Evaluating the impact of using digital soil mapping products as input for spatializing a crop model: The case of drainage and maize yield simulated by STICS in the Berambadi catchment (India)

Lagacherie, P and Bui, S and Constantin, J and Dharumarajan, S and Ruiz, L and Sekhar, M (2022) Evaluating the impact of using digital soil mapping products as input for spatializing a crop model: The case of drainage and maize yield simulated by STICS in the Berambadi catchment (India). In: Geoderma, 406 .

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Abstract

Digital Soil Mapping (DSM) can be an alternative data source for spatializing crop models over large areas. The objective of the paper was to evaluate the impact of DSM products and their uncertainties on a crop model's outputs in an 80 km2 catchment in south India. We used a crop model called STICS and evaluated two essential soil functions: the biomass production (through simulated yield) and water regulation (via calculated drainage). The simulation was conducted at 217 sites using soil parameters obtained from a DSM approach using either Random Forest or Random Forest Kriging. We first analysed the individual STICS simulations, i.e., at two cropping seasons for 14 individual years, and then pooled the simulations across years, per site and crop season. The results show that i) DSM products outperformed a classical soil map in providing spatial estimates of STICS soil parameters, ii) although each soil parameters were estimated separately, the correlations between soil parameters were globally preserved, ii) Errors on STICS’ yearly outputs induced by DSM estimations of soil parameters were globally low but were important for the few years with high impacts of soil variations, iii) The statistics of the STICS simulations across years were also affected by DSM errors with the same order of magnitude as the errors on soil inputs and iv) The impact of DSM errors was variable across the studied soil parameters. These results demonstrated that coupling DSM with a crop model could be a better alternative to the classical Digital Soil Assessment techniques. As such, it will deserve more work in the future.

Item Type:	Journal Article
Publication:	Geoderma
Publisher:	Elsevier B.V.
Additional Information:	The copyright for this article belongs to the Elsevier B.V.
Keywords:	Catchments; Crops; Decision trees; E-learning; Errors; Function evaluation; Machine learning; Mapping; Parameter estimation; Random forests; Runoff; Soil surveys; Soils, Crop modeling; Digital soil assessment; Digital soil mappings; Mapping error; Mapping products; Random forests; Soil assessment; Soil function; Soil mapping; Soil parameters, Uncertainty analysis, agricultural modeling; agricultural science; agricultural soil; crop plant; crop yield; maize; mapping method; simulation; soil property
Department/Centre:	Division of Mechanical Sciences > Civil Engineering Others
Date Deposited:	02 Jul 2022 05:03
Last Modified:	02 Jul 2022 05:03
URI:	https://eprints.iisc.ac.in/id/eprint/74067

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