Dan Li, Vassili Kitsios, David Newth, Terence John O'Kane
arXiv 29 Jul 2025 · Statistics — Applications
arXiv:2507.21559 · PDF · Extracted main text
This paper introduces a Bayesian hierarchical modeling framework within a fully probabilistic setting for crop yield estimation, model selection, and uncertainty forecasting under multiple future greenhouse gas emission scenarios. By informing on regional agricultural impacts, this approach addresses broader risks to global food security. Extending an established multivariate econometric crop-yield model to incorporate country-specific error variances, the framework systematically relaxes restrictive homogeneity assumptions and enables transparent decomposition of predictive uncertainty into contributions from climate models, emission scenarios, and crop model parameters. In both in-sample and out-of-sample analyses focused on global wheat production, the results demonstrate significant improvements in calibration and probabilistic accuracy of yield projections. These advances provide policymakers and stakeholders with detailed, risk-sensitive information to support the development of more resilient and adaptive agricultural and climate strategies in response to escalating climate-related risks.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Li, D., Kitsios, V., Newth, D., and O’Kane, T. J (2025) Machine learning projection of climate and technology impacts on crops key to food security self | 1.000 | 8 | 6 | 100% |
| 2 | Kitsios, V., O’Kane, T. J., and Newth, D (2023) A machine learning approach to rapidly project climate responses under a multitude of net-zero emission pathways self | 0.644 | 2 | 2 | 100% |
| 3 | Müller, C., Franke, J., Jägermeyr, J., Ruane, A. C., Elliott, J., Mo… (2021) Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios | 0.585 | 3 | 1 | 100% |
| 4 | Bürkner, P.-C., Gabry, J., and Vehtari, A (2020) Approximate leave-future-out cross-validation for Bayesian time series models | 0.511 | 3 | 2 | 33% |
| 5 | Vehtari, A., Gelman, A., and Gabry, J (2017) Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC | 0.511 | 2 | 2 | 50% |
| 6 | Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L.… (2013) Uncertainty in simulating wheat yields under climate change | 0.511 | 2 | 1 | 100% |
| 7 | Wang, E., Martre, P., Zhao, Z., Ewert, F., Maiorano, A., Rötter, R.… (2017) The uncertainty of crop yield projections is reduced by improved temperature response functions | 0.511 | 2 | 1 | 100% |
| 8 | Zhang, Y., Zhao, Y., Chen, S., Guo, J., and Wang, E (2015) Prediction of maize yield response to climate change with climate and crop model uncertainties | 0.511 | 2 | 1 | 100% |
| 9 | Population Reference Bureau (2018) 2018 World Population Data Sheet | 0.405 | 1 | 1 | 100% |
| 10 | United Nations (2022) World population prospects 2022: Summary of results | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 35 scored citations.