The agricultural sector is not only an important component of economic growth, but the environmental degradation caused by the sector has also been a significant research topic in recent years. This study aims to examine the time-frequency dependencies between agricultural activities, economic growth (GDP), and environmental degradation during the period 1961-2021. Instead of traditional time series analyses, the Cross-Wavelet Transform (XWT) and Wavelet Coherence (WTC) approaches, which can detect asymmetric, nonlinear, and dynamic relationships in the short, medium, and long term, were used as the methodology. Empirical findings from WTC demonstrate the existence of a strong, co-directional movement between agriculture and GDP in the medium and long term, despite periodic differences. A high level of positive correlation was found between agriculture and CO₂ emissions, particularly in long-term periods and after 1980. The direction of the phase arrows indicates that agricultural expansion triggers environmental degradation as a precursor. WTC results obtained from robustness analysis using the ecological footprint strongly confirm the long-term positive correlation between agriculture and environmental degradation. Consequently, this study empirically demonstrates that agricultural growth has a significant environmental cost. It is critically important for policymakers to implement green agriculture and low-carbon production strategies that prioritize environmental sustainability over purely economic growth and food security objectives in order to achieve environmentally friendly agricultural strategies.