Canonical Analysis of the Impact of Climate Predictors on Sugarcane Yield in the Eastern Region of Pernambuco, Brazil

Rodrigo Rogério da Silva, Geber Barbosa de Albuquerque Moura, Pabrício Marcos Oliveira Lopes (), Cristina Rodrigues Nascimento and Pedro Rogério Giongo
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Rodrigo Rogério da Silva: Department of Agronomy, Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, SN, Dois Irmãos, Recife 52171-900, PE, Brazil
Geber Barbosa de Albuquerque Moura: Department of Agronomy, Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, SN, Dois Irmãos, Recife 52171-900, PE, Brazil
Pabrício Marcos Oliveira Lopes: Department of Agronomy, Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, SN, Dois Irmãos, Recife 52171-900, PE, Brazil
Cristina Rodrigues Nascimento: Department of Agronomy, Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, SN, Dois Irmãos, Recife 52171-900, PE, Brazil
Pedro Rogério Giongo: Campus of Santa Helena of Goiás, State University of Goiás, Via Protestado Joaquim Bueno, No. 945 Urban Perimeter, Santa Helena of Goiás 75920-000, GO, Brazil

Agriculture, 2025, vol. 15, issue 20, 1-26

Abstract: Sugarcane yield plays a crucial role in food safety and biofuel production, and it is strongly influenced by climatic variations. In this context, this study applies canonical correlation analysis (CCA) to identify the climatic predictors, such as sea surface temperature, atmospheric pressure, and wind speed, that affect sugarcane yield from 1990 to 2019. Hierarchical cluster analysis applied to the performance of 58 municipalities in the eastern region of Pernambuco identified three distinct and homogeneous groups. An analysis of the CCA for the three sugarcane yield groups and climatic variables revealed that the first canonical function was significant with R = 0.82 and precision of 0.67 ( p ≤ 0.05 at 95% confidence level), and that the sugarcane yield groups and climatic variables were different (Wilks’ lambda = 0.14), but they were associated. Climatic variables affected the three sugarcane productivity groups, with redundancy indices of 29.7%, 52.2%, and 59.9%. Climatic variables with positive canonical weights enhance performance, while those with negative weights decrease yields. The structural canonical loads and cross-loadings reveal that sea surface temperature plays a crucial role in determining sugarcane yield, potentially influencing precipitation and temperature patterns in the region. The sensitivity analysis confirms the stability of the canonical loads and the robustness of the results, demonstrating that this research can support yield forecasting, regional agricultural policy, and drought management. Identifying climate predictors, such as sea surface temperature, wind speed, and atmospheric pressure, enables the creation of accurate models to predict sugarcane productivity, assisting farmers in planning input management, irrigation during dry periods, and harvesting. Furthermore, climate data can inform policies that encourage sustainable agricultural practices and adaptation to climate conditions, strengthening food security and guiding the selection of more resilient sugarcane varieties, increasing production resilience.

Keywords: agricultural planning; wind trade; temperature sea surface; hierarchical cluster; El Niño (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
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