Modeling Impacts Of Climate Change On Crop Yield PDF Download

Climate change is threatening food security as it is generally perceived to have negative impacts on agricultural production. Understanding this impact is central to adaptations to reduce potential yield loss. However, yield responses to changes in climate are complicated and have not been well understood. This project aims to characterize yield responses to the changing climate by utilizing modeling approaches, which in turn will help develop decision-supporting tools to inform policy or adaptation strategies. In this dissertation, we address several questions in modeling the impact of climate change on crop yield. First, in Chapter 2, we reviewed and synthesized current progress and findings from studies in the last 21 years using data-driven approaches. We found that previous studies generally agree that warming will negatively affect crop yields. For example, maize, wheat, soybean, and rice yield could be reduced by 7.5 ± 5.3%, 6.0 ± 3.3%, 6.8 ± 5.9%, and 1.2 ± 5.2% with 1 °C warming. Climate change could account for 37% of yield variability across the world. We also identified challenges and issues in previous studies, and thus developed a Bayesian model framework in Chapter 3 to overcome part of these challenges. The proposed Bayesian model framework was used in Chapter 4 to characterize spatial variations in yield responses to changes in climate variables with response curves. These response curves could help us identify what threats crop yield of a county is facing or will face and inform adaptation strategies to deal with these threats. If without adaptions, projected climate conditions of more than 36 climate models under four Coupled Model Intercomparison Project 5 (CMIP5) scenarios would benefit crops in some areas but could also cause severe yield loss in others. These yield changes are location- and scenario-specific. The Henry County in northern Ohio, for example, would have a yield increase of 1.2% and 0.7% under RCP 2.6 and 6.0 (both scenarios are moderate warming), and a yield decline of 0.1% and 3.1% under RCP 4.5 and 8.5 when both temperature and precipitation increase too much. In addition to the data-driven approach, Chapters 5 and 6 proposed a mechanism-driven approach to simulate crop growth by combining radiative transfer and photosynthesis processes (RP). This approach holds the promise to simulate crop growth under future climate conditions which are featured with elevated CO2 concentration, frequent extreme events such as heatwaves. Our assessment of this approach indicates that its simulations have overall good agreement with either flux data measured by Eddy covariance (correlation > 0.8) or field observations of crop yields (a correlation of 0.79). We then integrated the RP approach to a widely used agroecosystem model—the Environmental Policy Integrated Climate (EPIC) and test its utility in quantifying crop yield and soil organic carbon (SOC) changes in the context of climate change through a case study at a watershed level. The simulated crop yields have an agreement of 0.92 with reported yields by USDA-NASS during 2006 and 2013. The model estimated that SOC change (-31.7 Mg C ha-1) is also consistent with estimates using inventory methods or model simulations in previous studies. Projected yields of maize, soybean, and winter wheat could be reduced by -26.12%, -27.3%, and -4.4%, and SOC stock could experience a reduction with a range from 36.1~41.5 Mg C ha-1 for 2036-2043 under climate conditions projected by 5 climate models form four CMIP6 scenarios (i.e., SSP 2.6, 4.5, 7.0, and 8.5). The results from this dissertation highlight the potential of these tools in helping us understand the impacts of climate change on crop yields and make adaptation strategies to fight climate change.