Supplementary material to "How well does Predictive Soil Mapping represent soil geography? An investigation from the USA"

The geography of soil is more important today than ever before. Models of environmental systems and myriad direct field applications depend on accurate information about soil properties and their spatial distribution. Many of these applications play a critical role in managing and preparing for issues of food security, water supply, and climate change. The capability to deliver soil maps with the accuracy and resolution needed by land use planning, precision agriculture, as well as hydrologic and meteorologic models is, fortunately, on the horizon due to advances in the geospatial revolution. Digital soil mapping, which utilizes spatial statistics and data provided by modern geospatial technologies, has now become an established area of study for soil scientists. Over 100 articles on digital soil mapping were published in 2018. The first and second generations of soil mapping thrived from collaborations between Earth scientists and geographers. As we enter the dawn of the third generation of soil maps, those collaborations remain essential. To that end, we review the historical connections between soil science and geography, examine the recent disconnect between those disciplines, and draw attention to opportunities for the reinvigoration of the long-standing field of soil geography. Finally, we emphasize the importance of this reinvigoration to geographers.

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How well does Predictive Soil Mapping represent soil geography? An investigation from the USA

10.5194/soil-2021-80 ◽

2021 ◽

Author(s):

David G. Rossiter ◽

Laura Poggio ◽

Dylan Beaudette ◽

Zamir Libohova

Keyword(s):

New York ◽

Soil Properties ◽

Spatial Patterns ◽

Landscape Metrics ◽

New York State ◽

Histogram Equalization ◽

Soil Mapping ◽

Partial Replacement ◽

Field Surveys ◽

The Usa

Abstract. We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by Predictive Soil Mapping (PSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales, and compare them to spatial patterns known from detailed field surveys (gSSURGO). The methods are illustrated with an example: topsoil pH for an area in central New York State. A companion report examines other areas, soil properties, and depth slices. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case we discover and discuss substan- tial discrepancies between PSM products, as well as large differences between the PSM products and legacy field surveys. These differences are in whole-map statistics, visually-identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, co-occurence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine-learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not better than the global model, and in some cases introduced artefacts from a lithology covariate. Uncertainty (5–95% confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the PSM products as a (partial) replacement for field-based soil surveys.

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Supplementary material to "No Silver Bullet for Digital Soil Mapping: Country-specific Soil Organic Carbon Estimates across Latin America"

10.5194/soil-2017-40-supplement ◽

2018 ◽

Author(s):

Mario Guevara ◽

Guillermo Federico Olmedo ◽

Emma Stell ◽

Yusuf Yigini ◽

Yameli Aguilar Duarte ◽

...

Keyword(s):

Latin America ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Supplementary Material ◽

Silver Bullet ◽

Country Specific

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Soil Mapping Based on Globally Optimal Decision Trees and Digital Imitations of Traditional Approaches

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9110664 ◽

2020 ◽

Vol 9 (11) ◽

pp. 664

Author(s):

Arseniy Zhogolev ◽

Igor Savin

Keyword(s):

Random Forest ◽

Decision Trees ◽

Regional Scale ◽

Soil Formation ◽

Test Site ◽

Soil Mapping ◽

Optimal Decision ◽

Formation Factor ◽

Trend Prediction ◽

Soil Geography

Most digital soil mapping (DSM) approaches aim at complete statistical model extraction. The value of the explicit rules of soil delineation formulated by soil-mapping experts is often underestimated. These rules can be used for expert testing of the notional consistency of soil maps, soil trend prediction, soil geography investigations, and other applications. We propose an approach that imitates traditional soil mapping by constructing compact globally optimal decision trees (EVTREE) for the covariates of traditionally used soil formation factor maps. We evaluated our approach by regional-scale soil mapping at a test site in the Belgorod region of Russia. The notional consistency and compactness of the decision trees created by EVTREE were found to be suitable for expert-based analysis and improvement. With a large sample set, the accuracy of the predictions was slightly lower for EVTREE (59%) than for CART (67%) and much lower than for Random Forest (87%). With smaller sample sets of 1785 and 1000 points, EVTREE produced comparable or more accurate predictions and much more accurate models of soil geography than CART or Random Forest.

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Migration and Multiplication of Pathogenic Bursaphelenchus xylophilus Isolates of Diverse Geographic Origins

Environmental Sciences Proceedings ◽

10.3390/iecf2020-07774 ◽

2020 ◽

Vol 3 (1) ◽

pp. 84

Author(s):

María Menéndez-Gutiérrez ◽

Lucía Villar ◽

Raquel Díaz

Keyword(s):

Geographic Origin ◽

Pine Wilt Disease ◽

Bursaphelenchus Xylophilus ◽

Fungal Culture ◽

Multiplication Rate ◽

Differential Host ◽

Geographic Origins ◽

Seedling Inoculation ◽

The Usa ◽

Supplementary Material

Unfavorable pine wilt disease expansion predictions require a rapid advance in genetic breeding against the causative agent of this disease, Bursaphelenchus xylophilus. The main strategy for breeding more resistant trees to B. xylophilus, is the use of highly virulent isolates in inoculation experiments. Different inoculation assays were conducted on Botrytis cinerea cultures, in addition to P. pinaster and P. radiata branch sections and seedlings. Seven virulent isolates of different geographic origin (The Japanese nematode isolates S10 and Ka4, the Portuguese Pt72CH and Pt72T, the Spanish SpSA1 and SpPO1, and the American USA745.) were used in the experiments. The main aim of this work is to investigate differences among the seven isolates. The experiments determined that the studied isolates are significantly different. On fungal culture, the isolate from the USA showed the highest multiplication rate. Both seedling inoculation and branch sections experiments pointed to the Portuguese isolate Pt52T and the Spanish SpPo1 as the most virulent to P. pinaster. Conversely, higher numbers of the Pt72CH isolate passed through P. pinaster branch sections. The most virulent isolate for P. radiata was the Japanese S10, though it only showed significant differences in mortality when compared to the Spanish SpSA1. These results suggest that B. xylophilus have differential host specificities. The supplementary material depicts the methodology used in the inoculation assays, as well as shows figures of the most relevant results.

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