Better models, more wheat

Accuracy in prediction of the growth and development of annual crops depends specifically on the ability to predict the time of the change from vegetative to reproductive growth. This determines the weather conditions in which the crop grows so has a major impact on yield. For wheat, the point that marks the transition from growing vegetative to growing reproductive structures occurs at anthesis, the period during which a flower is open and functional. Accurate anthesis models would simulate the underlying processes that lead to anthesis and provide quantitative estimates of occurrence in any specified environment for any specified genotype by linking genetic information to environmental response coefficients. This would enable rapid characterization of the anthesis behaviour of specific genotypes. It would also enable rapid screening of the adaptive fitness of progeny in a breeding programme by linking molecular markers for development genes/alleles to model coefficients and running simulations to determine the range of anthesis times that will occur in the location for which it is being selected. A model that is suitable for these purposes is yet to be created.

A recent paper in Annals of Botany develops a quantitative model of the expression of specific developmental genes and combines it with physiological models that predict anthesis time in response to the environment. This new model provides a framework to test the current genetic models of floral transition in wheat, and will ultimately inform the development of genotypes adapted to specific environments.

Brown, H.E., Jamieson, P.D., Brooking, I.R., Moot, D.J., & Huth, N.I. (2013) Integration of molecular and physiological models to explain time of anthesis in wheat. Annals of botany, 112(9), 1683-1703.
Background: A model to predict anthesis time of a wheat plant from environmental and genetic information requires integration of current concepts in physiological and molecular biology. This paper describes the structure of an integrated model and quantifies its response mechanisms.
Methods: Literature was reviewed to formulate the components of the model. Detailed re-analysis of physiological observations are utilized from a previous publication by the second two authors. In this approach measurements of leaf number and leaf and primordia appearance of near isogenic lines of spring and winter wheat grown for different durations in different temperature and photoperiod conditions are used to quantify mechanisms and parameters to predict time of anthesis.
Conclusions: The analysis integrates molecular biology and crop physiology concepts into a model framework that links different developmental genes to quantitative predictions of wheat anthesis time in different field situations.