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This article in AJ

  1. Vol. 99 No. 1, p. 158-165
     
    Received: Mar 24, 2006


    * Corresponding author(s): armen@brc.tamus.edu
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doi:10.2134/agronj2006.0090

Estimating Grain and Straw Nitrogen Concentration in Grain Crops Based on Aboveground Nitrogen Concentration and Harvest Index

  1. Armen R. Kemanian *a,
  2. Claudio O. Stöckleb and
  3. David R. Hugginsc
  1. a Biological Systems Engineering Dep., Washington State Univ., Pullman, WA 99164-6120 (current address: Texas Agricultural Experiment Station, Blackland Research and Extension Center, Temple, TX 76502)
    b Biological Systems Engineering Dep., Washington State Univ., Pullman, WA 99164-6120
    c USDA-ARS, Washington State Univ., Pullman, WA 99164-6421

Abstract

Simulating grain (N g) and straw (N s) nitrogen (N) concentration is of paramount importance in cropping systems simulation models. In this paper we present a simple model to partition N 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 N at physiologic maturity based on the relative availability of biomass and N 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 N concentration (N t) at harvest, representing the availability of N. The model has five parameters, of which four (the maximum and minimum achievable grain and straw N concentrations) are readily available; the parameter C requires calibration. The model was calibrated and tested for these four species without differentiating genotypes within species. The testing included diverse experiments in wheat; comparing observed and estimated N g the relative RMSE ranged from 3 to 10% (five experiments) and was 31% in one experiment in which the estimated N g exceeded consistently the observed values. For barley, maize, and sorghum, the data availability for testing was limited, but the model performed well (relative RMSE values of 7, 7, and 18%, respectively). Therefore, the model proposed seems to be robust. It remains to be determined if the parameters and the method are useful to discriminate genotypic differences in N g within a species and if the method can be applied to legume crops.

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