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

Simulating grain (Ng) and straw (Ns) 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 (Nt) 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 Ng the relative RMSE ranged from 3 to 10% (five experiments) and was 31% in one experiment in which the estimated Ng 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 Ng within a species and if the method can be applied to legume crops. SIMULATING grain (Ng) and straw (Ns) nitrogen (N) concentration is of paramount importance in cropping systems simulation models. Ng is a major quality determinant of cereal and legume crops. For crop simulation models to be useful in helping producers make informed decision regarding N management, they must provide accurate estimates of Ng. In addition, accurate estimates of the N removed with the grain are needed to keep accurate N balances in shortand long-term simulations. The basic approach to simulateNg in process-oriented crop models is to allocate dry matter and N to the grain during grain filling depending on the balance between the grain demand and the supply of these two resources. The degree to which the demand is satisfied by the supply depends on environmental and crop conditions affecting photosynthesis and on the N status of the crop. The approach used by Ritchie et al. (1985) in wheat, which was modified by Asseng et al. (2002), assumes that the daily demand of dry matter and N for each grain is independent. The demand is determined by the maximum daily grain growth and N deposition rates, which are empiric functions of temperature. The optimum temperature for N deposition in the grain is higher than that for dry matter, and therefore the simulated Ng tends to increase as temperature increases. The supply of drymatter depends on current photosynthesis and pre-stored reserves, and the supply of N depends on the N concentration of roots, leaves, and stems, which can be depleted until they reach a minimum allowable N concentration. Larmure and Munier-Jolain (2004) proposed a conceptually similar approach to model Ng in peas. This model is not linked to a comprehensive cropping system simulation model and requires considerable input of physiologic parameters to run (number of grains and individual grain growth rate at each reproductive node, rate of progression of the beginning and end of grain filling along the nodes in the stem, and genotype-dependent maximum grain and N deposition rate). Jamieson and Semenov (2000) followed a slightly different approach. They assumed that the minimum Ng is 15 g N kg 21 and that the N harvest index (NHI) increases linearly during grain filling as a function of thermal time, so that the NHI at physiologic maturity is 0.8. An allowance is made for NHI to be greater than 0.8 in the event that the demand of N by the grain is met by postanthesis N uptake. The practical effect is that Ng is basically determined by the supply of total dry matter during grain filling: the lower the supply of dry matter, the higher Ng. None of these models was built as a generic model for grain crops. The objective of this paper is to present a simple model of N partitioning between grain and straw at harvest. The inputs for the model are the harvest index (HI) and the aboveground biomass N concentration at physiologic maturity (Nt). This information is readily produced by cropping systems simulation models like CropSyst (Stöckle et al., 2003) and EPIC (Williams, 1995), which calculate Nt directly (i.e., independently of Ng and Ns). The model requires minimum calibration to accommodate differences between genotypes or species.