Current Estimates of Soil Organic Carbon Stocks Are Not Four to Six Times Underestimated. Comment on "Non-Flat Earth Recalibrated for Terrain and Topsoil. Soil Syst. 2018, 2, 64"

In the interesting paper "Non-Flat Earth Recalibrated for Terrain and Topsoil" published in Soil Systems [...

Digital mapping of GlobalSoilMap soil properties at a broad scale: a review

Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent >10,000 km2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. We observed that DSM publications continued to increase exponentially; however, the majority (74.6%) focused on applications rather than methodology development. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. Approximately 78% of articles focused on mapping soil organic matter/carbon content and soil organic carbon stocks because of their significant role in food security and climate regulation. Half the articles focused on soil information in topsoil only (<30 cm), and studies on deep soil (100–200 cm) were less represented (21.7%). Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Nonlinear models (i.e. machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Soil pH was the best predicted soil property (average R2 of 0.60, 0.63, and 0.56 at 0–30, 30–100, and 100–200 cm). Other relatively well-predicted soil properties were clay, silt, sand, soil organic carbon (SOC), soil organic matter (SOM), SOC stocks, and bulk density, and coarse fragments and soil depth were poorly predicted (R2 < 0.28). In addition, decreasing model performance with deeper depth intervals was found for most soil properties. Further research should pursue rescuing legacy data, sampling new data guided by well-designed sampling schemas, collecting representative environmental covariates, improving the performance and interpretability of advanced spatial predictive models, relating performance indicators such as accuracy and precision to cost-benefit and risk assessment analysis for improving decision support; moving from static DSM to dynamic DSM; and providing high-quality, fine-resolution digital soil maps to address global challenges related to soil resources

High resolution soil property maps over mainland France : application to soil organic carbon and its additional sequestration and storage potentials

Cette thèse est une contribution à la Cartographie Numérique des Sols (CNS) sur de vastes espaces. Dans le Chapitre 1, je discute des principaux facteurs à l’origine de l’émergence et du développement de la CNS et j’en retrace brièvement l’historique. Dans le Chapitre 2, je réalise une revue générale de la CNS sur de vastes espaces en me fondant sur 160 articles publiés de 2003 à mi-2019. J’y synthétise et discute les principales avancées et défis pour la communauté de la CNS. Je me focalise ensuite sur le carbone organique des sols (COS) en raison de son importance fondamentale pour les services écosystémiques et le cycle global du carbone. Dans le Chapitre 3, je montre comment améliorer une carte nationale du COS en agrégeant différentes cartes de COS et je donne des des pistes sur la façon de tirer parti deprédictions globales pour les pays disposant de peu de données, en utilisant une stratégie d’échantillonnage peu coûteuse et efficace.Ensuite, dans les Chapitres 4 et 5, je me concentre sur le domaine de validité de fonctions de pédotranfert utilisées pour la prédiction de la densité apparente et je développe une approche nouvelle pour traiter l’épaisseur des sols en France. J’y propose également des stratégies efficientes pour améliorer la précision de leurs prédictions. Je passe ensuite de la CNS à la cartographie de fonctions des sols en prenant comme exemple la cartographie du potentiel de séquestation (Chapitre 6) et de stockage (Chapitre 7) en COS. Ces chapitres contribuent à améliorer de nombreux aspects concernant la CNS et son application au programme GlobThis thesis is a contribution to Digital Soil Mapping (DSM) at broad scale, with applications on the French mainland territory. In Chapter 1, I discussed the main drivers for the rise and development of DSM and gave a brief history about DSM. In Chapter 2, I made a general review about broad-scale DSM by reviewing 160 selected articles from 2003 to mid-2019. I synthetized and discussed the main achievements and challenges for the DSM community. Then I decided to focus on soil organic carbon (SOC) because of its main importance for ecosystem services and global carbon cycle. In Chapter 3, I showed how to improve a national SOC map by merging various SOC maps and provided inputs on how to take advantage of global predictions in ‘data-poor’ countries using a low cost and efficient sampling strategy. Then in Chapters 4 and 5, I focused onthe validity domain of pedotransfer functions used for bulk density predictions and on developing a novel approach to deal with soil thickness prediction over France. I also proposed efficient sampling strategies to improve the accuracy of their predictions. I moved from DSM to Digital Soil Assessment (DSA), exemplified by SOC sequestration potential in Chapter 6 and SOC storage potentials in Chapter 7. They contribute to improving some aspects related to DSM and GlobalSoilMap. In Chapter 8, I finished this thesis by discussing the most important findings of my work and relating them to main challenges of Pedometrics. I outlined the inputs that my work provided to reaching these challenges and highlighted the remaining issues to be solved i

Cartographie à haute résolution de propriétés du sol à l’échelle de la France métropolitaine : application au carbone organique du sol et à ses potentiels additionnels de séquestration et de stockage

This thesis is a contribution to Digital Soil Mapping (DSM) at broad scale, with applications on the French mainland territory. In Chapter 1, I discussed the main drivers for the rise and development of DSM and gave a brief history about DSM. In Chapter 2, I made a general review about broad-scale DSM by reviewing 160 selected articles from 2003 to mid-2019. I synthetized and discussed the main achievements and challenges for the DSM community. Then I decided to focus on soil organic carbon (SOC) because of its main importance for ecosystem services and global carbon cycle. In Chapter 3, I showed how to improve a national SOC map by merging various SOC maps and provided inputs on how to take advantage of global predictions in ‘data-poor’ countries using a low cost and efficient sampling strategy. Then in Chapters 4 and 5, I focused onthe validity domain of pedotransfer functions used for bulk density predictions and on developing a novel approach to deal with soil thickness prediction over France. I also proposed efficient sampling strategies to improve the accuracy of their predictions. I moved from DSM to Digital Soil Assessment (DSA), exemplified by SOC sequestration potential in Chapter 6 and SOC storage potentials in Chapter 7. They contribute to improving some aspects related to DSM and GlobalSoilMap. In Chapter 8, I finished this thesis by discussing the most important findings of my work and relating them to main challenges of Pedometrics. I outlined the inputs that my work provided to reaching these challenges and highlighted the remaining issues to be solved inCette thèse est une contribution à la Cartographie Numérique des Sols (CNS) sur de vastes espaces. Dans le Chapitre 1, je discute des principaux facteurs à l’origine de l’émergence et du développement de la CNS et j’en retrace brièvement l’historique. Dans le Chapitre 2, je réalise une revue générale de la CNS sur de vastes espaces en me fondant sur 160 articles publiés de 2003 à mi-2019. J’y synthétise et discute les principales avancées et défis pour la communauté de la CNS. Je me focalise ensuite sur le carbone organique des sols (COS) en raison de son importance fondamentale pour les services écosystémiques et le cycle global du carbone. Dans le Chapitre 3, je montre comment améliorer une carte nationale du COS en agrégeant différentes cartes de COS et je donne des des pistes sur la façon de tirer parti deprédictions globales pour les pays disposant de peu de données, en utilisant une stratégie d’échantillonnage peu coûteuse et efficace.Ensuite, dans les Chapitres 4 et 5, je me concentre sur le domaine de validité de fonctions de pédotranfert utilisées pour la prédiction de la densité apparente et je développe une approche nouvelle pour traiter l’épaisseur des sols en France. J’y propose également des stratégies efficientes pour améliorer la précision de leurs prédictions. Je passe ensuite de la CNS à la cartographie de fonctions des sols en prenant comme exemple la cartographie du potentiel de séquestation (Chapitre 6) et de stockage (Chapitre 7) en COS. Ces chapitres contribuent à améliorer de nombreux aspects concernant la CNS et son application au programme Glo

Role of Environment Variables in Spatial Distribution of Soil C, N, P Ecological Stoichiometry in the Typical Black Soil Region of Northeast China

The effects of environmental factors on topsoil nutrient distribution have been extensively discussed, but it remains unclear how they affect spatial characteristics of soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry at different depths. We collected 184 soil samples in the typical black soil region of northeast China. Ordinary kriging was performed to describe the spatial distribution of soil C, N, and P eco-stoichiometry. Redundancy analysis was used to explore relationships between C:N:P ratios and physicochemical characteristics. The soil classification was studied by hierarchical cluster analysis. The mean C, N, and P contents ranged from 15.67 to 20.08 g&middot;kg&minus;1, 1.15 to 1.51 g&middot;kg&minus;1, and 0.80 to 0.90 g&middot;kg&minus;1 within measured depths. C, N, and P concentrations and stoichiometry increased from southwest to northeast, and the Songhua River was identified as an important transition zone. At 0&ndash;20 cm, soil water content explained most of the C, N, and P content levels and ratios in cluster 1, while latitude had the highest explanatory ability in cluster 2. For 20&ndash;40 cm, soil bulk density was the main influencing factor in both clusters. Our findings contribute to an improved knowledge of the balance and ecological interactions of C, N, and P in northeast China for its sustainability

Role of Environment Variables in Spatial Distribution of Soil C, N, P Ecological Stoichiometry in the Typical Black Soil Region of Northeast China

The effects of environmental factors on topsoil nutrient distribution have been extensively discussed, but it remains unclear how they affect spatial characteristics of soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry at different depths. We collected 184 soil samples in the typical black soil region of northeast China. Ordinary kriging was performed to describe the spatial distribution of soil C, N, and P eco-stoichiometry. Redundancy analysis was used to explore relationships between C:N:P ratios and physicochemical characteristics. The soil classification was studied by hierarchical cluster analysis. The mean C, N, and P contents ranged from 15.67 to 20.08 g·kg−1, 1.15 to 1.51 g·kg−1, and 0.80 to 0.90 g·kg−1 within measured depths. C, N, and P concentrations and stoichiometry increased from southwest to northeast, and the Songhua River was identified as an important transition zone. At 0–20 cm, soil water content explained most of the C, N, and P content levels and ratios in cluster 1, while latitude had the highest explanatory ability in cluster 2. For 20–40 cm, soil bulk density was the main influencing factor in both clusters. Our findings contribute to an improved knowledge of the balance and ecological interactions of C, N, and P in northeast China for its sustainability

Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy

Visible and near-infrared (Vis&ndash;NIR) spectroscopy can provide a rapid and inexpensive estimation for soil organic carbon (SOC). However, with respect to field in situ spectroscopy, external environmental factors likely degrade the model accuracy. Among these factors, moisture has the greatest effect on soil spectra. The external parameter orthogonalization (EPO) algorithm in combination with the Chinese soil spectroscopic database (Dataset A, 1566 samples) was investigated to eliminate the interference of the external parameters for SOC estimation. Two different methods of EPO development, namely, laboratory-rewetting archive soil samples and field-collecting actual moist samples, were compared to balance model performance and analytical cost. Memory-based learning (MBL), a local modeling technique, was introduced to compare with partial least square (PLS), a global modeling method. A total of 250 soil samples from Central China were collected. Of these samples, 120 dry ground samples (Dataset B) were rewetted to different moisture levels to develop EPO P1 matrix. Seventy samples (Dataset C) containing field-moist intact and laboratory dry ground soils were used to establish EPO P2 matrix. The remaining 60 samples (Dataset D) also containing field-moist intact and laboratory dry ground soils were employed to validate the spectral models developed based on Dataset A. Results showed that EPO could correct the effect of external factors on soil spectra. For PLS, the validation statistics were as follows: no correction, validation R2 = 0.02; P1 correction, validation R2 = 0.56; and P2 correction, validation R2 = 0.57. For MBL, the validation results were as follows: no correction, validation R2 = 0.06; P1 correction, validation R2 = 0.65; and P2 correction, validation R2 = 0.69. The P2 consistently yielded better results than P1 did but simultaneously increased the sampling time and economic cost. The use of the P1 matrix and the MBL algorithm was recommended because it could reduce the cost of establishing in situ models for SOC

Fixed Spraying Systems Application in Citrus Orchards: Nozzle Type and Nozzle Position Effects on Droplet Deposition and Pest Control

Pesticide application is an essential means of controlling plant diseases and pests in citrus orchards. In recent years, fixed spraying systems have gradually been used as alternatives to traditional sprayers and manual sprayers in some hilly citrus orchards. In this paper, influences of fixed system spraying parameters, such as droplet size and spraying height, on spraying quality were elucidated and analyzed. The performances of two nozzle types, pressure-swirl nozzles and fixed spray plate sprinklers, were assessed and compared by effective droplet coverage ratio (DCR), droplet distribution uniformity coefficient of variation (CV), and droplet penetration ratio (DPR). The results showed that appropriately increasing droplet size and spraying height could improve the DCR and distribution uniformity of pressure-swirl nozzles. The DCR and distribution uniformity of fixed spray plate sprinklers had a positive correlation with droplet size, while spraying height had no significant effect on these variables. Additionally, with the increase in droplet size, DPR initially increased and then gradually decreased. The optimized results showed that the optimal parameters for pressure-swirl nozzles were a droplet size of 240 μm and spraying height of 100 cm, while for fixed spray plate sprinklers, the results were a droplet size of 240 μm and spraying height of 50 cm. Comparison results showed that the spraying quality of fixed spray plate sprinklers was better overall, with values of DCR, CV, and DPR being 37.15%, 24.20%, and 71.67%, respectively, while the corresponding values for pressure-swirl nozzles were 39.65%, 35.41%, and 56.02%. Based on the above results and the occurrence rule of citrus pests and disease, the optimal spraying parameters of fixed spraying systems were selected to control the Asian citrus psyllid Diaphorina citri. Furthermore, the effect of fixed spraying systems on controlling Diaphorina citri reached the maximum at 3 days after spraying, which was 97.83%, and the effect declined at 14 days after spraying, which was 85.47%. This study provides valuable scientific references for guiding the application of fixed spraying systems in hilly citrus orchards

Towards Optimal Variable Selection Methods for Soil Property Prediction Using a Regional Soil Vis-NIR Spectral Library

Soil visible and near-infrared (Vis-NIR, 350–2500 nm) spectroscopy has been proven as an alternative to conventional laboratory analysis due to its advantages being rapid, cost-effective, non-destructive and environmentally friendly. Different variable selection methods have been used to deal with the high redundancy, heavy computation, and model complexity of using full spectra in spectral modelling. However, most previous studies used a linear algorithm in the variable selection, and the application of a non-linear algorithm remains poorly explored. To address the current knowledge gap, based on a regional soil Vis-NIR spectral library (1430 soil samples), we evaluated seven variable selection algorithms together with three predictive algorithms in predicting seven soil properties. Our results showed that Cubist overperformed partial least squares regression (PLSR) and random forests (RF) in most soil properties (R2 > 0.75 for soil organic matter, total nitrogen and pH) when using the full spectra. Most of variable selection can greatly reduce the number of spectral bands and therefore simplified predictive models without losing accuracy. The results also showed that there was no silver bullet for the optimal variable selection algorithm among different predictive algorithms: (1) competitive adaptive reweighted sampling (CARS) always performed best for the PLSR algorithm, followed by forward recursive feature selection (FRFS); (2) recursive feature elimination (RFE) and genetic algorithm (GA) generally had better accuracy than others for the Cubist algorithm; and (3) FRFS had the best model performance for the RF algorithm. In addition, the performance was generally better when the algorithm used in the variable selection matched the predictive algorithm. The outcome of this study provides a valuable reference for predicting soil information using spectroscopic techniques together with variable selection algorithms