Comparing process-based wheat growth models in their simulation of yield losses caused by plant diseases

Bregaglio, Simone; Willocquet, Laetitia; Kersebaum, Kurt Christian; Ferrise, Roberto; Stella, Tommaso; Ferreira, Thiago Berton; Pavan, Willingthon; Asseng, Senthold; Savary, Serge

doi:10.1016/j.fcr.2021.108108

Cited by 20 publications

(15 citation statements)

References 70 publications

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“…Crop losses by pest and diseases are rarely considered in crop models. Within MACSUR and AgMIP first attempts have been made to implement generic damage mechanisms to assess crop loss by pest and diseases, which builds on early modelling efforts during the 1980s (Bregaglio et al 2021). However, following the roadmap of Donatelli et al (2017) models are under development, which consider the interaction between crops and pests and diseases through the link between crop models and pest and disease models to cope with future changes of biotic pressures under climate change.…”

Section: Conclusion and Recommendations For Model Improvementmentioning

confidence: 99%

Modelling to Evaluate Climate Resilience of Crop Rotations Under Climate Change

Kersebaum

2022

Springer Climate

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Diversification of crop rotations is considered as an option to increase the resilience of European crop production under climate change. Although crop rotation design and management has been identified as an important measure to adapt to and mitigate climate change, most studies on climate change impact or adaptation so far use single-year simulations and/or single crop assessments. Crop response to various management options within a growing season is generally taken into account by most crop models. However, if simulations neglect processes and fluxes between growing seasons and potential carry-over effects related to agronomic management, the long-term sustainability of adaptation and mitigation strategies cannot be properly evaluated. Therefore, the integrated assessment of impacts, adaptation and mitigation options under current and future climatic conditions requires a continuous long-term analysis of crop sequences to take into account carry-over effects as in real conditions. The present paper provides information on crop rotation aspects, which should be considered in modelling, presents the current state of modelling for climate impact assessment, address points of uncertainty and missing aspects in modelling and draws an outlook on potential future developments with special emphasis on crop rotations. In conclusion, crop models require suitable experimental data to parameterize additional crops, which were so far not sufficiently investigated to cope with multiple opportunities in crop rotations.

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Section: Conclusion and Recommendations For Model Improvementmentioning

confidence: 99%

Modelling to Evaluate Climate Resilience of Crop Rotations Under Climate Change

Kersebaum

2022

Springer Climate

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“…Climate change will directly affect crop pathogens and the diseases they cause. This topic has been widely investigated and reported for the last few decades, even under controlled conditions, for example, free air concentration enrichment (FACE) systems and open-topped chambers (Eastburn et al, 2011) or with modelling studies (e.g., Bregaglio et al, 2021;West et al, 2012). Juroszek et al…”

Section: Climate Chang E Impac Ts On Crops and Pathog En Smentioning

confidence: 99%

“…One challenge for estimation of yield loss is how to combine these epidemiological models with process-based crop growth models (Bregaglio et al, 2021;Cunniffe et al, 2015;Donatelli et al, 2017). To respect ICM principles, there is a need to develop decision support systems (DSS) with economic thresholds to estimate when it is economically feasible to apply crop protection products with a limited impact on the environment (Gilligan, 2008).…”

Section: Forecastingmentioning

confidence: 99%

Control of crop diseases through Integrated Crop Management to deliver climate‐smart farming systems for low‐ and high‐input crop production

Richard

Fitt

2021

Plant Pathology

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“…Whish et al (2015) estimated the impact of wheat rust on the green leaf area of wheat crops by coupling the Agricultural Production Systems Simulator (APSIM) with a rust model developed in the DYMEX population-modeling platform. Bregaglio et al (2021) incorporated a pest damage mechanism into five different wheat models to simulate four major wheat diseases (brown and yellow rust, Septoria tritici blotch and powdery mildew). Their approach used injury drivers (Willocquet et al, 2008(Willocquet et al, , 2018 that represented the time course of multiple injuries through a disease progress curve (DPC) of disease severity on a 0-1 scale.…”

mentioning

confidence: 99%

“…Their approach used injury drivers (Willocquet et al, 2008(Willocquet et al, , 2018 that represented the time course of multiple injuries through a disease progress curve (DPC) of disease severity on a 0-1 scale. Bregaglio et al (2021) also outlined the rationale for implementing more dynamic pest damage mechanisms into crop models, as their approach was very static. Therefore, simulating the interaction between pest damage and wheat growth should be further explored to add more flexibility to pest damage mechanisms to create case-specific simulation scenarios not bound to a static DPC that shapes the simulated crop losses.…”

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confidence: 99%

Coupling a Pest and Disease Damage Module with CSM-NWheat: A Wheat Crop Simulation Model

Ferreira¹,

Pavan²,

Fernandes³

et al. 2021

Transactions of the ASABE

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HighlightsCSM-NWheat, a DSSAT wheat crop model, was coupled with a pest module named PEST.The coupled model can simulate the impact of pest and disease damage on wheat crops.Pest damage is expressed in daily steps by communication links called coupling points.Coupling points are linked with state variables at which pest damage can be applied.Field pest-scouting reports and linear interpolation are used to compute damage rates.Abstract. Wheat is one of the most important global staple crops and is affected by numerous pests and diseases. Depending on their intensity, pests and diseases can cause significant economic losses and even crop failures. Pest models can assist decision-making, thus helping reduce crop losses. Most wheat simulation models account for abiotic stresses such as drought and nutrients, but they do not account for biotic stresses caused by pests and diseases. Therefore, the objective of this study was to couple a dynamic pest and disease damage module to the DSSAT model CSM-NWheat. Coupling points were integrated into the CSM-NWheat model for applying daily damage to all plant components, including leaves, stems, roots, and grains, the entire plant, and to the assimilate supply. The coupled model was tested by simulating a wheat crop with virtual damage levels applied at each coupling point. Measured foliar damage caused by tan spot (Pyrenophora tritici-repentis) was also simulated. The modified model accurately estimated the reduction in leaf area growth and the yield loss when compared with observed data. With the incorporation of the pest module, CSM-NWheat can now predict the potential impact of pests and diseases on wheat growth and development, and ultimately economic yield. Keywords: Biotic stress, Decision support, DSSAT, Model coupling, Yield loss.

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Comparing process-based wheat growth models in their simulation of yield losses caused by plant diseases

Cited by 20 publications

References 70 publications

Modelling to Evaluate Climate Resilience of Crop Rotations Under Climate Change

Modelling to Evaluate Climate Resilience of Crop Rotations Under Climate Change

Control of crop diseases through Integrated Crop Management to deliver climate‐smart farming systems for low‐ and high‐input crop production

Coupling a Pest and Disease Damage Module with CSM-NWheat: A Wheat Crop Simulation Model

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