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Paper   IPM / Biological Sciences / 16398
School of Biological Sciences
  Title:   A parametric empirical Bayes (PEB) approach for estimating maize progress percentage at field scale
1.  Mahdi Ghamghami
2.  Nozar Ghahreman
3.  Parviz Irannejad
4.  Hamid Pezeshk
  Status:   Published
  Journal: Agricultural and Forest Meteorology
  Vol.:  281
  Year:  2020
  Pages:   https://doi.org/10.1016/j.agrformet.2019.107829
  Supported by:  IPM
The Crop Progress Percentage (CPP) in a given phenology stage reflects growth status in the crop life cycle. Generally, routine field measurements of this variable are missing, hence various alternative approaches have been proposed for its estimation. Hidden Markov Models (HMMs) which follow the Bayesian structure are helpful tools for this aim. In the current study, an approach based on the parametric empirical Bayes (PEB) method is used for more accurate estimation of the maize CPP at field scale. The CPP information recorded in three experiment sites i.e. Karaj, Darab and Zarghan were used to validate the performance of the PEB method and to test the robustness. The PEB method includes a non-homogeneous HMM along with an empirical method based on fitting a gamma probability density function (PDF) on prior probabilities. Temporal sequence of phonological stages is regarded as the hidden layer and temporal sequence of NDVI and AGDD indices as the observable layer. The procedure of CPPs estimation is based on calculation of prior and posterior probabilities and an inverse normalization. The overall RMSE of the non-homogeneous HMM before applying the empirical method was 15.1, 11.5 and 7.8

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