@article{PAR00000544,
  title = {{F}rom {GCM} grid cell to agricultural plot : scale issues affecting modelling of climate impact},
  author = {{B}aron, {C}. and {S}ultan, {B}. and {B}alme, {M}. and {S}arr, {B}. and {T}raore, {S}. and {L}ebel, {T}. and {J}anicot, {S}. and {D}ingkuhn, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{G}eneral circulation models ({GCM}) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. {T}his is particularly important for semi-arid {W}est {A}frica where climate variability and drought threaten food security. {T}ranslating {GCM} outputs into attainable crop yields is difficult because {GCM} grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. {T}his study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. {A} detailed case study was conducted using historical weather data for {S}enegal, applied to the crop model {SARRA}-{H} (version for millet). {T}he study was then extended to a 10 degrees {N}-17 degrees {N} climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. {F}inally, a down-scaling model called {LGO} ({L}ebel-{G}uillot-{O}nibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. {R}esults indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10-50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. {A}ggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. {W}here climatic gradients are steep, these two situations can occur within the same {GCM} grid cell. {D}isaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. {I}t is concluded that coupling of {GCM} outputs with plot level crop models can cause large systematic errors due to scale incompatibility. {T}hese errors can be avoided by transforming {GCM} outputs, especially rainfall, to simulate the variability found at plot level.},
  keywords = {millet ; drought ; {W}est {A}frica ; grain yield ; biomass ; aggregation},
  booktitle = {},
  journal = {{P}hilosophical {T}ransactions of the {R}oyal {S}ociety {B} {B}iological {S}ciences},
  volume = {360},
  numero = {1463},
  pages = {2095--2108},
  ISSN = {0962-8436},
  year = {2005},
  DOI = {10.1098/rstb.2005.1741},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00000544},
}