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

Ratjen, Arne M.; Kage, Henning

doi:10.1017/s0021859615001082

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…Variation in nitrogen dilution curves for wheat has been reported that relates to phenological development, hence the attempts to model critical nitrogen against phenological stage ( Angus, 2007 ; Yue et al, 2012 ; Zhao et al, 2014 ; Ratjen and Kage, 2016 ). Variation in allometric relations, between organs as well as between structural and labile carbohydrates, partially underlies the effect of phenology on nitrogen dilution curves ( Gastal et al, 2015 ; Hoogmoed and Sadras, 2016 ; Yan et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Water Stress Scatters Nitrogen Dilution Curves in Wheat

Hoogmoed

Sadras

2018

Front. Plant Sci.

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Nitrogen dilution curves relate a crop’s critical nitrogen concentration (%Nc) to biomass (W) according to the allometric model %Nc = a W-b. This model has a strong theoretical foundation, and parameters a and b show little variation for well-watered crops. Here we explore the robustness of this model for water stressed crops. We established experiments to examine the combined effects of water stress, phenology, partitioning of biomass, and water-soluble carbohydrates (WSC), as driven by environment and variety, on the %Nc of wheat crops. We compared models where %Nc was plotted against biomass, growth stage and thermal time. The models were similarly scattered. Residuals of the %Nc - biomass model at anthesis were positively related to biomass, stem:biomass ratio, Δ13C and water supply, and negatively related to ear:biomass ratio and concentration of WSC. These are physiologically meaningful associations explaining the scatter of biomass-based dilution curves. Residuals of the thermal time model showed less consistent associations with these variables. The biomass dilution model developed for well-watered crops overestimates nitrogen deficiency of water-stressed crops, and a biomass-based model is conceptually more justified than developmental models. This has implications for diagnostic and modeling. As theory is lagging, a greater degree of empiricism might be useful to capture environmental, chiefly water, and genotype-dependent traits in the determination of critical nitrogen for diagnostic purposes. Sensitivity analysis would help to decide if scaling nitrogen dilution curves for crop water status, and genotype-dependent parameters are needed.

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Section: Introductionmentioning

confidence: 99%

Water Stress Scatters Nitrogen Dilution Curves in Wheat

Hoogmoed

Sadras

2018

Front. Plant Sci.

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“…R Foundation for Statistical Computing, Vienna, Austria). Results of this approach have been implemented at the HumeWheat crop-soil model, which has been designed for the detection of interannual variability in N fertilization demand (Ratjen 2012, Ratjen andKage 2015b).…”

Section: Introductionmentioning

confidence: 99%

Modelling N and Dry Matter Partitioning between Leaf and Stem of Wheat under Varying N Supply

Ratjen¹,

Kage²

2016

J Agronomy Crop Science

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The fraction of dry matter and nitrogen (N) allocated to leaves is of high importance for wheat crop simulators, because it has an impact on N demand, evapotranspiration, light interception and light use efficiency. For the vegetative phase of wheat, a robust empiric description of the influence of N nutrition status (NNI) on this pattern is still pending. We investigated the influence of NNI on leaf: stem partitioning of wheat (Triticum aestivum L.), until end of leaf growth. The evaluation based on N rate experiments (0–320 kg N ha−1) from three subsequent seasons (2003/4 to 2005/6), with five modern bread wheat cultivars, carried out near Kiel (northern Germany). A previously published model was adapted for describing the influence of NNI on dry matter partitioning. Starting from the allometric leaf: stem relationship of situations with NNI ≥1, a correction term was used, to describe the partitioning under varying N supply. The correction term assumes a linear increase of the stem fraction, proportional to the difference between a NNI threshold and actual NNI. The allometric partitioning was found to shift towards stem fraction, if the NNI falls below 1.38. The slope of the relative increase was determined with 0.6. Both dimensionless parameters of the correction term were found to be highly significant. The second aim was to quantify how N status impacts the N allocation between shoot organs. Dry matter‐based N dilution curves were fitted for stem (plateau power) and leaf (plateau linear), for situations with sufficient N supply (NNI ≥1). The ‘expected’ shoot N demand was calculated as a function of observed dry matter and N dilution for both organs. The N deficit was defined as difference between expected and observed shoot N. Plotting stem against shoot N deficit reveals that 63 % of the shoot deficit was allocated to the stem (R2 = 0.98). Implications for crop modelling are discussed.

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Spectral reflection and crop parameters: can the disentanglement of primary and secondary traits lead to more robust and extensible prediction models?

Rose

Kage

2022

Precision Agric

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Recently the application of spectral reflection data for the prediction of crop parameters for applications in precision agriculture, such as green area index (GAI), total aboveground dry matter (DM), and total aboveground nitrogen content (N content) increases. However, the usability of vegetation indices (VI) for the prediction of crop parameters is strongly limited by the fact that most VI calibrations are only valid for specific crops and growth periods. The results of the presented study based on the differentiation of primary (main driver of the reflectance signal) and secondary (not directly related to reflectance signal) crop parameters. For GAI prediction, a universal (without crop-specific parametrization) simple ratio vegetation index (SR) provided good calibration (R2 adj. = 0.90, MAE = 0.32, rMAE = 22%) and evaluation results (MAE = 0.33, rMAE = 18%). The disentanglement of primary and secondary traits allowed the development of a functional two-step model for the estimation of the N content during vegetative growth (MAE = 19.2 g N m−1, rMAE = 44%). This model was based on fundamental, crop-specific relationships between the crop parameters GAI and N content. Additionally, an advanced functional approach was tested enabling the whole-season prediction of DM and confirming a reliable GAI estimation throughout the whole growing season (R2 = 0.89–0.93).

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

Cited by 9 publications

References 30 publications

Water Stress Scatters Nitrogen Dilution Curves in Wheat

Water Stress Scatters Nitrogen Dilution Curves in Wheat

Modelling N and Dry Matter Partitioning between Leaf and Stem of Wheat under Varying N Supply

Spectral reflection and crop parameters: can the disentanglement of primary and secondary traits lead to more robust and extensible prediction models?

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