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<p>The present study aimed to model the pomegranate area and production trends in Himachal Pradesh, over a&#xa0;period of 23&#xa0;years (from 2001–2002 to 2023–2024). The area, production, and productivity of pomegranate increased with a&#xa0;compound growth rate of 10.27%, 18.98%, and 7.90% annually, respectively. The compound growth rate was found to be higher than linear growth rate for area (7.51%), production (11.56%), and productivity (6.86%), indicating an accelerated growth trend rather than a&#xa0;constant linear increase. Production is growing faster than the area, suggesting improved farming techniques or favorable growing conditions. Productivity growth rates have depicted a&#xa0;steady increase, confirming the enhanced efficiency of pomegranate farming over time. Parameters of the various nonlinear models were estimated for pomegranate area and production. The best-fit models were obtained based on the lowest values of mean square error, root mean square error, mean absolute error, Akaike’s information criterion, and Bayesian information criterion. The assumptions of independence and normality of error terms were examined by the run test and Shapiro–Wilk’s, test respectively. The Durbin–Watson test was used to examine autocorrelation among residuals for the various fitted models. From the present analysis, it was observed that all the models followed the assumptions of nonlinear models. The nonlinear logistic model performed better for describing both pomegranate area and production in Himachal Pradesh.</p>

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Modeling of Pomegranate Cultivation and Production Trends in Himachal Pradesh

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Anju Sharma,
  • Satish K. Sharma,
  • Sahil Verma,
  • Vishal Thakur

摘要

The present study aimed to model the pomegranate area and production trends in Himachal Pradesh, over a period of 23 years (from 2001–2002 to 2023–2024). The area, production, and productivity of pomegranate increased with a compound growth rate of 10.27%, 18.98%, and 7.90% annually, respectively. The compound growth rate was found to be higher than linear growth rate for area (7.51%), production (11.56%), and productivity (6.86%), indicating an accelerated growth trend rather than a constant linear increase. Production is growing faster than the area, suggesting improved farming techniques or favorable growing conditions. Productivity growth rates have depicted a steady increase, confirming the enhanced efficiency of pomegranate farming over time. Parameters of the various nonlinear models were estimated for pomegranate area and production. The best-fit models were obtained based on the lowest values of mean square error, root mean square error, mean absolute error, Akaike’s information criterion, and Bayesian information criterion. The assumptions of independence and normality of error terms were examined by the run test and Shapiro–Wilk’s, test respectively. The Durbin–Watson test was used to examine autocorrelation among residuals for the various fitted models. From the present analysis, it was observed that all the models followed the assumptions of nonlinear models. The nonlinear logistic model performed better for describing both pomegranate area and production in Himachal Pradesh.