Evaluation of Trend Dynamics of Lentil Crop in India using Statistical Models

This paper examines the trend patterns in area, production, and yield of lentil in India. The analysis is based on secondary time series data spanning the period from 2001-2023. Trend values have been determined by applying well-known statistical models, namely the linear model, exponential model, quadratic model, and cubic model. Furthermore, the accuracy of these fitted models has been evaluated using statistical measures such as the coefficient of determination (, root mean square error (RMSE), and relative mean absolute percentage error (RMAPE). The findings of the study reveal that the applied models effectively represented the trend in the area, production, and yield of lentil. In terms of area, the value of is 0.333 for cubic model, which is greater than the other fitted models. Also cubic model attains the least (i.e., 0.090) and RMAPE (i.e., 5.170) for area, as compared to the other models. Furthermore, in terms of production, cubic model attains maximum (i.e., 0.744) and the least RMSE (i.e., 0.127) and RMAPE (i.e., 7.862). Moreover, in terms of yield, cubic model attains maximum (i.e., 0.807) and the least RMSE (i.e., 0.061) and RMAPE (i.e., 6.084). Hence, the cubic model exhibited higher accuracy compared to the other fitted models, making it a suitable choice for forecasting the future scenario of lentil in India. These results offer valuable insights for policymakers in formulating strategies to enhance lentil production, thereby contributing to global food security and nutritional sustainability.

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Publication Details

Journal
Journal of Advances in Food Science & Technology
Published
2025-03-01
DOI
https://doi.org/10.56557/jafsat/2025/v12i29147
Citations
1
Primary Topic
Agricultural Economics and Practices
Type
article
Field-Weighted Citation Impact
4.83
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article

Evaluation of Trend Dynamics of Lentil Crop in India using Statistical Models

Arpit Mishra, Manish Kumar, Gulshan Singh, Subhi Singh
1 citations
Journal of Advances in Food Science & Technology
Agricultural Economics and Practices
4.83
article

Evaluation of Trend Dynamics of Lentil Crop in India using Statistical Models

Arpit Mishra, Manish Kumar, Gulshan Singh, Subhi Singh
article en
1 citations

Abstract

This paper examines the trend patterns in area, production, and yield of lentil in India. The analysis is based on secondary time series data spanning the period from 2001-2023. Trend values have been determined by applying well-known statistical models, namely the linear model, exponential model, quadratic model, and cubic model. Furthermore, the accuracy of these fitted models has been evaluated using statistical measures such as the coefficient of determination (, root mean square error (RMSE), and relative mean absolute percentage error (RMAPE). The findings of the study reveal that the applied models effectively represented the trend in the area, production, and yield of lentil. In terms of area, the value of is 0.333 for cubic model, which is greater than the other fitted models. Also cubic model attains the least (i.e., 0.090) and RMAPE (i.e., 5.170) for area, as compared to the other models. Furthermore, in terms of production, cubic model attains maximum (i.e., 0.744) and the least RMSE (i.e., 0.127) and RMAPE (i.e., 7.862). Moreover, in terms of yield, cubic model attains maximum (i.e., 0.807) and the least RMSE (i.e., 0.061) and RMAPE (i.e., 6.084). Hence, the cubic model exhibited higher accuracy compared to the other fitted models, making it a suitable choice for forecasting the future scenario of lentil in India. These results offer valuable insights for policymakers in formulating strategies to enhance lentil production, thereby contributing to global food security and nutritional sustainability.

Journal of Advances in Food Science & TechnologyVol. 12(2)
Acharya Narendra Deva University of Agriculture and Technology (IN)
Openalex Percentile: Top 8%
Agricultural Economics and Practices
4.83
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Evaluation of Trend Dynamics of Lentil Crop in India using Statistical Models — Arpit Mishra, Manish Kumar, et al. · Journal of Advances in Food Science & Technology (2025) | TGRS Research Map | TGRS