Mapping cation exchange capacity and exchangeable potassium using proximal soil sensing data at the multiple-field scale
Cation exchange capacity (CEC – cmol (+) kg−1) is the capacity of a soil to hold exchangeable cations, one of which is exchangeable potassium (K). The data from both CEC and exchangeable cations are frequently useful for fertiliser recommendations; however, they are expensive to measure in the labor...
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| Veröffentlicht in: | Soil & tillage research Jg. 232; S. 105735 |
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| Sprache: | Englisch |
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Elsevier B.V
01.08.2023
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| ISSN: | 0167-1987, 1879-3444 |
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| Abstract | Cation exchange capacity (CEC – cmol (+) kg−1) is the capacity of a soil to hold exchangeable cations, one of which is exchangeable potassium (K). The data from both CEC and exchangeable cations are frequently useful for fertiliser recommendations; however, they are expensive to measure in the laboratory. To add value to limited CEC and K data, digital data can be beneficial where large amounts can be acquired expeditiously and are often strongly correlated. In this research, we seek to develop a linear regression (LR) between apparent soil electrical conductivity (ECa – mS m–1) from Veris-3100 shallow (0 – 0.3 m) and deep (0 – 0.9 m) array configurations and measured topsoil (0 – 0.2 m) and subsoil (0.5 – 0.7 m) CEC. Moreover, we compare these LRs, with a LR between estimates of σ derived from the inversion of ECa using a quasi-three-dimensional algorithm (invVeris V1.1) and topsoil and subsoil CEC. We also want to determine the minimum number of calibration sample sites (i.e., 45, 40, 35, …, 5) required to produce a strong LR (i.e., coefficient of determination: R2 > 0.7) and substantial (Lin’s concordance correlation coefficient; LCCC > 0.8) or consistent prediction agreement. This requires dividing the n = 60 sample sites into calibration (n = 45) and validation (n = 15) sets. A similar approach was used to develop a multiple LR (MLR) to predict topsoil K considering digital data (i.e., ECa, elevation and trend surface parameters). While the LRs between topsoil CEC with shallow ECa (R2 = 0.38), and subsoil CEC with deep ECa (0.36) were weak (0.5 > R2 > 0.3), the LR (0.75) between σ and CEC was strong (using the S2 algorithm and a damping factor [λ] = 1). A poor LR (0.47) was also found between K and ECa; however, a MLR model (ECa, elevation and trend surface parameters) was strong (0.73). In terms of minimum calibration sample size for CEC, it was found that n = 10 sites (i.e., at 2 depths) or more were required. With respect to K, the minimum calibration sample size was n = 10 (i.e., single depth). To improve calibration equations and areal prediction agreement of CEC and K, and reduce confidence intervals (CI) across the four fields, we recommend the use of tighter transect spacings (< 6 m), and the inclusion of soil (i.e., small CEC and K) and digital (e.g., small ECa) data from adjacent fields and on nearby farms. The final DSM of CEC and K could be used to prescribe potash (K2O) fertilisers.
•A linear regression (LR) could not be developed between ECa and measured CEC at different depths.•LR model was built between CEC and inverted σ.•Identify optimal calibration sample size.•Prescribe potash (K2O) fertilisers based on CEC and K. |
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| AbstractList | Cation exchange capacity (CEC – cmol (+) kg−1) is the capacity of a soil to hold exchangeable cations, one of which is exchangeable potassium (K). The data from both CEC and exchangeable cations are frequently useful for fertiliser recommendations; however, they are expensive to measure in the laboratory. To add value to limited CEC and K data, digital data can be beneficial where large amounts can be acquired expeditiously and are often strongly correlated. In this research, we seek to develop a linear regression (LR) between apparent soil electrical conductivity (ECa – mS m–1) from Veris-3100 shallow (0 – 0.3 m) and deep (0 – 0.9 m) array configurations and measured topsoil (0 – 0.2 m) and subsoil (0.5 – 0.7 m) CEC. Moreover, we compare these LRs, with a LR between estimates of σ derived from the inversion of ECa using a quasi-three-dimensional algorithm (invVeris V1.1) and topsoil and subsoil CEC. We also want to determine the minimum number of calibration sample sites (i.e., 45, 40, 35, …, 5) required to produce a strong LR (i.e., coefficient of determination: R2 > 0.7) and substantial (Lin’s concordance correlation coefficient; LCCC > 0.8) or consistent prediction agreement. This requires dividing the n = 60 sample sites into calibration (n = 45) and validation (n = 15) sets. A similar approach was used to develop a multiple LR (MLR) to predict topsoil K considering digital data (i.e., ECa, elevation and trend surface parameters). While the LRs between topsoil CEC with shallow ECa (R2 = 0.38), and subsoil CEC with deep ECa (0.36) were weak (0.5 > R2 > 0.3), the LR (0.75) between σ and CEC was strong (using the S2 algorithm and a damping factor [λ] = 1). A poor LR (0.47) was also found between K and ECa; however, a MLR model (ECa, elevation and trend surface parameters) was strong (0.73). In terms of minimum calibration sample size for CEC, it was found that n = 10 sites (i.e., at 2 depths) or more were required. With respect to K, the minimum calibration sample size was n = 10 (i.e., single depth). To improve calibration equations and areal prediction agreement of CEC and K, and reduce confidence intervals (CI) across the four fields, we recommend the use of tighter transect spacings (< 6 m), and the inclusion of soil (i.e., small CEC and K) and digital (e.g., small ECa) data from adjacent fields and on nearby farms. The final DSM of CEC and K could be used to prescribe potash (K2O) fertilisers.
•A linear regression (LR) could not be developed between ECa and measured CEC at different depths.•LR model was built between CEC and inverted σ.•Identify optimal calibration sample size.•Prescribe potash (K2O) fertilisers based on CEC and K. The cation exchange capacity (CEC - cmol (+) kg⁻¹) is the capacity of a soil to hold exchangeable cations, such as exchangeable potassium (K). Knowledge of these properties is often used to provide recommendations for fertilizers; however, they are expensive to measure in the laboratory. To create maps, proximal sensed instruments can be used. In this research, we explore the potential to develop a linear regression (LR) between apparent soil electrical conductivity (ECₐ - mS/m⁻¹) from Veris-3100 shallow (0-0.3 m) and deep (0-0.9 m) array configurations and measured topsoil (0-0.2 m) and subsoil (0.5-0.7 m) CEC. We compare these with a LR between estimates of ' from inversion of Veris-3100 ECₐ using a quasi-three-dimensional algorithm (invVeris V1.1). We also determine the minimum number of calibration sample sites (i.e., 45, 40, 35, 5) required to produce a strong LR (i.e., coefficient of determination: R² > 0.7) and substantial (Lin's concordance correlation coefficient; LCCC > 0.8) or consistent prediction agreement. We do this by dividing the n = 60 sample sites into calibration (n = 45) and validation (n = 15) sets. A similar approach is used to develop a multiple-LR (MLR) to predict topsoil K considering digital data (i.e., ECₐ, elevation and trend surface parameters). The LR between topsoil CEC and shallow ECa (R² = 0.38) and subsoil CEC with deep ECₐ (0.36) were poor (0.5 > R² > 0.3). However, LR (0.75) between s and CEC was strong (using S2 algorithm and a damping factor ['] = 1). A poor LR (0.47) was also found between K and ECₐ, however, a MLR model (ECₐ, elevation and trend surface parameters) was strong (0.73). In terms of minimum calibration sample size for CEC, it was found n = 10 sites (i.e., at 2 depths) or more were required. With respect to K, the minimum calibration sample size was n = 10 (i.e., single depth). To improve areal prediction agreement of CEC and K and reduce CI across the four fields, we recommend the use of tighter transect spacings (< 6m), and inclusion of soil (i.e., small CEC and K) and digital (e.g., small ECₐ) data from adjacent fields and on nearby farms to improve calibration equations. The final DSM of CEC and K could be used to prescribe potash (K₂O) fertilizers. |
| ArticleNumber | 105735 |
| Author | Fung, Evangeline Levison, Philip Triantafilis, John Zhao, Xueyu Farzamian, Mohammad Allred, Barry Clevenger, William Bruce Wang, Jie |
| Author_xml | – sequence: 1 givenname: Evangeline surname: Fung fullname: Fung, Evangeline organization: School of Biological, Earth and Environmental Sciences, UNSW, Sydney, NSW 2052, Australia – sequence: 2 givenname: Jie surname: Wang fullname: Wang, Jie organization: School of Biological, Earth and Environmental Sciences, UNSW, Sydney, NSW 2052, Australia – sequence: 3 givenname: Xueyu surname: Zhao fullname: Zhao, Xueyu organization: School of Biological, Earth and Environmental Sciences, UNSW, Sydney, NSW 2052, Australia – sequence: 4 givenname: Mohammad surname: Farzamian fullname: Farzamian, Mohammad organization: Instituto Nacional de Investigação Agrária e Veterinária, Ministério da Agricultura, Oeiras 2780-157, Portugal – sequence: 5 givenname: Barry surname: Allred fullname: Allred, Barry organization: US Department of Agriculture - Agricultural Research Service, Soil Drainage Research Unit, Columbus, OH, United States – sequence: 6 givenname: William Bruce surname: Clevenger fullname: Clevenger, William Bruce organization: Ohio State University Extension Defiance County, Defiance, OH 43512, United States – sequence: 7 givenname: Philip surname: Levison fullname: Levison, Philip organization: USDA ARS, Columbus Ohio, OH 43210, United States – sequence: 8 givenname: John surname: Triantafilis fullname: Triantafilis, John email: triantafilisj@landcareresearch.co.nz organization: Manaaki Whenua – Landcare Research, P.O. Box 69040, Lincoln 7640, New Zealand |
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| CitedBy_id | crossref_primary_10_3390_plants14020169 crossref_primary_10_1016_j_still_2024_106429 crossref_primary_10_1016_j_compag_2025_110448 crossref_primary_10_3390_agriculture15151627 crossref_primary_10_3390_su16167002 |
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| Keywords | Quasi-3d inversion algorithm Cation exchange capacity Electrical conductivity Digital soil mapping Electromagnetic inversion |
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| Snippet | Cation exchange capacity (CEC – cmol (+) kg−1) is the capacity of a soil to hold exchangeable cations, one of which is exchangeable potassium (K). The data... The cation exchange capacity (CEC - cmol (+) kg⁻¹) is the capacity of a soil to hold exchangeable cations, such as exchangeable potassium (K). Knowledge of... |
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| SubjectTerms | algorithms Cation exchange capacity digital database Digital soil mapping Electrical conductivity Electromagnetic inversion exchangeable potassium prediction Quasi-3d inversion algorithm regression analysis soil electrical conductivity subsoil tillage topsoil |
| Title | Mapping cation exchange capacity and exchangeable potassium using proximal soil sensing data at the multiple-field scale |
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