IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i14p6250-d1697092.html

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xuenan Li & Kun Han & Jiaze Li & Chunsheng Li, 2025. "A Sustainable Solution for High-Standard Farmland Construction—NGO–BP Model for Cost Indicator Prediction in Fertility Enhancement Projects," Sustainability, MDPI, vol. 17(14), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:14:p:6250-:d:1697092
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/14/6250/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/14/6250/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yata, Kazuyoshi & Aoshima, Makoto, 2012. "Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 193-215.
    2. Zhao Xue-hua & Miao Xu-juan & Zhang Zhen-gang & Hao Zheng, 2019. "Research on Prediction Method of Reasonable Cost Level of Transmission Line Project Based on PCA-LSSVM-KDE," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, August.
    3. Xuenan Li & Kun Han & Wenhe Liu & Tieliang Wang & Chunsheng Li & Bin Yan & Congming Hao & Xiaochen Xian & Yingying Yang, 2025. "Prediction Model of Farmland Water Conservancy Project Cost Index Based on PCA–DBO–SVR," Sustainability, MDPI, vol. 17(6), pages 1-17, March.
    4. Liu, Luyao & Zhao, Yi & Chang, Dongliang & Xie, Jiyang & Ma, Zhanyu & Sun, Qie & Yin, Hongyi & Wennersten, Ronald, 2018. "Prediction of short-term PV power output and uncertainty analysis," Applied Energy, Elsevier, vol. 228(C), pages 700-711.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiaze Li & Xuenan Li & Kun Han & Chunsheng Li, 2025. "Application of an Integrated DEMATEL-ISM-BN and Gray Clustering Model to Budget Quota Consumption Analysis in High-Standard Farmland Projects," Sustainability, MDPI, vol. 17(16), pages 1-26, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Yata, Kazuyoshi & Aoshima, Makoto, 2013. "PCA consistency for the power spiked model in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 334-354.
    3. Wang, Shao-Hsuan & Huang, Su-Yun, 2022. "Perturbation theory for cross data matrix-based PCA," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    4. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    5. Tsukuda, Koji & Matsuura, Shun, 2025. "Estimators for multivariate allometric regression model," Journal of Multivariate Analysis, Elsevier, vol. 210(C).
    6. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    7. Wu, Thomas & Hu, Ruifeng & Zhu, Hongyu & Jiang, Meihui & Lv, Kunye & Dong, Yunxuan & Zhang, Dongdong, 2024. "Combined IXGBoost-KELM short-term photovoltaic power prediction model based on multidimensional similar day clustering and dual decomposition," Energy, Elsevier, vol. 288(C).
    8. Zhiyuan Zeng & Tianyou Li & Jun Su & Longyi Sun, 2023. "Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers," Energies, MDPI, vol. 16(20), pages 1-21, October.
    9. Gómez-Amo, J.L. & Freile-Aranda, M.D. & Camarasa, J. & Estellés, V. & Utrillas, M.P. & Martínez-Lozano, J.A., 2019. "Empirical estimates of the radiative impact of an unusually extreme dust and wildfire episode on the performance of a photovoltaic plant in Western Mediterranean," Applied Energy, Elsevier, vol. 235(C), pages 1226-1234.
    10. Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
    11. Ma, Chao & Xu, Ximeng & Pang, Xiulan & Li, Xiaofeng & Zhang, Pengfei & Liu, Lu, 2024. "Scenario-based ultra-short-term rolling optimal operation of a photovoltaic-energy storage system under forecast uncertainty," Applied Energy, Elsevier, vol. 356(C).
    12. Han, Youhua & Liu, Yang & Lu, Shixiang & Basalike, Pie & Zhang, Jili, 2021. "Electrical performance and power prediction of a roll-bond photovoltaic thermal array under dewing and frosting conditions," Energy, Elsevier, vol. 237(C).
    13. Wang, Hu & Mao, Lei & Zhang, Heng & Wu, Qiang, 2024. "Multi-prediction of electric load and photovoltaic solar power in grid-connected photovoltaic system using state transition method," Applied Energy, Elsevier, vol. 353(PB).
    14. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    15. Martina Radicioni & Valentina Lucaferri & Francesco De Lia & Antonino Laudani & Roberto Lo Presti & Gabriele Maria Lozito & Francesco Riganti Fulginei & Riccardo Schioppo & Mario Tucci, 2021. "Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center," Energies, MDPI, vol. 14(3), pages 1-22, January.
    16. Bo Gu & Xi Li & Fengliang Xu & Xiaopeng Yang & Fayi Wang & Pengzhan Wang, 2023. "Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    17. Choi, Jihwan & Eom, Dongguen & Park, Byunghwa & Song, Junseok & Kook, Myungchul & Park, Sangwook, 2025. "Clean energy supply and demand strategies of Jeju Island to achieve carbon net-zero by using digital twin technology," Energy, Elsevier, vol. 314(C).
    18. Hao, Ying & Dong, Lei & Liang, Jun & Liao, Xiaozhong & Wang, Lijie & Shi, Lefeng, 2020. "Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid," Renewable Energy, Elsevier, vol. 155(C), pages 1191-1210.
    19. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    20. Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:14:p:6250-:d:1697092. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.