@article{fdi:010083781,
  title = {{S}ugarcane yield forecast in {I}vory {C}oast ({W}est {A}frica) based on weather and vegetation index data},
  author = {{P}ign{\`e}de, {E}douard and {R}oudier, {P}. and {D}iedhiou, {A}rona and {B}i, {V}. {H}. {N}. and {K}obea, {A}. {T}. and {K}onat{\'e}, {D}. and {P}{\'e}n{\'e}, {C}. {B}.},
  editor = {},
  language = {{ENG}},
  abstract = {{O}ne way to use climate services in the case of sugarcane is to develop models that forecast yields to help the sector to be better prepared against climate risks. {I}n this study, several models for forecasting sugarcane yields were developed and compared in the north of {I}vory {C}oast ({W}est {A}frica). {T}hese models were based on statistical methods, ranging from linear regression to machine learning algorithms such as the random forest method, fed by climate data (rainfall, temperature); satellite products ({NDVI}, {EVI} from {MODIS} {V}egetation {I}ndex product) and information on cropping practices. {T}he results show that the forecasting of sugarcane yield depended on the area considered. {A}t the plot level, the noise due to cultivation practices can hide the effects of climate on yields and leads to poor forecasting performance. {H}owever, models using satellite variables are more efficient and those with {EVI} alone may explain 43% of yield variations. {M}oreover, taking into account cultural practices in the model improves the score and enables one to forecast 3 months before harvest in 50% and 69% of cases whether yields will be high or low, respectively, with errors of only 10% and 2%, respectively. {T}hese results on the predictive potential of sugarcane yields are useful for planning and climate risk management in this sector.},
  keywords = {crop modeling ; sugarcane ; {I}vory {C}oast ; machine learning ; vegetation index ; yield forecast ; {COTE} {D}'{IVOIRE}},
  booktitle = {},
  journal = {{A}tmosphere},
  volume = {12},
  numero = {11},
  pages = {1459 [22 p.]},
  year = {2021},
  DOI = {10.3390/atmos12111459},
  URL = {https://www.documentation.ird.fr/hor/fdi:010083781},
}