Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

Salo, Tapio; Palosuo, Taru; Kersebaum, Kurt Christian; Nendel, Claas; Angulo, Carlos; Ewert, Frank; Bindi, Marco; Calanca, Pierluigi; Klein, Tommy; Moriondo, Marco; Ferrise, Roberto; Olesen, Jørgen E.; Patil, R. K.; Ruget, Françoise; Takáč, Jozef; Hlavinka, Petr; Trnka, Miroslav; Rötter, Reimund P.

doi:10.1017/s0021859615001124

Cited by 74 publications

(37 citation statements)

References 76 publications

(91 reference statements)

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“…The variation in the nitrogen response of the models can add to the uncertainty of crop modeling. In their comparison of eleven models regarding their response to different nitrogen levels [48], came to the conclusion that uncertainty regarding the simulation of nitrogen release by mineralization was one of the main factors influencing the performance of crop models. Uncertainties are related to different structures of the N turnover modules in models [49], differences in their temperature responses [50] or in the estimation of initial mineralization parameters, which might be even a consequence of lacking long term history data on land management.…”

Section: Discussionmentioning

confidence: 99%

Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

Kersebaum

Kroes

Gobin

et al. 2016

Water

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Crop productivity and water consumption form the basis to calculate the water footprint (WF) of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%-19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%-39%. Models responded differently to elevated CO 2 in a FACE (Free-Air Carbon Dioxide Enrichment) experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%-49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%-68% of the total actual evapotranspiration.

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Section: Discussionmentioning

confidence: 99%

Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

Kersebaum

Kroes

Gobin

et al. 2016

Water

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“…The APSIM model is a modular, process‐oriented simulation framework maintained by the APSIM Initiative (http://www.apsim.info), which simulates crop growth and corresponding carbon, water, and N dynamics in the plant and the soil in response to daily weather input data. The development of APSIM initially focused on the prediction of crop yield as influenced by the availability of water and N but since then has been tested for simulating soil water movement, nitrate leaching, crop water uptake (Cichota, Snow, Vogeler, Wheeler, & Shepherd, 2012; Connolly, Bell, Huth, Freebairn, & Thomas, 2002; Teixeira et al., 2018), and crop growth and development, including barley (Ibrahim, Harrison, Meinke, & Zhou, 2019; Salo et al., 2016). For the current study, the APSIM–Barley cultivar Oxford was used with default parameters.…”

Section: Methodsmentioning

confidence: 99%

Nitrate leaching from suction cup data: Influence of method of drainage calculation and concentration interpolation

et al. 2020

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The use of suctions cups is a common practice for estimating nitrate (NO3–N) leaching under agricultural systems despite the various uncertainties associated with the approach. One major uncertainty is water flux, which is required for calculating NO3–N leaching loads from measured concentrations. Another problem is the interpolation of NO3–N concentrations between measurement days. We investigated how differences in water flux, obtained from two different models (EVACROP and APSIM), affect NO3–N leaching loads. The effect of interpolation of NO3–N concentrations based on days or drainage was also addressed. The models were set up according to a 2‐yr field experiment with spring barley (Hordeum vulgare L. Quinch) with different levels of N fertilization rates on a loamy soil at Flakkebjerg, Denmark. Due to small differences in measured NO3–N concentrations between sequential samplings, the method of interpolation did not significantly affect NO3–N leaching in the two periods investigated. Although there is no standard against which leaching losses from different approaches can be tested, results highlight that the modeling of water uptake as affected by N supply influences the amount of drainage and thus calculated NO3–N leaching. Therefore, for experiments with varying N fertilization levels, the APSIM model, which accounts for N nutrition on crop water use, is likely more accurate. For common fertilization rates, the simpler EVACROP seems appropriate. Thus, when using suction cup data for testing models or for evaluating mitigation options for nitrate leaching, the use of an appropriate model for estimating water fluxes is important.

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“…Detailed field experimental data, including soils, tillage, fertilization, phenology, aboveground biomass at anthesis and maturity, yield and agronomic management practices were obtained for two growing seasons at Jokioinen in 2002 and 2009 (Salo et al., ) and for three growing seasons at Lleida from 1996 to 1999 (Cantero‐Martinez, Angas, & Lampurlanes, ). The barley cultivar in the experiment was Annabell at Jokioinen, and Hispanic at Lleida.…”

Section: Methodsmentioning

confidence: 99%

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

Tao

Rötter

Palosuo

et al. 2018

Global Change Biology

169

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Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.

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Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

Cited by 74 publications

References 76 publications

Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

Nitrate leaching from suction cup data: Influence of method of drainage calculation and concentration interpolation

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

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