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Nature‐inclusive scenarios of the future can help address numerous societal challenges related to soil health. As nature‐inclusive scenarios imply sustainable management of natural systems and resources, land use and soil health are assumed to be mutually beneficial in such scenarios. However, the interplay between nature‐inclusive land use scenarios and soil health has never been modelled using digital soil mapping. We predicted soil organic matter (SOM), an important indicator of soil health, in 2050, based on a recently developed nature‐inclusive scenario and machine learning in 3D space and time in the Netherlands. By deriving dynamic covariates related to land use and the occurrence of peat for 2050, we predicted SOM and its uncertainty in 2050 and assessed SOM changes between 2022 and 2050 from 0 to 2 m depth at 25 m resolution. We found little changes in the majority of mineral soils. However, SOM decreases of up to 5% were predicted in grasslands used for animal‐based production systems in 2022, which transitioned into croplands for plant‐based production systems by 2050. Although increases up to 25% SOM were predicted between 0 and 40 cm depth in rewetted peatlands, even larger decreases, on reclaimed land even surpassing 25% SOM, were predicted on non‐rewetted land in peat layers below 40 cm depth. There were several limitations to our approach, mostly due to predicting future trends based on historic data. Furthermore, nuanced nature‐inclusive practices, such as the adoption of agroecological farming methods, were too complex to incorporate in the model and would likely affect SOM spatial variability. Nonetheless, 3D‐mapping of SOM in 2050 created new insights and raised important questions related to soil health behind nature‐inclusive scenarios. Using machine learning explicit in 3D space and time to predict the impact of future scenarios on soil health is a useful tool for facilitating societal discussion, aiding policy making and promoting transformative change. [ABSTRACT FROM AUTHOR]

In response to the growing societal awareness of the critical role of healthy soils, there has been an increasing demand for accurate and high-resolution soil information to inform national policies and support sustainable land management decisions. Despite advancements in digital soil mapping and initiatives like GlobalSoilMap, quantifying soil variability and its uncertainty across space, depth and time remains a challenge. Therefore, maps of key soil properties are often still missing on a national scale, which is also the case in the Netherlands. To meet this challenge and fill this data gap, we introduce BIS-4D, a high-resolution soil modeling and mapping platform for the Netherlands. BIS-4D delivers maps of soil texture (clay, silt and sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity and their uncertainties at 25 m resolution between 0 and 2 m depth in 3D space. Additionally, it provides maps of soil organic matter and its uncertainty in 3D space and time between 1953 and 2023 at the same resolution and depth range. The statistical model uses machine learning informed by soil observations amounting to between 3815 and 855 950, depending on the soil property, and 366 environmental covariates. We assess the accuracy of mean and median predictions using design-based statistical inference of a probability sample and location-grouped 10-fold cross validation (CV) and prediction uncertainty using the prediction interval coverage probability. We found that the accuracy of clay, sand and pH maps was the highest, with the model efficiency coefficient (MEC) ranging between 0.6 and 0.92 depending on depth. Silt, bulk density, soil organic matter, total nitrogen and cation exchange capacity (MEC of 0.27 to 0.78), and especially oxalate-extractable phosphorus (MEC of -0.11 to 0.38) were more difficult to predict. One of the main limitations of BIS-4D is that prediction maps cannot be used to quantify the uncertainty in spatial aggregates. We provide an example of good practice to help users decide whether BIS-4D is suitable for their intended purpose. An overview of all maps and their uncertainties can be found in the Supplement. Openly available code and input data enhance reproducibility and help with future updates. BIS-4D prediction maps can be readily downloaded at 10.4121/0c934ac6-2e95-4422-8360-d3a802766c71. BIS-4D fills the previous data gap of the national-scale GlobalSoilMap product in the Netherlands and will hopefully facilitate the inclusion of soil spatial variability as a routine and integral part of decision support systems. [ABSTRACT FROM AUTHOR]

In response to the growing societal awareness of the critical role of healthy soils, there is an increasing demand for accurate and high-resolution soil information to inform national policies and support sustainable land management decisions. Despite advancements in digital soil mapping and initiatives like GlobalSoilMap, quantifying soil variability and its uncertainty across space, depth, and time remains a challenge. Therefore, maps of key soil properties are often still missing on a national scale, which is also the case in the Netherlands. To meet this challenge and fill this data gap, we introduce BIS-4D, a high resolution soil modelling and mapping platform for the Netherlands. BIS-4D delivers maps of soil texture (clay, silt and sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity and their uncertainties at 25m resolution between 0 - 2m depth in 3D space. Additionally, it provides maps of soil organic matter and its uncertainty in 3D space and time between 1953 - 2023 at the same resolution and depth range. The statistical model uses machine learning informed by soil observations numbering between 3815 - 855 950, depending on the soil property, and 366 environmental covariates. We assess the accuracy of mean and median predictions using design-based statistical inference of a probability sample and location-grouped 10-fold cross-validation, and prediction uncertainty using the prediction interval coverage probability. We found that the accuracy of clay, sand and pH maps was highest, with the model efficiency coefficient (MEC) ranging between 0.6 - 0.92 depending on depth. Silt, bulk density, soil organic matter, total nitrogen and cation exchange capacity (MEC = 0.27 - 0.78), and especially oxalate-extractable phosphorus (MEC = -0.11 - 0.38), were more difficult to predict. One of the main limitations of BIS-4D is that prediction maps cannot be used to quantify the uncertainty of spatial aggregates. A step-by-step manual helps users decide whether BIS-4D is suitable for their intended purpose, an overview of all maps and their uncertainties can be found in the supplementary information (SI), openly available code and input data enhance reproducibility and future updates, and BIS-4D prediction maps can be easily downloaded at https://doi.org/10.4121/0c934ac6-2e95-4422-8360-d3a802766c71 (Helfenstein et al., 2024a). BIS-4D fills the previous data gap of a national scale GlobalSoilMap product in the Netherlands and will hopefully facilitate the inclusion of soil spatial variability as a routine and integral part of decision support systems. [ABSTRACT FROM AUTHOR]

Soil compaction is a severe threat to agricultural productivity, as it can lead to yield losses ranging from 5% to 40%. Quantification of the state of compaction can help farmers and land managers to determine the optimal management to avoid these losses. Bulk density is often used as an indicator for compaction. It is a costly and time-consuming measurement, making it less suitable for farmers and land managers. Alternatively, measurements of penetration resistance can be used. These measurements are cheaper and quicker but are prone to uncertainty due to the existence of a wide array of thresholds. Classifications using either measurement may provide different outcomes when used in the same location, as they approximate soil compaction using different mechanisms. In this research, we assessed the level of agreement between soil compaction classifications using bulk density and penetration resistance for an agricultural field in Flevoland, the Netherlands. Additionally, we assessed the possible financial implications of misclassification. Balanced accuracy results indicate that most thresholds from the literature show around 70% agreement between both methods, with a maximum level of agreement of 76% at 1.8 and 1.9 MPa. The expected cost of misclassification shows a dip between 1.0 and 3.0 MPa, with an effect of crop value on the shape of the cost function. Although these results are specific to our study area, we believe they show that there is a substantial effect of the choice of measurement on the outcome of soil compaction studies. [ABSTRACT FROM AUTHOR]

There is a growing demand for high‐quality soil data. However, soil measurements are subject to many error sources. We aimed to quantify uncertainties in synthetic and real‐world wet chemistry soil data through a linear mixed‐effects model, including batch and laboratory effects. The use of synthetic data allowed us to investigate how accurately the model parameters were estimated for various experimental measurement designs, whereas the real‐world case served to explore if estimates of the random effect variances were still accurate for unbalanced datasets with few replicates. The variance estimates for synthetic pHH2O data were unbiased, but limited laboratory information led to imprecise estimates. The same was observed for unbalanced synthetic datasets, where 20, 50 and 80% of the data were removed randomly. Removal led to a sharp increase of the interquartile range (IQR) of the variance estimates for batch effect and the residual. The model was also fitted to real‐world pHH2O and total organic carbon (TOC) data, provided by the Wageningen Evaluating Programmes for Analytical Laboratories (WEPAL). For pHH2O, the model yielded unbiased estimates with relatively small IQRs. However, the limited number of batches with replicate measurements (5.8%) caused the batch effect to be larger than expected. A strong negative correlation between batch effect and residual variance suggested that the model could not distinguish well between these two random effects. For TOC, batch effect was removed from the model as no replicates were available within batches. Again, unbiased model estimates were obtained. However, the IQRs were relatively large, which could be attributed to the smaller dataset with only a single replicate measurement. Our findings demonstrated the importance of experimental measurement design and replicate measurements in the quantification of uncertainties in wet chemistry soil data. Highlights: Accurate uncertainty quantification depends on the experimental measurement design.Linear mixed‐effects models can be used as a tool to quantify uncertainty in wet chemistry soil data.Lack of replicate measurements leads to poor estimates of error variance components.Measurement error in wet chemistry soil data should not be ignored. [ABSTRACT FROM AUTHOR]

Soil properties that are considered difficult to measure are frequently determined through pedotransfer functions (PTFs). Calibration and validation datasets, containing measurements of the target soil property as well as widely available basic soil attributes, are needed to construct and validate PTFs. However, the target soil property data are prone to measurement errors, which are typically ignored when deriving new PTFs. In this contribution, we considered a PTF that predicts the soil cation exchange capacity (CEC) and aimed to study the effect of additive and multiplicative measurement error in the CEC calibration data on multiple linear regression (MLR) and random forest (RF) PTF predictions and associated prediction uncertainty. We used data from the National Cooperative Soil Survey Soil Characterization (NCSS-SC) Database, which unfortunately did not contain repeated measurements. Therefore, data from the Wageningen Evaluating Programmes for Analytical Laboratories (WEPAL) were used to quantify measurement error variance in CEC data. We assumed that the obtained measurement error variance was representative for the NCSS-SC Database CEC data, and used it to generate a probability distribution of the CEC measurement error. We used Monte Carlo simulations to add measurement errors to the original CEC values and fitted MLR- and RF-PTFs to 'error-free' and 'error-contaminated' datasets to study the effect of measurement error. For the MLR-PTFs, measurement error did not lead to large differences in the mean estimated model coefficients when compared to the estimates based on the 'true' CEC. Similarly, the variable importance scores of the RF-PTFs were comparable. However, a relationship between dataset size and the standard deviations of the model coefficient estimates and variable importance scores was present. Furthermore, the model performance reduced slightly (MEC decreased between 0.09 and 2.53%) compared to the MLR-PTFs fitted on the 'true' CEC. A larger effect of measurement error on model performance was found for the RF-PTFs, where the MEC decreased between 1.52 and 31.59%. The study showed that measurement error in the calibration data can impact the calibration and statistical validation of PTFs, especially when using small datasets or when measurement error variance is large. • Effect of measurement error on pedotransfer functions was studied. • Measurement error in calibration data reduces pedotransfer function performance. • Smaller calibration datasets lead to larger impact of measurement error. • Random forest pedotransfer models were sensitive to large measurement errors. [ABSTRACT FROM AUTHOR]

• Tier 4 GlobalSoilMap products that include spatially explicit accuracy thresholds. • Assessment of map accuracy using various statistical validation strategies, including design-based inference of a probability sample. • Soil pH prediction maps at six standard depth layers between 0 m and 2 m for the Netherlands at 25 m resolution. • Quantile Regression Forest model can predict at any desired depth. • Evaluation of uncertainty using prediction interval coverage probability. Accurate and high resolution spatial soil information is essential for efficient and sustainable land use, management and conservation. Since the establishment of digital soil mapping (DSM) and the goals set by the GlobalSoilMap (GSM) working group, great advances have been made to attain spatial soil information worldwide. Highly populated areas such as the Netherlands demand multi-functional land use, for which information of key soil properties such as pH is essential to make decisions. We a) provide soil pH prediction maps at six standard depth layers between 0 m to 2 m for the Netherlands at 25 m resolution, whereby the calibrated Quantile Regression Forest (QRF) model allows for prediction at any desired depth, and b) determine map accuracy using various statistical validation strategies and evaluation of prediction uncertainty. This study is unique among GSM products by including design-based inference of a probability sample as an external accuracy assessment and providing Tier 4 maps with spatially explicit accuracy thresholds for end-users based on GSM specifications. QRF models were tuned and calibrated using 15 338 soil observations between 0 m and 2 m depth from 4230 locations and 195 covariates representing the soil-forming factors. The following statistical validation strategies were used for external accuracy assessment of map quality: out-of-bag, location-grouped 10-fold cross-validation, an independent validation set (5677 observations, 1367 locations) and a stratified random sample of the independent validation set separated by depth layer. Mean error (ME), root mean squared error (RMSE), model efficiency coefficient (MEC) and the prediction interval coverage probability (PICP) were calculated in all four strategies. In addition, the 90th prediction intervals were used to categorize each 25 m pixel into "none", A, AA or AAA quality as a measure of the internal accuracy assessment. We obtained large differences depending on the four external accuracy assessment strategies and depth layer (ME = −0.08–0.20, RMSE = 0.41–0.83, MEC = 0.64–0.90, PICP of PI90 = 0.80–0.94). Design-based inference (LSK-SRS) was most indicative of map accuracy based on sampling theory (ME = 0.09–0.17, RMSE = 0.7–0.79, MEC = 0.73–0.82). QRF prediction uncertainty was slightly overestimated. Less than 10 % of pixels were designated with AA and AAA and therefore we recommend future studies to also test the achievability of high quality thresholds for Tier 4 GSM maps. We believe these 3D soil pH maps at 25 m resolution are useful for a variety of end users and that our workflow can be applied elsewhere and for other soil properties to further diminish the gap of missing spatial soil information. [ABSTRACT FROM AUTHOR]

Site-specific estimation of lime requirement requires high-resolution maps of soil organic carbon (SOC), clay and pH. These maps can be generated with digital soil mapping models fitted on covariates observed by proximal soil sensors. However, the quality of the derived maps depends on the applied methodology. We assessed the effects of (i) training sample size (5–100); (ii) sampling design (simple random sampling (SRS), conditioned Latin hypercube sampling (cLHS) and k-means sampling (KM)); and (iii) prediction model (multiple linear regression (MLR) and random forest (RF)) on the prediction performance for the above mentioned three soil properties. The case study is based on conditional geostatistical simulations using 250 soil samples from a 51 ha field in Eastern Germany. Lin's concordance correlation coefficient (CCC) and root-mean-square error (RMSE) were used to evaluate model performances. Results show that with increasing training sample sizes, relative improvements of RMSE and CCC decreased exponentially. We found the lowest median RMSE values with 100 training observations i.e., 1.73%, 0.21% and 0.3 for clay, SOC and pH, respectively. However, already with a sample size of 10, models of moderate quality (CCC > 0.65) were obtained for all three soil properties. cLHS and KM performed significantly better than SRS. MLR showed lower median RMSE values than RF for SOC and pH for smaller sample sizes, but RF outperformed MLR if at least 25–30 or 75–100 soil samples were used for SOC or pH, respectively. For clay, the median RMSE was lower with RF, regardless of sample size. [ABSTRACT FROM AUTHOR]

The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study's findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide. [ABSTRACT FROM AUTHOR]

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring. [ABSTRACT FROM AUTHOR]

But this is far from the full value that could be extracted from a dataset, if the ingenuity of other researchers, perhaps combined with other datasets, could be applied to the dataset. Data articles should give sufficient information to enable other scientists, whether soil scientists or those in related disciplines, to properly use the datasets in their own research. These datasets are sometimes re-used or integrated with other datasets by the original researchers or selected collaborators. [Extracted from the article]

• Community-based research effort. • Define ten challenging and unsolved pedometrics questions. • Review and proposed agenda for each challenge. • Guide for future pedometrics research. • Foster collaboration among soil scientists to solve the challenges. Pedometrics, the application of mathematical and statistical methods to the study of the distribution and genesis of soils, has broadened its scope over the past two decades. The primary focus of pedometricians has traditionally been on spatial and spatio-temporal soil inventories with numerical soil classification, geostatistical modelling of spatial variation and mapping. The rapid development of remote and proximal soil sensing as well as data-driven statistical modelling techniques have had a major impact on pedometrics over the past decades. During this time, a general demand for quantitative digital soil information for environmental modelling and management has compelled pedometricians to address other soil-related questions from a quantitative point of view: soil genesis and utility and quality of soil. While scientific progress is largely an autonomous process that is difficult to steer, research efforts could benefit from an agenda with pressing pedometric research topics. This paper defines and discusses ten recent or longstanding pedometrics challenges, with the attempt to identify knowledge gaps and suggest new concepts and methods to overcome them. The ten challenges were selected through a collaborative effort and may serve as a guidance for future pedometrics research and to foster collaboration among soil scientists. The challenges discussed in this paper are also indicators of the current understanding and state of knowledge from which progress can be measured in the future. [ABSTRACT FROM AUTHOR]