Diagnostic Systems for Plant Nutrient Analysis (DRIS, MDRIS, PASS)

Provides implementations of the Diagnosis and Recommendation Integrated System (DRIS), the Modified DRIS (MDRIS), and the Plant Analysis with Standardized Scores (PASS) approaches for nutrient diagnosis in crops. These methods allow quantitative evaluation of nutrient imbalances using ratio-based indices and standardized scores, supporting improved fertilizer use efficiency and crop management decisions. The DRIS method is described in Walworth, J.L. and Sumner, M.E. (1987) . The MDRIS approach is detailed in Beverly, R.B. (1987) . The PASS method combining DRIS and sufficiency ranges is presented in Baldock, J.O. and Schulte, E.E. (1996) .

drisdiagnostics

The goal of drisdiagnostics is to provide tools for plant nutrient diagnostics using the Diagnosis and Recommendation Integrated System (DRIS), Modified DRIS (MDRIS) and the PASS approach. It supports splitting crop populations into high- and low-yielding subgroups, calculating diagnostic norms and indices, and visualizing nutrient balance through diverging bar charts and tri-axial DRIS wheel plots. It also provide MDRIS and PASS indices.

Installation

install.packages("drisdiagnostics")

Example: Standard DRIS workflow

my_crop_data below is a placeholder for your own data frame of plant tissue nutrient concentrations plus a yield column. Substitute it with your own dataset before running.

library(drisdiagnostics)

# Split populations using calculated mean + a custom SD
processed_data <- split_populations(
  data = my_crop_data,
  yield_col = "Yield",
  method = "mean_sd",
  value = 2/3
)

# Calculate DRIS norms from the split populations
dris_norms <- calculate_dris_norms(
  data = processed_data,
  nutrient_cols = c("N", "P", "K", "S",  "Ca", "Mg")
)

# Generate the optimal HYP ratio reference table
hyp_ratios_results <- generate_hyp_ratios_table(
  data = processed_data,
  dris_norms = dris_norms,
  sample_col = "Sample"
)

# Run diagnostics on the sample set
diagnostics_results <- calculate_dris_diagnostics(
  data = processed_data,
  dris_norms = dris_norms,
  nutrient_cols = c("N", "P", "K", "S",  "Ca", "Mg"),
  sample_col = "Sample",
  hyp_only = TRUE
)

# Run diagnostics separately for each level of a grouping factor (e.g. leaf age)
grouped_results <- calculate_dris_by_group(
  data = processed_data,
  dris_norms = dris_norms,
  nutrient_cols = c("N", "P", "K", "S",  "Ca", "Mg"),
  filter_col = "Age",   # The column to filter and group by
  sample_col = "Sample",
  hyp_only = FALSE      # TRUE to analyze only HYP; FALSE to analyze everyone
)

# Generate the nutrient sufficiency ranges table
ranges_table <- sufficiency_ranges(
  data = processed_data,
  nutrient_cols = c("N", "P", "K", "Zn", "B")
)

Visualizing results

# Diverging bar chart of nutrient indices for a single sample
indices <- c(
  N_index  = -15.4,
  P_index  = -8.2,
  K_index  = 12.6,
  Ca_index = 5.3
)

plot_dris_diverging_bar(
  indices = indices,
  sample_name = "Sample 1"
)

# Tri-axial DRIS wheel using three custom ratios
plot_dris_norms_wheel(
  dris_norms = dris_norms,
  ratios_to_plot = c("P/N", "N/K", "P/K")
)

Example: MDRIS workflow

mdris_norms <- calculate_mdris_norms(
  data = my_crop_data,
  nutrient_cols = c("N", "P", "K", "Zn", "B")
)

mdris_results <- calculate_mdris_diagnostics(
  data = my_crop_data,
  mdris_norms = mdris_norms,
  nutrient_cols = c("N", "P", "K", "Zn", "B"),
  sample_col = "Sample"
)

Example: PASS workflow

Unlike the workflows above, this example is fully self-contained and can be copied and run as-is.

# Define the pre-established INI norms (critical levels & SDs)
ini_norms <- data.frame(
  nutrient = c("N", "P", "K", "S", "Zn", "Ca", "Mg", "B", "Mn", "Cu", "Fe"),
  CL       = c(25.0, 2.5, 17.5, 1.6, 19.0, 3.0, 1.6, 6.0, 19.0, 3.0, 21.0),
  SD       = c(4.159, 0.464, 4.394, 0.426, 7.490, 1.254, 0.900, 3.426, 24.296, 2.640, 74.753),
  group    = c("common", "common", "common", "common", "common",
               "rare", "rare", "rare", "rare", "rare", "rare"),
  stringsAsFactors = FALSE
)

# Define the DNI norms (pairwise ratio means & SDs)
dni_norms <- data.frame(
  ratio = c("N/P", "N/K", "N/S", "N/Zn", "P/K", "P/S", "P/Zn", "K/S", "K/Zn", "S/Zn"),
  mean  = c(9.030, 1.460, 11.900, 0.118, 0.169, 1.420, 0.0113, 8.770, 0.0714, 0.0095),
  SD    = c(2.140, 0.426, 2.610, 0.0446, 0.054, 0.3449, 0.0056, 2.990, 0.0476, 0.0036),
  stringsAsFactors = FALSE
)

# Input tissue concentrations for your samples (Macros in g/kg, Micros in mg/kg)
my_corn_samples <- data.frame(
  Sample = c("Field_1", "Field_2"),
  N  = c(22.6, 26.5),
  P  = c(3.1, 2.8),
  K  = c(16.2, 19.5),
  S  = c(2.0, 1.8),
  Zn = c(21.0, 24.0),
  Ca = c(4.4, 4.8),
  Mg = c(2.1, 1.9),
  B  = c(7.0, 8.5),
  Cu = c(3.0, 4.2),
  Mn = c(22.0, 31.0),
  Fe = c(150.0, 185.0),
  stringsAsFactors = FALSE
)

# Run the PASS analysis
pass_report <- calculate_pass_analysis(
  data = my_corn_samples,
  ini_norms = ini_norms,
  dni_norms = dni_norms,
  sample_col = "Sample"
)

pass_report