Prediction of Soil Fertility Change Trend Using a Stochastic Petri Net

被引:2
作者
Geng, Xia [1 ]
Zhu, Changsheng [2 ,3 ]
Zhang, Jijun [1 ]
Xiong, Zenggang [4 ]
机构
[1] Shandong Agr Univ, Sch Informat Sci & Engn, Tai An 271018, Shandong, Peoples R China
[2] Shandong Univ Sci & Technol, Sch Intelligent Equipment, Tai An 271019, Shandong, Peoples R China
[3] Shandong Univ, Sch Control Sci & Engn, Jinan 250100, Shandong, Peoples R China
[4] Hubei Engn Univ, Sch Comp & Informat, Xiaogan 432000, Hubei, Peoples R China
来源
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY | 2021年 / 93卷 / 2-3期
基金
中国国家自然科学基金;
关键词
Influential factors; Prediction method; Probability; Soil fertility; Stochastic Petri net;
D O I
10.1007/s11265-020-01594-3
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:285 / 297
页数:13
相关论文
共 42 条
  • [1] [Anonymous], 2014, THESIS
  • [2] [Anonymous], 2004, THESIS
  • [3] [Anonymous], 2006, THESIS
  • [4] GEMAS: Spatial distribution of chemical elements in agricultural and grazing land soil of Italy
    Cicchella, Domenico
    Giaccio, Lucia
    Dinelli, Enrico
    Albanese, Stefano
    Lima, Annamaria
    Zuzolo, Daniela
    Valera, Paolo
    De Vivo, Benedetto
    [J]. JOURNAL OF GEOCHEMICAL EXPLORATION, 2015, 154 : 129 - 142
  • [5] Soil health and global sustainability: translating science into practice
    Doran, JW
    [J]. AGRICULTURE ECOSYSTEMS & ENVIRONMENT, 2002, 88 (02) : 119 - 127
  • [6] Dugan J. B., 1984, EXTENDED STOCHASTI
  • [7] Privacy-Preserving Data Encryption Strategy for Big Data in Mobile Cloud Computing
    Gai, Keke
    Qiu, Meikang
    Zhao, Hui
    [J]. IEEE TRANSACTIONS ON BIG DATA, 2021, 7 (04) : 678 - 688
  • [8] Security-Aware Efficient Mass Distributed Storage Approach for Cloud Systems in Big Data
    Gai, Keke
    Qiu, Meikang
    Zhao, Hui
    [J]. 2016 IEEE 2ND INTERNATIONAL CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY), IEEE INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING (HPSC), AND IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS), 2016, : 140 - 145
  • [9] Girault C., 2002, Petri Nets for Systems Engineering: A Guide to Modeling, Verification, and Applications
  • [10] [何杰 HE Jie], 2009, [中国安全科学学报, China Safety Science Journal], V19, P77