Analysis Weather To Instability Price Of Large Red Chili Using Time Series-Based Prediction
DOI:
https://doi.org/10.59944/jipsi.v5i3.978Keywords:
weather conditions, price instability, large red chili, farmers, time series, price volatilityAbstract
This study was motivated by the high price volatility of large red chili at the farmer level, which is influenced by weather conditions, seasonal changes, and production factors. Price instability reduces farmers’ income and affects the economic stability of the agricultural sector. This study aims to analyze the influence of weather conditions on large red chili production and price instability at the farmer level in Paiton District. A quantitative approach was employed using primary and secondary data. Primary data were collected through questionnaires administered to 70 large red chili farmers, while secondary data consisted of historical price and weather data. Data analysis included validity and reliability tests, simple linear regression, and time series analysis.
The findings indicate that weather conditions have a significant effect on price instability, with a significance value of 0.000 and a coefficient of determination of 52.5%. High rainfall, extreme temperatures, and seasonal changes were found to affect production levels, thereby influencing price fluctuations. The novelty of this study lies in integrating weather-related variables with a time-series-based analytical approach to examine price instability at the farmer level, an aspect that has received limited attention in previous studies focusing mainly on market prices.
This study contributes to the development of agricultural econometric and predictive agricultural economics studies by providing empirical evidence regarding the relationship between weather conditions, production, and farmer-level price instability. Practically, the findings can assist farmers in determining planting schedules and production strategies, support policymakers in designing weather-based price stabilization programs, and encourage agricultural stakeholders to develop early warning systems and weather-based price forecasting models.
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