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Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change. Highlights • Modelling plays a key role in adapting agriculture to climate change. • Modellers' views and literature were analysed to identify adaptation-specific issues. • Novel challenges to modelling climate change adaptation were explored. • Challenges related to Responsibility, Scope, Optimisation, Information, Collaboration. • Adaptation to progressive climate change highlights social impacts of model use. Graphical abstract Image 1

Abstract • Key message Dynamic global vegetation models are key tools for interpreting and forecasting the responses of terrestrial ecosystems to climatic variation and other drivers. They estimate plant growth as the outcome of the supply of carbon through photosynthesis. However, growth is itself under direct control, and not simply controlled by the amount of available carbon. Therefore predictions by current photosynthesis-driven models of large increases in future vegetation biomass due to increasing concentrations of atmospheric CO2may be significant over-estimations. We describe how current understanding of wood formation can be used to reformulate global vegetation models, with potentially major implications for their behaviour.

42), two lines with elevated activity (AO+14 and AO+16), and the non-transgenic line (WVa106) were grown and irrigated with 75 mM and 150 mM NaCl in 2015 and 2016. Growth, yield, and chemical composition of the lines under salinity stress were evaluated. Lines with lower ascorbate oxidase activity resulted in higher plant growth parameters (plant height, leaf number, flower, and cluster number in 2015 and stem diameter and flower number in 2016), and improved fruit quality (firmness in 2016 and soluble solid content in 2015) and total yield per plant under salinity stress over both years. In addition, we show that ascorbic acid, lycopene, and carotene contents of fruits were higher in lines with lower ascorbate oxidase activity compared to lines with elevated activity and the non-transgenic line under conditions of moderate and high salinity in both years. Ascorbic acid is considered to be one of the most important antioxidants in plants and plays a vital role in the adaptation of plants to unfavorable conditions. In the present study, an ascorbate oxidase gene (Solyc04g054690) was over-expressed in cherry tomato cv. West Virginia 106 lines and compared with previously studied RNAi silenced ascorbate oxidase lines. Two lines with lower ascorbate oxidase activity (AO&minus 15 and AO&minus

Predicting the evolutionary potential of natural tree populations requires the estimation of heritability and genetic correlations among traits on which selection acts, as differences in evolutionary success between species may rely on differences for these genetic parameters. In situ estimates are expected to be more accurate than measures done under controlled conditions which do not reflect the natural environmental variance. The aim of the current study was to estimate three genetic parameters (i.e., heritability, evolvability, and genetic correlations) in a natural mixed oak stand composed of Quercus petraea and Quercus robur about 100 years old, for 58 traits of ecological, and functional relevance (growth, reproduction, phenology, physiology, resilience, structure, morphology, and defense). First, we estimated genetic parameters directly in situ using realized genomic relatedness of adult trees and parentage relationships over two generations to estimate the traits' additive variance. Secondly, we benefited from existing ex situ experiments (progeny tests and conservation collection) installed with the same populations, thus allowing comparisons of in situ heritability estimates with more traditional methods. Heritability and evolvability estimates obtained with different methods varied substantially and showed large confidence intervals; however, we found that in situ were less precise than ex situ estimates, and assessments over two generations (with deeper relatedness) improved estimates of heritability while large sampling sizes are needed for accurate estimations. At the biological level, heritability values varied moderately across different ecological and functional categories of traits, and genetic correlations among traits were conserved over the two species. We identified limits for using realized genomic relatedness in natural stands to estimate the genetic variance, given the overall low variance of genetic relatedness and the rather low sampling sizes of currently used long-term genetic plots in forestry. These limits can be overcome if larger sample sizes are considered, or if the approach is extended over the next generation. International audience

International Agrophysics, 32 (4) ISSN:0236-8722 ISSN:2300-8725