Predicting future climate scenarios: a machine learning perspective on greenhouse gas emissions in agrifood systems

Global climate change is an extensive phenomenon characterized by alterations in weather patterns, temperature trends, and precipitation levels. These variations substantially impact agrifood systems, encompassing the interconnected components of farming, food production, and distribution. This article analyzes 8,100 data points with 27 input features that quantify diverse aspects of the agrifood system’s contribution to predicted Greenhouse Gas Emissions (GHGE). The study uses two machine learning algorithms, Long-Short Term Memory (LSTM) and Random Forest (RF), as well as a hybrid approach (LSTM-RF). The LSTM-RF model integrates the strengths of LSTM and RF. LSTMs are adept at capturing long-term dependencies in sequential data through memory cells, addressing the vanishing gradient problem. Meanwhile, with its ensemble learning approach, RF improves overall model performance and generalization by combining multiple weak learners. Additionally, RF provides insights into the importance of features, helping to understand the significant contributors to the model’s predictions. The results demonstrate that the LSTM-RF algorithm outperforms other algorithms (for the test subset, RMSE = 2.977 and R 2 = 0.9990). These findings highlight the superior accuracy of the LSTM-RF algorithm compared to the individual LSTM and RF algorithms, with the RF algorithm being less accurate in comparison. As determined by Pearson correlation analysis, key variables such as on-farm energy use, pesticide manufacturing, and land use factors significantly influence GHGE outputs. Furthermore, this study uses a heat map to visually represent the correlation coefficient between the input variables and GHGE, enhancing our understanding of the complex interactions within the agrifood system. Understanding the intricate connection between climate change and agrifood systems is crucial for developing practices addressing food security and environmental challenges.