Projecting climate change impacts on grain maize based on three different crop model approaches

Holzkämper, Annelie; Calanca, Pierluigi; Honti, Márk; Fuhrer, Jürg

doi:10.1016/j.agrformet.2015.08.263

Cited by 29 publications

(23 citation statements)

References 56 publications

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“…These projections remained unchanged when the variation of downloaded weather from 12 GCMs used in this study was considered or when yields with a probability of exceedance of 70% were used to classify AECs. One weakness of the study is the use of a single crop model for yield projections, although the CropSyst model has been used extensively in wheat studies in the IPNW (Pannkuk et al, 1998;Peralta and Stöckle, 2002;Stöckle et al, 2010; and around the world, Pala et al, 1996;Sommer et al, 2013;Donatelli et al, 2015;Holzkämper et al, 2015;O'Leary et al, 2015). Previous studies conducted with other models have also shown positive impacts of climate change in the region (Thomson et al, 2002;Rosenzweig and Tubiello, 2007).…”

Section: Aec Classification Based On Yield Percentilesmentioning

confidence: 99%

“…These simulations were performed using CropSyst (Stockle et al, 1994;Stöckle et al, 2003), a cropping system model that has been widely used for climate change assessment studies under different climatic conditions around the world (Sommer et al, 2013;Donatelli et al, 2015;Holzkämper et al, 2015;O'Leary et al, 2015). Downscaled gridded daily weather data (4 × 4 km) for the period 1979-2010 (Abatzoglou, 2013) were used for baseline simulations.…”

Section: Crop Yield Simulationsmentioning

confidence: 99%

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Projected Dryland Cropping System Shifts in the Pacific Northwest in Response to Climate Change

et al. 2017

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Section: Aec Classification Based On Yield Percentilesmentioning

confidence: 99%

Section: Crop Yield Simulationsmentioning

confidence: 99%

Projected Dryland Cropping System Shifts in the Pacific Northwest in Response to Climate Change

et al. 2017

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“…Another risk of applying models for supporting decisions in adaptation planning lies in the potentially large uncertainties in model estimates originating from (1) context uncertainty, (2) input uncertainty, (3) model structure uncertainty, (4) parameter uncertainty, and (5) modelling technical uncertainty [10,32,33]. Uncertainties in model estimates cascade down from pathways of radiative forcing to climate models, downscaled climate projections, impact estimates and finally to recommendations for appropriate adaptation responses [34].…”

Section: The Risk Of Maladaptation In Agriculturementioning

confidence: 99%

Adapting Agricultural Production Systems to Climate Change—What’s the Use of Models?

Holzkämper

2017

Agriculture

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Climate change poses a challenge to agricultural production and its impacts vary depending on regional focus and on the type of production system. To avoid production losses and make use of emerging potentials, adaptations in agricultural management will inevitably be required. Adaptation responses can broadly be distinguished into (1) short-term incremental responses that farmers often choose autonomously in response to observed changes and based on local knowledge and experiences, and (2) long-term transformative responses that require strategic planning, and which are usually implemented at a larger spatial scale. Models can be used to support decision making at both response levels; thereby, different features of models prove more or less valuable depending on the type of adaptation response. This paper presents a systematic literature review on the state-of-the-art in modelling for adaptation planning in agricultural production systems, investigating the question of which model types can be distinguished and how these types differ in the way they support decision making in agricultural adaptation planning. Five types of models are distinguished: (1) empirical crop models; (2) regional suitability models; (3) biophysical models; (4) meta-models; and (5) decision models. The potential and limitations of these model types for providing decision-support to short-and long-term adaptation planning are discussed. The risk of maladaptation-adaptation that implies negative consequences either in the long term or in a wider context-is identified as a key challenge of adaptation planning that needs more attention. Maladaptation is not only a risk of decision making in the face of incomplete knowledge of future climate impacts on the agricultural production system; but it can also be a threat if the connectedness of the agroecosystem is not sufficiently acknowledged when management adaptations are implemented. Future research supporting climate change adaptation efforts should thus be based on integrated assessments of risk and vulnerabilities (considering climate variability and uncertainty). To secure adaptation success in the long term, frameworks for monitoring management adaptations and their consequences should be institutionalised.

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“…PROC ANOVA in SAS, Version 9.2 (SAS Institute Inc., 2010), was used to obtain the sums of squares for targeted effects, and an Uncertainty Index (UI) was calculated by dividing the treatment sums of squares by the total sums of squares (Holzkämper et al, 2015). The resulting UI is a measure FIGURE 3 | Cumulative probability distribution for simulated winter wheat yield changes during three 31-year time periods, centered on 2030, 2050, and 2070, relative to the baseline period under two representative concentration pathways (RCP4.5 and 8.5) and five crop models with ensembles of crop models at an irrigated site, Moses Lake, WA.…”

Section: Simulations and Analysismentioning

confidence: 99%

Assessment of Climate Change and Atmospheric CO2 Impact on Winter Wheat in the Pacific Northwest Using a Multimodel Ensemble

et al. 2017

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Simulations of crop yields under climate change are subject to uncertainties whose quantification is important for effective use of projected results for adaptation and mitigation strategies. In the US Pacific Northwest (PNW), studies based on single crop models and weather projections downscaled from a few general circulation models (GCM) have indicated mostly beneficial effects of climate change on winter wheat production for most of the twenty-first century. In this study we evaluated the uncertainty in the projection of winter wheat yields at seven sites in the PNW using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS, and EPIC) and daily weather data downscaled from 14 GCMs for 2 representative concentration pathways (RCP) of atmospheric CO 2 (RCP4.5 and 8.5). All crop models were calibrated for high, medium, and low precipitation dryland sites and one irrigated site using 1979-2010 as the baseline period. All five models were run from years 2000 to 2100 to evaluate the effect of future conditions (precipitation, temperature and atmospheric CO 2 ) on winter wheat grain yield. Simulations of future climatic conditions and impacts were organized into three 31-year periods centered around the years 2030, 2050, and 2070. All models predicted a decrease of the growing season length and crop transpiration, and increase in transpiration-use efficiency, biomass production, and yields, but with substantial variation that increased from the 2030s to 2070s. Most of the uncertainty (up to 85%) associated with predictions of yield was due to variation among the crop models. Maximum uncertainty due to GCMs was 15% which was less than the maximum uncertainty associated with the interaction between the crop model effect and GCM effect (25%). Large uncertainty associated with the interaction between crop models and GCMs indicated that the effect of GCM on yield varied among the five models. The mean of the ensemble of all crop models and GCMs provided a robust indication of positive effects of future environmental conditions on winter wheat yield during this century at all sites studied, with greater beneficial effect under water stressed conditions than under well-watered conditions, and under RCP8.5 than RCP4.5.

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Projecting climate change impacts on grain maize based on three different crop model approaches

Cited by 29 publications

References 56 publications

Projected Dryland Cropping System Shifts in the Pacific Northwest in Response to Climate Change

Projected Dryland Cropping System Shifts in the Pacific Northwest in Response to Climate Change

Adapting Agricultural Production Systems to Climate Change—What’s the Use of Models?

Assessment of Climate Change and Atmospheric CO2 Impact on Winter Wheat in the Pacific Northwest Using a Multimodel Ensemble

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