Cotton yield prediction with Markov Chain Monte Carlo-based simulation model integrated with genetic programing algorithm: A new hybrid copula-driven approach
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- @Article{ALI:2018:AFM,
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author = "Mumtaz Ali and Ravinesh C. Deo and Nathan J. Downs and
Tek Maraseni",
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title = "Cotton yield prediction with Markov Chain Monte
Carlo-based simulation model integrated with genetic
programing algorithm: A new hybrid copula-driven
approach",
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journal = "Agricultural and Forest Meteorology",
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volume = "263",
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pages = "428--448",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Crop yield
prediction, Cotton yield, Climate data, Markov Chain
Monte Carlo based copula model",
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ISSN = "0168-1923",
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DOI = "doi:10.1016/j.agrformet.2018.09.002",
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URL = "http://www.sciencedirect.com/science/article/pii/S0168192318302971",
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abstract = "Reliable data-driven models designed to accurately
estimate cotton yield, an important agricultural
commodity, can be adopted by farmers, agricultural
system modelling experts and agricultural policy-makers
in strategic decision-making processes. In this paper a
hybrid genetic programing model integrated with the
Markov Chain Monte Carlo (MCMC) based Copula technique
is developed to incorporate climate-based inputs as the
predictors of cotton yield, for selected study regions:
Faisalabad",
- }
Genetic Programming entries for
Mumtaz Ali
Ravinesh C Deo
Nathan J Downs
Tek Maraseni
Citations