Abstract

Advanced level modelling systems are essential to understanding the individual activities which occur within very complex behavioural, socio-economic and ecological systems. However, the scales at which models can be developed, and the subsequent problems they can inform, are often limited by our inability or challenges to simulate microdata that mimic interactions at the finest spatial, temporal, or organizational resolutions. Recent developments in remote-sensing technology now allow environmental characteristics like climate, land cover and topography to be measured at fine spatial resolutions. But, reliable small area level data with enough comprehensive information for measuring human socio-economic activities are often unavailable because they contain confidential or proprietary information. For instances, spatially-disaggregated and explicit data depicting the distribution of livestock, crops, and invasive animals (e.g. feral pigs) with the farming households social and economic characteristics are needed to inform potential agricultural production increases and to manage the risks to food safety and biosecurity. Just for a particular example, invasive animals in Australia are estimated to cause losses in excess of one billion dollars per year through environmental and agricultural production losses. The lack of a small area datasets often negatively impacts the ability of agencies to manage serious health risks. The attributes of individual farms, e.g. their distances to other farms, the size of the population, invasive animals, and labour dynamics data are needed to parameterize spatial epidemiological and economic models. While knowledge from various sources or previous epidemics can inform the necessary agencies, the data is not always available or accessible to risk managers and farmers. Therefore, advanced level models are essential in agricultural industries to produce reliable statistics for decision makers and stakeholders.

Microsimulation modellings are powerful tools for small area estimation and policy analysis. They simulate the characteristics of populations and model the complex interactions of individuals’ activities at the finest spatial resolutions. This talk will present an overview of a project in developing new algorithmic methods for a tractable class of spatial microsimulation models for agricultural industries. An intelligent system based Bayesian matrix analytic approach has been designed to deliver new results on the simulation methods, efficient estimation of model parameters, and on the effects of random environments and policy changes on farming dynamics. These results would be used further to study key problems in agriculture, thereby providing useful statistics at small area level for decision makers about cost-effective policies which will minimise future concerns facing agriculture and improve productivity.
Original languageEnglish
Pages1
Number of pages1
Publication statusPublished - 2018
EventNew directions in microsimulation: A workshop hosted by NATSEM, University of Canberra and Commonwealth Treasury - Ann harding conference centre at UC, Canberra, Australia
Duration: 08 Feb 201808 Feb 2018
http://www.governanceinstitute.edu.au/magma/media/upload/ckeditor/files/Microsimulation%20Workshop%20-%20Canberra%20Final(1).pdf

Workshop

WorkshopNew directions in microsimulation
Country/TerritoryAustralia
CityCanberra
Period08/02/1808/02/18
OtherMicrosimulation is used in Australia and internationally to assess the impact of different Government policies. The aim of this workshop is to bring together those working in the microsimulation field, to discuss the latest methods, learn from different areas and different groups and establish connections.
Internet address

Fingerprint

Dive into the research topics of 'An intelligent system based microsimulation modelling for estimating and mapping of small area agriculture data'. Together they form a unique fingerprint.

Cite this