@article{fdi:010069978,
  title = {{P}rediction of topsoil texture for {R}egion {C}entre ({F}rance) applying model ensemble methods},
  author = {{D}obarco, {M}. {R}. and {A}rrouays, {D}. and {L}agacherie, {P}. and {C}iampalini, {R}ossano and {S}aby, {N}. {P}. {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{W}ith the rapid development of digital soil mapping it is not unusual to find several maps for the same soilproperty in an area of interest. {W}e applied two standard methods of model averaging for combining two regional maps and a {E}uropean map of topsoil texture in agricultural land for the {R}egion {C}entre ({F}rance). {T}he two methods for model ensemble were the {G}ranger-{R}amanathan ({G}-{R}) and the {B}ates-{G}ranger ({B}-{G}). {A} calibration dataset was used for fitting the coefficients of the {G}-{R} model, and for calculating a global variance: prediction error ratio which was then used to re-scale the weights of the {B}-{G} model. {T}he prediction performance of the three primary maps and the two ensemble maps was compared with an independent validation dataset consisting on 100 observations from the {F}rench soil monitoring network. {T}he prediction accuracy of the ensemble models improved only for day in comparison to the primary maps ({D}elta {R}-2 = 0.02-0.06, {D}elta {RMSE} = -1.56- - 4.97 g kg(-1)). {O}verall, the {G}-{R} models obtained smaller {RMSE} and greater bias than {B}-{G}, and {G}-{R} estimated better the prediction uncertainty. {T}he dissimilarities between the methods for estimating the prediction variance and non-optimal estimated uncertainties were important limitations for the {B}-{G} models despite applying a global correction factor for the prediction variances. {T}he results suggested that both the calibration and validation datasets should represent the patterns of spatial variation and range of values of the soil property for the prediction space. {N}onetheless, model ensemble methods proved to be useful for merging maps with different types of datasets, spatial coverage, and methodological approaches.},
  keywords = {{FRANCE}},
  booktitle = {},
  journal = {{G}eoderma},
  volume = {298},
  numero = {},
  pages = {67--77},
  ISSN = {0016-7061},
  year = {2017},
  DOI = {10.1016/j.geoderma.2017.03.015},
  URL = {https://www.documentation.ird.fr/hor/fdi:010069978},
}