Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Tao, Fulu; Rötter, Reimund P.; Palosuo, Taru; Díaz‐Ambrona, Carlos Gregorio Hernández; Mínguez, M. Inés; Semenov, Mikhail A.; Kersebaum, Kurt Christian; Nendel, Claas; Specka, Xenia; Hoffmann, Holger; Ewert, Frank; Dambreville, Anaëlle; Martre, Pierre; Rodríguez, L.; Ruiz‐Ramos, Margarita; Gaiser, Thomas; Höhn, Jukka; Salo, Tapio; Ferrise, Roberto; Bindi, Marco; Cammarano, Davide; Schulman, Alan H.

doi:10.1111/gcb.14019

Cited by 152 publications

(76 citation statements)

References 67 publications

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“…Crop modelling is also permeated with uncertainties that challenge the confidence placed in the results of model-based systems (Ogle et al, 2010). The design choices made by the modellers during model development combined with the im perfect knowledge about biophysical processes and the short age of high-quality experimental data result in model structural and parameter uncertainties (Post, Hattermann, Krysanova, & Suckow, 2008;Tao et al, 2018). The MONICA model has been previously tested for simulating the main crops that make up the rotations of this study (see Section 2.3.1) and the effects of agricultural management on SOC (Specka et al, 2016).…”

Section: Methodological Approachmentioning

confidence: 99%

“…For example, it currently does not account for the ef fect of soil organic matter, and consequently residue retention, on soil hydraulic properties or evaporation (Bescansa, Imaz, Virto, Enrique, & Hoogmoed, 2006) or the protection they provide against water and wind erosion (Wilhelm, Johnson, Karlen, & Lightle, 2007); factors that sustain primary produc tion while preventing soil degradation processes (Lal, 2005). This calls for the application of multi-model ensembles, ideally considering models that have such missing functionalities built in, to investigate the contribution of model structure, parame ters and climate projections (Tao et al, 2018) towards quantify ing the uncertainty that afflicts model predictions.…”

Section: Methodological Approachmentioning

confidence: 99%

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Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

et al. 2019

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Crop residue exploitation for bioenergy can play an important role in climate change mitigation without jeopardizing food security, but it may be constrained by impacts on soil organic carbon (SOC) stocks, and market, logistic and conversion challenges. We explore opportunities to increase bioenergy potentials from residues while reducing environmental impacts, in line with sustainable intensification. Using the case study of North Rhine‐Westphalia in Germany, we employ a spatiotemporally explicit approach combined with stakeholder interviews. First, the interviews identify agronomic and environmental impacts due to the potential reduction in SOC as the most critical challenge associated with enhanced crop residue exploitation. Market and technological challenges and competition with other residue uses are also identified as significant barriers. Second, with the use of agroecosystem modelling and estimations of bioenergy potentials and greenhouse gas emissions till mid‐century, we evaluate the ability of agricultural management to tackle the identified agronomic and environmental challenges. Integrated site‐specific management based on (a) humus balancing, (b) optimized fertilization and (c) winter soil cover performs better than our reference scenario with respect to all investigated variables. At the regional level, we estimate (a) a 5% increase in technical residue potentials and displaced emissions from substituting fossil fuels by bioethanol, (b) an 8% decrease in SOC losses and associated emissions, (c) an 18% decrease in nitrous oxide emissions, (d) a 37% decrease in mineral fertilizer requirements and emissions from their production and (e) a 16% decrease in nitrate leaching. Results are spatially variable and, despite improvements induced by management, limited amounts of crop residues are exploitable for bioenergy in areas prone to SOC decline. In order to sustainably intensify crop residue exploitation for bioenergy and reconcile climate change mitigation with other sustainability objectives, such as those on soil and water quality, residue management needs to be designed in an integrated and site‐specific manner.

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Section: Methodological Approachmentioning

confidence: 99%

Section: Methodological Approachmentioning

confidence: 99%

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

et al. 2019

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“…Addressing such uncertainty calls for the application of multi-model ensembles (e.g. Martre et al 2015), ideally considering the contribution of model structure, parameters and climate projections (Tao et al 2018) in the assessment of future trajectories of SOC in agricultural lands. Multimodel ensembles could also establish the basis for standardized methods to determine the initial distribution of C among soil organic matter pools.…”

Section: Modelling Soc Dynamics: Signals and Uncertaintiesmentioning

confidence: 99%

Estimating the contribution of crop residues to soil organic carbon conservation

Stella

Mouratiadou

Gaiser

et al. 2019

Environ. Res. Lett.

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Crop residues contribute to the maintenance of soil organic carbon (SOC) stores, a key component of soil fertility and soil-based climate change mitigation strategies, such as the '4 per 1000' initiative. Residues are also in demand in sectors coupled to crop production, such as the supply chain of livestock and bioenergy production. Ongoing debate revolves around balancing these competing uses, but science-based assessments of the long-term sustainability of residue exploitation are rare. This work uses biophysical simulation modelling to explore the likely response of SOC to different management strategies, using the land area of North Rhine-Westphalia (Germany) as a case study. Four strategies are tested: zero, one third and 100% removal of cereal residues, plus an approach proposed by the State farm advisory service. Simulations are carried out for the period 1971-2050 and 19 crop rotations coincident with land use throughout the study area. Uncertainty associated with the modelled SOC changes is explored by sampling values of relevant parameters for SOC turnover and running an ensemble of model configurations. Simulated SOC is used to trace time-dependent response functions following a change in residue management under different soil textures, initial SOC levels and crop rotations. Results highlight a general exponential decrease in SOC, with relative changes in 2050 distributed between +10% and −40% with respect to a reference period. SOC loss can be buffered or offset by returning all crop residues to the soil. Under such management, an SOC increase can be achieved on clayey soils characterized by a low initial SOC. Under moderate crop residue removal, positive SOC trends are limited to a few crop rotations. In this context, 4 per 1000 increase rate in SOC appears largely out of reach through residue management, calling for additional measures to meet the targets of land-based mitigation of anthropogenic emissions.

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“…An effective way to quantify this uncer tainty is to compare multiple climate-crop simulations of the same climate change problem 7,8 . Most current impact assessments are conducted for just a few wellcharacterized sites [9][10][11][12] , so while model accuracy can be improved through understanding where and how uncertainty arises in a multimodel ensemble 13 , the uncertainty of predictions across the mass of diverse arable lands around the world is difficult to estimate.…”

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Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat

et al. 2019

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I n the global gridded crop model (GGCM) approach, the world is divided into grid cells defined by latitude and longitude, and crop yield for the landmass in each grid cell is simulated 1-4 . To estimate the potential impact of climate change on food produc tion, researchers aggregate simulated results into nations, regions or the world to aid economic analysis and inform policymaking at different scales 5,6 . To be deemed trustworthy, GGCM results must provide accurate estimates of yield-climate relationships, or other wise give explicit information of the uncertainty of projections. Current crop models produce different results due to underlying differences in climate projections, model structure, inputs and parameterization, so overall there is a large degree of uncertainty in crop yield projections 1 . An effective way to quantify this uncer tainty is to compare multiple climate-crop simulations of the same climate change problem 7,8 . Most current impact assessments are conducted for just a few wellcharacterized sites 9-12 , so while model accuracy can be improved through understanding where and how uncertainty arises in a multimodel ensemble 13 , the uncertainty of predictions across the mass of diverse arable lands around the world is difficult to estimate.Estimating impact consistently across the globe by applying a GGCM ensemble is expensive and difficult in terms of labour, tim ing, computational ability and resources, and expertise. Compared with crop modelling at individual sites, GGCMs introduce additional sources of uncertainty such as those that arise from geospatial data and data processing. For example, recent GGCM ensemble studies compared simulation results from different research groups 14 . Even when committed to following a common simulation protocol 15 , groups adopted their unique ways of processing data and param eterizing the models for the globe. A further aspect that has received insufficient attention in GGCMs is model parameterization and how it can affect the uncertainty of globalscale predictions, par ticularly for parameters describing localscale spatial variation in the use and performance of cultivars, varieties or hybrids.Here we present a range of global wheat yield responses to future warming scenarios based on a large simulation ensemble. The ensemble was composed of 1,440 global simulations, with combina tions of 20 climate projections (5 climate models under 4 representa tive concentration pathways (RCPs) for greenhouse gas emissions), 3 crop models, 4 parameterization strategies and 3 management inputs of sowing date ( Supplementary Fig. 1). We linked the uncer tainty of gridded predictions to the latitude of grid cells. To include the uncertainty of crop yield predictions due to CO 2 fertilization, we conducted two sets of simulations: the first accounted for the effects of both increased CO 2 as projected by the RCPs and changes in climate (CC w/CO 2 ); the second accounted for changes in climate at 360 ppm CO 2 , assuming CO 2 concentration would remain constant at its...

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Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Cited by 152 publications

References 67 publications

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

Estimating the contribution of crop residues to soil organic carbon conservation

Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat

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