Multinomial logistic regression on Markov chains for crop rotation modelling

Paton, Lewis; Troffaes, Matthias C.M.; Boatman, Nigel; Hussein, Mohamud; Hart, Andy

Abstract

Often, in dynamical systems such as farmer’s crop choices, the dynamics are driven by external non-stationary factors, such as rainfall, temperature and agricultural input and output prices. Such dynamics can be modelled by a non-stationary Markov chain, where the transition probabilities are multinomial logistic functions of such external factors. We extend previous work to investigate the problem of estimating the parameters of the multinomial logistic model from data. We use conjugate analysis with a fairly broad class of priors, to accommodate scarcity of data and lack of strong prior expert opinion. We discuss the computation of bounds for the posterior transition probabilities. We use the model to analyse some scenarios for future crop growth.

Citation

Paton, L., Troffaes, M. C., Boatman, N., Hussein, M., & Hart, A. (2014, July). Multinomial logistic regression on Markov chains for crop rotation modelling. Presented at Information Processing and Management of Uncertainty in Knowledge-Based Systems, Montpellier, France

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Information Processing and Management of Uncertainty in Knowledge-Based Systems
• Publication Date: Jul 19, 2014
• Deposit Date: Sep 15, 2014
• Pages: 476-485
• Series Title: Communications in computer and information science
• Series Number: 444
• Series ISSN: 1865-0929
• Book Title: Information processing and management of uncertainty in knowledge-based systems : 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014 ; proceedings, part III.
• ISBN: 9783319088518
• DOI: https://doi.org/10.1007/978-3-319-08852-5_49
• Public URL: https://durham-repository.worktribe.com/output/1155033