A multiple crop model ensemble for improving broad-scale yield prediction using Bayesian model averaging

Huang, Xiao; Huang, Guorui; Yu, Chunyan; Ni, Shaoqiang; Yu, Le

doi:10.1016/j.fcr.2017.06.011

Cited by 45 publications

(36 citation statements)

References 56 publications

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“…Given the highly localized, management‐specific nature of cropping systems, crop model selection and a plausible range of parameters should be carefully justified for a specific environment. This point is supported by several previous studies, which show that some crop models perform well in one environment but not in another environment, and vice versa (Asseng et al., ; Bassu et al., ; Huang, Huang, Yu, Ni, & Yu, ; Li et al., ). Many crop models are developed for a specific focus and spatial scale, consequently strong in some aspects but weak in others; these should be kept in mind in the selection of crop model for a specific task.…”

Section: Discussionsupporting

confidence: 79%

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

171

102

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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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Section: Discussionsupporting

confidence: 79%

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

171

102

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“…Extreme heat events can cause severe yield reductions, but the projection remains largely uncertain. This study demonstrated that the major rice growth models underestimated the negative impacts of SEHS in the periods of flowering and early grain filling (Figure 4), and that multi-model ensembles failed to provide reliable estimates unlike many previous studies which reported that MMEs agreed better with observations than individual models (Huang et al, 2017;Rahman et al, 2018;Ruane et al, 2016;Sándor et al, 2017). The variations among models and biases between simulations and observations were larger notably in the heated treatments which commenced at flowering (0 DAF; Figure 4a), but none of the mod- This study showed that grain-setting under SEHS is the primary source of uncertainty in the yield estimates (Figure 5a,b).…”

Section: Main Source Of Uncertainty In Yield Projection Under Sehscontrasting

confidence: 64%

Current rice models underestimate yield losses from short‐term heat stresses

Sun

Hasegawa

Tang

et al. 2020

Global Change Biology

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Crop production will likely face enormous challenges against the occurrences of extreme climatic events projected under future climate change. Heat waves that occur at critical stages of the reproductive phase have detrimental impacts on the grain yield formation of rice (Oryza sativa). Accurate estimates of these impacts are essential to evaluate the effects of climate change on rice. However, the accuracy of these predictions by crop models has not been extensively tested. In this study, we evaluated 14 rice growth models against four year phytotron experiments with four levels of heat treatments imposed at different times after flowering. We found that all models greatly underestimated the negative effects of heat on grain yield, suggesting that yield projections with these models do not reflect food shocks that may occur under short‐term extreme heat stress (SEHS). As a result, crop model ensembles do not help to provide accurate estimates of grain yield under heat stress. We examined the functions of grain‐setting rate response to temperature (TRF_GS) used in eight models and showed that adjusting the effective periods of TRF_GS improved the model performance, especially for models simulating accumulative daily temperature effects. For TRF_GS which uses daily maximum temperature averaged for the effective period, the models provided better grain yield estimates by using maximum temperatures averaged only when daily maximum temperatures exceeded the base temperature (Tbase). An alternative method based on heating‐degree days and stage‐dependent heat sensitivity parameters further decreased the prediction uncertainty of grain yield under heat stress, where stage‐dependent heat sensitivity was more important than heat dose for model improvement under SEHS. These results suggest the limitation of the applicability of existing rice models to variable climatic conditions and the urgent need for an alternative grain‐setting function accounting for the stage‐dependent heat sensitivity.

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“…To reduce the predictive uncertainty of model structure, we use the Bayesian Model Averaging (BMA) method to obtain the multimodel ensemble by the linear combination of individual model predictions (Huang et al, ). The DREAM algorithm is also used to derive the weights and variance for the individual ensemble members.…”

Section: Methodsmentioning

confidence: 99%

Assessing the Impacts of Extreme Agricultural Droughts in China Under Climate and Socioeconomic Changes

Huang

Han

et al. 2018

Earth's Future

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Agricultural food production in China is deeply vulnerable to extreme droughts. Although there are many studies to evaluate this issue from different aspects, comprehensive assessments with full consideration of climate change, crop rotations, irrigation effects, and socioeconomic factors in broad scales have not been well addressed. Considering both the probability of drought occurrence and the consequential yield losses, here we propose an integrated approach for assessing past and future agricultural drought risks that relies on multimodel ensemble simulations calibrated for rice, maize, and wheat (RMW) in China. Our results show that irrigation has reduced drought‐related yield losses by 31 ± 2%; the largest reductions in food production were primarily attributable to socioeconomic factors rather than droughts during 1955–2014. Unsustainable water management, especially groundwater management, could potentially cause disastrous consequences in both food production and water supply in extreme events. Our simulations project a rise of 2.5~3.3% in average rice, maize, and wheat productivity before 2050 but decrease thereafter if climate warming continues. The frequency of extreme agricultural droughts in China is projected to increase under all examined Representative Concentration Pathway (RCP). A current 100‐year drought is projected to occur once every 30 years under RCP 2.6, once every 13 years under RCP 4.5, and once every 5 years under RCP 8.5. This increased occurrence of severe droughts would double the rate of drought‐induced yield losses in the largest warming scenario. Policies for future food security should prioritize sustainable intensification and conservation of groundwater, as well as geographically balanced water resource and food production.

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A multiple crop model ensemble for improving broad-scale yield prediction using Bayesian model averaging

Cited by 45 publications

References 56 publications

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

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

Current rice models underestimate yield losses from short‐term heat stresses

Assessing the Impacts of Extreme Agricultural Droughts in China Under Climate and Socioeconomic Changes

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