Is it useful to combine measurements taken during the growing season with a dynamic model to predict the nitrogen status of winter wheat?

Dynamic crop models can evaluate the effects of weather, soil and farming practices and the interactions between these three sources of variability on production and the environment. However, because of uncertainty about the system equations, the parameters and the input variables of these models, their predictions are imprecise. The objective of this article is to evaluate the usefulness of measurements obtained during the growing season to improve the precision of the values of the nitrogen nutrition index predicted by a dynamic model of winter wheat crop. A Bayesian method, called “interacting particle filter” was used to correct three state variables of the model from biomass and nitrogen uptake measurements obtained between the end of winter and flowering. The value of this approach was assessed using 16 experiments carried out between 1995 and 2002. The results show that when they were used alone, the biomass measurements did not reduce the prediction errors of the model. On the other hand, the use of measurements of nitrogen uptake alone or in combination with the biomass measurements reduced the values of root mean square error by 32% in predicting the nitrogen nutrition index. The correction of the model at a single date appeared to be sufficient to improve the quality of the predictions so long as this date was late and close to the prediction date. The results also show that the use of less precise measurements than those made on our experiments would still be worthwhile in practice provided that the measurement error does not exceed 15%.