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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| Vydáno v: | Soil & tillage research Ročník 232; s. 105735 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.08.2023
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| Témata: | |
| ISSN: | 0167-1987, 1879-3444 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0167-1987 1879-3444 |
| DOI: | 10.1016/j.still.2023.105735 |