Winter Wheat Yield Prediction and Influencing Factors Analysis Based on FourierGNN–Random Forest Combined Modeling
In order to investigate the changes in winter wheat yield and the factors influencing it, five meteorological factors—such as rainfall and soil moisture—collected from the experimental area between 2010 and 2022 were used as characteristic features. A combined model of GNN (Graph Neural Network), ba...
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2025-03-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/3/641 |
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| author | Jianqin Ma Yijian Chen Bifeng Cui Yu Ding Xiuping Hao Yan Zhao Junsheng Li Xianrui Su |
| author_facet | Jianqin Ma Yijian Chen Bifeng Cui Yu Ding Xiuping Hao Yan Zhao Junsheng Li Xianrui Su |
| author_sort | Jianqin Ma |
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| description | In order to investigate the changes in winter wheat yield and the factors influencing it, five meteorological factors—such as rainfall and soil moisture—collected from the experimental area between 2010 and 2022 were used as characteristic features. A combined model of GNN (Graph Neural Network), based on the Fourier transform and the Random Forest algorithm was developed to predict winter wheat yield. Matrix multiplication in Fourier space was performed to predict yield, while the Random Forest algorithm was employed to quantify the contribution of various yield factors to winter wheat yield. The combined model effectively captured the dynamic dependencies between yield factors and time series, improving predictive accuracy by 5.00%, 10.00%, and 27.00%, and reducing the root mean square error by 26.26%, 29.31%, and 88.20%, respectively, compared to the StemGNN, Informer, and Random Forest models. The predicted outputs ranged from 520 to 720 g/m<sup>2</sup>, with an average error of 2.69% compared to the actual measure outputs. Under the insufficient real-time irrigation mode, winter wheat yield was highest at 90% irrigation upper limit and 70% irrigation lower limit, with a medium fertilization level (850 mg/kg). The yield showed an overall decreasing trend as both irrigation limits and fertilizer application decreased. Rainfall and soil moisture were the most significant factors influencing winter wheat yield, followed by air temperature and evapotranspiration. Solar radiation and sunshine duration had the least impact. The results of this study provide a valuable reference for accurately predicting winter wheat yield. |
| format | Article |
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| institution | DOAJ |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Agronomy |
| spelling | doaj-art-3cc848e237ef44e1b0dfc7bb949b937b2025-08-20T02:41:58ZengMDPI AGAgronomy2073-43952025-03-0115364110.3390/agronomy15030641Winter Wheat Yield Prediction and Influencing Factors Analysis Based on FourierGNN–Random Forest Combined ModelingJianqin Ma0Yijian Chen1Bifeng Cui2Yu Ding3Xiuping Hao4Yan Zhao5Junsheng Li6Xianrui Su7College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaCollege of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaCollege of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaCollege of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaCollege of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaCollege of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaHenan Bizhixiao Inspection Technology Co., Ltd., Zhengzhou 450046, ChinaHenan Bizhixiao Inspection Technology Co., Ltd., Zhengzhou 450046, ChinaIn order to investigate the changes in winter wheat yield and the factors influencing it, five meteorological factors—such as rainfall and soil moisture—collected from the experimental area between 2010 and 2022 were used as characteristic features. A combined model of GNN (Graph Neural Network), based on the Fourier transform and the Random Forest algorithm was developed to predict winter wheat yield. Matrix multiplication in Fourier space was performed to predict yield, while the Random Forest algorithm was employed to quantify the contribution of various yield factors to winter wheat yield. The combined model effectively captured the dynamic dependencies between yield factors and time series, improving predictive accuracy by 5.00%, 10.00%, and 27.00%, and reducing the root mean square error by 26.26%, 29.31%, and 88.20%, respectively, compared to the StemGNN, Informer, and Random Forest models. The predicted outputs ranged from 520 to 720 g/m<sup>2</sup>, with an average error of 2.69% compared to the actual measure outputs. Under the insufficient real-time irrigation mode, winter wheat yield was highest at 90% irrigation upper limit and 70% irrigation lower limit, with a medium fertilization level (850 mg/kg). The yield showed an overall decreasing trend as both irrigation limits and fertilizer application decreased. Rainfall and soil moisture were the most significant factors influencing winter wheat yield, followed by air temperature and evapotranspiration. Solar radiation and sunshine duration had the least impact. The results of this study provide a valuable reference for accurately predicting winter wheat yield.https://www.mdpi.com/2073-4395/15/3/641winter wheatmultivariate dataproduction forecastsFourierGNNrandom forestout-of-bag error |
| spellingShingle | Jianqin Ma Yijian Chen Bifeng Cui Yu Ding Xiuping Hao Yan Zhao Junsheng Li Xianrui Su Winter Wheat Yield Prediction and Influencing Factors Analysis Based on FourierGNN–Random Forest Combined Modeling Agronomy winter wheat multivariate data production forecasts FourierGNN random forest out-of-bag error |
| title | Winter Wheat Yield Prediction and Influencing Factors Analysis Based on FourierGNN–Random Forest Combined Modeling |
| title_full | Winter Wheat Yield Prediction and Influencing Factors Analysis Based on FourierGNN–Random Forest Combined Modeling |
| title_fullStr | Winter Wheat Yield Prediction and Influencing Factors Analysis Based on FourierGNN–Random Forest Combined Modeling |
| title_full_unstemmed | Winter Wheat Yield Prediction and Influencing Factors Analysis Based on FourierGNN–Random Forest Combined Modeling |
| title_short | Winter Wheat Yield Prediction and Influencing Factors Analysis Based on FourierGNN–Random Forest Combined Modeling |
| title_sort | winter wheat yield prediction and influencing factors analysis based on fouriergnn random forest combined modeling |
| topic | winter wheat multivariate data production forecasts FourierGNN random forest out-of-bag error |
| url | https://www.mdpi.com/2073-4395/15/3/641 |
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