Soil Mapping, Monitoring, and Assessment

Kimsey, Mark; Laing, Larry; Anderson, Sarah M.; Bruggink, Jeff; Campbell, Steven; Diamond, David M.; Domke, Grant M.; Gries, James; Holub, Scott M.; Nowacki, Gregory J.; Page‐Dumroese, Deborah S.; Perry, Charles H.; Rustad, Lindsey E.; Stephens, Kyle; Vaughan, R. E.

doi:10.1007/978-3-030-45216-2_9

Cited by 10 publications

(6 citation statements)

References 34 publications

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“…Among different soil units (SU), factor analysis of soil properties showed that upon varimax rotation, the principal components were characterized as indicators, explaining 78.9, 81.1, 82.0, and 80.5% of the total variance for Fluvisols, Calcisols, Cambisols, and Luvisols respectively (Tables 7 and 8). As our findings showed, the use of soil maps can efficiently deliver soil information to meet user needs for soil management and crop performance decisions, which is in accordance with previous studies [7,51] As was observed, the spatial variability in agricultural land is attributed both to geogenic factors and anthropogenic interventions. Principal component analysis (PCA) showed that the clustering of soil properties based on the SU and LU could better describe the variability of soil properties in Mediterranean smallholder farming, which was in line with previous studies [52,53].…”

Section: Discussionsupporting

confidence: 91%

Assessing Spatial Variability of Soil Properties in Mediterranean Smallholder Farming Systems

Kosma

Triantafyllidis

Zotos

et al. 2022

Land

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Smallholder farming systems are typical of the European Mediterranean region. Small farms of less than 2 hectares cover approximately 15% of cropland in the southern EU and only 5% across the EU. The greater variability of cultivated species per unit of cropland (ha), the different approaches, and empirical application of cultivation practices by smallholder farmers increase the spatial variability of soil properties. Therefore, a decision support tool for effective management practices was formed based on a soil indicators set, which is sensitive to changes under agricultural management practices and different LUs. The data for this task were collected from 364 crop fields. The data were clustered and correlated based on (a) the existing soil units (SU): Fluvisols, Cambisols, Luvisols, and Calcisols, and (b) the LU: pastureland, annual, and permanent crops. Principal component analysis (PCA) identified up to seven main components that can better explain soil variability properties. The results indicated that the selected soil indicators can explain only 70.98% of soil variability. Clustering the parameters based on LU and SU can explain up to 80% and 82% of soil properties’ variability, respectively. Factor analysis could function as a decision support tool for soil fertility management by farmers or policy makers, who aim to achieve higher yields, promote sustainable practices, maintaining, at the same time, a low cost of cultivation.

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Section: Discussionsupporting

confidence: 91%

Assessing Spatial Variability of Soil Properties in Mediterranean Smallholder Farming Systems

Kosma

Triantafyllidis

Zotos

et al. 2022

Land

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“…Traditionally, it has been done using polygon‐based approaches where equal values are given to defined categories, such as soil types (Cheng et al, 2019), or by parameterizing process‐based models to predict soil N across space and time (Grunwald, 2009). Spatial modeling could offer insight into trends, how changes in climate, environmental contaminants, and land management can alter soil properties by utilizing measurements from monitoring networks (Kimsey et al, 2020). However, these efforts have usually missed facets such as depth, uncertainty estimates, and information about ecological or functional relationships (Villarreal & Vargas, 2021).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

et al. 2022

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Soil nitrogen (N) is an important driver of plant productivity and ecosystem functioning; consequently, it is critical to understand its spatial variability from local‐to‐global scales. Here, we provide a quantitative assessment of the three‐dimensional spatial distribution of soil N across the United States (CONUS) using a digital soil mapping approach. We used a random forest‐regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) at 5‐km spatial grids. Across CONUS, there is a strong spatial dependence of soil N, where soil N concentrations decrease but uncertainty increases with soil depth. Soil N was higher in Pacific Northwest, Northeast, and Great Lakes National Ecological Observatory Network (NEON) ecoclimatic domains. Model uncertainty was higher in Atlantic Neotropical, Southern Rockies/Colorado Plateau, and Southeast NEON domains. We also compared our soil N predictions with satellite‐derived gross primary production and forest biomass from the National Biomass and Carbon Dataset. Finally, we used uncertainty information to propose optimized locations for designing future soil surveys and found that the Atlantic Neotropical, Pacific Northwest, Pacific Southwest, and Appalachian/Cumberland Plateau NEON domains may require larger survey efforts. We highlight the need to increase knowledge of biophysical factors regulating soil processes at deeper depths to better characterize the three‐dimensional space of soils. Our results provide a national benchmark regarding the spatial variability and uncertainty of soil N and reveal areas in need of a better representation.

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“…They also used an external validation data set and found a good fit with an RMSE = 0.26% C. Other researchers that have measured the 350-2500 nm spectrum have found similar predictive capabilities within the visible spectrum for soil N and N at 400-480 nm, 640-700 nm, 418 nm, 470 nm, 760 nm, and ferrous and ferric iron oxides at 400 nm, 450 nm, 510 nm, 550 nm, 700 nm, 870 nm and 1000 nm across a range of Oxisols in Madagascar. Use of the visible spectrum to monitor soil carbon constituents has been scaled up to regional, national and global scales via efforts such as the NRCS Soils2026 initiative [29,30] and for implementing global policies that foster Sustainable Development Goals. The application of portable field -based active and passive remote and spectroradiometers proximal sensors to measure soil properties is a growing area of research and is a natural progression to expedite further the measurement of soil properties.…”

Section: Discussionmentioning

confidence: 99%

“…A number of agencies and organizations are involved in creating and maintaining soil spectral libraries to predict soil properties [32][33][34]. United States Department of Agriculture (USDA) initiatives that inventory and assess the impact of land use and management on soil resources are limited by a lack of site-specific information and scope of measurements required to aggregate and interpret natural resource databases at regional, national and global scales [29,35]. The current Natural Resources Conservation Service (NRCS) digital mapping of dynamic soil properties to quantify soil landscapes and properties to enhance conservation planning is also limited by the number of measurements required to complete the Soils2026 initiative [30,36].…”

Section: Discussionmentioning

confidence: 99%

Comparable Discrimination of Soil Constituents Using Spectral Reflectance Data (400–1000 nm) Acquired with Hyperspectral Radiometry

Starks

Fortuna

2021

Soil Systems

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Currently, a gap exists in inventorying and monitoring the impact of land use and management on soil resources. Reducing the number of samples required to determine the impact of land management on soil carbon (C) and mineral constituents via proximal sensing techniques such as hyper-spectral radiometry can reduce the cost and personnel required to monitor changes in our natural resource base. Previously, we used an expensive, high signal-to-noise ratio (SNR) field spectrometer to correlate soil constituents to hyperspectral diffuse reflectance (HDR), over the 350–2500 nm (VIS-SWIR) wavelength range. This research is an extension of preceding research but focuses solely on the 400–1000 nm (VIS-NIR) region of the electromagnetic spectrum. This region can be measured using less expensive (albeit with lower SNR), miniaturized, field spectrometers that allow minimal sample preparation. Our objectives are to: (1) further evaluate the use of soil HDR in the visible and near-infrared (VIS-NIR) region acquired using an expensive field hyperspectral spectroradiometer for prediction of soil C and selected fractions and nitrogen (N) constituents, (2) repeat the above measurements using HDR data from samples examined in objective (1) using lower SNR hyperspectral radiometers, and (3) add to the limited literature that addresses determinations of selected soil properties using proximal sensing in the VIS-NIR region. Data analyzed in this study confirms that good to satisfactory prediction equations for soil constituents can be developed from spectral reflectance data within the 400–1000 nm wavelength region obtained using relatively inexpensive field radiometers. This application could reduce the time and resources required to monitor gains or losses in carbon constituents, information that can be used in programing such as Conservation Technical Assistance (CTA), the Conservation Reserve Program (CRP) and Climate-smart agriculture (CSA).

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Soil Mapping, Monitoring, and Assessment

Cited by 10 publications

References 34 publications

Assessing Spatial Variability of Soil Properties in Mediterranean Smallholder Farming Systems

Assessing Spatial Variability of Soil Properties in Mediterranean Smallholder Farming Systems

Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

Comparable Discrimination of Soil Constituents Using Spectral Reflectance Data (400–1000 nm) Acquired with Hyperspectral Radiometry

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