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Nitrogen Nutrition Index (NNI)

Source: R/compute_NNI.R
compute_NNI.Rd

Computes the Nitrogen Nutrition Index, the standard scalar indicator of crop N status (Lemaire & Gastal 1997). NNI = N_actual / N_c, where N_actual is the measured aboveground N concentration (% DM) and N_c is the critical N concentration derived from the species-specific dilution curve \(N_c = a \cdot W^{-b}\).

Usage

compute_NNI(N_content, biomass, crop, curve = NULL, is_percent = FALSE)

Arguments

N_content

Aboveground N concentration as a fraction of DM (e.g. 0.025 = 2.5pct) or percentage DM (pass is_percent = TRUE).

biomass

Aboveground dry biomass in t DM / ha (tonnes).

crop

Crop identifier, passed to critical_N_curve.

curve

Optional list with a and b (and optionally W_min) to override the default curve. Useful for custom local calibration.

is_percent

Logical. Is N_content supplied as a percentage (e.g. 2.5 for 2.5pct) rather than a fraction? Default FALSE (fraction). Auto-detected and warned if values > 1.

Value

A numeric scalar / vector or a RasterLayer of NNI values.

Details

Interpretation:

  • NNI < deficient_threshold (default 0.90): N-deficient

  • deficient_threshold <= NNI <= excessive_threshold (0.90 to 1.10 by default): optimal / balanced

  • NNI > excessive_threshold (default 1.10): luxury consumption / N-excess

All three arguments can be scalar, numeric vectors of the same length (pixel-wise / plot-wise), or raster::RasterLayers aligned to the same grid. If any of them is a RasterLayer, the output is a RasterLayer; otherwise a numeric vector is returned.

References

Lemaire, G. & Gastal, F. (1997). N uptake and distribution in plant canopies. In: Lemaire G. (ed.) Diagnosis of the Nitrogen Status in Crops. Springer, Berlin.

Lemaire, G., Jeuffroy, M.-H., Gastal, F. (2008). Diagnosis tool for plant and crop N status in vegetative stage: theory and practices for crop N management. Eur. J. Agron. 28, 614-624.

Examples

if (FALSE) { # \dontrun{
# Scalar: wheat at GS30, N% = 3.2, biomass = 2.5 t/ha
compute_NNI(N_content = 3.2, biomass = 2.5, crop = "wheat",
            is_percent = TRUE)
# ~ 0.94 -> slightly deficient

# Field-average maize at V8, N% = 2.8, biomass = 4 t/ha
compute_NNI(2.8, 4, crop = "maize", is_percent = TRUE)
# ~ 1.52 -> luxury consumption

# Raster (pixel-wise): N_content and biomass as RasterLayers
nni_map <- compute_NNI(N_content = n_map, biomass = w_map,
                       crop = "wheat", is_percent = TRUE)
} # }