Regional ensemble modeling reduces uncertainty for digital soil mapping

Brungard, Colby; Nauman, Travis; Duniway, Michael C.; Veblen, Kari E.; Nehring, Kyle C.; White, David S.; Salley, Shawn W.; Anchang, J.

doi:10.1016/j.geoderma.2021.114998

Cited by 32 publications

(15 citation statements)

References 69 publications

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“…Recent advancements in soil modeling have occurred for both digital and conventional soil mapping. For digital soil mapping, advances in machine learning, and in particular ensemble modeling [ 74 – 76 ], have resulted in improvements in model accuracy. Recent digital soil models also provide spatial estimates of model uncertainty [ 72 , 77 ], which can help end-users assess how reliable a model might be in a certain area.…”

Section: Discussionmentioning

confidence: 99%

Errors in soil maps: The need for better on-site estimates and soil map predictions

Buenemann

Coetzee

Kutuahupira³

et al. 2023

PLoS ONE

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High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need.

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Section: Discussionmentioning

confidence: 99%

Errors in soil maps: The need for better on-site estimates and soil map predictions

Buenemann

Coetzee

Kutuahupira³

et al. 2023

PLoS ONE

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“…Mapping soil texture has been suggested as a guide for shrubland management elsewhere (Wonkka et al 2016), and high‐resolution raster‐based maps of soil physical properties (30‐m resolutions; e.g. Nauman & Duniway 2020 a ; Brungard et al 2021) are increasingly available. This information can be used to help prioritize areas for restoration because soil texture and depth are relatively simple measures that can be used to delineate landscapes and serve as an accessible, common language between land managers and researchers.…”

Section: Discussionmentioning

confidence: 99%

“…The soil depth (B) and surface sand content (C) of the study sites (black outlines; Beef Basin in upper left, Hart's Draw upper right, Alkali Flat bottom right, and Black Mesa bottom left) and surrounding areas. Sagebrush ecosystem from LANDFIRE existing vegetation type (Hanser 2011), soil depth based on soil taxonomic depth classes and mapped by Brungard et al (2021), and sand is depth‐weighted percent by weight from 0 to 15 cm from Nauman and Duniway (2020 b ). Also see Supplement S3.…”

Section: Introductionmentioning

confidence: 99%

“…We therefore tested how big sagebrush seedling survival responded to different combinations of soil depth and texture on the Colorado Plateau, U.S.A., a region characterized by bedrock-controlled, sandy soils. We hypothesized that (1) survival would be highest in coarser soils with a shallower depth to restrictive layer (approximately 75 cm) due to reduced evaporative soil water loss and improved retention of soil water within the seedling rooting zone, and (2) survival would be (Hanser 2011), soil depth based on soil taxonomic depth classes and mapped by Brungard et al (2021), and sand is depth-weighted percent by weight from 0 to 15 cm from Nauman and Duniway (2020b). Also see Supplement S3.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Soil depth and precipitation moderate soil textural effects on seedling survival of a foundation shrub species

Veblen

Nehring

Duniway

et al. 2022

Restoration Ecology

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In drylands, there is a need for controlled experiments over multiple planting years to examine how woody seedlings respond to soil texture and the potentially interactive effects of soil depth and precipitation. Understanding how multiple environmental factors interactively influence plant establishment is critical to restoration ecology and in this case to broadscale restoration efforts in western U.S. drylands dominated by big sagebrush (Artemisia tridentata). We planted sagebrush seedlings across a range of soil textures and depths in the southern portion of the species' range, on the Colorado Plateau, USA. We evaluated survival of repeated plantings of caged and uncaged seedlings over 2 years across 20 plots in wet versus average precipitation years at one site, and examined broader patterns of sagebrush seedling survival during an average precipitation year in 56 plots across four sites. First‐year survival was >9× higher under wet than average precipitation. Under favorable (wet) conditions, early sagebrush seedling survival was highest on coarser soils, especially those that also had a shallower restrictive layer (e.g. 50–100 cm). Under average precipitation, soil texture and depth effects on survival of newly planted seedlings were much weaker, but older (>1 year) seedlings benefitted from growing on coarser‐textured soils. It may be possible to increase survival by sheltering seedlings with small mesh cages, which likely improve moisture availability. Our results provide new insights into environmental factors that limit woody seedling survival in drylands and illustrate that planting in wet years and incorporating detailed soil setting information could increase survival of sagebrush seedlings in restoration projects.

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“…The results of this work demonstrate a possible methodology that can be expanded upon to develop such a universal model. While developing a prediction for a single STR across the eastern United States was possible with a single, relatively simple model, when translat-ing this approach to the continental scale, it is likely that an ensemble modeling approach (Brungard et al, 2021) will be required to properly predict multiple STR across more ecophysiographically diverse areas. This project identifies the need to proceed with research to evaluate models developed from different MD and how to assemble continental, subcontinental, and global datasets developed from various MD.…”

Section: Discussionmentioning

confidence: 99%

Modeling the thermic soil temperature regime boundary of the eastern United States

D'Avello¹,

Bathgate

Thompson

2021

Soil Science Soc of Amer J

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Within U.S. soil taxonomy,soil temperature regimes (STRs) are recognized and incorporated in the classification and mapping of soils. Although knowledge of STR can inform crop selection, yield estimates, and nutrient management decisions, detailed and current maps of STRs derived from measured data are not available for most of the United States. Soil temperature measurements at the prescribed depth of 50 cm were summarized for 69 Soil Climate Analysis Network (SCAN) stations in the eastern United States. Mean annual soil temperature (MAST) was calculated for the 2005-2015 period from monthly averages calculated from daily soil temperature observations. A prediction equation for MAST was developed using mean annual air temperature (MAAT) from the PRISM (Parameter-elevation Regressions on Independent Slopes Model) group as a predictor: MAST = 2.49 + (0.91 × MAAT). The adjusted r 2 was .96 with a RMSE of 0.74 ˚C and mean absolute error of 0.59 ˚C based on 10-fold cross-validation. Orographic influence is incorporated in PRISM MAAT, yielding a simple model. The MAST surfaces were calculated by applying the prediction equation to MAAT grids for the 1895-2015 period. The yearly MAST surfaces allow estimation of STR over time and for various summary periods including the 1895-2015 record, 30-yr normal, and probabilities based on individual years for the 1895-2015 period. Based on this investigation, the mesic-thermic STR boundary occurs north of the presently recognized location, is dynamic, and is supported by existing native plant communities associated with thermic STR. INTRODUCTIONSoil temperature is a dynamic soil property that influences the physical, chemical, and biological processes in the soil (Soil Survey Staff, 2014). Solar radiation is the primary source of heat entering the soil (Shul'gin, 1965). Local site characteristics including land cover, slope aspect, slope gradient, and the

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Regional ensemble modeling reduces uncertainty for digital soil mapping

Cited by 32 publications

References 69 publications

Errors in soil maps: The need for better on-site estimates and soil map predictions

Errors in soil maps: The need for better on-site estimates and soil map predictions

Soil depth and precipitation moderate soil textural effects on seedling survival of a foundation shrub species

Modeling the thermic soil temperature regime boundary of the eastern United States

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