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The prediction of crop yields internationally is a crucial objective inagricultural research. Thus, this study implements 6 regression models (Linear,Tree, Gradient Descent, Gradient Boosting, K- Nearest Neighbors, and RandomForest) to predict crop yields in 196 countries. Given 4 key trainingparameters, pesticides (tonnes), rainfall (mm), temperature (Celsius), andyield (hg/ha), it was found that our Random Forest Regression model achieved adetermination coefficient (r^2) of 0.94, with a margin of error (ME) of .03.The models were trained and tested using the Food and Agricultural Organizationof the United Nations data, along with the World Bank Climate Change DataCatalog. Furthermore, each parameter was analyzed to understand how varyingfactors could impact overall yield. We used unconventional models, contrary togenerally used Deep Learning (DL) and Machine Learning (ML) models, combinedwith recently collected data to implement a unique approach in our research.Existing scholarship would benefit from understanding the most optimal modelfor agricultural research, specifically using the United Nations data.

Innovations in Agricultural Forecasting: A Multivariate Regression Study on Global Crop Yield Prediction