Prediction and Analysis of Sturgeon Aquaculture Production in Guizhou Province Based on Grey System Model DOI Open Access
Yi Wang, Meng Ni,

Zhiqiang Lu

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(8), P. 3292 - 3292

Published: April 8, 2025

In this study, grey system theory is applied through the implementation of GM(1,1) modelling and Grey Relational Analysis (GRA) to forecast evaluate sturgeon aquaculture production dynamics in Guizhou Province. The results demonstrate a marked temporal dependency predictive efficacy, with exhibiting superior short-term forecasting performance that progressively diminishes extension. Utilizing 2018–2022 observational data, framework achieved Grade 2 precision (mean absolute percentage error, MAPE = 4.172%; 1% < ∆k¯ ≤ 5%), projecting sustained annual growth. decade-long (2023–2032) yielded following estimates (×103 tons): 32.3, 39.1, 47.3, 57.2, 69.2, 83.7, 101.2, 122.4, 148.1, 179.2. GRA identified three principal determinants: aquatic seed value (X9, r 0.8336), freshwater fishery output (X2, 0.8019), per capita fisher income (X5, 0.8003). Furthermore, technological promotion funding (X6) workforce parameters (X4), while demonstrating weaker correlations (r 0.75), maintain critical roles advancement labour competency enhancement. This methodological provides empirical support for sustainable development strategies Guizhou’s sector, emphasizing necessity temporal-scale considerations multifactorial optimization management.

Language: Английский

Prediction and Analysis of Sturgeon Aquaculture Production in Guizhou Province Based on Grey System Model DOI Open Access
Yi Wang, Meng Ni,

Zhiqiang Lu

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(8), P. 3292 - 3292

Published: April 8, 2025

In this study, grey system theory is applied through the implementation of GM(1,1) modelling and Grey Relational Analysis (GRA) to forecast evaluate sturgeon aquaculture production dynamics in Guizhou Province. The results demonstrate a marked temporal dependency predictive efficacy, with exhibiting superior short-term forecasting performance that progressively diminishes extension. Utilizing 2018–2022 observational data, framework achieved Grade 2 precision (mean absolute percentage error, MAPE = 4.172%; 1% < ∆k¯ ≤ 5%), projecting sustained annual growth. decade-long (2023–2032) yielded following estimates (×103 tons): 32.3, 39.1, 47.3, 57.2, 69.2, 83.7, 101.2, 122.4, 148.1, 179.2. GRA identified three principal determinants: aquatic seed value (X9, r 0.8336), freshwater fishery output (X2, 0.8019), per capita fisher income (X5, 0.8003). Furthermore, technological promotion funding (X6) workforce parameters (X4), while demonstrating weaker correlations (r 0.75), maintain critical roles advancement labour competency enhancement. This methodological provides empirical support for sustainable development strategies Guizhou’s sector, emphasizing necessity temporal-scale considerations multifactorial optimization management.

Language: Английский

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