Evidence of synchronization between solar activity and agricultural performance in Germany - Sierra-Figueredo, Pablo; Sol-Sánchez, Angel; Zúniga-González, Carlos Alberto (Frontiers in Agronomy, 2025)
BackgroundOver the past decades, extensive research has examined the interactions between space weather, solar activity, and the biosphere, particularly their implications for agricultural productivity. However, the specific mechanisms linking solar activity to agricultural performance remain underexplored, particularly in the context of bioeconomic efficiency. Understanding these connections is crucial for mitigating adverse environmental impacts and optimizing agricultural productivity. Research has highlighted the synchronization between multi-year solar activity cycles, particularly the 11-year solar cycles, and agricultural performance. Forecasting these cycles holds the potential to anticipate fluctuations in agricultural productivity, thus enabling more effective resource planning and enhancing profitability within the agri-food industry.MethodologyThis study employs Fast Fourier Transform (FFT) and advanced statistical tools from Microcal Origin 6.0 to analyze time-series data for 10 key agricultural products in Germany over a 61-year period. The study integrates solar radiation data, meteorological variables, and regional agricultural production data to investigate the relationship between solar activity and crop yields.ResultsThe analysis reveals significant temporal associations between solar activity cycles and agricultural yields, identified through both cross-correlation and spectral analysis. These findings indicate that solar activity, particularly during the 11-year solar cycle, plays a notable role in shaping agricultural productivity.ConclusionThe results confirm the synchronization between solar activity and agricultural performance. These insights have significant implications for the agri-food sector, suggesting that incorporating solar activity forecasts into agricultural management strategies could enhance resource allocation, improve crop yield predictions, and promote sustainable agricultural practices.