Numerous climate change models are currently in use for predicting ecological changes as well as assisting in mitigation strategies and policies. However, the substantial variability in methodologies limits opportunities for cross-study comparisons and introduces bias. This study reviewed 221 papers involving climate change models and examined the model/models used, impacts considered, risk assessment, crops evaluated, regions evaluated, adaptation strategies, and CO2 concentration data origin and application. Soybean, wheat, rice, and maize were most frequently studied, and radiation use efficiency and transpiration adjustments were the most frequent approach in simulating changes in CO2 concentration. The authors found that the diversity in methodologies and parameters constrains comparisons across multiple studies, and the use of deterministic models is not sufficient for deciphering highly variable agroecological processes. They call for further integration among disciplines to develop more holistic, stochastic methodologies that will better predict the impacts of climate change.