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Inferring weed spatial distribution from multi-type data

Author

Listed:
  • Bourgeois, A.
  • Gaba, S.
  • Munier-Jolain, N.
  • Borgy, B.
  • Monestiez, P.
  • Soubeyrand, S.

Abstract

An accurate weed management in a context of sustainable agriculture relies on the knowledge about spatial weed distribution within fields. To improve the representation of patchy spatial distributions of weeds, several sampling strategies are used and lead to various weed measurements (abundance, count, patch boundaries). Here, we propose a hierarchical Bayesian model which includes such multi-type data and which allows the interpolation of weed spatial distributions (using a MCMC algorithm). The weed pattern is modeled with a log Gaussian Cox process and the various weed measurements are modeled with different observation processes. The application of the method to simulated data shows the advantage of combining several types of data (instead of using only one type of data). The method is also applied to infer the weed spatial distribution for real data.

Suggested Citation

  • Bourgeois, A. & Gaba, S. & Munier-Jolain, N. & Borgy, B. & Monestiez, P. & Soubeyrand, S., 2012. "Inferring weed spatial distribution from multi-type data," Ecological Modelling, Elsevier, vol. 226(C), pages 92-98.
    • Handle: RePEc:eee:ecomod:v:226:y:2012:i:c:p:92-98
    DOI: 10.1016/j.ecolmodel.2011.10.010
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    References listed on IDEAS

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    1. Gilles Guillot & Niklas Lorén & Mats Rudemo, 2009. "Spatial prediction of weed intensities from exact count data and image‐based estimates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 525-542, September.
    2. Anders Brix & Jesper Moller, 2001. "Space‐time Multi Type Log Gaussian Cox Processes with a View to Modelling Weeds," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(3), pages 471-488, September.
    3. Christopher K. Wikle, 2003. "Hierarchical Models in Environmental Science," International Statistical Review, International Statistical Institute, vol. 71(2), pages 181-199, August.
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    1. Munier-Jolain, N.M. & Guyot, S.H.M. & Colbach, N., 2013. "A 3D model for light interception in heterogeneous crop:weed canopies: Model structure and evaluation," Ecological Modelling, Elsevier, vol. 250(C), pages 101-110.

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