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Prediction of Soil Fertility Change Trend Using a Stochastic Petri Net

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Abstract

Grasping the future change trend of soil fertility has great significance in improving the soil quality and achieving high-quality crop production and sustainable agricultural development. However, studies predicting the future change trend of farmland soil fertility are scarce. In this paper, with Yanzhou District of Shandong Province as the research area, a study was conducted based on the sampled data from 2012 to 2017. The data extracted from 2012 to 2016 was used for prediction and that from 2017 was applied for verification. The pH, organic matter, available phosphorus, alkali-hydrolyzed nitrogen and available potassium were selected as indexes of soil fertility. From a socioeconomic perspective, the factors affecting the changes in soil fertility selected in this study include fertilization measures, crop yield, area of arable land, farmers’ income, degree of mechanized operation, irrigated area, pesticide dosage, mulch dosage and rural electricity consumption. Based on this, a stochastic Petri net was used to build a model for predicting the soil fertility change trend. According to the relevant statistical data, the parameters of the model were determined, and by using the solid mathematical basis of the model, the probability of about 0.7852 was calculated out for the soil fertility to decline in the study area in the coming year. By comparing the soil fertility in 2016 and 2017, and further analyzing the changes in soil fertility from 2012 to 2016, the method of predicting the variation trend of soil fertility proposed in this study was verified to be effective.

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Acknowledgements

This work is supported in part by the Key Program for Joint Funds of the National Natural Science Foundation of China under Grant U1813215, and in part by the National Natural Science Foundation of China under Grant 61773239, and in part by Tai’an Science and Technology development program (2018GX0039), and in part by Project of Shandong Province Higher Educational Science and Technology Program (J17KB169), the National Nature Science Foundation of China (No. 61972136), and Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation (No.T201410). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Agricultural University, Tai’an, 271018, Shandong, China

    Xia Geng & Jijun Zhang

  2. School of Intelligent Equipment, Shandong University of Science and Technology, Tai’an, 271019, Shandong, China

    Changsheng Zhu

  3. School of Control Science and Engineering, Shandong University, Jinan, 250100, Shandong, China

    Changsheng Zhu

  4. School of Computer and Information, Hubei Engineering University, Xiaogan, 432000, Hubei, China

    Zenggang Xiong

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  1. Xia Geng
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  2. Changsheng Zhu
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  4. Zenggang Xiong
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Correspondence to Changsheng Zhu.

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Geng, X., Zhu, C., Zhang, J. et al. Prediction of Soil Fertility Change Trend Using a Stochastic Petri Net. J Sign Process Syst 93, 285–297 (2021). https://doi.org/10.1007/s11265-020-01594-3

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  • DOI: https://doi.org/10.1007/s11265-020-01594-3

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