@article{fdi:010053765,
  title = {{P}redicting the spatio-temporal dynamic of soil surface characteristics after tillage},
  author = {{P}are, {N}. and {A}ndrieux, {P}. and {L}ouchart, {X}. and {B}iarn{\`e}s, {A}nne and {V}oltz, {M}arc},
  editor = {},
  language = {{ENG}},
  abstract = {{S}oil surface characteristics ({SSC}) influence strongly hydrological processes and are known to vary largely in space and time according to soil characteristics and soil management. {B}ecause tillage is a main source of variation, the goal of this study was to present and evaluate a prediction model of the temporal variation of the {SSC} after tillage at the catchment level. {T}he study focused on bare soils prevailing in spring and summer. {A} logistic regression approach was used to predict the evolution along three stages, starting from the fresh tillage stage to the crusted soil stage. {T}his method provides the probabilities of occurrence of each stage. {T}he predictor candidates tested were a rainfall characteristic, namely cumulative rainfall depth or cumulative kinetic energy, basic soil properties and tillage features. {T}he results showed that a model based on cumulative kinetic energy since tillage and soil stoniness accurately predicts the dynamics of {SSC}: the rate of well classified {SSC} was 91%. {H}owever, no significant difference in the prediction performance was found using as predictor either cumulative kinetic energy or cumulative rainfall amount since tillage. {I}n the prediction model, the rainfall characteristic was the most significant predictor for the {SSC} evolution and the only one during the first stages of crust development since tillage. {S}toniness was also shown to influence {SSC} evolution but only during the last stages of crust development: high stone cover speeds up soil surface evolution. {T}he same approach using logistic regression can be applied elsewhere but will require a re-examination of the most relevant predicting variables. {F}inally, to be able to predict the soil surface characteristic evolution on an annual scale, weed growth characteristics must be considered in the list of predictor candidates.},
  keywords = {{L}ogistic regression ; {V}ineyard ; {S}oil crusting ; {C}atchment ; {R}unoff ; {I}nfiltration},
  booktitle = {},
  journal = {{S}oil and {T}illage {R}esearch},
  volume = {114},
  numero = {2},
  pages = {135--145},
  ISSN = {0167-1987},
  year = {2011},
  DOI = {10.1016/j.still.2011.04.003},
  URL = {https://www.documentation.ird.fr/hor/fdi:010053765},
}