A Sustainable Solution for High-Standard Farmland Construction—NGO–BP Model for Cost Indicator Prediction in Fertility Enhancement Projects

Xuenan Li, Kun Han, Jiaze Li and Chunsheng Li ()
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Xuenan Li: College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China
Kun Han: College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China
Jiaze Li: College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China
Chunsheng Li: College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China

Sustainability, 2025, vol. 17, issue 14, 1-19

Abstract: High-standard farmland fertility enhancement projects can lead to the sustainable utilization of arable land resources. However, due to difficulties in project implementation and uncertainties in costs, resource allocation efficiency is constrained. To address these challenges, this study first analyzes the impact of geography and engineering characteristics on cost indicators and applies principal component analysis (PCA) to extract key influencing factors. A hybrid prediction model is then constructed by integrating the Northern Goshawk Optimization (NGO) algorithm with a Backpropagation Neural Network (BP). The NGO–BP model is compared with the RF, XGBoost, standard BP, and GA–BP models. Using data from China’s 2025 high-standard farmland fertility enhancement projects, empirical validation shows that the NGO–BP model achieves a maximum RMSE of only CNY 98.472 across soil conditioning, deep plowing, subsoiling, and fertilization projects—approximately 30.74% lower than those of other models. The maximum MAE is just CNY 88.487, a reduction of about 32.97%, and all R 2 values exceed 0.914, representing an improvement of roughly 5.83%. These results demonstrate that the NGO–BP model offers superior predictive accuracy and generalization ability compared to other approaches. The findings provide a robust theoretical foundation and technical support for agricultural resource management, the construction of projects, and project investment planning.

Keywords: fertility enhancement project; principal component analysis; BP neural network; northern goshawk optimization; cost indicator (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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