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@ARTICLE{Hao:897475,
author = {Hao, Shirui and Ryu, Dongryeol and Western, Andrew and
Perry, Eileen and Bogena, Heye and Hendricks-Franssen,
Harrie-Jan},
title = {{P}erformance of a wheat yield prediction model and factors
influencing the performance: {A} review and meta-analysis},
journal = {Agricultural systems},
volume = {194},
issn = {0308-521X},
address = {Amsterdam [u.a.]},
publisher = {Elsevier},
reportid = {FZJ-2021-03808},
pages = {103278 -},
year = {2021},
abstract = {CONTEXTProcess-based crop models provide ways to predict
crop growth, evaluate environmental impacts on crops, test
various crop management options, and guide crop breeding.
They can be used to explore options for mitigating climate
change impacts when combined with climate projections and
explore mitigation of environmental impacts of production.
The Agricultural Production Systems SIMulator (APSIM) is a
widely adopted crop model that offers modules for simulation
of various crops, soil processes, climate, and grazing
within a modelling system that enables robust addition of
new components.OBJECTIVEThis study uses APSIM Classic-Wheat
as an example to examine yield prediction accuracy of
biophysically based crop yield modelling and to analyse the
factors influencing the model performance.METHODSWe analysed
yield prediction results of APSIM Classic-Wheat from 76
published studies across thirteen countries on four
continents. In addition, a meta-database of modelled and
observed yields from 30 studies was established and used to
identify factors that influence yield prediction
uncertainty.RESULTS AND CONCLUSIONSOur analysis indicates
that, with site-specific calibration, APSIM predicts yield
with a root mean squared error (RMSE) smaller than 1 t/ha
and a normalised RMSE (NRMSE) of about $28\%,$ across a wide
range of environmental conditions for independent evaluation
periods. The results show increasing errors in yield with
limited modelling information and adverse environmental
conditions. Using soil hydraulic parameters derived from
site-specific measurements and/or tuning cultivar parameters
improves yield prediction accuracy: RMSE decreases from 1.25
t/ha to 0.64 t/ha and NRMSE from $32\%$ to $14\%.$ Lower
model accuracy was found where APSIM overestimates yield
under high water deficit condition and when it
underestimates yield under nitrogen limitation. APSIM
severely over-predicts yield when some abiotic stresses such
as heatwaves and frost affect the crop growth.},
cin = {IBG-3},
ddc = {640},
cid = {I:(DE-Juel1)IBG-3-20101118},
pnm = {2173 - Agro-biogeosystems: controls, feedbacks and impact
(POF4-217)},
pid = {G:(DE-HGF)POF4-2173},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000701028300010},
doi = {10.1016/j.agsy.2021.103278},
url = {https://juser.fz-juelich.de/record/897475},
}