%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Parkes, B.
%A Sultan, Benjamin
%A Ciais, P.
%A Wang, X. H.
%T Modelling fertiliser significance in three major crops + [Erratum]
%D 2017
%L fdi:010071288
%G ENG
%J European Journal of Agronomy
%@ 1161-0301
%K Fertiliser ; Statistical modelling ; Maize ; Rice ; Wheat
%M ISI:000413128500001
%P 1-11 [+ Erratum paru Vo. 93,  135]
%R 10.1016/j.eja.2017.06.012
%U https://www.documentation.ird.fr/hor/fdi:010071288
%> https://www.documentation.ird.fr/intranet/publi/depot/2018-03-12/010071288.pdf
%V 90
%W Horizon (IRD)
%X We present work using two long-term climate datasets to show that nitrogen fertiliser is an important aspect of yield projection for three major crops. The ability of linear models using climate variables as predictors to accurately project the yield of maize, rice and wheat over multi-decadal scales is improved with the addition of fertiliser as an input. Highly productive nations including Argentina, India, Poland and South Africa show significant improvement in yield simulations and show that fertiliser use should not be discounted when estimating yield variability. The use of nitrogen fertiliser in the generalised linear models improves yield forecast by 18% using the Princeton climate dataset and 23% using the WFDEI climate dataset. This work therefore supports the use of additional predictors than climate for improving the ability of statistical models to reconstruct yield variability.
%$ 076 ; 020