Date of Graduation
Doctor of Philosophy in Biology (PhD)
Second Committee Member
Third Committee Member
data analytics, drought, food and agriculture, phenotyping, rice, sustainability
The increasing concentration of anthropogenic greenhouse gases in the atmosphere is altering the climate, posing a serious threat to global agriculture and food security. Agriculture and food production contribute a quarter of all GHG emissions produced, so there is a critical need to limit emissions in this area while increasing food production to feed the anticipated 10 billion people by 2050. To address the needs of the future, data-driven solutions are needed to guide decision-making and provide support for actionable climate mitigation and survival strategies. Research efforts must be focused on analyzing problems on multiple scales, identifying new ways to answer relevant questions, and translating available data into useful solutions.
This dissertation examines three diverse areas of research with an overarching goal of supporting sustainable food and agriculture future through developing data analytics tools. The first chapter evaluated the public GHG emission disclosure practices and climate goals of the top 100 global food and beverage companies, the second chapter developed a novel mechanistic model in a probabilistic framework to quantify and predict photosynthesis response to drought to support crop phenotyping, and the third chapter parameterized a common phenotyping model in a probabilistic framework to understand phenotypic plasticity of a major cereal crop of the world.
The first chapter highlights the gap in current GHG reporting practices of the largest food and beverage companies in the world. Roughly a third of companies assessed had some sort of climate goal, though the ability of those goals to significantly reduce global climate emissions is negligible in the majority of cases. Many companies lack any sort of public disclosure as well, and companies that fail to measure emissions are unable to set reduction goals. There is a significant disconnect between what is needed to keep global warming under 1.5 °C and the action currently being taken to do so.
The second chapter describes a novel multilevel Bayesian drought response curve model based on Michaelis-Menten equation for phenotyping drought sensitivity in rice genotypes. The model was successfully implemented in eight rice genotypes and drought sensitivity ranking was validated using yield data from the field.
The third chapter highlights the need to understand the plastic nature of plants and tested a fast non-sequential photosynthesis light response curve model in field condition. The growing environment of the rice plants in this research significantly altered their maximum photosynthesis rate, and the method of generating such curves was less important than the environment.
Reavis, M. L. (2021). Data Analytics for Sustainable Food and Agriculture Systems. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/4270
Available for download on Friday, February 17, 2023