Towards an enhanced use of soil databases for assessing water availability in (sub)tropical regions using fractal-based methods

Following the completion of numerous elaborate soil surveys in many (sub)tropical regions of the African continent during the past decades, vast databases with soil properties of the prevailing soil orders in these regions have been assembled in order to support agricultural stakeholders throughout crucial decision-making processes. Unfortunately, even though soil hydraulic properties are of primary interest for designing sustainable farming practices, guiding crop choice and irrigation scheduling, a substantial share of the soil surveys is restricted to the collection of soil chemical properties. This bias principally originates from the fact that soil chemical characteristics like pH, organic carbon/matter (OC/OM), cation exchange capacity (CEC), base saturation (BS) can be determined readily. On the other hand, determination of the hydraulic properties of a soil on the field or in the lab, is much more time consuming, particularly the soil-water retention curve (SWRC) which is generally considered as one of the most important physical property since it constitutes the footprint of a soil. Owing to the incompleteness of most soil databases in (sub)tropical regions, either much valuable information is discarded because the assessment of meaningful indices in land evaluation such as the soil available water capacity (AWC), the hydraulic conductivity are merely based upon those soil samples for which hydraulic properties were measured, or one has to resort to pedotransfer functions (PTFs). The latter are equations for deducing hydraulic properties of a soil from physico-chemical data that are commonly available in soil survey reports (sand, silt, clay, OC/OM, CEC, etc.). Yet, such PTFs are only locally applicable because their derivation rests on statistical or machine learning techniques and has no physical basis. Recently, however, physically-based, and hence globally applicable, fractal methods have been put forward for assessing a soil's SWRC based upon its particle-size distribution, which is a significantly more available property in dedicated soil databases than its hydraulic properties. Notwithstanding the fact that these methods offer a means to fully exploit soil databases of (sub)tropical regions, their applicability methods has only been demonstrated for soils from temperate regions. Here, we first demonstrate the applicability of such fractal-based methods for some soil orders (Acrisols and Ferrasols) that are typically tied up with (sub)tropic climate zones. Then, the fractal-based method that gave rise to the best performance indices for the considered soil families is used to retrieve the spatio-temporal distribution of the AWC across Lower Congo, which is one of the agricultural hotspots of Democratic Republic of Congo whose soil physical properties have therefore been surveyed elaborately. Since this map is based upon the entire soil database of Lower Congo, which is the advent of using the aforementioned fractal-based methods for translating the measured soil physical properties like texture into the otherwise unknown soil hydraulic properties, it reflects the spatial variability of the AWC in more detail, such that it is of greater value for the involved stakeholders during the process of crop, harvest and tillage selection.