A reappraisal of the critical nitrogen concentration of wheat and its implications on crop modeling

Asseng

et al. 2016

Global Change Biology

124

Higher temperatures caused by future climate change will bring more frequent heat stress events and pose an increasing risk to global wheat production. Crop models have been widely used to simulate future crop productivity but are rarely tested with observed heat stress experimental datasets. Four wheat models (DSSAT-CERES-Wheat, DSSAT-Nwheat, APSIM-Wheat, and WheatGrow) were evaluated with 4 years of environment-controlled phytotron experimental datasets with two wheat cultivars under heat stress at anthesis and grain filling stages. Heat stress at anthesis reduced observed grain numbers per unit area and individual grain size, while heat stress during grain filling mainly decreased the size of the individual grains. The observed impact of heat stress on grain filling duration, total aboveground biomass, grain yield, and grain protein concentration (GPC) varied depending on cultivar and accumulated heat stress. For every unit increase of heat degree days (HDD, degree days over 30 °C), grain filling duration was reduced by 0.30-0.60%, total aboveground biomass was reduced by 0.37-0.43%, and grain yield was reduced by 1.0-1.6%, but GPC was increased by 0.50% for cv Yangmai16 and 0.80% for cv Xumai30. The tested crop simulation models could reproduce some of the observed reductions in grain filling duration, final total aboveground biomass, and grain yield, as well as the observed increase in GPC due to heat stress. Most of the crop models tended to reproduce heat stress impacts better during grain filling than at anthesis. Some of the tested models require improvements in the response to heat stress during grain filling, but all models need improvements in simulating heat stress effects on grain set during anthesis. The observed significant genetic variability in the response of wheat to heat stress needs to be considered through cultivar parameters in future simulation studies.

Section: Wheat Simulation Modelsmentioning

confidence: 99%

Testing the responses of four wheat crop models to heat stress at anthesis and grain filling

Asseng

et al. 2016

Global Change Biology

124

“…However, Zhao et al . (2014) used different methods to derive the critical N concentrations for wheat: N crit was defined by data from selected treatments of the experiments where the N rates were considered to be optimal, i.e. below which reduction in either biomass at the time of measurement or final grain yield occurred (Zhao et al .…”

Section: Discussionmentioning

confidence: 99%

“…Zhao et al . (2014) pooled N crit values derived from alternative methods with the data published by Justes et al . (1994).…”

Section: Discussionmentioning

confidence: 99%

Nitrogen-limited light use efficiency in wheat crop simulators: comparing three model approaches

Ratjen¹,

Kage²

2015

J. Agric. Sci.

SUMMARYThree different explanatory indicators for reduced light use efficiency (LUE) under limited nitrogen (N) supply were evaluated. The indicators can be used to adapt dry matter production of crop simulators to N-limited growth conditions. The first indicator, nitrogen factor (NFAC), originates from the CERES-Wheat model and calculates the critical N concentration of the shoot as a function of phenological development. The second indicator, N nutrition index (NNI), calculates a critical N concentration as a function of shoot dry matter. The third indicator, specific leaf nitrogen (SLN) index (SLNI), has been newly developed. It compares the actual SLN with the maximum SLN (SLNmax). The latter is calculated as a function of the green area index (GAI). The comparison was based on growth curves and fitted to empirical data, and was carried out independently from a dynamic crop model. The data set included four growing seasons (2004–2006, 2012) in Northern Germany and seven modern bread wheat cultivars with varying N fertilization levels (0–320 kg N/ha). The influence of N shortage on LUE was evaluated from the beginning of stem elongation until flowering. With the exception of 2005, the highest productivity was observed for the highest N level. A moderate N shortage primarily reduced GAI and therefore light interception, while LUE remained stable under moderate N shortage. The relative LUE (rLUE) of a specific day was defined as the ratio of actual to maximal LUE. None of the indicators was proportional to rLUE, but the relationships were described well by quadratic plateau curves. The correlation between simulated and measured rLUE was significant for all explanatory indicators, but different in terms of mean absolute error and coefficient of determination (R2). The performance of SLNI and NNI was similar, but the goodness of prediction was much lower for NFAC. Compared with NNI and NFAC, SLNI corresponded to leaf N and was therefore sensitive to N translocation from leaves to growing grains during the reproductive stage. For this reason, SLNI may have the potential to improve simulation of dry matter production in wheat crop simulators.

“…Nitrogen nutrition index (NNI) and accumulated N deficit were developed to assess crop N status based on the concept of Nc (Justes et al ., 1994; Ata-Ul-Karim et al ., 2013). This approach has also been widely used in crop models (Godwin and Jones, 1991; Gayler et al ., 2002; Jones et al ., 2003; Bouman and Van Laar, 2006; Zhao et al ., 2014 b ) due to its flexibility, successful application to different spatial scales and suitability for scenario analysis. Existing rice models also adopt this approach widely.…”

Section: Introductionmentioning

confidence: 99%

“…After a plant reaches its Nc, N uptake will continue up to the maximum plant N concentration (Nmax) (Justes et al ., 1994; Ata-Ul-Karim, et al ., 2013). The Nmax is a determinant in the estimation of maximum N accumulation capacity of shoots (Lemaire and Gastal, 1997; Zhao et al ., 2014 b ) and can be considered as the estimate of a maximum N dilution curve (i.e. a curve corresponding to the maximum N uptake).…”

Section: Introductionmentioning

confidence: 99%

Improving the estimation and partitioning of plant nitrogen in the RiceGrow model

et al. 2018

Plant nitrogen (N) links with many physiological progresses of crop growth and yield formation. Accurate simulation is key to predict crop growth and yield correctly. The aim of the current study was to improve the estimation of N uptake and translocation processes in the whole rice plant as well as within plant organs in the RiceGrow model by using plant and organ maximum, critical and minimum N dilution curves. The maximum and critical N (Nc) demand (obtained from the maximum and critical curves) of shoot and root and Nc demand of organs (leaf, stem and panicle) are calculated by N concentration and biomass. Nitrogen distribution among organs is computed differently pre- and post-anthesis. Pre-anthesis distribution is determined by maximum N demand with no priority among organs. In post-anthesis distribution, panicle demands are met first and then the remaining N is allocated to other organs without priority. The amount of plant N uptake depends on plant N demand and N supplied by the soil. Calibration and validation of the established model were performed on field experiments conducted in China and the Philippines with varied N rates and N split applications; results showed that this improved model can simulate the processes of N uptake and translocation well.