 20 PCs from two PXRF spectra (XRF40 + 10), and (e) concatenating 30 PCs from two PXRF spectra and vis–NIR spectra (XRF40 + 10 + NIR). In addition, the performances of the preprocessing methods (four smoothing methods with or without background removal) of PXRF spectra were compared. Multiple linear regression was also used to predict soil properties directly from 11 PXRF‐estimated elements. Our results suggest that smoothing should be applied to the PXRF spectra prior to developing predictive models. PXRF spectra and elemental data can predict soil texture with validation R² > 0.85, better than using solely vis–NIR spectra, and combining vis–NIR and PXRF spectra improved the prediction. Combining PXRF and vis–NIR can also predict TC and TN moderately well. Soil pH cannot be predicted from vis–NIR or PXRF spectra in this dataset due to its weak correlations with other soil properties and elements. It is concluded that PXRF spectra can be solely used to estimate soil texture, whereas combining vis–NIR and PXRF spectra via PCA should be used to estimate TC and TN. HIGHLIGHTS: The use of front‐end data fusion method to combine vis–NIR and PXRF spectra for predicting soil properties. Different preprocessing algorithms were compared systematically for PXRF spectra. Spectral smoothing should be applied to the PXRF spectra prior to developing predictive models. PXRF spectra can be used solely to estimate soil texture, whereas combining vis–NIR and PXRF spectra via PCA should be used to estimate TC and TN.

In this study, we evaluate the use of visible-near infrared (vis-NIR) and portable X-ray fluorescence (pXRF) for delineating soil horizons and how these are affected by soil moisture in a Mollisol (A, Ab, 2E, and 2Bt) profile of south-central Wisconsin. The soil profile was scanned in the field (moist) and soil samples were taken that were scanned in the laboratory (dry) using a vis-NIR spectrometer and a pXRF spectrometer. The CIE L*a*b* color was extracted from the vis-NIR spectra, and the elemental ratios and weathering indices were calculated from the pXRF data. The CIE L*a*b* color, elemental concentrations, elemental ratios, and weathering indices of the vertical transect were interpolated into 1-cm interval up to 100-cm soil depth for moist and dry measurements which were further used in the clustering analysis for identifying soil horizons. It was found that soil moisture decreased L*, a*, and b* color coordinates and the elemental concentrations. Soil moisture had little effect on the vertical distribution of the CIE L*a*b* color and pXRF measurements. Moisture effects can be partly reduced by taking elemental ratios and calculating weathering indices, or corrected by a linear function. The color variables (a* and b*), Ca, and Calcium/Titanium ratio (CTR) distinguished A horizons (Ap, A2, and Ab), whereas the Al, Fe, Ruxton index (R), and Sesquioxide ratio (Saf) distinguished 2E, 2Bt1, and 2Bt2 horizons. The CIE L*a*b* color and pXRF data were excellent variables for delineating soil horizons, and the measurements on moist soil samples performed better than those measured on dry soil samples.

We developed a novel method using image analysis to quantify the content and shape characteristics of coarse fragments in an Alfisol developed from loess over outwash. A total of 45 soil samples (about 600–1000 g each) were collected at 10-cm depth interval from 0 to 150-cm deep at three transects in the profile. The coarse fragments were separated from the soil fine-earth particles and photographed in the laboratory. The images were processed using ImageJ processing software that provides total count, total area, and mean circularity of each sample, as well as shape characteristics (circularity, aspect ratio, roundness, and solidity) of each coarse fragment. The amount of coarse fragments (coarse fragments, total count/mass, and total area/mass) varied with depth and was highest in the 2C1 and BC horizons where the soil was developed from coarse glacial outwash and a mixing of coarse outwash and loess. The coarse fragments content was lower in the loess and fine glacial outwash horizons. Approximately 9.6% of coarse fragments in weight or 14.5% by count moved from the glacial outwash layer into the loess layer (Ap and Bt horizons). The shape characteristics showed large variations at 50–110 cm depth where the soil was developed from coarse glacial outwash and a mixing of coarse outwash and loess. The mean circularity, roundness, and solidity of the coarse fragments at the topsoil were larger than those at the 50-110 cm, indicating that rounder stones tend to move faster than irregularly shaped stones during the frost heaving. This method provides a robust, fast and quantitative way to estimate the distribution and the shape characteristics of coarse fragments in a soil sample that can be applied to help better understand soil formation.

Advances in sensor technologies provide the potential for real-time monitoring of soil moisture status. Little work has been conducted to study the spatial and temporal variations of soil water content at the field scale and the interactions between soil water content and environmental controlling factors in cropped fields during the growing season. In this study, we assessed the spatial and temporal variations of surface soil water content (0–0.1 m) using seven in-situ soil moisture probes and Sentinel-1 satellite data over the 2016 and 2017 cropping seasons in irrigated fields of the Central Sands Plains, Wisconsin, USA. An empirical multiple linear regression model was developed between the soil water content with Sentinel-1 backscatter data. The model was evaluated with leave-one-group-out cross-validation, which showed an R2 of 0.60, mean error of –0.00 m3 m−3, root mean square error of 0.02 m3 m−3, and Lin’s concordance correlation coefficient of 0.75. Soil volumetric water content (VWC) and soil water deficit (SWD) were mapped at 30-m resolution every 6–12 days across one field (DU field at the Wallendal farm). Using two statistical metrics (“first metric” and “last metric”) and a bootstrapping approach, the location-specific and time-dependent interactions between estimated soil water content and environmental controlling factors across the DU field during the cropping season were evaluated, and it was found that such interactions were affected by initial soil water content and plant stress status. The robustness of the model needs to be improved in the future by collecting more VWC measurements in different soil and crop conditions. The method has the potential to provide a baseline soil moisture map and can be coupled with mechanistic models and subsoil VWC measurements for improved irrigation management and water use efficiency under a changing climate.

Here we present a novel way to assess soil horizons, their boundaries, and purity. Soil profile images in JPEG format were taken from 17 morphologically different soil profiles using a digital camera. Soil color models and image texture features were extracted from the soil profile images, and k-means clustering was used to segment the soil profile. Four models with different input variables were compared, and soil horizon boundary features (distinctness, tortuosity, mean curvature, and topography) and horizon features (thickness and purity) were calculated. The CIE L*a*b* color and HSV color identified most horizons, but CIE L*a*b* was better than HSV for identifying soil colors and color differences between horizons. Image texture improved the horizonation when coarse fragments were present. The k-means clustering method identified soil master horizons in most soil profiles, particularly when the horizons showed distinct color features, e.g. reddish with a* coordinate >5, yellowish with b* coordinate >15, a dark A horizon with H coordinate >0.20, and color distinction between horizons was high with ratio > 2. The ploughed A horizons showed high purity in clustering, whereas the transitional EB and BE horizons had lower purity. The distinctness and topography of some boundaries did not match with the field description when there was a ragged pattern. Tortuosity and mean curvature can be used to assess the waviness of horizon boundaries. The method of automated soil horizonation is robust and yields quantitative metrics on horizon purity and boundaries.

Knowledge of soil water storage and deep drainage is important for improving irrigation efficiency, maximizing crop water use, and understanding groundwater table fluctuation. This is particularly important in sandy soils that depend on irrigation to produce high crop yields. In sandy soils under potato in the Wisconsin Central Sand Plains, soil water storage using tension probes was measured in three irrigation zones over the 2014 and 2015 growing seasons. Soil water storage was estimated across a 78 ha field using apparent electrical conductivity maps. Deep drainage was estimated using the Richards’ equation and Hydrus-1D software. It was found that the average soil water storage ranged from 74 to 110 mm in the top 0.45 m in three irrigation zones in 2014, and from 70 to 95 mm in 2015. Rainfall and irrigation was 387 and 269 mm in 2014, and 328 and 281 mm in 2015. Estimated deep drainage was uniform in three irrigation zones, and ranged from 222 to 244 mm in 2014, and from 167 to 180 mm in 2015. A negative correlation was found between soil water storage and potato yields possibly due to over-irrigation. The methods used in this study can be applied to improve irrigation and water use efficiency.

International audience

Current in situ soil moisture monitoring networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical global surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100m and performed moderately well under cultivated, herbaceous, forest, and shrub soils (R2 = 0.524, RMSE = 0.07 m3 m−3). It has a relatively good transferability at the regional scale among different continental and regional networks (mean RMSE = 0.08–0.10 m3 m−3). The global model was then applied to map SSM dynamics at 30–100m across a field-scale network (TERENO-Wüstebach) in Germany and an 80-ha irrigated cropland in Wisconsin, USA. Without local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was also affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and high-resolution soil maps as well as assimilation with process-based water flow models.

Previous studies of long-term soil change have been focusing on the impacts of climate and land-use change, while neglecting the impacts of soil taxonomy on soil’s response to vegetational and human disturbance. In this study, a spatial-temporal framework was used to study the change in soil organic carbon (SOC) across National Ecological Observatory Network (NEON), USA over 30 years. We hypothesize that: 1) on the continental scale, the hot-spots and cold-spots of SOC change vary with soil orders across different eco-climatic domains, controlled by all soil forming factors that affect carbon input and output; 2) within the same eco-climatic regime, the effects of disturbance on SOC change are controlled by physical and biogeochemical processes, represented by varying soil properties including clay, bulk density, pH, and CEC. To separate the effects of disturbance under different land-use scenarios on SOC change, space-for-time substitution was used in combination with the Continuous Change Detection and Classification algorithm and structural equation models. Results suggested that 1) under natural vegetation, Ultisols, Spodosols, and Inceptisols showed a large SOC accumulation especially in the eastern coast, while Inceptisols, Andisols, and Aridisols in the western US showed a large SOC loss; 2) compared with the same reference soils under natural vegetation, Mollisols and Alfisols showed a large SOC decrease due to human disturbance (e.g., farming and grazing); 3) Inceptisols (+6.2 g/kg) and Gelisols (+27.5 g/kg) in Alaska presented the largest SOC increase among all the soil orders within the subsoil (B horizon); 4) clay content and pH were the most dominant factors that affected SOC content across the NEON sites. This empirical analysis of the 30-years SOC change across eco-climatic regimes could be used for ecosystem modelers to benchmark the models across biomes and study the physical and biogeochemical controls on SOC change under different land management scenarios.

In February 1934, Charles Kellogg became assistant to Curtis Marbut at the Division of Soil Survey in Washington, and in 1935, he was appointed Chief. Upon his retirement in May 1971, he was the longest serving chief of the Division. Milton Whitney established the soil survey, largely expanded the organization, but had a narrow focus. Curtis Marbut cautiously led the soil survey out of its geological base into an organization that embraced pedology. Charles Kellogg inherited an organization at a crossroads. It could have continued its course in which quantity was somewhat favored over quality but, instead, Charles Kellogg brought science into soil survey, introduced new techniques such as the use of aerial photography, aimed for standardization in soil survey and classification, and worked with soil surveyors across the country and the globe. He delegated authority, decentralized the organization, and for many years he battled with Hugh Bennett who led the soil erosion program.

The first public university west of the Mississippi River was the Missouri State University, founded in 1839. Curtis Marbut was born in 1863 and enrolled at the Missouri State University and graduated with a BS in geology in 1889. He continued his studies in geology at Harvard University in 1893, completed his MS but did not finish his Ph.D. As director of the Missouri Soil Survey, he surveyed the soils of the Ozarks Mountains before joining Milton Whitney at the Bureau of Soils in 1910. Reading Russian soil literature and studying soils across the country, he slowly transformed from a geologist into a pedologist and with him the direction of the soil survey. He helped founding the International Society of Soil Science, led the excursion of the 1927 international congress, but died unexpectedly in 1935. Other well-known soil scientists wo worked in Missouri were Merritt Miller, Henry Krusekopf, William Albrecht and Hans Jenny who made major contributions to soil science after he had moved to the University of California Berkeley.

Early soil maps in the USA were made by the state geological surveys. One of the first soil maps was made in Wisconsin in 1882, followed by soil maps of Mississippi, Maryland, and Tennessee. In 1891, a soils division was established within the Department of Agriculture. In 1899, it began a program that aimed for a nationwide soil survey similar to the geological survey. In this new survey program, the soils would be mapped, studied, and described in reports that were to be publicly distributed. An important purpose of the soil survey was to help settlers. The focus was agrarian as in the 1890s over 40% of the population were farmers. Soil observations were made in the field, and soils were then clustered into soil types, series, and provinces. Some theory was developed as to why certain soils were found in certain locations. The first field manual was published in 1903, and Milton Whitney was the first chief. He held a geological view on the survey, was successful in obtaining funds, but also had arguments with the leading soil researchers at the time including Eugene Hilgard, Franklin King, and Cyril Hopkins. He was succeeded by Curtis Marbut. In order to exchange experiences and to form a community, the American Association of Soil Survey Workers was established in 1920 that became the foundation for the Soil Science Society of America in 1936.

In the late 1800s, several terms described the study of soil, including agrology, agrogeology, agricultural chemistry, and pedology. The term soil science was not used until the early 1900s and became more widely used after the journal Soil Science was established in 1916. In 1908, Peter Treitz from Hungary and Gheorghe Murgoci from Romania traveled to southern Russia to participate in a soil excursion led by Konstantin Glinka. During the excursion, they hatched the idea of bringing together researchers from various countries to present methods and Approaches and seek standardization. The First International Conference of Agrogeology was organized in Budapest in 1909. Several meetings were held after that but the First World War halted progress in the foundation of an international soil science community. The Fourth International Conference of Pedology was held in Rome in 1924 and brought together 300 soil researchers and was the largest soil conference to date. On the last day of the conference, the International Society of Soil Science was established, and six commissions were formed. It was agreed that the First International Congress of Soil Science was to be held in the USA in 1927, and Jacob Lipman was elected as President.

Chronicles and progressions are made daily and can be dissected and aggregated in diverse ways. They can be presented as series of sequential happenings (and then… and then… and then), or as a meandering flux of events—mostly backward and linear but at times curving or directionless. In all efforts, chronicles and progressions are about connections using knowledge of the past to relate events that led to the present.

The Second International Congress of Soil Science was held in St. Petersburg and Moscow in July 1930. Konstantin Glinka had been elected the President of the International Society of Soil Science, but he had suddenly died in 1927. The congress was attended by 460 soil scientists, and included presentations, exhibits and an excursion that lasted 24 days. The Third International Congress of Soil Science was held in Oxford, UK, in August 1935. The congress had a similar setup to the first two congresses but lacked the brilliance and vitality. A fourth congress was to be held in Heidelberg, Germany, in 1940 but the world went to war. In 1945, efforts were made to establish a new global organization of soil science and to disremember the war years of the International Society of Soil Science. However, in 1947, the society was resurrected. In 1950 the Fourth International Congress of Soil Science was held in Amsterdam, and there were almost 500 attendees, with 120 from the Netherlands. Soils of the tropics was an important theme at the congress, and it decided that the next congress was to be held in Congo in 1954. The congress was held in Kinshasa in 1954 had only 200 participants but had an impact on soil research in tropical countries. The sixth congress was held in Paris in 1956, had almost 800 attendees, of which 200 were from France, and over 40 American attendees.

The First International Congress of Soil Science was held from 13th until 22nd of June 1927. The congress was organized by the International Society of Soil Science, the American Society of Agronomy, and the United States Department of Agriculture. Jacob Lipman and Curtis Marbut were the main organizers. The congress was attended by 507 people (366 from the USA) and there were 141 delegates from 31 countries; there were two women. Konstantin Glinka headed the Russian delegation, and with Curtis Marbut he was the pedologic leader of the congress. Jacob Lipman had made the program for the congress based on the recommendations of the six commissions. In total, 361 papers were received, with most for for the commission on Soil Biology and Biochemistry and the commission on Classification, Nomenclature, and Mapping of Soils. The congress included social events in Washington, day excursions, and was followed by a one-month excursion by train through 23 states and four Canadian Provinces. Over 200 people participated, and thirty-four stops were made, and twelve agricultural stations were visited. There were long discussions at the numerous soil profiles that were especially dug for the excursion or observed along road cuts. The congress and excursion were a success, forged a community of soil scientists, and became a model for successive soil congresses.

Roy Simonson was born in 1908 in Agate, North Dakota where his parents had settled in 1896. They grew wheat and other grains. In 1927, he enrolled at North Dakota Agricultural College as a student in civil engineering. A soil course taught by Charles Kellogg changed the direction of his studies and he graduated in 1934. At the recommendation of Charles Kellogg, he started his Ph.D. studies at the University of Wisconsin in Madison under Emil Truog. He graduated in 1938 and was hired at Iowa State College where he conducted soil survey and taught soils. In 1943, he was hired as soil correlator for the Division of Soil Survey in the south-east. After 6 years as soil inspector in Knoxville and several brief stints of soil survey work in the Pacific, Roy became the assistant chief of the Division of Soil Survey where he worked until his retirement in 1973. He died in 2008, 100 years old.

Emil Truog was born in 1884 in Independence, Wisconsin. He grew up on a farm, milked Guernsey cows, and from an early age on was interested in chemistry and the maintenance of soil fertility. In 1905, he enrolled at the University of Wisconsin in Madison where he took soil courses from Franklin King and Andrew Whitson. After his BS degree in Soils, he obtained a MS in chemistry and was hired in 1912 in the Department of Soils. Emil Truog worked on pH measurements, plant nutrients and exchange properties. He was an agronomist at heart and regarded deep soil understanding as essential for the maintenance of soil fertility, and an increase in agricultural production. He officially retired in 1954, and had supervised 175 MS and Ph.D. students. Emil Truog was the congress organizer for the Seventh International Congress of Soil Science held in Madison in 1960, and died in 1969.