@article{fdi:010083201,
  title = {{E}valuating 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 {B}erambadi catchment ({I}ndia)},
  author = {{L}agacherie, {P}. and {B}ui, {S}. and {C}onstantin, {J}. and {D}harumarajan, {S}. and {R}uiz, {L}aurent and {S}ekhar, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{D}igital {S}oil {M}apping ({DSM}) can be an alternative data source for spatializing crop models over large areas. {T}he 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 {I}ndia. {W}e used a crop model called {STICS} and evaluated two essential soil functions: the biomass production (through simulated yield) and water regulation (via calculated drainage). {T}he simulation was conducted at 217 sites using soil parameters obtained from a {DSM} approach using either {R}andom {F}orest or {R}andom {F}orest {K}riging. {W}e 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. {T}he 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) {E}rrors 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) {T}he 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) {T}he impact of {DSM} errors was variable across the studied soil parameters. {T}hese results demonstrated that coupling {DSM} with a crop model could be a better alternative to the classical {D}igital {S}oil {A}ssessment techniques. {A}s such, it will deserve more work in the future.},
  keywords = {{S}oil mapping ; {S}oil functions ; {D}igital soil assessment ; {C}rop model ; {M}achine learning ; {U}ncertainty analysis ; {INDE} ; {BERAMBADI} {BASSIN}},
  booktitle = {},
  journal = {{G}eoderma},
  volume = {406},
  numero = {},
  pages = {115503 [11 p.]},
  ISSN = {0016-7061},
  year = {2022},
  DOI = {10.1016/j.geoderma.2021.115503},
  URL = {https://www.documentation.ird.fr/hor/fdi:010083201},
}