Optimizing Greenhouse Gas Emissions Studies with Functional Data Analysis in Tropical Land Uses

Inadequate agricultural management and soil cover changes are key drivers of greenhouse gas (GHG) emissions. Traditional statistical methods for measuring GHG fluxes often fail to account for the variability and randomness inherent to these processes. Functional data analysis (FDA) offers a novel approach to model and optimize exploratory analyses by addressing continuous variation (linear or nonlinear) and randomness over time. This study highlights the advantages of the FDA in assessing GHG emissions across three land-use treatments─extensive pasture, intensive pasture, and sugar cane─over a 182-day sampling period. FDA demonstrates superior versatility compared to conventional models, capturing the dynamic and continuous nature of gas exchange between soil and atmosphere. It allows for comprehensive uni- and multivariate analyses, effectively representing linear and nonlinear behaviors in gas fluxes. The results emphasize FDA’s potential as a robust tool for studying GHG emissions in tropical agricultural systems.