000			03725nab a22003737a 4500
001			65646
003			MX-TxCIM
005			20230217172541.0
008			220930s2022 ne |||p|op||| 00| 0 eng d
022			_a0168-1923
024	8		_ahttps://doi.org/10.1016/j.agrformet.2022.109187
040			_aMX-TxCIM
041			_aeng
100	0		_913647 _aHuan Liu
245	1	0	_aExploring the uncertainty in projected wheat phenology, growth and yield under climate change in China
260			_aAmsterdam (Netherlands) : _bElsevier, _c2022.
500			_aPeer review
520			_aExploring and quantifying the uncertainties in climate impact assessment with multiple climate-crop models is crucial to reducing the total uncertainty and guiding adaptation strategies for crop production. Here, we carried out a climate-crop ensemble simulation to measure the uncertainty in estimated climate impacts on China's wheat productivity by the 2050s. The ensemble included the simulations conducted with the three-DSSAT wheat model ensemble. As for the future climate, five Global Climate projections (GCMs) under two Representative Concentration Pathways (RCP4.5 and 8.5) and two CO2 concentrations were selected. Our results indicate that the median of simulated yield change was between 4.5% ∼ 5.5%, and -7.7% ∼ -5.6% respectively under elevated and current CO2 concentrations by 2050s compared to 1981–2010. The median of simulated phenology change was nearly -12 ∼ -10 d In percentage terms, higher uncertainty in national yield change was observed compared to phenology change. The total relative contributions of climate projections, crop models, and RCP scenarios have been more than 70% of the total uncertainty of national phenology and yield change. Crop models have accounted for the largest uncertainty of irrigated yield, while crop models and climate projections almost contributed a similar share of the total uncertainty of rainfed yield. These findings highlight the distribution of uncertainty and sources of uncertainty both at the national and grid scales, which would provide a more comprehensive understanding of uncertainties in future yield prediction. Our results also showed that larger uncertainty has been observed in warmer regions (growing season average temperature > 20 °C) than in cooler regions, while the wet regions (growing season rainfall > 400 mm) would suffer smaller uncertainty than dry regions. These findings emphasize the relationships between uncertainty and climate factors, which offers insights for improving crop models and designing adaptation strategies.
546			_aText in English
650		7	_2AGROVOC _91310 _aWheat
650		7	_2AGROVOC _91045 _aClimate change
650		7	_2AGROVOC _99439 _aGrowth
650		7	_2AGROVOC _91313 _aYields
651		7	_2AGROVOC _93990 _aChina
700	1		_97946 _aWei Xiong _8001710466 _gSustainable Intensification Program _gSustainable Agrifood Systems
700	1		_96381 _aPequeno, D.N.L. _8001710201 _gSocioeconomics Program _gIntegrated Development Program _gSustainable Agrifood Systems
700	1		_97945 _aHernandez-Ochoa, I.M.
700	1		_aKrupnik, T.J. _gSustainable Intensification Program _gSustainable Agrifood Systems _8INT3222 _9906
700	1		_9907 _aBurgueño, J. _8INT3239 _gGenetic Resources Program
700	0		_911045 _aYinlong Xu
773	0		_dAmsterdam (Netherlands) : Elsevier, 2022. _gv. 326, art. 109187 _tAgricultural and Forest Meteorology _x0168-1923 _wu444454
942			_2ddc _cJA _n0
999			_c65646 _d65638