Digital soil mapping of soil fertility index for agricultural lands in tropical environment

Abstract

<p>Indicators of soil production function such as soil fertility index can potentially be a key decision tool in spatial planning for sustainable land management. The establishment of such soil fertility index requires basic soil properties which can be modelled for spatial mapping. The objective of this study was to take advantage of the soil legacy data of Benin to produce a digital soil map of soil fertility index at a national scale based on 8 soil properties (soil organic carbon matter, nitrogen, pH, exchangeable potassium, assimilable phosphorus, sum of base, cation exchange capacity and base saturation). Specific research aims were: (1) to model and develop digital soil maps; (2) to identify important factors influencing soil nutrients; (3) to establish soil fertility potentials using digital soil maps. 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 and Quantile Random Forest model. Results revealed that apart from N and exchangeable K, significant models can be produced for most of the soil properties with R-square varying between 28% and 72% with the Quantile Random Forest presenting a more accurate prediction interval coverage probability. The analysis revealed that the distance to the nearest stream has strong predictive ability for all the soil properties along with the bioclimatic variables. Visualisation of the soil fertility map showed that most of the soils in Benin have low fertility level suggesting that the use of fertilizers and organic materials will be critical in sustaining crop productivity. 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 agriculture purpose as well as promote sustainable management practices.</p>

Evaluation of pedotransfer functions to estimate some of soil hydraulic characteristics in North Africa: A case study from Morocco

“…PTFs based on random forest showed that clay and sand were the predictor variable most important in the prediction, and higher value of R 2 associated with w 330 prediction, medium value with w 15000 prediction, and lower value with w 0 prediction. The use of PTFs represents a useful tool, a low-cost method, and an easy technique to estimate soil hydraulic properties, to evaluate soil water retention capacity, and to optimize water management for farmers Hounkpatin et al, 2022.…”