Changes in Camelina sativa Yield Based on Temperature and Precipitation Using FDA

Abstract

Camelina (Camelina sativa) is an oilseed crop of increasing importance, valued not only for its adaptability to diverse environmental conditions and potential for sustainable agriculture but also for its economic advantages, including low input requirements and suitability for biofuel production and niche markets. This study examines the relationship between camelina yield and climatic variables—specifically temperature and precipitation—based on a ten-year field experiment conducted in Poland. To capture the temporal dynamics of weather conditions, Functional Data Analysis (FDA) was applied to daily temperature and precipitation data. The analysis revealed that yield variability was strongly influenced by the length of the vegetative period and specific weather patterns in April and July. Higher yields were recorded in years characterized by moderate spring temperatures, elevated temperatures in July, and evenly distributed rainfall during the early generative growth stages. The Maximal Information Coefficient ((Formula presented.)) confirmed the relevance of these variables, with the duration of the vegetative phase showing the strongest correlation with yield. Cluster analysis further distinguished high- and low-yield years based on functional weather profiles. The FDA-based approach provided clear, interpretable insights into climate–yield interactions and demonstrated greater effectiveness than traditional regression models in capturing complex, time-dependent relationships. These findings enhance our understanding of camelina’s response to climatic variability and support the development of predictive tools for resilient, climate-smart crop management.