Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments

Modelling the impact of climate change on cropping systems is crucial to support policy-making for farmers and stakeholders.Nevertheless,there exists inherent uncertainty in such cases.General Circulation Models(GCMs)and future climate change scenarios(different Representative Concentration Pathways(RCPs)in different future time periods)are among the major sources of uncertainty in projecting the impact of climate change on crop grain yield.This study quantified the different sources of uncertainty associated with future climate change impact on wheat grain yield in dryland environments(Shiraz,Hamedan,Sanandaj,Kermanshah and Khorramabad)in eastern and southern Iran.These five representative locations can be categorized into three climate classes:arid cold(Shiraz),semi-arid cold(Hamedan and Sanandaj)and semi-arid cool(Kermanshah and Khorramabad).Accordingly,the downscaled daily outputs of 29 GCMs under two RCPs(RCP4.5 and RCP8.5)in the near future(2030s),middle future(2050s)and far future(2080s)were used as inputs for the Agricultural Production Systems sIMulator(APSIM)-wheat model.Analysis of variance(ANOVA)was employed to quantify the sources of uncertainty in projecting the impact of climate change on wheat grain yield.Years from 1980 to 2009 were regarded as the baseline period.The projection results indicated that wheat grain yield was expected to increase by 12.30%,17.10%,and 17.70%in the near future(2030s),middle future(2050s)and far future(2080s),respectively.The increases differed under different RCPs in different future time periods,ranging from 11.70%(under RCP4.5 in the 2030s)to 20.20%(under RCP8.5 in the 2080s)by averaging all GCMs and locations,implying that future wheat grain yield depended largely upon the rising CO2 concentrations.ANOVA results revealed that more than 97.22% of the variance in future wheat grain yield was explained by locations,followed by scenarios,GCMs,and their interactions.Specifically,at the semi-arid climate locations(Hamedan,Sanandaj,Kermanshah and Khorramabad),most of the variations arose from the scenarios(77.25%),while at the arid climate location(Shiraz),GCMs(54.00%)accounted for the greatest variation.Overall,the ensemble use of a wide range of GCMs should be given priority to narrow the uncertainty when projecting wheat grain yield under changing climate conditions,particularly in dryland environments characterized by large fluctuations in rainfall and temperature.Moreover,the current research suggested some GCMs(e.g.,the IPSL-CM5B-LR,CCSM4,and BNU-ESM)that made moderate effects in projecting the impact of climate change on wheat grain yield to be used to project future climate conditions in similar environments worldwide.

Application of an Economy-Climate Model to Assess the Impact of Climate Change

An interdisciplinary investigation was conducted to assess the impact of climate change on grain yields using an economy-climate model （C-D-C）. The model was formulated by incorporating climate factors into the classic Cobb-Douglas （C-D） economic production function model. The economic meanings of the model output elasticities are described and elucidated. The C-D-C model was applied to the assessment of the impact of climate change on grain yields in China during the past 20 years, from 1983 through 2002. In the study, the land of China was divided into eight regions, and both the C-D-C and C-D models were applied to each individual region. The results suggest that the C-D-C model is superior to the classic C-D model, indicating the importance of climate factors. Prospective applications of the C-D-C model are discussed.

Construction of a novel economy-climate model

An attempt has been made to construct a novel economy-climate model by combining climate change research with agricultural economy research to evaluate the influence of global climate change on grain yields. The insertion of a climate change factor into the economic C-D (Cobb-Dauglas) production function model yields a novel evaluation model, which connects the climate change factor to the eco-nomic variation factor, and the performance and reasonableness of the novel evaluation model are also preliminarily simulated and verified.

构建一个经济-气候新模型评价气候变化对粮食产量的影响

将气候变化研究和农业经济研究相结合,构建了一个经济-气候新模型,用来评价全球气候变化对粮食产量影响的问题。提出在经济模型C-D生产函数中添加气候变化因子,建立一个新的评价模型,作为连接气候变化因素和经济变化因素的桥梁,并对该模型的性能及合理性进行了初步的模拟和验证。

气候变化对关中地区粮食产量的影响及趋势分析

以增温为主的气候变化对粮食产量具有显著影响。本文利用关中地区1983—2016年的站点气象要素、粮食产量统计数据和跨部门影响模型比较计划(Intersectoral Impact Model Intercomparison Project,ISIMIP)中4个全球气候模式2021—2050年降水、气温输出结果,采用突变分析、趋势分析和敏感性分析等方法,从粮食单产、气候产量和气候生产潜力等方面系统分析了我国主要粮食产地之一的陕西关中粮食产量对气候变化的响应特征。结果表明:1)1983—2016年,关中地区年平均气温呈显著上升趋势,升温速率为0.05℃·a^–1(P<0.01),其中,最高气温的上升起主要作用;年降水量则以–1 mm·a^–1的速率呈下降趋势,但不显著。2)关中地区多年平均粮食单产为3599 kg·hm^–2,且逐年波动上升,速率为57.17 kg·hm^–2·a^–1(P<0.001)。关中多地的气候产量与气温呈正相关,气温的升高一定程度上促进了关中气候产量的增加,但并不显著(平均增加率为0.85 kg·hm^–2·a^–1)。渭河关中地区多年以来的气候生产潜力为7000~12000 kg·hm^–2,受气温波动的影响,1995年后的平均气候生产潜力高于1995年之前,是整个研究时段气候生产潜力呈现增加趋势的主要时期。3)未来30年里(2021—2050年),关中地区在RCP2.6情景下的平均气候生产潜力略高于RCP6.0情景,但前者的生产潜力呈逐年下降趋势,后者则表现出逐年上升趋势。关中地区的作物对气候资源的利用空间还很大,且气候变化对关中粮食产量具有促进作用,但此正向作用并不是持续不变的。