Where Can We Grow?Machine Learning to Predict the Future of U.S.Maize - 2024

Aug 4, 2026

Upload by Ezra

This poster presents research on utilizing machine learning to predict the future of U.S. maize cultivation, focusing on the factors influencing growth. The study investigates the impact of weather, soil conditions, and terrain on crop yield, aiming to provide insights for optimizing agricultural practices. The research is part of the Three-Minute Thesis (3MT) competition at the University of Arkansas, Fayetteville, and is made available through ScholarWorks@UARK. The work, authored by Harrison Smith, a PhD Candidate in Environmental Dynamics, explores predictive modeling for agricultural sustainability and efficiency. The poster highlights key variables that affect maize production and suggests potential applications of machine learning in addressing future challenges in agriculture. The citation details are provided for further reference, along with information about the open access repository where the poster is archived. The focus is on how machine learning can contribute to understanding and enhancing the future of maize farming in the United States by analyzing environmental data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages3

Reading Time8 min

AccessFree to download

Where Can We Grow?

Machine Learning to Predict the Future of U.S. Maize

Factors Influencing Maize Growth

Weather

Soil

Terrain

FAQs

What is the main goal of this research?

The main goal is to use machine learning to predict the future of U.S. maize cultivation and identify factors that influence its growth.

Who conducted this research and where?

The research was conducted by Harrison Smith, a PhD Candidate in Environmental Dynamics at the University of Arkansas, Fayetteville.

What are the key factors considered in predicting maize growth?

The key factors considered are weather, soil conditions, and terrain.

Aug 4, 2026

Upload by Ezra