Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations

Rodríguez, Alfredo; Ruiz‐Ramos, Margarita; Palosuo, Taru; Carter, T. R.; Fronzek, Stefan; Lorite, Ignacio J.; Ferrise, Roberto; Pirttioja, Nina; Bindi, Marco; Baranowski, Piotr; Buis, Samuel; Cammarano, Davide; Chen, Y.; Dumont, Benjamin; Ewert, F.; Gaiser, Thomas; Hlavinka, Petr; Hoffmann, Holger; Höhn, Jukka; Jurečka, František; Kersebaum, Kurt Christian; Krzyszczak, Jaromir; Lana, Marcos; Mechiche-Alami, Altaaf; Minet, Julien; Montesino, M.; Nendel, Claas; Porter, John R.; Ruget, Françoise; Semenov, Mikhail A.; Steinmetz, Zacharias; Stratonovitch, Pierre; Supit, Iwan; Tao, Fulu; Trnka, Miroslav; Wit, Allard de; Rötter, Reimund P.

doi:10.1016/j.agrformet.2018.09.018

Cited by 41 publications

(25 citation statements)

References 34 publications

(74 reference statements)

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“…Mean number of seasons (out of 29) for the studied periods where chilling requirements are compromised at the indicated location. Roman et al (1998). g Richardson et al (1974).…”

Section: Discussionmentioning

confidence: 99%

“…It could be possible to use a hypothesis-based index such as the ensemble outcome agreement index (e.g. EOA; Rodríguez et al, 2019) to test the robustness of a hypothesis that imposes a conservative threshold, for example, considering the threshold for a variety to meet the "safe winter chill" requirements at a specific location and time (Luedeling et al, 2009a). By doing this, suitable zones for a given variety could be calculated.…”

Section: Discussionmentioning

confidence: 99%

“…The 10-model ensemble (hereafter EUR-11) used in this study is based on the availability of model runs (at the chosen resolution) at the time of data processing. This ensemble size is considered to be large enough by the agricultural impact community to retrieve robust results (Martre et al, 2015;Rodríguez et al, 2019). Due to the complex orography of the Iberian Peninsula and its remarkable climatic diversity (Bladé et al, 2010), no additional systematic selection was performed to reduce the number of RCM ensemble members (e.g.…”

Section: Observed and Simulated Climate Data Setsmentioning

confidence: 99%

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Chilling accumulation in fruit trees in Spain under climate change

Rodríguez

Pérez-López²,

Sánchez

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Growing trees are quite vulnerable to cold temperatures. To minimise the effect of these cold temperatures, they stop their growth over the coldest months of the year, a state called dormancy. In particular, endodormancy requires accumulating chilling temperatures to finish this sort of dormancy. The accumulation of cool temperatures according to specific rules is called chilling accumulation, and each tree species and variety has specific chilling requirements for correct plant development. Under global warming, it is expected that the fulfilment of the chilling requirements to break dormancy in fruit trees could be compromised. In this study, the impact of climate change on the chilling accumulation over peninsular Spain and the Balearic Islands was assessed. For this purpose, bias-adjusted results of 10 regional climate models (RCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 were used as inputs of four different models for calculating chilling accumulation, and the results for each model were individually compared for the 2021–2050 and 2071–2100 future periods under both RCPs. These results project a generalised reduction in chilling accumulation regardless of the RCP, future period or chilling calculation model used, with higher reductions for the 2071–2100 period and the RCP8.5 scenario. The projected winter chill decrease may threaten the viability of some tree crops and varieties in some areas where the crop is currently grown, but also shows scope for varieties with lower chilling requirements. The results are relevant for planning future tree plantations under climate change, supporting adaptation of spatial distribution of tree crops and varieties in Spain.

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“…Mean number of seasons (out of 29) for the studied periods where chilling requirements are compromised at the indicated location. Roman et al (1998). g Richardson et al (1974).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Observed and Simulated Climate Data Setsmentioning

confidence: 99%

See 1 more Smart Citation

Chilling accumulation in fruit trees in Spain under climate change

Rodríguez

Pérez-López²,

Sánchez

et al. 2019

Nat. Hazards Earth Syst. Sci.

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“…We documented the variability of the multi‐model simulation exercise across two stages (blind test and alternative calibration scenarios) while inspecting how the multi‐model median (MMM) converged to the observations. We used box‐plots to compare the variability of estimates by different models (with focus on multi‐year averages) to the observed variability, and we represented model ensembles with MMM, which has the advantage to exclude distinctly biased model members with a disproportionate influence on the mean (Rodríguez et al., 2019). The advantage of using MMM was established in practical studies in crop and grassland modelling but also on a theoretical basis (Wallach et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

Ensemble modelling, uncertainty and robust predictions of organic carbon in long‐term bare‐fallow soils

Farina¹,

Sándor

Abdalla

et al. 2020

Global Change Biology

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Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate‐change studies. It is imperative to increase confidence in long‐term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process‐based C models by comparing simulations to experimental data from seven long‐term bare‐fallow (vegetation‐free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi‐year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge‐based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin‐up initialization of SOC. Changes in the multi‐model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

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“…Such limitations include uncertainty about CC impacts and adaptive responses. The use of impact (Pirttioja et al, 2015) and adaptation response surfaces (Ruiz-Ramos et al, 2018) together with robustness indexes of adaptation recommendations (Rodríguez et al, 2018) have been used in biophysical models to address this challenge. Uncertainty makes developing sound protocols for adaptation modelling vital in preventing model misuse and reducing the potential for incorrect or misinterpreted outputs to drive mal-adaptive change (Ramirez-Villegas et al, 2015).…”

Section: Illustrative Reviews Of Modelling Climate Change Adaptationmentioning

confidence: 99%

To what extent is climate change adaptation a novel challenge for agricultural modellers?

Kipling

Topp

Bannink

et al. 2019

Environmental Modelling & Software

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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.

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Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations

Cited by 41 publications

References 34 publications

Chilling accumulation in fruit trees in Spain under climate change

Chilling accumulation in fruit trees in Spain under climate change

Ensemble modelling, uncertainty and robust predictions of organic carbon in long‐term bare‐fallow soils

To what extent is climate change adaptation a novel challenge for agricultural modellers?

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