Evaluation and Predictive Analysis of the Development of the First Industry in Fujian Province under the Background of Rural Revitalization Strategy

Abstract

As the number of factors impacting the primary industry grows, interdisciplinary research intensifies, and cross-species studies multiply, the economic implications conveyed by various indicators and their fluctuating patterns over the years become intricate. Randomly examining individual or a subset of these related indicators inevitably lacks systemic rigor, is susceptible to randomness, and fails to provide a comprehensive and visual representation of the overall changes across industries. It is inadequate for accurately forecasting the economic outlook of the primary sector. Presently, Principal Component Analysis (PCA) has seen ample application in agriculture, particularly in fruit and vegetable maturation harvesting and preservation techniques [1-4].Given the current theoretical frameworks, we are unable to differentiate among the 23 influencing factors in agriculture, fisheries, animal husbandry, and forestry – including per capita GDP, workforce numbers, forest nurturing areas, and expenditures on agricultural, forestry, and aquatic affairs – to identify those with statistically significant relevance to estimating the economic status of the primary industry. This research aims to synthesize and investigate recent data on these 23 factors, conducting correlation analyses. Leveraging the scientific methodology of PCA, we will derive representative factors indicative of the economic level of the primary industry. Further, by employing ARMA autoregressive models to predict the future five-year trends of these representative factors, we will forecast the economic prospects of the primary industry in Fujian Province. Consequently, this will facilitate the provision of well-timed and rational recommendations for adjusting the current development strategies of the primary industry.