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Exploration of Causal Inference Using Machine Learning for Food Policy Modeling in the United States
No full textThis dissertation explores econometrics and machine learning methodologies, focusing on food policy. Initially, the spotlight is on the food policy environment, but later, it shifts to studying food prices specifically. This study is crucial for those making decisions around food and nutrition, helping to clear up how various factors interlink. This dissertation broadens existing research by (1) adding new machine-learning methods into the model, (2) exploring different pathways within graphical models, and (3) studying how rising food prices affect policy recommendations.
The first paper uses Markov blankets to sift through variables in the U.S. food scene. It pinpoints applicable models for simulating scenarios around food insecurity, poverty, obesity, and food assistance programs. The results show that addressing certain variables is crucial for making effective policy. For example, income and marginalized groups should be a focus of policies that combat food insecurity. Additionally, policies should look into the relationship of high obesity rates and high poverty rates. Those in charge should use advanced techniques to tackle complex issues, leading to better policy creation.
The second study points out that policy modeling biases can be caused by incorrectly handling back-door pathways. Building from the first paper, it focuses on four significant variables: food insecurity, poverty, SNAP (a food assistance program), and obesity. It shows that mismanaging these pathways can cause errors by not closing open pathways or wrongly opening closed ones.
The third paper explores the reasons behind fluctuating food prices with three goals: (1) to understand changing factors affecting food price inflation in the U.S. using specific models, (2) to find out how different variables are related, and to create models using innovative methods, and (3) to make and validate predictions about U.S. food price inflation. The aim is to offer valuable insights for making policy. This information is crucial for those making food and nutrition policies in the U.S. The findings show a close relationship between food price inflation, the price of goods, and medical service costs. Furthermore, looking back at the COVID-19 period reveals links between food price inflation and energy price inflation
Fueling the mind, feeding the world: Communication in agriculture - Communicating accurately and concisely (COM02)
Get PDFFall 2024 version. Created at Texas A&M University as part of a grant sponsored by the USDA.
For additional information, visit the Texas A&M University Science Communications Lab at https://scicomm.tamu.edu/.This packet contains instructional materials and online modules prepared for Fueling the mind, feeding the world: Communication in agriculture - Communicating accurately and concisely (COM02). It includes curriculum, PowerPoint slides, activities, handouts, grading considerations, and notes for instructors. These materials were created as part of the USDA Grant entitled "Fueling the Mind, Feeding the World: Enhancing Communication and Decision-Making Skills of Secondary Agricultural Education Students."
MODULE OVERVIEW: Accurate and concise information is important in the decision-making process. Without accurate and concise information, scientists cannot conduct research, interpret scientific findings, or make recommendations based on their findings. These are all important as those who use this information���the majority of the global population���usually lack specialized knowledge but still look for and need understandable, usable scientific information.Secondary Education, Two-Year Postsecondary Education, and Agriculture in the K-12 Classroom (SPECA) Challenge Grants Program no. 2019-38414-30265 and Hatch Project No. TEX09825 from the USDA National Institute of Food and Agriculture
Global Lithium Competition: Examining The Social, Market and Geopolitical Factors of The Global Lithium Trade
Get PDFIn this dire race towards a sustainable tomorrow, one vital element that holds the power to reshape our world and mitigate the lasting impacts of climate change is Lithium. This lightweight metal, coined as the 21st century ���white gold��� will play a pivotal role in the development of green energy technologies, electrification of energy systems and electric vehicles. As the world grapples with solutions to tackle climate change, expand on innovative technologies and advance global decarbonization, Lithium will be the catalyst that determine whether superpower nations, like the United States and China, will sustain global dominance in this green transition race. Pre-existing research tends to draw relationships between industry trends and supply chains in hopes of predicting the outlay of the future, many others may dive into potentially impactful markets like Bolivia, China, or Argentina (Narins, 2017). However, this thesis seeks a different approach by diving into global trade dynamics and international competition from different angles to shed light on the nuances of varying market limitations, social/governmental challenges and environmental factors that may contribute to the intricate complexities of the global lithium trade. Using pre-existing literature, industry intelligence, government reports and press releases we hope to build a more comprehensive picture of the ferocious competition surrounding this limited resource, in addition to clarifying how vital this rare mineral is to a clean carbon reduced future
Advancing the Practice of Coprolite Research
No full textThis dissertation informs the practice of coprolite research by discussing ethical coprolite research and applying genetic and macroscopic analyses to dietary studies of coprolites from Bonneville Estates Rockshelter.
The work is divided into three sections, each of which addresses a question facing coprolite research. The first concerns the ethics of ancient DNA analyses on composite materials including coprolites, sediment, birch pitch, and dental calculus. I show that ethical concerns vary according to the composite used and provide historic context for genetic analyses on archaeological materials in the United States. The text contains discussion on how guidelines for working with ancient DNA from Ancestral remains can be applied to composite work. Finally, I offer a guide to planning ancient DNA research on composites under a framework of clear, open communication and collaboration with stakeholders.
The second section addresses differences between the chosen methods of coprolite analysis. I conducted a DNA metabarcoding and macroremains analysis on coprolites associated with Bonneville Estates��� middle and late Holocene. The results correlate to previous studies showing a reliance on small, dryland seeds and minimal wetland use throughout the Holocene. Inhabitants of Bonneville Estates likely visited in the summer/fall and utilized resources from both upland and lowland contexts. However, the genetic data revealed more taxa utilized for softer elements than the macroscopic data and the methods had little taxonomic overlap.
In the final section I consider coprolite quantification by using cluster analysis to detect dietary trends in coprolites recovered from all of Bonneville Estates��� cultural components. The data show a relatively consistent diet through time, though grass usage increases in the late Holocene. Foragers at the site predominantly visited in the summer/fall and relied on dependable dryland resources even in times of climate amelioration. Cluster analysis revealed individual diets were centered on pickleweed, dropseed sandgrass, unidentified cheno-ams, prickly pear, and unidentified fauna. Lagomorphs and pronghorn are common faunal supplements.
Together, this dissertation informs the practice of coprolite research and helps refine what is known about subsistence practices at Bonneville Estates Rockshelter
Applications of Causal Inference and Machine Learning in Food and Agricultural Policy
No full textThis dissertation explores the intersections of causal inference and machine learning in food and agricultural policy, offering in-depth analyses through advanced methodologies. It scrutinizes the implications of the Supplemental Nutrition Assistance Program (SNAP) policies and the effects of climate change on crop yield, emphasizing the practical applications of causal insights and predictive models in these areas.
Chapter II identifies the Broad-Based Categorical Eligibility (BBCE) policy���s impact on SNAP participation, utilizing a more appropriate estimator based on the difference-in-differences design. The study investigates the BBCE���s role in the surge of SNAP enrollment, revealing that the implementation of BBCE significantly increased SNAP participation. The results also shed light on the heterogeneous impacts of BBCE and emphasize its complex role in policy discussions and evaluations.
Chapter III explores the differential impacts of the augmentations of the maximum SNAP benefits on SNAP household food and nondurable, nonfood goods expenditures during the Great Recession and the COVID-19 pandemic with their different socio-political and economic conditions. This study uncovers the disparities in the impacts of similar benefit increases implemented during these two periods. It shows the American Recovery and Reinvestment Act of 2009 increased SNAP households��� expenditures on food during the Great Recession, highlighting the heterogeneity in impacts across households with different levels of food expenditures, but no effect is detected as a result of the similar benefit adjustment during the pandemic due to the Consolidated Appropriations Act of 2020 and the American Rescue Plan Act of 2021.
Chapter IV employs the quantile random forest model to investigate the interplay between crop yield deviation and climate change. This analysis provides insights into the diverse impacts of climate change on non-irrigated cotton yield across the United States under varied climate scenarios, elucidating how climate scenarios can potentially change the distribution of crop yields
