Forecasting Gross Yield in Agriculture Using The Arima Model: The Case of Cereal Crops
DOI:
https://doi.org/10.51699/cajitmf.v7i1.1096Keywords:
Agriculture, grain crops, regression equation, dynamic series, factors, model, perspective, forecast, elite seeds, efficiencyAbstract
Forecasting gross yield in agriculture, particularly for cereal crops, using the ARIMA (Autoregressive Integrated Moving Average) model has become a vital tool for agricultural planning and risk management. The ARIMA model is adept at analyzing time series data, allowing for the identification of trends and seasonal patterns in crop yields. In recent studies, ARIMA has been successfully applied to predict cereal crop yields over various time frames. For instance, forecasts indicate that cereal crops are expected to show increasing trends from 2020 to 2030, highlighting the model's capability to capture underlying growth patterns in agricultural production. The model's strength lies in its ability to incorporate historical yield data, which helps in making informed predictions about future outputs. This is particularly important in regions where agricultural productivity is influenced by climatic variations and market dynamics. By utilizing ARIMA, farmers and policymakers can better anticipate yield fluctuations, enabling them to implement strategies that enhance food security and optimize resource allocation. Overall, the application of the ARIMA model in forecasting cereal crop yields not only aids in understanding potential agricultural outputs but also supports the development of effective agricultural policies and practices.
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