Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

Zhao, Gang; Hoffmann, Holger; Bussel, L.G.J. van; Enders, Andreas; Specka, Xenia; Sosa, Carmen; Yeluripati, Jagadeesh; Tao, Fulu; Constantin, Julie; Raynal, Hélène; Teixeira, Edmar; Grosz, Balázs; Doro, Luca; Zhao, Zhigan; Nendel, Claas; Kiese, Ralf; Eckersten, Henrik; Haas, Edwin; Vanuytrecht, Eline; Wang, Enli; Kuhnert, Matthias; Trombi, Giacomo; Moriondo, Marco; Bindi, Marco; Lewan, Elisabet; Bach, Michaela; Kersebaum, Kurt Christian; Rötter, Reimund P.; Roggero, Pier Paolo; Wallach, Daniel; Cammarano, Davide; Asseng, Senthold; Krauss, Gunther; Siebert, Stefan; Gaiser, Thomas; Ewert, Frank

doi:10.3354/cr01301

Cited by 44 publications

(37 citation statements)

References 79 publications

(82 reference statements)

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“…The models were not calibrated for the study area, but were adjusted based on 30-year yield averages of about 8 t ha −1 for winter wheat and 14 t ha −1 for silage maize. The weather data, presented and discussed by Zhao et al (2015) and Hoffmann et al (2015), show a 30 year average temperature of 9.7 • C, an average annual precipitation of 899 mm and mean annual global radiation of 3758 MJ m −2 a −1 with the standard deviations of 1.2 • C, 214.0 mm a −1 and 169.4 MJ m −2 a −1 , respectively. The coldest year was 2004, with an average temperature of 8.9 • C and the warmest year was 1983 (11.2 • C).…”

Section: Study Areamentioning

confidence: 99%

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Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

Kuhnert

Yeluripati

Smith

et al. 2017

European Journal of Agronomy

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a b s t r a c tFor spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km 2 ), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1-100 km grid cells show changes of 0.4-7.8% and 0.0-4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9-4.2 g C m −2 d −1 and 2.7-6.1 g C m −2 d −1 for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data

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Section: Study Areamentioning

confidence: 99%

“…Several studies have highlighted the impact of data aggregation on simulation results (Cale et al, 1983;Rastetter et al, 1992;Ewert et al, 2015;Zhao et al, 2015). de Wit et al (2005) and Hoffmann et al (2015) investigated the impact of climate data aggregation on crop yields.…”

Section: Introductionmentioning

confidence: 98%

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

Kuhnert

Yeluripati

Smith

et al. 2017

European Journal of Agronomy

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“…The uncertainty in the choice of representative field is in fact uncertainty in the input variables. Uncertainty due to scale change is explicitly studied in (Zhao et al 2015). Secondly, in impact assessment studies, the uncertainty in the climate projections must be taken into account.…”

Section: Creating Ensembles Based On a Single Model With Multiple Inpmentioning

confidence: 99%

Lessons from climate modeling on the design and use of ensembles for crop modeling

et al. 2016

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Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically Climatic Change (2016) 139:551-564 DOI 10.1007/s10584-016-1803-1 Highlights ?•Ensembles of crop models can improve uncertainty estimates, impact projections, and collaboration.•The climate modeling community has identified and studied many of the questions related to design and analysis of ensembles.•Much of the climate experience could be adapted to crop modeling. oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

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“…The performance of MONICA in representing crop dynamics in field experiments has been evaluated against other models (e.g., the Agricultural Production and Externalities Simulator [APES], CropSyst, Daisy, the Decision Support System for Agrotechnology Transfer [DSSAT], etc.) and was found to be sufficient (Nendel et al, 2011;Asseng et al, 2015;Kollas et al, 2015;Makowski et al, 2015;Pirttioja et al, 2015;Zhao et al, 2015).…”

Section: Modeling Soil Organic Carbonmentioning

confidence: 98%

A Modeling Framework to Evaluate the Impacts of Future Climate on Soil Organic Carbon Dynamics

2018

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Soil organic C (SOC) is significantly affected by changes in climate that control the physical, chemical, and biological processes in the soil. Simulating the impacts of future climate on SOC is challenging due to the limited availability of soil and climate variables required to properly simulate future SOC dynamics. The main objective of this study was to develop a modeling framework to quantify the impacts of future climate on SOC dynamics. The framework was developed for the Model for Nitrogen and Carbon in Agro-Ecosystems (MONICA) using the data collected from three University of Illinois Crop Science Research Centers. Projected precipitation and air temperature, collected from 32 global circulation models, were used to estimate the climate variables and cropping operation schedules required as inputs in MONICA. Six corn ()-soybean [ (L.) Merr.] rotation scenarios were considered in the simulations. Results showed that the total SOC in the upper 30 cm of the soil was expected to decrease from 43 to 70% in 2015 to 2075, with an uncertainty range of 13 to 16% due to the variation in climate prediction. The SOC in corn-soybean rotation schemes did not vary significantly from that of continuous corn scheme. The ability of a model to simulate realistic results depends significantly on the reliability of input data used to parameterize the model. A methodology that comprehensively estimates the impacts of future climatic conditions on SOC allows for realistic model results that can be used as a basis for environmental options, leading to a sustainable agro-production system.

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Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

Cited by 44 publications

References 79 publications

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

Lessons from climate modeling on the design and use of ensembles for crop modeling

A Modeling Framework to Evaluate the Impacts of Future Climate on Soil Organic Carbon Dynamics

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