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.

Keywords

Soil fertility index; Digital soil mapping; Random forest; Multiple soil classes

Published in

Geoderma Regional
2022, Volume: 28, article number: e00444

SLU Authors

• Hounkpatin, Ozias

  • Department of Soil and Environment, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss
End hunger, achieve food security and improved nutrition and promote sustainable agriculture
Ensure sustainable consumption and production patterns


UKÄ Subject classification

Soil Science


Publication identifier

DOI: https://doi.org/10.1016/j.geodrs.2021.e00444

Permanent link to this page (URI)

https://res.slu.se/id/publ/114615