Parameter-estimation in the auto-binomial model using the coding- and pseudo-likelihood method approached with simulated annealing and numerical optimization

Johansson, Jan-Olof

doi:10.1016/s0167-8655(01)00055-1

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…The association between farming practices and incidence of herbicide resistance was analysed by using auto-binomial models (Besag 1974(Besag , 1975Johansson 2001), which include the usual logistic regression model as a special case. The usage of auto-binomial models allowed the study of spatial effects along all other variables.…”

Section: Statistical Analysesmentioning

confidence: 99%

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Cropping practices influence incidence of herbicide resistance in annual ryegrass (Lolium rigidum) in Australia

Broster

Pratley

et al. 2019

Crop Pasture Sci.

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Herbicide resistance is a common occurrence in southern Australia. The evolution of herbicide resistance is influenced by the selection pressure placed on the weed species controlled by that herbicide. Results from resistance screening of ~4500 annual ryegrass (Lolium rigidum Gaud.) samples were entered in a GIS database, together with several agricultural parameters used in the Australian Bureau of Statistics Agricultural Surveys. This allowed a study of the associations between mode of action of resistance, geographic distribution of resistance across southern Australia, and farming practices employed in particular regions. Cultivation was negatively associated with resistances in acetyl-CoA carboxylase (ACCase)-inhibiting cyclohexanedione and acetolactate synthase (ALS)-inhibiting herbicides. Higher proportions of wheat sown were associated with higher incidences of resistance. ACCase-inhibiting aryloxyphenoxypropionate and cyclohexanedione and ALS-inhibiting resistances were higher in those shires where soils were predominantly acidic. This study demonstrates the association between farm practice and the evolution of herbicide resistance. The analysis provides reinforcement to the principle of rotating chemical modes of action with non-chemical weed control measures to minimise the risk of herbicide resistance evolution in any farming system.

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Section: Statistical Analysesmentioning

confidence: 99%

“…Estimation of the model parameters was done by using the maximum pseudo-likelihood approach (Johansson 2001), and the standard errors of the parameters were estimated by using delete-one jackknife resampling (Friedl and Stampfer 2002). The covariates in the final model were selected by using backward selection.…”

Section: Statistical Analysesmentioning

confidence: 99%