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Master_Thesis_Revised -Tongxi Hu.pdf (1.73 MB)

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Impacts of climate variabilities on maize yield across the US: Insights from a Bayesian modeling analysis

Hu, Tongxi, Hu
http://orcid.org/0000-0003-4773-1022
http://rave.ohiolink.edu/etdc/view?acc_num=osu1531995498285494

Abstract Details

2018, Master of Science, Ohio State University, Environmental Science.
Climate change has reduced crop yields by 1~2% per decade over the past century, and such adverse impacts are projected to exacerbate in the future. Understanding the magnitude of these impacts is hindered by complex interactions between numerous biophysical and socioeconomic factors. To quantify the impacts of climate change on crop yields, many models have been developed. A common approach is to use statistical models (e.g., simple linear regression, quadratic or higher order regression models) trained on historical yields and some simplified climate factors, such as growing season temperature and precipitation. Responses of crop yields to climate change are still not well understood because of the complexities of relationships between climate factors and crop yields, and considerable uncertainties and limits of currently used statistical models. New approaches are needed to accelerate understanding of the climate impacts on crop yields. Thus, a highly flexible statistical model implemented according to the Bayes’ Theorem was employed to investigate how key climate factors (temperature and precipitation) affect maize yield in the U.S. We firstly generated simulated datasets using equations of a curve and surface to test a statistical model, Bayesian Multivariate Adaptive Regression Splines (BMARS). Although this model has been proved powerful in many former studies, an evaluation is still needed especially when the model is applied to raster data. The result showed that BMARS has capacities to capture complex relationships among variables by performing well in both curve and surface fitting. According to the favorable results, we feel confident to use this model to explore the potential relationships between crop yields and climate variables (mainly temperature and precipitation). Our results showed that growing season temperature (GST) can cause yield loss in many counties when increasing by more than 10% of a reference temperature (averaged GST of a county from 1895 to 2014). When GST increases by 10%, the average yield loss across all counties is 30.1%. While precipitation has a positive effect on maize yields before the amount rises by 20% of a reference value (averaged precipitation during growing season in the same period as temperature). When the amount increases by 20%, the precipitation helps increase maize yields averagely by 6.12% across all the counties. But the “increasing effect” decreases as the amount of precipitation increases. We also conducted sensitivity analysis of maize yields. The results demonstrated that mean growing season temperature has a negative effect on maize yields in many counties when it increases by 1¿ compared to the referenced temperature, while precipitation has a positive effect generally when it increases by 10 mm compared to the referenced amount of precipitation. But the effects of temperature and precipitation may be influenced by many other factors, such as irrigation.
Kaiguang Zhao (Advisor)
Karen Mancl (Committee Member)
Rattan Lal (Committee Member)
68 p.
Environmental Science
Impacts of climate variabilities

Recommended Citations

Citations

  • Hu, Hu, T. (2018). Impacts of climate variabilities on maize yield across the US: Insights from a Bayesian modeling analysis [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531995498285494

    APA Style (7th edition)

  • Hu, Hu, Tongxi. Impacts of climate variabilities on maize yield across the US: Insights from a Bayesian modeling analysis. 2018. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1531995498285494.

    MLA Style (8th edition)

  • Hu, Hu, Tongxi. "Impacts of climate variabilities on maize yield across the US: Insights from a Bayesian modeling analysis." Master's thesis, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531995498285494

    Chicago Manual of Style (17th edition)

osu1531995498285494
401
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