Lactation Curve Model Fitting for Dairy Animals

Fits up to 20 nonlinear lactation curve models to dairy animal milk yield data. Models fitted include exponential, polynomial, mixed logarithmic, inverse polynomial, and sigmoid families published between 1923 and 2000. Supports batch processing of multiple animals from a single CSV file, with flexible selection of animals and models. Produces per-animal parameter tables, goodness-of-fit metrics including R-squared (R2), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and a serial autocorrelation statistic, 15 diagnostic figures, and combined cross-animal comparison outputs. References: , , , , .

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.