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Influence of Regional Temperature Anomalies on Strawberry Yield: A Study Using Multivariate Copula Analysis

Author

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  • Poornima Unnikrishnan

    (Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Kumaraswamy Ponnambalam

    (Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Fakhri Karray

    (Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi 50819, United Arab Emirates)

Abstract

A thorough understanding of the impact of climatic factors on agricultural production is crucial for improving crop models and enhancing predictability of crop prices and yields. Fluctuations in crop yield and price can have significant implications for the market sector and farming community. Given the projected increase in frequency and intensity of extreme events, reliable modelling of cropping patterns becomes essential. Temperature anomalies are expected to play a prominent role in future extreme events, emphasizing the need to comprehend their influence on crop yield. Forecasting extreme yield, which encompasses both the highest and lowest levels of agricultural production within a given time period, along with peak crop prices representing the highest market values, poses greater challenges in forecasting compared to other values. Probability-based predictions, accounting for uncertainty and variability, offer a more accurate approach for extreme value estimation and risk assessment. In this study, we employ a multivariate analysis based on vine copula to explore the interdependencies between temperature anomalies and daily strawberry yield in Santa Maria, California. By considering the maximum and minimum daily yields each month, we observe an increased probability of yield loss with rising temperature anomalies. While we do not explicitly consider the specific impacts of temperature anomalies under individual Representative Concentration Pathway (RCP) scenarios, our analysis is conducted within the broader context of the current global warming scenario. This allows us to capture the overall anticipated effects of regional temperature anomalies on agriculture. The findings of this study have potential impacts and consequences for understanding the vulnerability of agricultural systems and improving crop model predictions. By enhancing our understanding of the relationships between temperature anomalies and crop yield, we can inform decision-making processes related to the impact of climate change on agriculture. This research contributes to the ongoing efforts in improving agricultural sustainability and resilience in the face of changing climatic conditions.

Suggested Citation

  • Poornima Unnikrishnan & Kumaraswamy Ponnambalam & Fakhri Karray, 2024. "Influence of Regional Temperature Anomalies on Strawberry Yield: A Study Using Multivariate Copula Analysis," Sustainability, MDPI, vol. 16(9), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3523-:d:1381105
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    1. Heidi Webber & Frank Ewert & Jørgen E. Olesen & Christoph Müller & Stefan Fronzek & Alex C. Ruane & Maryse Bourgault & Pierre Martre & Behnam Ababaei & Marco Bindi & Roberto Ferrise & Robert Finger & , 2018. "Diverging importance of drought stress for maize and winter wheat in Europe," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    2. Roch, Oriol & Alegre, Antonio, 2006. "Testing the bivariate distribution of daily equity returns using copulas. An application to the Spanish stock market," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1312-1329, November.
    3. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
    4. Ling Hu, 2006. "Dependence patterns across financial markets: a mixed copula approach," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 717-729.
    5. Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2013. "Analyzing the dependence structure of various sectors in the Brazilian market: A Pair Copula Construction approach," Economic Modelling, Elsevier, vol. 35(C), pages 199-206.
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