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

Małgorzata Graczyk, Danuta Kurasiak-Popowska () and Grażyna Niedziela
Additional contact information
Małgorzata Graczyk: Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, 60-656 Poznań, Poland
Danuta Kurasiak-Popowska: Department of Genetics and Plant Breeding, Faculty of Agriculture, Horticulture and Biotechnology, Poznan University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland
Grażyna Niedziela: Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, 60-656 Poznań, Poland

Agriculture, 2025, vol. 15, issue 19, 1-14

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 ( M I C ) 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.

Keywords: camelina; weather condition; Functional Data Analysis (FDA); climate–yield relationship (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/19/2051/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/19/2051/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:19:p:2051-:d:1761509

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().