Analysis and classification of data sets for calibration and validation of agro-ecosystem models

Kersebaum, Kurt Christian; Boote, Kenneth J.; Jorgenson, Jason; Nendel, Claas; Bindi, Marco; Frühauf, Cathleen; Gaiser, Thomas; Hoogenboom, Gerrit; Kollas, Chris; Olesen, Jørgen E.; Rötter, Reimund P.; Ruget, Françoise; Thorburn, Peter J.; Trnka, Miroslav; Wegehenkel, Martin

doi:10.1016/j.envsoft.2015.05.009

Cited by 130 publications

(62 citation statements)

References 79 publications

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“…Our study is among a few in the literature that tests a process-based model in the long-term (Ma et al, 2007). The long-term data were powerful in detecting weakness in the model, but did not provide guidance on which of the model’s processes or parameters needed to be improved (Kersebaum et al, 2015). Therefore, during calibration we aimed to improve the overall representation of the system based on previous knowledge of the site (for example, C:N ratio of soybean and corn residue, phenology, etc.)…”

Section: Discussionmentioning

confidence: 99%

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

et al. 2016

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Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

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Section: Discussionmentioning

confidence: 99%

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

et al. 2016

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“…Across the agricultural research community, the need for joined up approaches to tackling the issues of climate change have long been appreciated (Soussana et al, 2012) and current network initiatives are starting to move agricultural modellers towards the realisation of a more joined-up, focussed modelling community, as some of the resources developed in MACSUR, GRA and AgMIP (Antle et al, 2015;Bellocchi et al, 2013;Kersebaum et al, 2015;Yeluripati et al, 2015) demonstrate. However, long term support and governance will be required if these efforts are to be successfully extended (Kipling et al, in press) given the barriers to scientific collaboration, especially across disciplines (Siedlok and Hibbert, 2014).…”

Section: Synthesismentioning

confidence: 97%

“…However, data from other sources need better evaluation in terms of the methods used, their compatibility with specific models, and the level of detail they include. Through the MACSUR knowledge hub, Kersebaum et al (2015) developed a quantitative classification framework to evaluate the quality and consistency of existing agricultural datasets for use in crop models. This framework is likely to be applicable for the identification of data for grassland models, especially for models used to characterise both grassland and cropping systems .…”

Section: Providing Data For Modelsmentioning

confidence: 99%

Key challenges and priorities for modelling European grasslands under climate change

Kipling

Virkajärvi

Breitsameter

et al. 2016

Science of The Total Environment

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Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change.

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“…Agricultural ensemble modelling is currently bringing together large teams of people, and there is a need for an ontology that describes the data available for model 'proving'. Kersebaum et al (2014) build on the ontology put forward by Rosenzweig et al (2013).…”

Section: Reflections From This Thematic Issuementioning

confidence: 99%