Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis

  • Philip G. Oguntunde
  • Gunnar Lischeid
  • Ottfried Dietrich
Original Paper

Abstract

This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease (P < 0.001) in rice yield, pan evaporation, solar radiation, and wind speed declined significantly. Eight principal components exhibited an eigenvalue > 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986–1993 and the 2006–2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

Keywords

Rice yield Climate variables Linear regression Support vector machine NERICA 

Notes

Acknowledgements

The first author gratefully acknowledges the visiting fellowship support from Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany. Dr. C.O. Akinbile graciously gave the rice yield data, which he previously collected from the Africa Rice Center, for this study.

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Copyright information

© ISB 2017

Authors and Affiliations

  • Philip G. Oguntunde
    • 1
    • 2
  • Gunnar Lischeid
    • 1
    • 3
  • Ottfried Dietrich
    • 1
  1. 1.Leibniz Centre for Agricultural Landscape ResearchInstitute of Landscape HydrologyMünchebergGermany
  2. 2.Department of Agricultural and Environmental EngineeringFederal University of TechnologyAkureNigeria
  3. 3.Institute of Earth and Environmental ScienceUniversity of PotsdamPotsdamGermany

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