Lactation Curve Model Fitting for Dairy Animals.

LactCurveModels

Overview

LactCurveModels is an R package for fitting, comparing, and visualising nonlinear lactation curve models for dairy animals. It fits up to 20 published lactation curve models simultaneously to daily milk yield data, produces publication-ready diagnostic figures, and generates comprehensive goodness-of-fit tables -- all from a single function call.

The package supports batch processing of multiple animals from one CSV file, with flexible options to analyse all animals or a selected subset, and to fit all 20 models or any chosen combination.

The 20 Lactation Curve Models

Installation

From CRAN (once published)

install.packages("LactCurveModels")

From GitHub (development version)

# install.packages("devtools")
devtools::install_github("VenkatesanRaja/LactCurveModels")

Input Data Format

The package reads a CSV file with exactly three columns (column order does not matter):

Example CSV layout:

animal_id,dim,dmy
Animal_A,1,9.265
Animal_A,2,9.957
Animal_A,3,10.421
Animal_B,1,8.100
Animal_B,2,8.750

Usage

Analyse all animals with all 20 models

library(LactCurveModels)

results <- run_lactation_analysis(
  input_csv_path = "C:/data/myanimals.csv"
)

Analyse selected animals only

results <- run_lactation_analysis(
  input_csv_path   = "C:/data/myanimals.csv",
  selected_animals = c("Animal_A", "Animal_B")
)

Fit selected models only

results <- run_lactation_analysis(
  input_csv_path  = "C:/data/myanimals.csv",
  selected_models = c("Wood_1967", "Wilmink_k005", "Ali_Schaeffer")
)

Combine both selections with a custom output folder

results <- run_lactation_analysis(
  input_csv_path   = "C:/data/myanimals.csv",
  selected_models  = c("Wood_1967", "Wilmink_k005", "Ali_Schaeffer",
                       "Pollott_Multiplicative"),
  selected_animals = c("Animal_A", "Animal_C"),
  out_dir          = "C:/results/lactation_analysis"
)

Fit models directly (advanced use)

# Build the data frame manually
animal_df <- data.frame(
  x = 1:20,
  y = c(9.3, 9.9, 10.2, 10.4, 10.3, 10.1, 9.9, 9.6, 9.3, 9.0,
        8.7, 8.4, 8.1,  7.8,  7.5,  7.2,  6.9, 6.6, 6.3, 6.0),
  z = (1:20) / 365
)

fits <- fit_lactation_models(animal_df, selected_models = "all")

# Extract results for Wood (1967)
fits[["Wood_1967"]]$metrics
fits[["Wood_1967"]]$predictions
coef(fits[["Wood_1967"]]$model)

Outputs

For each animal, the following are saved in a dedicated sub-folder:

CSV Tables (4 files per animal)

Diagnostic Figures (15 PNG files per animal)

Combined Cross-Animal Outputs (produced when more than 1 animal is analysed)

Output Folder Structure

lactation_results/
|-- Animal_A/
|   |-- Animal_A_parameter_estimates.csv
|   |-- Animal_A_summary_metrics.csv
|   |-- Animal_A_actual_and_predicted_values.csv
|   |-- Animal_A_residuals.csv
|   |-- Fig01_All_Models_Overlay.png
|   |-- Fig02_Ranked_Panels.png
|   |-- Fig03_Best_vs_Worst.png
|   |-- Fig04_Family_Panels.png
|   |-- Fig05_Residuals_ModelName.png
|   |-- Fig06_Residual_BubbleChart.png
|   |-- Fig07_R2_AdjR2.png
|   |-- Fig08_RMSE.png
|   |-- Fig09_AIC_BIC.png
|   |-- Fig10_DurbinWatson.png
|   |-- Fig11_R2_RMSE_Bubble.png
|   |-- Fig12_ModelRanking.png
|   |-- Fig13_Pred_vs_Actual.png
|   |-- Fig14_Correlation_Heatmap.png
|   `-- Fig15_Peak_Yield_Time.png
|-- Animal_B/
|   `-- (same structure as Animal_A)
|-- COMBINED_summary_metrics_all_animals.csv
|-- COMBINED_parameter_estimates_all_animals.csv
|-- COMBINED_best_model_per_animal.csv
|-- COMBINED_Fig_R2_CrossAnimal.png
|-- COMBINED_Fig_RMSE_CrossAnimal.png
`-- COMBINED_Fig_BestModel_Overlay.png

Valid Model Names

Use these exact strings in the selected_models argument:

"Brody_1923"             "Brody_1924"             "Parabolic_Exp_Sikka"
"Wood_1967"              "Wood_Dhanoa"             "Cobby_LeDu"
"Quadratic_Dave"         "Mixed_Log_GS"            "Khandekar"
"Wilmink_k005"           "Wilmink_k_estimated"     "Ali_Schaeffer"
"Cappio_Borlino"         "Papajcsik_Bodero1"       "Papajcsik_Bodero2"
"Papajcsik_Bodero3"      "Inverse_Poly_Nelder"     "Log_Quadratic_Adediran"
"Morant_Gnanasakthy"     "Pollott_Multiplicative"

Goodness-of-Fit Metrics

The package computes and reports the following metrics for every fitted model:

Authors

License

This package is licensed under the GNU General Public License v3.0. See GPL-3.0 License for details.

Citation

If you use LactCurveModels in your research, please cite it as:

Raja TV, Lalremruati PC, Priyadharshini P, Nidhishree NS, Gurjar D,
Alex R, Vohra V (2025). LactCurveModels: Lactation Curve Model Fitting
for Dairy Animals. R package version 0.1.0.
https://github.com/VenkatesanRaja/LactCurveModels

To get the citation from within R:

citation("LactCurveModels")

Bug Reports and Contributions

• Bug reports: https://github.com/VenkatesanRaja/LactCurveModels/issues
• Pull requests are welcome for new models, additional figure types, or performance improvements.