A zero-inflated Poisson mixture model to analyse spread and abundance of the Western Corn Rootworm in Austria

被引:13
|
作者
Falkner, Katharina [1 ]
Mitter, Hermine [1 ]
Moltchanova, Elena [2 ]
Schmid, Erwin [1 ]
机构
[1] Univ Nat Resources & Life Sci, Vienna BOKU, Inst Sustainable Econ Dev, Dept Econ & Social Sci, Feistmantelstr 4, A-1180 Vienna, Austria
[2] Univ Canterbury, Sch Math & Stat, Sci Rd,Erskine Bldg, Christchurch 8140, New Zealand
关键词
Western Corn Rootworm; Zero-inflated Poisson mixture model; Abundance model; Climate change; Pest modelling; SPECIES ABUNDANCE; DIGESTIBILITY; STRATEGIES; EUROPE;
D O I
10.1016/j.agsy.2019.04.010
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
The Western Corn Rootworm (WCR, Diabrotica virgifera virgifera) has become one of the main maize pests in Europe. Our objective was to develop a model for mapping spread and abundance of WCR in Austria as a function of the natural spread, climatic conditions and the maize share in crop rotations. Records of a total of 5,338 WCR monitoring traps spread over Austria are available for the period 2002-2015, with 2,520 (47.2%) showing zero counts. We developed a spatial zero-inflated Poisson mixture (ZIP) model to relate WCR counts to climatic conditions and maize shares and account for zero-inflation, and spatial correlation in the count data. The model was validated by a 40-fold cross validation procedure and applied to Austrian cropland on a spatial resolution of 1 km. Results show that increased probabilities of WCR occurrence and abundance are associated with higher maize shares in crop rotations combined with a positive influence of higher winter temperatures and summer precipitation. The developed model provides a scientifically sound basis for analysing impacts of future climate change scenarios and crop rotational maize restrictions on the spread and abundance of WCR. It supports the development of WCR control measures.
引用
收藏
页码:105 / 116
页数:12
相关论文
共 50 条
  • [41] Monitoring social networks based on Zero-inflated Poisson regression model
    Motalebi, Narges
    Owlia, Mohammad Saleh
    Amiri, Amirhossein
    Fallahnezhad, Mohammad Saber
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2023, 52 (07) : 2099 - 2115
  • [42] The Zero-Inflated Poisson - Probit regression model: a new model for count data
    Pho, Kim-Hung
    Truong, Buu-Chau
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2024,
  • [43] Control chart for detecting the scale parameter of the zero-inflated Poisson model
    Zhao, Aijun
    Liu, Liu
    Lai, Xin
    Chong, Ka Chun
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2024, 40 (06) : 3387 - 3406
  • [44] Semiparametric estimation of a zero-inflated Poisson regression model with missing covariates
    T. Martin Lukusa
    Shen-Ming Lee
    Chin-Shang Li
    Metrika, 2016, 79 : 457 - 483
  • [45] Bootstrap tests for overdispersion in a zero-inflated Poisson regression model -: Reply
    Ridout, M
    Hinde, JP
    Demétrio, CGB
    BIOMETRICS, 2005, 61 (02) : 628 - 629
  • [46] Consistency and Asymptotic Normality of the Maximum Likelihood Estimator in a Zero-inflated Poisson Mixture Distributions
    Yang Aijun
    Yang Zhenhai
    RECENT ADVANCE IN STATISTICS APPLICATION AND RELATED AREAS, VOLS I AND II, 2009, : 732 - 736
  • [47] A bivariate zero-inflated Poisson regression model to analyze occupational injuries
    Wang, K
    Lee, AH
    Yau, KKW
    Carrivick, PJW
    ACCIDENT ANALYSIS AND PREVENTION, 2003, 35 (04): : 625 - 629
  • [48] Bivariate zero-inflated generalized Poisson regression model with flexible covariance
    Faroughi, Pouya
    Ismail, Noriszura
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2017, 46 (15) : 7769 - 7785
  • [49] Liu Estimation Method in the Zero-Inflated Conway Maxwell Poisson Regression Model
    Amin, Muhammad
    Ashraf, Bushra
    Siddiqa, Syeda Maryam
    JOURNAL OF STATISTICAL THEORY AND APPLICATIONS, 2024,
  • [50] Statistical Inference of Journal Quality Parameters Under the Zero-Inflated Poisson Model
    Huang, Genfan
    Wen, Limin
    SSRN, 2023,