Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors,of which the most important one is the increasing carbon dioxide(CO_(2))concentrations.Estimates ...Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors,of which the most important one is the increasing carbon dioxide(CO_(2))concentrations.Estimates of CO_(2) fertilization effect(CFE)on rice,however,still had large uncertainties.Therefore,using the rice planting areas in East China as the study area,we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models,and found that the CFE predicted by these models had significant differences.We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province.Using CFE measurements from a field experiment as benchmark,we have developed an experiment–model integration approach aiming to reduce this variation.This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty,which is beneficial for the accurate prediction of future global rice yield in the context of climate change.展开更多
基金supported by the National Natural Science Foundation of China (32322064,32101340)the Jiangsu Provincial Natural Science Foundation for Distinguished Young Scholars (BK20220083)+1 种基金the Carbon Peak and Carbon Neutralization Key Science and technology Program of Suzhou (ST202228)Songhan Wang acknowledges the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (2021QNRC001).
文摘Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors,of which the most important one is the increasing carbon dioxide(CO_(2))concentrations.Estimates of CO_(2) fertilization effect(CFE)on rice,however,still had large uncertainties.Therefore,using the rice planting areas in East China as the study area,we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models,and found that the CFE predicted by these models had significant differences.We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province.Using CFE measurements from a field experiment as benchmark,we have developed an experiment–model integration approach aiming to reduce this variation.This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty,which is beneficial for the accurate prediction of future global rice yield in the context of climate change.
基金国家自然科学基金联合基金项目(U21A20485)浙江省“十四五”第二批本科省级教学改革备案项目(JGBA2024014)+2 种基金教育部产学合作协同育人项目(2501270945)2024年度浙江大学本科“AI赋能”示范课程建设项目(202424EE2501M)浙江大学第四批AI For Education系列实证教学研究项目(BKSY20251104)。
