Date of Graduation

Summer 2025

Degree

Master of Natural and Applied Science in Geography, Geology, and Planning

Department

Geography, Geology, and Planning

Committee Chair

Asif Ishtiaque

Abstract

Mapping plant hardiness zones is crucial to understanding and predicting the spatial distribution of plants in response to climatic conditions. The United States Department of Agriculture (USDA) developed the plant hardiness zones based on the average of annual extreme minimum temperatures using the Parameter elevation Regression on Independent Slope Model (PRISM). While the USDA’s 2012 and 2023 Plant Hardiness Zone maps remain standard references, they offer limited geostatistical transparency that causes more complications in replicating. This study applies Empirical Bayesian Kriging Regression Prediction (EBKRP) method to generate Plant Hardiness Zone maps, analyze spatial and temporal shifts in the plant hardiness zone boundaries from 1930 to 2023 for the contiguous United States, with the aim of assessing the impact of climate change on plant distribution and survivability. The Empirical Bayesian Kriging Regression Prediction derived Plant Hardiness Zone maps were evaluated against the USDA maps using a confusion matrix, Quantity and Allocation Disagreement Index, and multiple geostatistical accuracy metrics. The results demonstrated EBKRP’s high predictive capacities. The model has high R-square values (>0.96), explaining over 96% of the variance in PHZ predictions. The confusion matrices showed high overall accuracies (>0.85) and the QADI values of 0.11 and 0.13 reflect high confidence and a low level of disagreement with the USDA plant hardiness zone maps. The research further reveals a clear and northward shift in minimum temperature thresholds that define these zones. Early decades, such as 1930 and 1970, have shown relatively minor spatial changes, reflecting slower climate warming. In contrast, the period from 1990 onward exhibits accelerated shifts in PHZ boundaries. These findings demonstrate that the Empirical Bayesian Kriging Regression Prediction (EBKRP) method provides a statistically rigorous and spatially detailed approach to mapping plant hardiness zones. The findings are crucial not only for advancing scientific understanding but also for practical applications, thus enabling farmers, horticulturists, and land managers to make more informed decisions when selecting climate-resilient crops and plant varieties that are best suited to specific regions amid changing climate conditions

Keywords

plant hardiness zone, empirical Bayesian kriging regression prediction, geostatistical, GIS, USA

Subject Categories

Agricultural Science | Climate | Environmental Monitoring | Horticulture

Copyright

© Daniel Donkor

Download

Available for download on Wednesday, July 01, 2026

Open Access

Share

COinS