Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/1/2007
Publication Date: 1/1/2007
Citation: Kemanian, A.R., Stockle, C.O., Huggins, D.R. 2007. Estimating grain and straw nitrogen concentration in grain crops based on aboveground nitrogen concentration and harvest index. Agronomy Journal 99:158-165. Interpretive Summary: Background: Simulating grain and straw nitrogen concentration is important in the development of models for cropping systems. We present a simple model to estimate nitrogen in harvested grain and straw for barley, wheat, corn and sorghum. The model achieves this goal based on the relative availability of grain carbohydrate and nitrogen. Description: The model has only five input parameters, of which four are readily available. We calibrated and tested the model for all four species and found small and acceptable errors between observed and estimated values of grain and straw nitrogen concentrations. Impact: This research contributes to the development of crop models that are relatively simple and require less information to achieve reasonable prediction of crop N use. The estimates of crop N concentrations will be incorporated into crop models such as CropSyst to enhance model simulations.
Technical Abstract: Simulating grain (Ng) and straw (Ns) nitrogen concentration is important in cropping systems simulation models. In this paper we present a simple model to partition nitrogen between grain and straw at harvest for barley (Hordeum vulgare L.), wheat (Triticum aestivum L.), maize (Zea mays L), and sorghum (Sorghum bicolor Moench). The principle of the model is to partition the aboveground nitrogen at physiological maturity based on the relative availability of biomass and nitrogen to the grain. The inputs for the model are the harvest index (HI), representing the relative availability of biomass to the grain, and the aboveground nitrogen concentration (Nt) at harvest, representing the availability of nitrogen. The model has five parameters, of which four (the maximum and minimum achievable grain and straw nitrogen concentrations) are readily available, and only one, the parameter C, requires calibration. The model was calibrated and tested for these four species without differentiating genotypes within species. In wheat, comparisons of observed and estimated Ng had relative root mean square errors (RMSE) that ranged from 3 to 10% (five experiments) and was 31% in one experiment in which the estimated Ng consistently exceeded the observed values. For barley, maize, and sorghum the data availability for testing was limited, but still the model performed well with relative RMSE of 7, 7, and 18%, respectively. We concluded that the proposed model seems to be robust. It remains to be determined if the parameters and the method are useful to discriminate genotypic differences in Ng within a species, and if the method can be applied to legume crops.