E. F. Elli, N. Huth, P. Sentelhas, R. Carneiro, C. Alvares
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引用次数: 10
摘要
桉树育种工作已经进行,以确定生长和产量性能优越的克隆,以及它们将如何与全球气候变化相互作用。本研究对巴西在当前和未来气候情景下不同环境下遗传性状对桉树产量的影响进行了全球敏感性分析。利用APSIM下一代桉树模型对巴西23个地点的茎材生物量(t ha - 1)进行了7年轮作的模拟。利用中(RCP4.5)和高(RCP8.5)温室气体排放情景下的3种全球环流模式对2020 - 2049年进行了预估。采用Morris敏感性方法进行全局敏感性分析,以确定植物性状对茎材生物量的影响。在当前气候条件下,辐射利用效率、叶片分配、冠层光捕获和细根分配是影响桉树产量的最重要性状。育种者现在针对当前气候的一些性状在未来气候下仍然很重要。然而,在某些地区,桉树的育种应更加重视光合温度响应。全局敏感性分析是鉴别桉树适应气候变率和变化的有效工具。该方法可以通过更好地了解基因与环境相互作用对森林生产力的影响来改进育种策略。
Global sensitivity-based modelling approach to identify suitable Eucalyptus traits for adaptation to climate variability and change
Eucalyptus-breeding efforts have been made to identify clones of superior performance for growth and yield and how they will interact with global climate changes. This study performs a global sensitivity analysis for assessing the impact of genetic traits on Eucalyptus yield across contrasting environments in Brazil under present and future climate scenarios. The APSIM Next Generation Eucalyptus model was used to perform the simulations of stemwood biomass (t ha−1) for 7-year rotations across 23 locations in Brazil. Projections for the period from 2020 to 2049 using three global circulation models under intermediate (RCP4.5) and high (RCP8.5) greenhouse gas emission scenarios were performed. The Morris sensitivity method was used to perform a global sensitivity analysis to identify the influence of plant traits on stemwood biomass. Traits for radiation use efficiency, leaf partitioning, canopy light capture and fine root partitioning were the most important, impacting the Eucalyptus yield substantially in all environments under the present climate. Some of the traits targeted now by breeders for current climate will remain important under future climates. However, breeding should place a greater emphasis on photosynthetic temperature response for Eucalyptus in some regions. Global sensitivity analysis was found to be a powerful tool for identifying suitable Eucalyptus traits for adaptation to climate variability and change. This approach can improve breeding strategies by better understanding the gene × environment interactions for forest productivity.