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Research Article Open access CC BY 4.0

Modeling and Optimization of Groundnut Production in Vijayapura District of Karnataka, India

Sampatkumar S Biradar, Ragini H. R., Harshith K. V.

Journal of Experimental Agriculture International · pp. 202–219 · Published 18 Mar 2024

10.9734/jeai/2024/v46i52371

Abstract

Groundnut is a significant oilseed crop in India, with Karnataka being one of the largest producers. The agricultural economy of Vijayapura district relies heavily on crop production, including groundnut. Understanding the production patterns and forecasting future yields is crucial for agricultural planning and economic sustainability. The study aimed to investigate the production patterns and forecast the groundnut yield in Vijayapura district, Karnataka. The research problem focused on understanding the trends in groundnut area, production, and productivity over time and developing a forecasting model for future yields. Secondary data from 1966-67 to 2020-21 was collected from various sources. Statistical models including linear, quadratic, cubic, exponential, log-logistic and GAM were used to analyze the trends. The ARIMA method was employed for forecasting. The models' adequacy was assessed using MAPE, R2, AIC, and BIC criteria. The log-logistic model was found to be the best fit for groundnut area trends, while the cubic model and GAM were best for productivity and production, respectively. Forecasting using ARIMA initially indicated a slight increase in groundnut yield, but the GAM model predicted a decrease in future production. The findings provide insights for policymakers, agricultural extension services and farmers to make informed decisions regarding crop planning, resource allocation and economic sustainability. Understanding the production patterns and forecasting future yields is crucial for agricultural planning and economic sustainability in Vijayapura district.

Groundnut production patterns forecasting Vijayapura district Karnataka agricultural economy ARIMA model log-logistic model Generalized Additive Model (GAM)

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