Bioclimatic modelling of the future ranges of crop species: an analysis of propagated uncertainty

R.J. Corner; M.A. Marinelli

Bioclimatic modelling of the future ranges of crop species: an analysis of propagated uncertainty

in Precision agriculture '09

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R.J. Corner

R.J. Corner Department of Spatial Sciences, Curtin University, GPO Box U1987, Western Australia 6845, Australia; R.Corner@curtin.edu.au

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M.A. Marinelli

M.A. Marinelli Cooperative Research Centre for Spatial Information, Melbourne, Australia

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Art:: Kapitel

Seiten:: 575–582

DOI:: https://doi.org/10.1163/9789086866649_070

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Abstract

The agriculturally significant region of south west Western Australia, has a ‘Mediterranean’ climate and its agriculture is currently characterised by the rainfed broad-acre cultivation of cereals and pulses. The rainfall in this region has been trending downwards over the last 30 years and much agriculture is already practised close to climatic limits. It is inevitable that under predicted future trends the range in which crops are grown will need to be reconsidered. One way of simulating this is with the use of bioclimatic models such as BIOCLIM. BIOCLIM is a probabilistic model that can be used to investigate and predict species distributions for both native and agricultural species. It is best used for studying relatively large (subcontinental) areas. The version of BIOCLIM used in this study uses three basic spatial climatic inputs (monthly maximum and minimum temperature and precipitation layers) and a dataset describing the current geographic range of the species of interest. Our work has investigated how uncertainty in the input data propagates through to the estimated spatial distribution for Field Peas (Pisum sativum), using as current species distribution a dataset of successful field plot tests carried out across the study area. In the case of future distribution modelling the input climate data layers obviously contain uncertainty, difficult to quantify, which will vary depending on the climatic modelling and future emission scenarios used. However the uncertainty in ‘current condition’ is more readily quantifiable and we have identified two components to it. Uncertainty propagation was carried out using Monte Carlo methods with up to 4,500 simulations being run for any scenario. Our results clearly show the effect of uncertainty in the input layers on the predicted species distribution map. In places the uncertainty significantly influences the final validity of the result.

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Precision agriculture '09

ISBN:: 9789086866649

Verleger:: Wageningen Academic

Print-Publikationsdatum:: 01 Jan 2009

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- Biowissenschaften
  - Allgemein
  - Landwirtschaft und Umwelt

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Bioclimatic modelling of the future ranges of crop species: an analysis of propagated uncertainty

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Precision agriculture '09

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