Alexandre WadouxFrench National Institute for Agriculture, Food, and Environment (INRAE) | INRAE
Alexandre Wadoux
PhD Habil.
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80
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Introduction
Marie Skłodowska-Curie Fellow at INRAE in France and working on mapping of soil multifunctionality in Europe.
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Publications
Publications (80)
For decades scientists have produced maps of biological, ecological and environmental variables. These studies commonly evaluate the map accuracy through cross-validation with the data used for calibrating the underlying mapping model. Recent studies, however, have argued that cross-validation statistics of most mapping studies are optimistically b...
Participatory approaches to data gathering and research which involve farmers, laypeople, amateur soil scientists, concerned community members or school students have attracted much attention recently, not only to enable scientific progress but also to achieve social and educational outcomes. Non-expert participation in soil research and management...
Global, continental and regional maps of concentrations, stocks and fluxes of natural resources provide baseline data to assess how ecosystems respond to human disturbance and global warming. They are also used as input to numerous modelling efforts. But these maps suffer from multiple error sources and hence it is good practice to report estimates...
In 2023, the European Commission released a legislative proposal for a Directive on Soil Monitoring and Resilience which aims to define a legal framework to achieve healthy soils across the European Union (EU) by 2050. A key component of the initial Directive is the mandate for Member States to establish basic geographic soil governance units, refe...
CONTEXT The characterization and assessment of soil functions is a prerequisite for agricultural and environmental policies aimed at soil health. However, there is a lack of satisfactory models for the assessment of soil functions to support national and intergovernmental initiatives.
OBJECTIVE In this study we fill this gap by restructuring model...
Perusal of the environmental modelling literature reveals that the Lin's concordance correlation coefficient is a popular validation statistic to characterise model or map quality. In this communication we illustrate, with synthetic examples three undesirable statistical properties of this coefficient. We argue that ignorance of these properties ha...
The mapping of soils in Africa is at least a century old. We currently have access to various maps depicting mapping units locally and for the continent. In the past two decades, there has been a growing interest in alternatives for generating soil maps through digital soil mapping (DSM) techniques. There are, however, numerous challenges pertainin...
The quest for a global soil classification system has been a long-standing challenge in soil science. There currently exist two, seemingly disjoint, global soil classification systems, the USDA Soil Taxonomy and the World Reference Base for Soil Resources, and many regional and national systems. While both systems are acknowledged as international,...
Soil organic carbon (SOC) is instrumental in the global carbon cycle and in many functions relating to soil fertility. Land-use change such as the conversion of forests or grazing lands and an inappropriate management in many smallholder farming systems across sub-Saharan Africa (SSA) have led to an important loss of SOC at plot, farm, and regional...
Artificial neural network (ANN) models have been successfully used in infrared spectroscopy research for the prediction of soil properties. They often show better performance than conventional methods such as partial least squares regression (PLSR). In this paper we develop and evaluate a multivariate extension of ANN for predicting correlated soil...
Artificial neural network (ANN) models have been successfully used in infrared spectroscopy research for the prediction of soil properties. They often show better performance than conventional methods such as partial least squares regression (PLSR). In this paper we develop and evaluate a multivariate extension of ANN for predicting correlated soil...
Soil microbial diversity mediates a wide range of key processes and ecosystem services influencing planetary health. Our knowledge of microbial biogeography patterns, spatial drivers and human impacts at the continental scale remains limited. Here, we reveal the drivers of bacterial and fungal community distribution in Australian topsoils using 138...
The soil security concept has been put forward to maintain and improve soil resources inter alia to provide food, clean water, climate change mitigation and adaptation, and to protect ecosystems. A provisional framework suggested indicators for the soil security dimensions, and a methodology to achieve a quantification. In this study, we illustrate...
Soil is a central component to seven existential challenges humanity currently faces, including food, water and energy security, climate change abatement, biodiversity protection, human health and ecosystem services delivery. Soils, however, are under threat and securing soil has become an eighth existential challenge. Soil security can be understo...
Soil color is a key indicator of soil properties and conditions, exerting influence on both agronomic and environmental variables. Conventional methods for soil color determination have come under scrutiny due to their limited accuracy and reliability. In response to these concerns, we developed an innovative system that leverages 35 years of satel...
Mes recherches s’inscrivent dans une logique de modélisation spatiale à travers le développement de cadres conceptuels et méthodologiques pour la caractérisation spatio-temporelle d’indicateurs biophysiques des sols et de certaines fonctions fournies par les écosystèmes (érosion hydrique des sols, carbone des sols et biomasse). Les méthodes permett...
Spectroscopic modelling of soil has advanced greatly with the development of large spectral libraries, computational resources and statistical modelling. The use of complex statistical and algorithmic tools from the field of machine learning has become popular for predicting properties from their visible, near- and mid-infrared spectra. Many users,...
Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuous set of compounds with different chemical compositions, origins, and susceptibilities to decomposition that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contributi...
We introduce a new dataset of high-resolution gridded total soil organic carbon content data produced at 30 m × 30 m and 90 m × 90 m resolutions across Australia. For each product resolution, the dataset consists of six maps of soil organic carbon content along with an estimate of the uncertainty represented by the 90% prediction interval. Soil org...
Human societies face six existential challenges to their sustainable development. These challenges have been previously addressed by a myriad of concepts such as soil conservation, soil quality, and soil health. Yet, of these, only soil security attempts to integrate the six existential challenges concurrently through the five biophysical and socio...
Soil aggregate stability is an important indicator of soil condition and is directly related to soil degradation processes such as erosion and crusting. Aggregate stability is conventionally measured by testing the aggregate resistance to water disturbance mechanisms. Such measurements, however, are costly and time-consuming, which make them diffic...
Insights into the controlling factors of soil organic carbon (SOC) stock variation are necessary both for our scientific understanding of the terrestrial carbon balance and to support policies that intend to promote carbon storage in soils to mitigate climate change. In recent years, complex statistical and algorithmic tools from the field of machi...
Many areas in the world suffer from relatively sparse soil data availability. This results in inefficient implementation of soil-related studies and inadequate recommendations for improving soil management strategies. Commonly, this problem is tackled by collecting new soil data which are used to update legacy soil surveys. New soil data collection...
Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuum set of compounds 10 with different chemical composition, origin and susceptibilities to decomposition, that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contributi...
Insights into the controlling factors of soil organic carbon (SOC) stocks variation is necessary both for our scientific understanding of the terrestrial carbon balance and to support policies that intend to promote carbon storage in soils to mitigate climate change. In recent years, complex statistical and algorithmic tools from the field of machi...
http://www.pedometrics.org/Pedometron/Pedometron46.pdf
Understanding the spatial variation of soil properties is central to many sub-disciplines of soil science. Commonly in soil mapping studies, a soil map is constructed through prediction by a statistical or non-statistical model calibrated with measured values of the soil property and environmental covariates of which maps are available. In recent y...
Human societies face six existential challenges to their sustainable development. These challenges have been previously addressed by a myriad of concepts such as soil conservation, soil quality, and soil health. Yet of these only soil security attempts to integrate the six existential challenges concurrently through the five biophysical and socio-e...
Since the early 2000s, digital soil maps have been successfully used for various applications, including precision agriculture, environmental assessments and land use management. Globally, however, there are large disparities in the availability of soil data on which digital soil mapping (DSM) models can be fitted. Several studies attempted to tran...
Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validati...
For many decades, soil scientists have produced spatial estimates of soil properties using statistical and non-statistical mapping models. Commonly in soil mapping studies the map quality is assessed through pairwise comparison of observed and predicted values of a soil property, from which statistical indices summarizing the quality of the entire...
Pedometrics is concerned with the application of mathematical and statistical methods to the study of the distribution and genesis of soils. Here, we describe the main areas that pedometric research addresses: distribution of the soil pattern in character space, spatial and spatio-temporal soil variation, quantitative evaluation of the utility and...
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 spat...
Understanding the spatial variation of soil properties is central to many sub-disciplines of soil science. Commonly in soil mapping studies, a soil map is constructed through prediction by a statistical or non-statistical model calibrated with measured values of the soil property and environmental covariates of which maps are available. In recent y...
Digital convergence is helping us to better understand and study the soil. Fixed and mobile sensors, and wireless communication systems aided by the internet produce cheap and abundant streams of digital soil data that can readily be used for modeling and information generation. Here, we explore the ways in which digital science and technology have...
Soil is a complex system in which biological, chemical and physical interactions take place. The behaviour of these interactions changes in spatial scale from the atomic to the global, and in time. To understand how this system works, soil scientists usually rely on incremental improvements in the knowledge by refinement of theories through hypothe...
In digital soil spectroscopy, similarity or distance metrics between soil spectra are necessary for a large number of applications, such as for assessing the reliability of a spectrometer over repeated scans, to search for a similar soil sample based on spectra from a large database, to classify spectra into groups of similar characteristics or mor...
Measurement protocols for the same material often vary from laboratory to laboratory. Similarly, while the same spectrometer or sensor can be used between laboratories, the difference in terms of sensor or spectrometer manufacturer is likely to introduce additional variation in the recorded spectrum.
Usually, spectra are obtained for all the soil samples available, but only a subset of these samples are sent to the laboratory for chemical and physical analysis. The reason is that spectra are fast and cheap to retrieve, while a single soil analysis (e.g. for soil clay) is relatively slow, and significantly more costly, to obtain. One must select...
The most common way of estimating soil properties from pre-processed spectra is by calibrating a statistical model. If the response of the spectra at a particular wavelength follows the Beer-Lambert law, the degree of reflectance at a particular wavelength is proportional to the concentration of a soil property. In this case, a linear model can be...
This book provides a didactic overview of techniques for inferring information from soil spectroscopic data, and the codes in the R programming language for performing such analyses. It is intended for students, researchers and practitioners looking to infer soil information from spectroscopic data, focusing mainly on, but not restricted to, the in...
Hypotheses are of major importance in scientific research. In current applications of machine learning algorithms for soil mapping the hypotheses being tested or developed are often ambiguous or undefined. Mapping soil properties or classes, however, does not tell much about the dynamics and processes that underly soil genesis and evolution. When t...
R provides a convenient and flexible data-analytic environment for soil spectral data. R is a programming language and a software facility for data manipulation, statistical analysis and graphics. R is an implementation of the S language developed at Bell Laboratories (Venables et al. 2009) in the 1980s. While R is an integrated environment for dat...
This chapter describes the datasets and R packages used in the book. A total of five datasets are provided and described. They originate from several studies and are made available through a book-associated R package. Most R functions used in this book are either provided in the text or available online in R packages.
The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is transforming the way soil scientists produce their maps. Within the past two decades, soil scientists have applied ML to a wide range of scenarios, by mapping soil properties or classes with various ML algorithms, on spatial scale from the local to the global, and with...
This paper provides a history of the investigation of the soils and organic matter of Deli in Sumatra, Indonesia, for growing tobacco in the early 20th century and an interpretation based on current data, knowledge and understanding. We first review some early chemists and agrogeologists’ investigations on the soils of Deli to increase tobacco prod...
River discharges are often predicted based on a calibrated rainfall-runoff model. The major sources of uncertainty, namely input, parameter and model structural uncertainty must all be taken into account to obtain realistic estimates of the accuracy of discharge predictions. Over the past years, Bayesian calibration has emerged as a suitable method...
Soil organic matter is important for nutrient exchange in the soil environment, carbon sink, and soil fertility. Soil scientists usually estimate the amount of organic matter in a soil from its carbon content using the 1.724 conversion factor. The origin of this conversion factor is conventionally attributed to Jacob Maarten Van Bemmelen, a Dutch c...
If a map is constructed through prediction with a statistical or non‐statistical model, the sampling design used for selecting the sample on which the model is fitted plays a key role in the final map accuracy. Several sampling designs are available for selecting these calibration samples. Commonly, sampling designs for mapping are compared in real...
The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is transforming the way soil scientists produce their maps. Machine learning is currently applied to mapping soil properties or classes much in the same way as other unrelated fields of science. Mapping of soil, however, has unique aspects which require adaptations of the...
In digital soil mapping, machine learning (ML) techniques are being used to infer a relationship between a soil property and the covariates. The information derived from this process is often translated into pedological knowledge. This mechanism is referred to as knowledge discovery. This study shows that knowledge discovery based on ML must be tre...
Digital soil mapping (DSM) techniques are widely employed to generate soil maps. Soil properties are typically predicted individually, while ignoring the interrelation between them. Models for predicting multiple properties
exist, but they are computationally demanding and often fail to provide accurate description of the associated uncertainty. In...
Space-time monitoring and prediction of environmental variables requires measurements of the environment. But environmental variables cannot be measured everywhere and all the time. Scientists can only collect a fragment, a sample of the property of interest in space and time, with the objective of using this sample to infer the property at unvisit...
Machine learning techniques are widely employed to generate digital soil maps. The map accuracy is partly determined by the number and spatial locations of the measurements used to calibrate the machine learning model. However, determining the optimal sampling design for mapping with machine learning techniques has not yet been considered in detail...
With the advances of new proximal soil sensing technologies, soil properties can be inferred by a variety of sensors, each having its distinct level of accuracy. This measurement error affects subsequent modelling and therefore must be integrated when calibrating a spatial prediction model. This paper introduces a deep learning model for contextual...
A geostatistical survey for soil requires rational choices regarding the sampling strategy. If the variogram of the property of interest is known then it is possible to optimize the sampling scheme such that an objective function related to the survey error is minimized. However, the variogram is rarely known prior to sampling. Instead it must be a...
With the advances of new proximal soil sensing technologies, soil properties can be inferred by a variety of sensors, each having its distinct level of accuracy. This measurement error affects subsequent modelling and therefore must be integrated when calibrating a spatial prediction model. This paper introduces a deep learning model for contextual...
Sustainable agriculture practices are often hampered by the prohibitive costs associated with the generation of fine‐resolution soil maps. Recently, several papers have been published highlighting how visible and near infrared (vis–NIR) reflectance spectroscopy may offer an alternative to address this problem by increasing the density of soil sampl...
The turn of the nineteenth century witnessed the emergence of a new geographical and global vision on soils. This is particularly due to the work of a Russian school of thought, from which the notion of soil was differentiated as an independent and varying natural body. This notion is confronted with the vision of the agronomists, chemists or geolo...
Simple and ordinary kriging assume a constant mean and variance of the soil variable of interest. This assumption is often implausible because the mean and/or variance are linked to terrain attributes, parent material or other soil forming factors. In kriging with external drift (KED) non-stationarity in the mean is accounted for by modelling it as...
This QUICS project deliverable 3.1:
(1) Gives an overview of statistical methods for spatial sampling design optimisation, with a focus on applications in the environmental sciences, including a brief review of key recent publications.
(2) Presents a specific application to sampling design optimisation of rain gauge locations for rainfall mapping i...
The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially average...
Soil erosion by water outlines a major threat to the Three Gorges Reservoir Area in China. A detailed assessment of soil conservation measures requires a tool that spatially identifies sediment reallocations due to rainfall–runoff events in catchments. We applied EROSION 3D as a physically based soil erosion and deposition model in a small mountain...
Most calibration sampling designs for Digital Soil Mapping (DSM) demarcate spatially distinct sample sites. In practical applications major challenges are often limited field accessibility and the question on how to integrate legacy soil samples to cope with usually scarce resources for field sampling and laboratory analysis. The study focuses on t...