@article{fdi:010053349,
  title = {{Y}ield estimation and sowing date optimization based on seasonal climate information in the three {CLARIS} sites},
  author = {d'{O}rgeval, {T}. and {B}oulanger, {J}ean-{P}hilippe and {C}apalbo, {M}. {J}. and {G}uevara, {E}. and {P}enalba, {O}. and {M}eira, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he present article is a contribution to the {CLARIS} {W}ork{P}ackage "{C}limate and {A}griculture", and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites ({P}ergamino, {M}arcos {J}uarez and {A}nguil) which are representative of different climate and soil conditions in {A}rgentina. {C}onsidering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the {DSSAT} ({D}ecision {S}upport {S}ystem for {A}grotechnology {T}ransfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). {W}e simulated yields for three different sowing dates for each crop in each of the three sites. {A}lso considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. {T}his type of information is used as input (predictand) for nonlinear statistical methods ({M}ultivariate {A}daptive {R}egression {S}plines, {MARS}; and classification trees) in order to predict yields and their dependency to the chosen sowing date. {MARS} models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. {C}lassification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. {I}n order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. {A}bove normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. {U}sing this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. {M}ore generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. {H}owever, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions.},
  keywords = {},
  booktitle = {},
  journal = {{C}limatic {C}hange},
  volume = {98},
  numero = {3-4},
  pages = {565--580},
  ISSN = {0165-0009},
  year = {2010},
  DOI = {10.1007/s10584-009-9746-4},
  URL = {https://www.documentation.ird.fr/hor/fdi:010053349},
}