Use of the USDA National Cooperative Soil Survey Soil Characterization Data to Detect Soil Change: A Cautionary Tale

Tomer, Mark D.; James, David; Schipper, Louis A.; Wills, Skye

doi:10.2136/sssaj2017.06.0198

Cited by 4 publications

(3 citation statements)

References 30 publications

(46 reference statements)

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“…Owusu et al (2020) described the soil sampling scheme adopted for the pedon characterization and the limitations of such long‐term datasets. In essence, these national soil survey datasets serve as a reservoir of soil information that basically provides a set of snapshot evidence about soil properties (Tomer et al, 2017). Nevertheless, the pedons were sampled in diverse geomorphic areas and land use types.…”

Section: Methodsmentioning

confidence: 99%

Exchangeable acidity and pedotransfer functions for the soils of Ghana

Owusu,

Hartemink,

Zhang

et al. 2024

European J Soil Science

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Soil exchangeable acidity (EA) is an indicator of aluminium toxicity potential in acidic soils. Predicting the distribution and dynamics of EA is needed for the identification and management of acidic soils. In this study, we used datasets of 355 pedons from across Ghana and the Cubist rule‐based algorithm to generate pedotransfer functions (PTFs) of EA. Eight soil properties (pH, organic carbon, calcium, sodium, magnesium, total exchangeable bases, cation exchange capacity [CEC] to clay ratio and soil depth) were used to predict EA. We first used the whole dataset to construct generic PTFs and then stratified the dataset based on World Reference Base‐Reference Soil Groups (WRB‐RSGs) to generate soil‐specific PTFs. Goodness‐of‐fit statistics comprising the root mean squared error (RMSE), Lin's concordance correlation coefficient () and coefficient of determination (R2) were used to evaluate the prediction accuracy and reliability of the developed PTF models on both calibration and validation datasets. The Fluvisols EA‐PTF exhibited lower performance metrics in the validation (RMSE = 0.17 cmolc kg−1, = 0.19, R2 = 0.24), whereas the EA‐PTFs for all other WRB‐RSGs and whole dataset had above‐average performance metrics in the validation (0.05 ≤ RMSE ≤ 0.97 cmolc kg−1; 0.34 ≤ ≤ 0.94; 0.52 ≤ R2 ≤ 0.96). Soil pH sufficed for predicting EA in soils with pH above 5.0, but in soils with a pH < 5.0, the levels of exchangeable bases (e.g., Na+, K+, Mg2+, Ca2+), CEC to clay ratio (CCR) and soil depth improved the prediction of EA. The developed EA‐PTFs are useful for estimating the missing values of EA in soil databases.

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Section: Methodsmentioning

confidence: 99%

Exchangeable acidity and pedotransfer functions for the soils of Ghana

Owusu,

Hartemink,

Zhang

et al. 2024

European J Soil Science

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“…Traditionally, the understanding of soil N content and its changes on a large spatial scale require extensive soil geochemical surveys. The high costs and low efficiency of soil surveys, commonly done by field sampling, remain a significant restriction on monitoring and mapping soil properties [12][13][14]. Therefore, it is necessary to develop and optimize more economical and effective techniques to estimate and map the N content of topsoil on a regional or even larger scale.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1–02D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China

Chen

Zhu

et al. 2022

Remote Sensing

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Soil nitrogen (N) content plays a vital role in agriculture and biogeochemical processes, ranging from the N fertilization management for intensive agricultural production to the patterns of N cycling in agroecological systems. While proximal sensing in laboratory settings can achieve ideal soil N estimation accuracy, the estimation and mapping by using remote sensing methods in a large spatial scale diplays low ability. A new hyperspectral imager with 166 spectral channels, the ZY1-02D, makes possible the detection of subtle but important spectral features of soil. This study aimed at exploring the capability of the ZY1-02D to estimate and map the topsoil N content of the black soil-covered farmlands in northeast China. To this aim, 646 soil samples from study sites were collected, processed, spectrally and geochemically measured for the soil N sensitive bands detection and partial least squares regression (PLSR) calibration and validation. The sensitive bands detection results showed an appealing regularity of the variability and stable tendency of the soil N sensitive spectral bands with the change of the sample size. Based on this, we compared the estimation capacity of the models developed with the full wavelength spectra and the models developed with the sensitive bands. The estimation based on ZY1-02D full wavelength spectral reflectance were robust, with R2 of 0.64 in validation. Further, the results of model developed with the sensitive bands showed better validation accuracy with R2 of 0.66 and were applied to create a map of topsoil N content of farmlands in the northeast China black soil area. The results demonstrated that sensitive bands modelling could enhance the accuracy of the estimation and simplify model, and what is more, showed the ideal capability of ZY1-02D for soil N content estimation at the regional scale.

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“…Despite the advantages to agricultural producers, the long‐term usage of CS rotation potentially jeopardizes soil health and crop production by altering the soil microbial activities and other soil properties. Previous studies demonstrated that long‐term use of CS rotation led to decline in soil organic C (Drinkwater et al., 1998), reduction in soil reactive N (Hall et al., 2019; Tomer et al., 2017), loss of soil aggregate stability (Zuber et al., 2015), and reduction in crop yield (Smith et al., 2008). Moreover, CS rotation can potentially reduce bacterial richness and diversity (Venter et al., 2016; Zhou et al., 2015) and fungal biodiversity and abundance (Ding et al., 2017), meanwhile posing the risk of long‐term soil degeneration (Katsvairo & Cox, 2000; Yin et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Yield and economic performance of crop rotation systems in South Dakota

Feng

Wang

Osborne

et al. 2021

Agrosystems Geosci & Env

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Crop yield and economic profitability, both highly dependent on local crop management, soil characteristics, and weather conditions, are among the most influential factors to consider when considering a cropping system. The objective of this study was to compare the economic returns of three different 4-yr diverse crop rotations with that of a 2-yr traditional crop rotation in eastern South Dakota. The rotations included were (a) corn (Zea mays L.)-soybean [Glycine max (L.) Merr.]-spring wheat (Triticum aestivum L.)-pea (Pisum sativum L.) (CSSwP), (b) corn-pea-winter wheat-soybean (CPWwS), (c) corn-oat (Avena sativa L.)-winter wheat-soybean (COWwS), and (d) corn-soybean (CS). Results showed that total cost for the CS rotation was 7.2, 14.9, and 18.2% greater than the COWwS, CSSwP, and CPWwS rotations, respectively. Whereas CS rotation had comparable corn yield with CSSwP and COWwS rotations, its soybean yield ranked the lowest among all the rotations.When N fertilizer application fell below the level necessary to achieve for yield maximization, the CS rotation demonstrated a lack of resilience as indicated by a continual decline in economic returns over time. In comparison, the CSSwP rotation demonstrated high resilience to reduced N fertilizer application rate, and its net revenue was the highest among all rotations and surpassed the CS. Our results suggest that extending the traditional CS rotation to the more diversified CSSwP rotation could simultaneously reduce input costs and overreliance on N fertilizer.Abbreviations: COWwS, corn-oat-winter wheat-soybean; CPWwS, corn-pea-winter wheat-soybean; CS, corn-soybean; CSSwP, corn-soybean-spring wheat-pea.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Use of the USDA National Cooperative Soil Survey Soil Characterization Data to Detect Soil Change: A Cautionary Tale

Cited by 4 publications

References 30 publications

Exchangeable acidity and pedotransfer functions for the soils of Ghana

Exchangeable acidity and pedotransfer functions for the soils of Ghana

Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1–02D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China

Yield and economic performance of crop rotation systems in South Dakota

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