首页|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)

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)

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  • NSTL
  • Elsevier
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 km(2) 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.

Soil mappingSoil functionsDigital soil assessmentCrop modelMachine learningUncertainty analysis

Lagacherie, P.、Bui, S.、Constantin, J.、Dharumarajan, S.、Ruiz, L.、Sekhar, M.

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Univ Montpellier, INRAE, IRD, Montpellier SupAgro,LISAH, F-34060 Montpellier, France

INRAE Avignon, EMMAH, Avignon, France

INRAE, AGIR, Toulouse, France

ICAR Natl Bur Soil Survey & Land Use Planning, Bangalore 560024, Karnataka, India

Univ Paul Sabatier, CNRS, IRD, Geosci Environm Toulouse, F-31400 Toulouse, France

Indian Inst Sci, Civil Engn Dept, Bangalore 560012, Karnataka, India

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2022

10.1016/j.geoderma.2021.115503

Geoderma: An International Journal of Soil Science

Geoderma: An International Journal of Soil Science

ISSN：0016-7061
年,卷(期)：2022.406
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