Assessing lead-time for predicting wheat growth using a crop simulation model

A - Papers appearing in refereed journals

Lawless, C. and Semenov, M. A. 2005. Assessing lead-time for predicting wheat growth using a crop simulation model. Agricultural and Forest Meteorology. 135 (1-4), pp. 302-313.

AuthorsLawless, C. and Semenov, M. A.

In order to use crop simulation models to predict crop yield, unobserved daily weather, an important input for crop models, must be forecast in some sense. Due to the chaotic nature of weather and the non-linear response of crop simulation models to weather input, this forecast weather cannot simply be a single weather series (e.g. average historical weather for the upcoming growing season), but must be an ensemble of weather series, incorporating site-specific climatic variability. To capture weather uncertainty, we used the LARS-WG stochastic weather generator to produce a probabilistic ensemble of weather series by mixing observed weather from the beginning of a season with stochastically generated (synthetic) weather for the remainder of the growing season. This ensemble was used with the crop simulation model Sirius to generate distributions of crop characteristics. Progressing through the growing season, as the proportion of synthetic weather in these ensembles decreased, the distribution means converged towards the true values, allowing us to make predictions with a high level of confidence before crop maturity. In this fashion, we analysed six sites with diverse climates in Europe and New Zealand, comparing lead-times for predicting different crop characteristics at various geographic locations. We demonstrated that that there is a large difference between lead-times amongst different crop characteristics at a single location, and that there is a large variation in lead-times for predicting selected crop characteristics between locations. Variation in climates places a quantifiable limit on our ability to make crop predictions using crop simulation models.

Year of Publication2005
JournalAgricultural and Forest Meteorology
Journal citation135 (1-4), pp. 302-313
Digital Object Identifier (DOI)
Open accessPublished as non-open access
FunderDepartment of Environment, Food and Rural Affairs
Biotechnology and Biological Sciences Research Council
Funder project or code513
Application of non-linear mathematics and stochastic modelling to biological systems
Project: 4441
Output statusPublished
Publication dates
Online02 Feb 2006
Publication process dates
Accepted03 Jan 2006
Copyright licensePublisher copyright
PublisherElsevier Science Bv

Permalink -

Restricted files

Publisher's version

Under embargo indefinitely

109 total views
1 total downloads
0 views this month
0 downloads this month