SSS10.2

As an alternative to the costly wet chemical analysis of soil, Machine Learning (ML) algorithms can be applied for the quantification of soil properties through prediction models. In this study, we evaluate the performances of the Random Forest (RF) algorithm and Partial Least Square Regression (PLSR), in the prediction of soil variables, including CEC, pH, total contents of C and N as wells as other elements (Al, Fe, Ca, Mn, Mg, K, and Na) based on FTIR-spectra of Relict Charcoal Hearth (RCH) soils and reference forest soils (Non-RCH). We investigate the effect of high quantities of charcoal in the soil on the prediction models. Preliminary results suggest that there is no significant difference in the results of prediction models for total N, C, and Fe contents, while the accuracy of PLSR in the prediction of pH, Mg, and Ca, and RF performance in prediction of pH and C decreased for RCH soils. Both algorithms demonstrate higher accuracies in the prediction of Al within the RCH soils. Prediction of CEC, Na, K, Mg (RF) within the RCH soils, Al (PLSR) for Non-RCH soils, and Mn and Ca (RF) for both soil types resulted in lower quality of predictions. 

It can be inferred from the results that the performance of FTIR-based prediction models can be affected by the presence of charcoal in soil due to the nature of spectral features reflecting the soil composition. The presence of charcoal in soil likely alters the absorption interference and peak overlaps, which can result in lower accuracy of the prediction models. In addition to the accuracy of the prediction models, we evaluate the reliability of the weighted wavenumbers (as important variables) in each prediction, which provides information about the correlation of spectral features and chemical properties. It can be studied through the Variable Importance Plot in RF and Variable Importance on Projection through PLSR (VIPs), which show high potential for studying soil composition and metal distribution in mineral and organic soil fractions despite the observed weaknesses in weighing wavenumbers in predictions. Therefore, we assess and compare the quality of information gained regarding soil chemical properties from the algorithms besides a sole quantification of soil parameters. Furthermore, we applied the developed prediction models on a large number (n > 600) of FTIR-spectra of RCH soils to investigate the practical application of the models and thereby compare spectral derived chemical properties of the studied Technosols and reference forest soils.

How to cite: Ramezany, S., Bonhage, A., and Raab, T.: Comparing prediction algorithms in FTIR-based chemometric analysis to predict soil variables within centuries-old charcoal rich Technosols, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5507, https://doi.org/10.5194/egusphere-egu22-5507, 2022.

Water is the main constrain for yield in semiarid vineyards, as in Spain. Therefore, effective management of the water resource is a priority to alleviate the instability in productivity and negative socio-economic impacts that the drought phenomena may cause. Vine growers always seek a certain level of water stress in vineyards, increasing wine quality. This target implies monitoring of crop water status during the agronomic campaign.

Traditional methods for field data acquisition involve extensive sampling and time-consuming, destructive and discrete measurements, thus impractical for monitoring large areas and commercial-scale farming. Nonetheless, vineyards are heterogeneous and sparse crop systems with significant intra- and inter-field variability, being soil one of the sources of this variability(Taylor, J., & Bramley, R., 2004). Remote sensing technology is a valuable tool for studying the significant complexity of vineyards agroecosystems. Among the remote sensing techniques, unmanned aerial vehicles (UAVs) have become a technology with affordable operational costs, non-invasive, and high spatial and temporal resolution used in commercial vineyards. UAVs are coupled with multispectral and thermal cameras that acquire aerial images of specific spectral responses of the vegetation and thermal infrared region of the spectrum.

This work aims to study how soil variability influences the monitoring of crop water status through multispectral and thermal infrared sensors installed in the UAV platform. In order to do it, a commercial Merlot variety vineyard located in Yepes, Toledo, was arranged on a trellis with a plantation frame 2.60 x 1.10 m established in 2002. This variety is located in two soil types present distinctive hydraulic properties and different water retention capacity

Canopy temperature and thermal-based indicators from airborne thermal imaging are used to map spatial variability and quantify crop water status. The crop water stress index (CWSI) is one of viticulture's most common water stress indices(Idso, et al., 1981) The normalized difference vegetation index (NDVI) has been proven to represent crop structural characteristics and vigour, correlated to vine water status in environments where soil water deficit is a determinant factor for vine crop(Hall, et al., 2002)

The results point out that the difference between both zones is statistically significant, indicating the role of soil in the water status. Several conclusions are obtained when comparing the physiological parameters.

REFERENCES

• Hall, A., Lamb, D. W., Holzapfel, B., & Louis, J. (2002). Optical remote sensing applications in viticulture––a review. Australian Journal of Grape and Wine Research, 8, 36–47. doi:10.1111/j.1755- 0238.2002.tb00209.x.
• Idso, S. B., Jackson, R. D., Pinter, P. J., Reginato, R. J., & Hatfield, J. L. (1981). Normalizing the stressdegree day parameter for environmental variability. Agricultural Meteorology, 24, 45–55.
• Taylor, J., & Bramley, R. (2004). Precision viticulture: Managing vineyard variability. In R. Blair, P. Williams, & S. Pretorius (Eds.), Proceeding of 12th Australian Wine Industry Technical Conference, Workshop 30B (pp. 51–55). Australia: Melbourne Convention Centre

Acknowledgements: Financial support provided by Comunidad de Madrid through calls for grants for the completion of Industrial Doctorates is greatly appreciated.

How to cite: Atencia Payares, L. K., Rico Prieto, I., Camacho Alonso, G., Cámara, J., Gómez del Campo, M., and Tarquis Alfonso, A. M.: Discrimination of agro-zones using UAVs platform in a commercial vineyard. Case study of the Merlot variety in Yepes-Toledo., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11818, https://doi.org/10.5194/egusphere-egu22-11818, 2022.

Water availability in vineyards plays an integral role in the sustainability of high-quality grapes and prevention of devastating crop loses. Stem water potential (Ψstem) reflects consistently vineyard water status, serving as an aid in irrigation management. However, some drawbacks make the Ψstem little used in commercial vineyards. It requires a pressure chamber, contracting a gas supplier, the need for one or two technicians to carry out the measurement, and the small size of the sample obtained limits the use to control large areas that normally present high intra-field variability.

The objective of this work was to establish a relationship between the Ψstem and the hyperspectral vegetation indices. Four irrigation doses were imposed in a commercial vineyard. Ψstem was measured five days during three-time intervals a day in 2019. The data for the calculation of vegetation indices can be taken quickly by means of a multispectral camera mounted on a UAV, be recorded and processed later. Two different indexes were calculated: NDVI and TCARI/OSAVI.

A total of 12 flights have been made, in addition to 320 measured data for the Ψstem, on 5 different dates at three different time intervals (morning, noon and afternoon) during 2019.There was a significant linear correlation (R² = 0.69, P <0.001) between the TCARI / OSAVI and the Ψstem. Despite the fact that the most widely used is the NDVI, in this study, the TCARI/OSAVI has obtained a tighter adjustment in all cases than NDVI.

The relationship allows estimating the Ψstem from the index (TCARI/OSAVI), which allows the knowledge of the vineyard water status of a larger area, improving the irrigation management in a more functional way for commercial plantations.

ACKNOWLEDGEMENTS

Grant AGL2016-77282-C3-2R funded by MCIN/AEI/ 10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union NextGenerationEU/PRTR”.

This work has been possible thanks to Licinia Wines (Morata de Tajuña, Madrid).

REFERENCES

Cancela, J.J.; Fandiño, M.; Rey, B.J.; Dafonte, J.; González, X.P. (2017). Discrimination of irrigation water management effects in pergola trellis system vineyards using a vegetation and soil index. Agricultural Water Management 183, 70-77. https://doi.org/ 10.1016/j.agwat.2016.11.003

Espinoza, C.Z., Khot, L.R., Sankaran, S., Jacoby, P.W. (2017). High resolution multispectral and thermal remote sensing based water stress assessment in surface irrigated grapevines. Remote Sensing, 9 (9). https://doi.org/10.3390/rs9090961

How to cite: Camacho, G., Hueso, A., Mendoza, G., Ramos, J., Tarquis, A., Gómez del Campo, M., Baeza, P., and López, J.: Use of vegetation indices for irrigation management in commercial vineyards, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11144, https://doi.org/10.5194/egusphere-egu22-11144, 2022.

Few studies to date have investigated the effect of uncertainty in soil property estimates from spectroscopy on soil management. In this study we considered the implications for variable rate application of phosphorus (P) and potassium (K) fertiliser. First, the uncertainty in soil available P and K estimates from spectroscopy was quantified as a function of the calibration set size at the field-scale.

Based on the observed variation in P and K in four experimental fields, we simulated 100 realisations per field for an in silico experiment. To simulate the process of sampling soil and predicting fertiliser requirement, we performed sampling on our simulated fields using a spatial coverage design. We added a calibration error to each sample value to simulate the error associated with spectroscopic prediction. Kriging was used to estimate the variation in the soil property of interest. We then computed the fertiliser requirement needed to minimise the expected loss associated with predictions. Here, the expected loss is defined as the difference in profit between applying fertiliser based on the estimated soil nutrient concentration accounting for uncertainty relative to the profit that would be gained from fertiliser application given the true soil nutrient concentration in known. We also accounted for data acquisition costs in computing the expected profit.

Results showed that calibration sample size outweighed the effect of total sample size on the uncertainty associated with predictions. Equally, for the same calibration set size, there were large differences in the kriging variance between total sample sizes. When data acquisition costs were disregarded, the expected loss for available P was particularly affected by the total sample size. For available K, the calibration sample size had a predominant effect on the expected loss. The expected loss showed diminishing returns on investment suggesting that there is an optimum sample size. However, the expected profit was dominated by the costs of sampling and spectroscopy, indicating that currently using spectral methods to inform fertiliser management is not cost effective. That is, no combination of the total- and calibration sample sizes considered would result in a financial gain and could thus be considered optimal. Should costs substantially reduce then spectral methods offer a promising method for informing variable rate management. We conclude that the loss function approach is an appropriate method to assess whether soil spectroscopy is a cost-effective means to inform soil management. We further suggest its application in different case-studies to gain more robust insight in the value of applied soil spectroscopy.

How to cite: Milne, A., Breuer, T., Haefele, S., Hannam, J., Webster, R., and Corstanje, R.: Quantifying the effect of prediction uncertainty from soil spectroscopy on soil management, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10580, https://doi.org/10.5194/egusphere-egu22-10580, 2022.

The goal of this work is to perform soil classification with uncertainty quantification for a structured treatment of the output classes. Uncertainty can help in this setting to make predictions more informative with regard to class relationships. This is of particular interest due to the often highly related nature of the distinguished soil types. Incorporating knowledge about class structure into the model also provides opportunity for improving the model accuracy. Our main focus, however, is to enable modellers to better understand and work with this structure during analysis.
For example, post-hoc aggregation of class labels into supersets facilitates applications such as letting the model choose an ontological level on which it can confidently distinguish the output class. It can likewise be used to determine the combined probability of specified classes that share a property of interest.
Technically, this works by learning a latent Gaussian distribution, for example using a Gaussian Process model, and mapping it to a distribution over the class probabilities. We demonstrate this approach, explore possible applications for exploiting uncertainty information, in particular with regard to the class hierarchy, and compare the performance of different model variants in terms of accuracy and calibration.

How to cite: Gläßle, T., Rau, K., Scholten, T., and Hennig, P.: Hierarchical Soil Classification using Gaussian Processes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5164, https://doi.org/10.5194/egusphere-egu22-5164, 2022.

Spatial information, inferred from samples, is needed for decision-making but is uncertain. One way to convey uncertain information is with probabilities (e.g., that a value falls below a critical threshold). We examined how different professional groups (agricultural scientists or health and nutrition experts) interpret information, presented this way when making a decision about interventions to address human selenium (Se) deficiency. The information provided was a map, either of the probability that Se concentration in local staple grain falls below a nutritionally-significant threshold (negative framing) or of the probability that grain Se concentration is above the threshold (positive framing). There was evidence for an effect of the professional group and of framing on the decision process. Negative framing led to more conservative decisions; intervention was recommended at a smaller probability that the grain Se is inadequate than if the question were framed positively, and the decisions were more comparable between professional groups under negative framing.  Our results show the importance of framing in probabilistic presentations of uncertainty, and of the background of the interpreter. Our experimental approach could be used to elicit threshold probabilities that represent the preferences of stakeholder communities to support them in the interpretation of uncertain information.

How to cite: Chagumaira, C., Nalivata, P., Chimungu, J., Gashu, D., Broadley, M., Milne, A., and Lark, M.: Stakeholder interpretation of probabilistic representations of uncertainty in spatial information: an example on the nutritional quality of staple crops, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9222, https://doi.org/10.5194/egusphere-egu22-9222, 2022.

Any model we use is an approximation of the real world and associated with some uncertainty about the transition of the model result into the real world. This issue is even stressed in situations where models are used to predict a set of variables over time. A prominent and widely used example are runs of climate models that generate ensembles of possible future paths of our climate including several correlated variables. Typically, these ensembles are presented and assessed in an univariate approach where e.g. spaghetti plots of single variables depict the variability within the future paths. What remains hidden are the dependencies among the variables. As the correlation measures might be ambiguous and summarize the possibly complex dependence structures in a single value, we use the concept of copulas to illustrate the variability of multivariate distributions. Going back to Sklar’s theorem any continuous multivariate distribution can be decomposed into its univariate margins and their copula describing the dependence between the univariate marginals. Copulas can serve two purposes in this context, to model and quantify i) the multivariate variability within the ensemble and ii) the variability in the dependence between the variables among the ensembles.

We illustrate the effect of different copula families on the inherent uncertainties in the ensemble based on synthetic data. Furthermore, we use climate predictions of the current century to identify and study the dependence and its variability in the data set. In order to increase the data and to avoid the expectation that we can do an assessment precisely for each year, the data is grouped by decades. Our approach is illustrated in R and an exploratory analysis is supported through an interactive Shiny application. The Shiny application also serves to communicate the multivariate uncertainty in the data.

How to cite: Gräler, B.: Communicating Uncertainties in Bi- and Multivariate Distributions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11075, https://doi.org/10.5194/egusphere-egu22-11075, 2022.

Biological soil crusts (BSC) are an integral part of dryland ecosystems and  They are intimate association between soil particles and diverse microorganism, including cyanobacteria, algae, lichen and bryophites, which live on the uppermost millimeters of soil. The successional stage of BSCs cause changes in soil surface conditions such as soil stability, soil aggregate stability and roughness, and affects some ecosystem processes including nutrient cycling, erosion, runoff, water retention and increase carbon secuestration.
Fractal dimension can be associated with the roughness of a surface represented by a digital image. Because surface roughness is so related to scale and to BSCs successional stage, a fractal analysis is worth pursuing and it would give realistic results. In order to differentiate the successional stages of  BSCs in quantity, we determined several fractal parameters in a series of different developmental BSCs in a tropical semiarid region of Venezuela. A progressive  classification of soil crust type from incipient crust through various BSCs successional stages were selected in two semiarid ecosystems (Quibor and Ojo de agua) in the Quibor Depression, Venezuela. This study focus on characterize the development stage of the BSC based on Fractal image analysis.
To this end, grayscale images of different biological soil crust at different successional stages were taken, each image corresponding to an area of 12.96 cm² with a resolution of 1024x1024 pixels. For each image lacunarity and fractal dimension through the differential box counting method were  calculated, using the software ImageJ/Fraclac. Fractal dimension and lacunarity could be good descriptors of BSCs successional stages, and could be very useful in a further exploration of the link between the BSCs successional stages and other soil properties.

How to cite: Florentino, A., Ospina, A., and Tarquis, A. M.: A Fractal Approach to Evaluate Biological Soil Crust in Arid and Semiarid Ecosystems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13174, https://doi.org/10.5194/egusphere-egu22-13174, 2022.

The mechanical conditions of the soil is essential for the planning and implementation of agricultural tillage. Determining the variability of the soil by sensors helps to optimize the intensity of the use of tillage components, control their wear and improve the energy consumption of the operation. This work presents the development of hardness and plasticity parameters by means of sensors implanted in tillage components such as harrow discs. The vibration measurement by an electronic sensor integrated in a harrow disc is packaged and sent to a control node where it is transformed to the frequency space and parameterized through the power spectral density (PSD). The energy analysis makes it possible to establish the variability of the terrain in degrees of hardness and plasticity; and thereby determine differential harrow management or plot mapping for decision making.

Lately, a Detrended Flucutation Analysis (DFA) of Hurst Index on these series were applied for first time. The presistence and antipersistant character of the Hurst index can help to identify the  hardeness of the plot analysed. Results are showed comparing different soil texture and soil humidity scenarios.

How to cite: López-Cuervo, S., Lamas López, F., Lazcano Lasa, M. B., Saa-Requejo, A., Martín-Sotoca, J. J., Pérez Martín, E., López Herrera, J., Caso Diaz-Aguilar, J. G., and Tarquis, A. M.: Hust Index applied in the soil resistance measurements based on a harrow discs sensors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13177, https://doi.org/10.5194/egusphere-egu22-13177, 2022.

The spatial variability of soil water content (SWC) and its temporal evolution are two essential factors to optimize the irrigation efficiency. This work presents an application of the Generalized Structure Function (GSF) to analyse the SWC evolution during two types of drip irrigation: surface and subsurface. In this way, we will compare both types of irrigation.

The GSF has been normally applied on time series. In our context we have used is on transect series of SWC measured at two different soil depth and at different times. From this type of analysis, two parameters are calculated: Hurst Index (HI) and Multifractality (DH). A set of experimental runs were performed in two irrigated plots with either surface or subsurface drip irrigation. SWC was estimated through the cumulative temperature (T_cum) from a Distributed Temperature Sensor (DTS) recordings aided by the Active Heated Fiber Optic (AHFO) technique. The fiber optic cable was deployed at 5 and 25 cm underneath the soil in both plots. Soil was a loamy sand textures (77% sand, 16% loam and 7% clay), 982 kg·m^-1 bulk density, 3.67 mm·min^-1 saturated hydraulic conductivity and 62% porosity.

The SWC evolution during the experiments in subsurface irrigation presented an HI around 0.50 (random) and more constant than in surface irrigation. The spatial and temporal variability of data revealed a HI>0.50 (persistent character) at the upper layer of the surface irrigated plot caused by the unequal distribution of ponded water around certain emitters and a subsequent anti-persistent character (HI<0.50) at the bottom because of the heterogeneous infiltration. The DH values estimated from surface irrigated plot with wider variation range that those from subsurface irrigated and greater similarity between both depths in the subsurface irrigated plot.

The results are discussed from an agronomic point of view providing an insight into the required adaptation of both irrigation water depths and frequency to avoid water loss in either surface or subsurface irrigation systems.

How to cite: Rodriguez-Sinobas, L., Zubelzu, S., Martín-Sotoca, J. J., and Tarquis Alfonso, A. M.: Generalized Structure Function applied to Soil Water Content during surface and subsurface drip irrigation in a loamy soil., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13526, https://doi.org/10.5194/egusphere-egu22-13526, 2022.

Degradation of soils due to land use change driven by economic factors represents a major concern in many parts of the world. Important questions regarding soil degradation demand further efforts to better understand the effect of land use change on soil functions. With the advent of 3d Computer Tomography techniques and computing power, new methods are becoming available to address these questions. In this work, we investigate how land use change affects soil structure by using information theory to quantify the complexity of soil 3d X-ray CT soil samples in northeastern Brazil. We implement the Fisher-Shannon method, borrowed from information theory, to quantify the complexity of 14 3d CT soil samples from native Atlantic forest sites, and15 samples from nearby sites converted to sugarcane plantation. The distinction found between the samples from the Atlantic forest and the sugarcane plantation is found to be quite pronounced. The discrimination results at the level of 89.6% accuracy were obtained in terms of Fisher information measure (FIM) alone, and 93% level accuracy was attained considering the complexity in the Fisher Shannon plane (FSP). Atlantic forest samples are found to be generally more complex than those from the sugar plantation. The approach introduced in the current work does not use arbitrary parameters, and it provides a rather precise quantitative FSP complexity measure, that may be seen as a quantifier of soil degradation level.

How to cite: Stosic, B., Albuquerque-Aguiar, J., Menezes, R., Dantas-Antonino, A., Stosic, T., and Tarquis, A. M.: Quantifying soil complexity using Fisher Information of 3d X-ray CT scan images, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13157, https://doi.org/10.5194/egusphere-egu22-13157, 2022.

Analyzing the spatial features of soil pore networks is very important to obtain different parameters that will be useful in obtaining simulation models for a range of physical, chemical, and biological soil processes. Over the last decade, technological advances in X-ray computed tomography (CT) have improved the reconstruction of natural porous soils at very fine scales. Delimiting the pore network (pore space) by different binarization methods can result in different spatial distributions of pores influencing the connectivity and geometry parameters used in the simulation models [1].

The 3D Combining Singularity-CV method is applied in this work. It combines the Singularity – CV (Concentration Volume) method [2] and a global one (the Maximum Entropy method) to improve 3D pore space detection [3].

Random walks have been applied in global soil pore networks to obtain parameters such as spectral dimensions or tortuosity to explain the diffusion processes better [4,5]. In this work, random walks are locally applied to obtain information about the local geometry and connectivity in 3D pore networks for the first time. The results show what is gained in this local analysis that at the global scale is missing. 

References:
[1] Sezgin, M., Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13 (1), 146–165. https://doi.org/10.1117/1.1631315.
[2] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2018). Local 3D segmentation of soil pore space based on fractal properties using singularity maps. Geoderma, vol. 311, February 2018, pp 175-188. http://dx.doi.org/10.1016/j.geoderma.2016.11.029.
[3] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B., Paz-González, A., and Tarquis, A.M. (2018). Combining global and local scaling methods to detect soil pore space. J. of Geo. Exploration, vol. 189, June 2018, pp 72-84. http://dx.doi.org/10.1016/j.gexplo.2017.06.017.
[4] Tarquis, A.M., Sanchez, M.E., Antón, J.M., Jimenez, J., Saa-Requejo, A., Andina, D. and Crawford, J. W. (2012). Variation in Spectral and Mass Dimension on Three-Dimensional Soil Image Processing. Soil Science: February 2012 - Volume 177 - Issue 2 - p 88-97. doi: 10.1097/SS.0b013e31824111b6.
[5] T.G. Tranter, M.D.R. Kok, M. Lam and J.T. Gostick. (2019). Pytrax: A simple and efficient random walk implementation for calculating the directional tortuosity of images. SoftwareX 10, 100277. https://doi.org/10.1016/j.softx.2019.100277

Acknowledgements:
The authors acknowledge the support from Project No. PGC2018-093854-B-I00 of the "Ministerio de Ciencia, Innovación y Universidades" of Spain and the funding from the “Comunidad de Madrid” (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330.

How to cite: Martin Sotoca, J. J., Saa-Requejo, A., Zubelzu, S., and Tarquis, A. M.: Using random walks to characterize local geometry and connectivity in 3D soil pore networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5039, https://doi.org/10.5194/egusphere-egu22-5039, 2022.

Soil salinization is one of the significant constraints to food security. Anthropogenic activities such as improper agricultural practices and poor drainage systems can accelerate salinization. The reclamation of these saline areas, such as the coastal areas in Jiangsu province (China), is vital to produce sufficient food, fodder, and fibre sustainably.

Soil washing was applied at Rudong location (Jiangsu) in 2007 as a method of soil reclamation. The monitoring of coastal saline soil improvement at three different depths (0-20, 20-40, 40-60 cm) has been followed through physical, chemical, and biological measures.

At the same time, soil samples collected by plastic rings were scanned, obtaining a central region of interest (ROI) of 512×512×512 voxels. These grey images, and their binary images, were analyzed in 2D and 3D using multiscaling techniques extracting standard multifractal parameters.

The results found different relationships between laboratory measures and image analysis parameters in which soil depth influences. Grey images parameters showed a stronger relation compared to binary images.

ACKNOWLEDGEMENTS

This research has been partially founded by the National Natural Science Foundation of China through Grant no. 42177393, MICINN through project no. Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain, the funding from the Comunidad de Madrid (Spain), the Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330.

How to cite: Sun, X., Tarquis, A., She, D., Han, X., Wang, H., and Tang, S.: Relations between multifractal soil pore characteristics and soil properties. A case study in coastal saline soils., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7901, https://doi.org/10.5194/egusphere-egu22-7901, 2022.

Biochar, the product of pyrolysis of biomass in the absence of oxygen, added to soil, has been explored in the last years to mitigate global warming. The biochar's nature could come from crop and tree residues, urban organic waste materials, and pig slurry, among others. A rising body of work quantifies the effect of pyrolysis conditions, mainly temperature, on chemical and physical biochar properties. One of these characteristics is porosity that is related with properties as water holding capacity. Besides quantifying the macro and micro-porosity, other parameters can be extracted from the pore size distribution (PSD). This work aims to extract scaling parameters from it to differentiate the biochar properties.

Biochars were prepared  from pig manure at three different pyrolysis temperatures: 300 (BPC-300), 450 (BPC-450) and 600ºC (BPC-600). Mercury injection porosimeter (MIP) was used to determine PSD of biochar for equivalent pore diameter from 1 mm to 0.005 mm. 

The multifractal formalism was employed to extract the scaling parameters. Mass exponent function and multifractal spectra showed that this method is suitable for mercury injection curves. The results show that with higher temperature applied in the pyrolysis process to obtain the biochar, the PSD multifractal characteristics decrease, pointing out a lower complexity in the PSD.

How to cite: Gascó Guerrero, G., Saa-Requejo, A., Tarquis, A. M., Martín-Sotoca, J. J., and Méndez, A. M.: Influence of pyrolysis temperature in the pore size distribution of biochar, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13272, https://doi.org/10.5194/egusphere-egu22-13272, 2022.