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February 2, 2026Plant Science Today0 citationsOpen Access

Statistical assessment and trend analysis of major crop yields in Tamil Nadu: A multi-metric evaluation using descriptive statistics, CAGR and ANOVA

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MRM RaghulSHS HemalathaCVC Velavan

Key Points

  • The central aim is to analyze long-term yield trends for major crops in Tamil Nadu from 1965-66 to 2023-24.
  • Used descriptive statistics to describe yield trends
  • Applied compound annual growth rate (CAGR) for assessing growth rates
  • Utilized ANOVA to determine yield disparities between crops
  • Employed linear regression for trend analysis of crop yields
  • Maize and groundnut showed the highest productivity increases, while pulses and minor millets underperformed.
  • Significant yield disparities among crops were confirmed through ANOVA analysis.
  • Groundnut and paddy demonstrated consistent yield increases, while maize showed volatility.
  • Recommendations indicate that some crops require focused support, while others may need reduced emphasis for optimal productivity.

Abstract

This study analyses long-term yield trends of major crops in Tamil Nadu from 1965-66 to 2023-24 using descriptive statistics, compound annual growth rate (CAGR), Analysis of Variance (ANOVA) and linear regression. The analysis revealed notable inter-crop yield inequalities, with maize and groundnut exhibiting the greatest productivity increase. Meanwhile, pulses and minor millets continued to underperform. ANOVA validated the existence of significant yield disparities between the crops, emphasising the need for differentiated policy interventions. The trends also revealed consistent increases in groundnut and paddy, though maize exhibited more volatile yields. The results suggest some crops need more concentrated efforts, while some need to be neglected to optimise productivity and climate-resilient approaches need to be implemented. This study, in addition to advanced seamless frameworks for yield assessments, also provided India and other developing countries with a planning model for evidence-based agricultural strategies.

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Cite This Study

Raghul et al. (2026) studied this question.

synapsesocial.com/papers/6980fe13c1c9540dea80fe65https://doi.org/10.14719/pst.10908
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