Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

Müller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Arneth, Almut; Balkovič, Juraj; Ciais, Philippe; Deryng, Delphine; Folberth, Christian; Glotter, Michael; Hoek, Steven; Iizumi, Toshichika; Izaurralde, R. C.; Jones, Curtis D.; Khabarov, Nikolay; Lawrence, P.; Liu, Wenfeng; Olin, Stefan; Pugh, Thomas A. M.; Ray, D. K.; Reddy, Ashwan; Rosenzweig, Cynthia; Ruane, Alex C.; Sakurai, Gen; Schmid, Erwin; Skalský, Rastislav; Song, Carol X.; Wang, Xuhui; Wit, Allard de; Yang, Hong

doi:10.5194/gmd-10-1403-2017

Cited by 244 publications

(262 citation statements)

References 71 publications

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“…Nitrate movement with water fluxes is simulated as in SWAT (Neitsch et al, 2002(Neitsch et al, , 2005. Nitrate is assumed to be fully dissolved in water and moves with surface runoff, lateral runoff, and percolation water.…”

Section: Nitrogen Leaching and Movementmentioning

confidence: 99%

Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

et al. 2018

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Abstract. The well-established dynamical global vegetation, hydrology, and crop growth model LPJmL is extended with a terrestrial nitrogen cycle to account for nutrient limitations. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included in the model. All new model features are described in full detail and the results of a global simulation of the historic past ) are presented for evaluation of the model performance. We find that the implementation of nitrogen limitation significantly improves the simulation of global patterns of crop productivity. Regional differences in crop productivity, which had to be calibrated via a scaling of the maximum leaf area index, can now largely be reproduced by the model, except for regions where fertilizer inputs and climate conditions are not the yield-limiting factors. Furthermore, it can be shown that land use has a strong influence on nitrogen losses, increasing leaching by 93 %.

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Section: Nitrogen Leaching and Movementmentioning

confidence: 99%

Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

et al. 2018

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“…photosynthesis, respiration and evapotranspiration) and modules specifically designed to represent crop processes. The version of ORCHIDEE-CROP used in this study includes a crop phenology module (Wu et al, 2016) and crop management modules (Wang, 2016), and has also submitted results for global gridded crop model intercomparison (Müller et al, 2017). ORCHIDEE-CROP calculates thermal unit accumulation, photosynthesis and energy exchange on a half-hourly time step, while leaf area dynamics, carbon allocation, and biomass and soil organic carbon change are simulated on a daily time step.…”

Section: Orchidee-cropmentioning

confidence: 99%

Projected changes in crop yield mean and variability over West Africa in a world 1.5 K warmer than the pre-industrial era

et al. 2018

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Abstract. The ability of a region to feed itself in the upcoming decades is an important issue. The West African population is expected to increase significantly in the next 30 years. The responses of crops to short-term climate change is critical to the population and the decision makers tasked with food security. This leads to three questions: how will crop yields change in the near future? What influence will climate change have on crop failures? Which adaptation methods should be employed to ameliorate undesirable changes?An ensemble of near-term climate projections are used to simulate maize, millet and sorghum in West Africa in the recent historic period (1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) and a near-term future when global temperatures are 1.5 K above preindustrial levels to assess the change in yield, yield variability and crop failure rate. Four crop models were used to simulate maize, millet and sorghum in West Africa in the historic and future climates.Across the majority of West Africa the maize, millet and sorghum yields are shown to fall. In the regions where yields increase, the variability also increases. This increase in variability increases the likelihood of crop failures, which are defined as yield negative anomalies beyond 1 standard deviation during the historic period. The increasing variability increases the frequency of crop failures across West Africa. The return time of crop failures falls from 8.8, 9.7 and 10.1 years to 5.2, 6.3 and 5.8 years for maize, millet and sorghum respectively.The adoption of heat-resistant cultivars and the use of captured rainwater have been investigated using one crop model as an idealized sensitivity test. The generalized doption of a cultivar resistant to high-temperature stress during flowering is shown to be more beneficial than using rainwater harvesting.

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“…The temporal variance of simulated crop yields is often not affected We also use the online tool as supplied by Müller et al (2017) for comparing the crop yield simulations against the Global Gridded Crop Model Intercomparison (GGCMI) model ensemble. Also here, results show that LPJmL 5 improves with respect to reproducing absolute yield levels across different countries, but that there is little effect on the simulated inter-annual 10 variability of crop yields.…”

Section: Crop Yieldsmentioning

confidence: 99%

“…Climate-carbon cycle feedbacks have become integral parts of Earth System Models (ESMs) for climate change projections. However, the terrestrial carbon cycle dynamic are not only driven by climate and carbon dioxide (CO 2 ) fertilization (Schimel et al, 2015;Norby et al, 2005), but also by land-use change (Müller et al, 2006(Müller et al, , 2016Arneth et al, 2017;Le Quéré et al, 2016) and vegetation dynamics (Müller et al, 2016, and references therein). Nutrient limitations, especially from nitro-15 gen, are also important constraints on vegetation growth and the terrestrial carbon cycle: Smith et al (2016) suggested that Earth System Models contributing to the CMIP5 data archive overestimate the response of net primary productivity to elevated CO 2 because the models largely miss the constraints from nutrient limitation.…”

mentioning

confidence: 99%

Implementing the Nitrogen cycle into the dynamic global vegetation, hydrology and crop growth model LPJmL (version 5)

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Schaphoff²,

Müller³

et al. 2017

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Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

Abstract: International audienc

Cited by 244 publications

References 71 publications

Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

Projected changes in crop yield mean and variability over West Africa in a world 1.5 K warmer than the pre-industrial era

Implementing the Nitrogen cycle into the dynamic global vegetation, hydrology and crop growth model LPJmL (version 5)

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