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- Adler, Karl, et al.
(författare)
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Digital soil mapping of copper in Sweden: Using the prediction and uncertainty as decision support in crop micronutrient management
- 2022
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Ingår i: Geoderma Regional. - : Elsevier BV. - 2352-0094. ; 30
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Tidskriftsartikel (refereegranskat)abstract
- Digital soil mapping (DSM) of topsoil copper (Cu) concentrations and prediction intervals covering 90% of agricultural land in Sweden was performed, in order to identify areas at risk of Cu deficiency. A total of 12,527 soil samples were used to calibrate the DSM model, using airborne gamma radiation data, climate data, topographical data and soil texture class data. Among the samples included, 11,093 had no laboratory-analysed Cu concentrations, so their Cu concentrations were predicted using portable X-ray fluorescence (PXRF) measurements. Cross-validation of the PXRF model resulted in Nash-Sutcliffe model efficiency coefficient (E) of 0.66 and mean absolute error (MAE) of 3.3 mg kg−1. Cross-validation of the DSM model showed somewhat lower performance (E = 0.57, MAE = 4.1 mg kg−1). Based on the lower bound of the prediction interval (5th percentile), 48% of agricultural soils in Sweden are most likely not at risk of Cu deficiency (>7 mg kg−1). The Cu map was also validated against concentrations in soil samples from five fields (25–47 ha in size; four samples per ha). The field means were predicted with a MAE of 1.0 mg kg−1 and within-field variation was reproduced with a field-wise squared Pearson correlation coefficient (r2) of 0–0.36. The classification metric ‘recall’ showed that the map of soil Cu concentrations might not predict all possible areas at risk of being Cu deficient, as observational data indicates that about 22% of soils in the mapped area should have Cu concentrations below the risk limit. However, the metric ‘precision’ showed that when the soil map predicted a concentration at or below 7 mg kg−1, it was generally correct. Increasing the limit resulted in the recall and precision increasing rapidly. The remaining 52% of agricultural soils at risk of being below the Cu concentration limit can be targeted by laboratory analysis or monitoring.
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| 2. |
- Hounkpatin, Ozias
(författare)
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Assessment of the soil fertility status in Benin (West Africa) – Digital soil mapping using machine learning
- 2022
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Ingår i: Geoderma Regional. - : Elsevier BV. - 2352-0094. ; 28
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Tidskriftsartikel (refereegranskat)abstract
- A soil fertility index map (SFIm) can provide key information to decision-makers in regard to spatial planning in the context of sustainable land management. The establishment of such SFIm requires basic soil properties that can be modelled for spatial mapping. The objective of this study was to take advantage of Benin soil legacy data to produce a digital SFIm at a national level based on 8 soil properties (soil organic matter, nitrogen, pH (water), exchangeable potassium, assimilable phosphorus, sum of bases, cation exchange capacity and base saturation). Specific research aims were (1) to model and develop digital soil maps, (2) to identify the key covariates influencing soil nutrients, and (3) to build an SFIm using digital maps of the soil properties. For each soil property, modelling procedures involved the use of different covariates, including soil type, topographic, bioclimatic and spectral data, along with the comparative assessment of the cubist (CB) and quantile random forest (QRF) models. Models were evaluated not only on the basis of classical error metrics (RMSE, R2) but also on the ability to predict local uncertainty based on the prediction interval coverage probability (PICP). The results revealed that CB performed marginally better than the QRF based on classical error metrics (R2, RMSE) but produced the worst uncertainty with an overestimation of the local uncertainty. This suggested that the use of accuracy plots such as PICP to evaluate models can identify accuracy problems not evident with classical error metrics. The analysis revealed that the distance to the nearest stream, which was part of topographic covariates, had strong predictive ability for all the soil properties along with the bioclimatic variables. The spatial distribution of the different classes of SFIm showed a preponderance of low fertility levels with severe limitations for crop development. A limited number of high and average fertility level soils were found in the low elevation areas of southern Benin, and policy could advocate for their sole use for agricultural purposes and promote sustainable management practices.
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