nasem_dairy 1.0.2

Implementation of the NASEM Nutrient Requirements of Dairy Cattle 8th Edition in Python

The NASEM 2021 Nutrient Requirements of Dairy Cattle Model in Python

• Installation
• Using the Model
  • Import package
  • Get input data
  • Execute Model

This is a python version of the Nutrient Requirments of Dairy Cattle 8th revisied edition (NASEM 2021)[https://nap.nationalacademies.org/catalog/25806/nutrient-requirements-of-dairy-cattle-eighth-revised-edition\] model. The equations have been copied from the R functions released with the official NASEM software: https://nap.nationalacademies.org/resource/25806/Installation_Instructions_NASEM_Dairy8.pdf

License

This project is distributed under the terms of the MIT License.

Code Attribution

The NASEM equations were coppied from the R code that was distributed under different licensing terms. The original R code is distributed under a separate license.

Please refer to both the MIT License and the R Code License for detailed terms.

Contributing

We welcome contributions! Please read our Contributing Guidelines to get started.

Installation

This package can be installed through a terminal using:

pip install nasem_dairy

The environment that this is installed into will require python >= 3.10. It will also install pandas and numpy automatically.

The suggested way to install this is inside a virtual environment. For example, by using Miniconda.

1. Open a terminal window, e.g., Anaconda Prompt.

2. Create a new conda environment with Python installed:

   conda create --name myenvironment python>=3.10
   
   conda activate myenvironment
   

3. Install this package via pip:

   pip install nasem_dairy
   

Using the Model

Import package

First, import nasem_dairy. pandas is also required if you want to modify the user_diet input.

import nasem_dairy as nd
import pandas as pd

Get input data

There are several demo scenarios built into the app which can be used as input to the model. These can be acessed using nd.demo():

user_diet, animal_input, equation_selection, infusion_input = nd.demo("lactating_cow_test")

These 4 inputs can be modified to simulate different scenarios:

• user_diet (pd.DataFrame): The name and kg/d to feed for each ingredient in the diet.
• animal_input (dict): Parameters to descibe the animal being modelled.
• equation_selection (dict): Select which equation to use when there are multiple options.
• infusion_input (dict): If simulating infusions specify this here. This is optional.

Execute Model

To run the model use nd.nasem and pass the input data. This will return an instance of the nd.ModelOutput. Printing this will provide a snapshot of the results.

## RUN MODEL ###
output = nd.nasem(
    user_diet = user_diet_in, 
    animal_input = animal_input_in, 
    equation_selection = equation_selection_in, 
    infusion_input = infusion_input)

print(output)

=====================
Model Output Snapshot
=====================
Milk production kg (Mlk_Prod_comp): 34.197
Milk fat g/g (MlkFat_Milk): 0.053
Milk protein g/g (MlkNP_Milk): 0.037
Milk Production - MP allowable kg (Mlk_Prod_MPalow): 35.906
Milk Production - NE allowable kg (Mlk_Prod_NEalow): 31.261
Animal ME intake Mcal/d (An_MEIn): 59.976
Target ME use Mcal/d (Trg_MEuse): 52.195
Animal MP intake g/d (An_MPIn_g): 2565.214
Animal MP use g/d (An_MPuse_g_Trg): 1989.985
Animal RDP intake g/d (An_RDPIn_g): 3556.058
Diet DCAD meq (An_DCADmeq): 96.453

This is a `ModelOutput` object with methods to access all model outputs. See help(ModelOutput).

Exploring the Results

The nd.ModelOutput class provides several methods to help you explore the results.

If you need a specific value you can used get_value().

Du_MiCP = output.get_value("Du_MiCP")
print(Du_MiCP)

2.0524052134583415

If you don't know the exact name of the variable you can use the search() method. For example if you want to see values related to microbial crude protein (MiCP) you could search for "MiCP".

print(output.search("MiCP"))

                Name                                              Value    Category     Level 1           Level 2
0           Du_EndCP                                           0.280988  Production        MiCP          Du_EndCP
1         Du_EndCP_g                                          280.98834  Production        MiCP        Du_EndCP_g
2            Du_EndN                                            0.04507  Production        MiCP           Du_EndN
3          Du_EndN_g                                           45.07041  Production        MiCP         Du_EndN_g
4        Du_IdEAAMic                                         767.796581  Production        MiCP       Du_IdEAAMic
5            Du_MiCP                                           2.052405  Production        MiCP           Du_MiCP
6          Du_MiCP_g                                        2052.405213  Production        MiCP         Du_MiCP_g
7   Du_MiN_NRC2001_g                                         334.939405  Production        MiCP  Du_MiN_NRC2001_g
8           Du_MiN_g                                         328.384834  Production        MiCP          Du_MiN_g
9            Du_MiTP                                           1.691182  Production        MiCP           Du_MiTP
10         Du_MiTP_g                                        1691.181896  Production        MiCP         Du_MiTP_g
11        Du_NANMN_g                                         302.751473  Production        MiCP        Du_NANMN_g
12          Du_NAN_g                                         631.136307  Production        MiCP          Du_NAN_g
13         Du_idMiCP                                           1.641924  Production        MiCP         Du_idMiCP
14       Du_idMiCP_g                                        1641.924171  Production        MiCP       Du_idMiCP_g
15         Du_idMiTP                                           1.352946  Production        MiCP         Du_idMiTP
16       Du_idMiTP_g                                        1352.945517  Production        MiCP       Du_idMiTP_g
17         EndAAProf  [4.61, 2.9, 4.09, 7.67, 6.23, 1.26, 3.98, 5.18...  Production        MiCP         EndAAProf
18      Fe_DEMiCPend                                           2.319218   Excretion       fecal      Fe_DEMiCPend
19        Fe_RumMiCP                                           0.410481   Excretion       fecal        Fe_RumMiCP
20              MiCP                                         Dictionary  Production        MiCP                  
21            MiN_Vm                                         340.791931  Production        MiCP            MiN_Vm
22        MiTPAAProf  [5.47, 2.21, 6.99, 9.23, 9.44, 2.63, 6.3, 6.23...  Production        MiCP        MiTPAAProf
23      RDPIn_MiNmax                                            2.94252  Production        MiCP      RDPIn_MiNmax
24   Rum_MiCP_DigCHO                                           0.326383  Production        MiCP   Rum_MiCP_DigCHO
25         SI_dcMiCP                                               80.0      Inputs  coeff_dict         SI_dcMiCP
26        fMiTP_MiCP                                              0.824      Inputs  coeff_dict        fMiTP_MiCP

This will print a table with all the variables related to the search string and their value. The "Category" and "Level" columns show where the values are stored within ModelOutput. In this case "MiCP" is the name of a dictionary containg values related to microbial crude protein production, so all values in this dictionary are displayed even if "MiCP" is not in the variable name.

The nasem_dairy package also includes all of the reports from the R version. These can be accessed through the get_report() method by specifying the report name.

print(output.get_report("table1_1"))

                    Description         Values
0                      Animal Type  Lactating Cow
1                     Animal Breed       Holstein
2                  Body Weight, kg        624.795
3                     Empty BW, kg     512.395561
4                 Birth Weight, kg           44.1
5                Mature Weight, kg          700.0
6              Mature Empty BW, kg          574.0
7                           Age, d          820.8
8          Condition Score, 1 to 5            3.0
9             Percent First Parity          100.0
10                    Days in Milk            100
11         Age At First Calving, d               
12                   Days Pregnant             46
13                  Temperature, C           22.0
14         Use In vitro NDF digest              0
15   Feeding Monensin, 0=No, 1=Yes              0
16                         Grazing              0
17                      Topography            0.0
18    Dist. (Pasture to Parlor, m)            0.0
19  One-Way Trips to the Parlor, m              0

The NASEM Directed Acyclic Graph (DAG)

nasem_dairy comes with an optional dag subpackage. This subpackage requires you install the graph-tool package. This package has several requirements depending on your operating system. For instructions on how to install visit their website

This subpackage parses the equations used to build the NASEM model and uses the function arguments to build a DAG. This can then be used to explore how varaibles in the model are connected.

Once you have graph-tool installed you can create a DAG.

dag = nd.ModelDAG()

Once you have created the DAG you can explore how functions are connected using dag.get_calculation_order().

dag.get_calculation_order("GrUter_Wt")

Requirements for Calculating GrUter_Wt

Order of Functions to Call:
1. calculate_Uter_Wtpart
2. calculate_Uter_Wt
3. calculate_GrUter_Wtpart
4. calculate_GrUter_Wt

Required User Inputs:
animal_input:
    - An_AgeDay
    - An_GestLength
    - An_Parity_rl
    - An_GestDay
    - Fet_BWbrth
    - An_LactDay

Required Constants:
coeff_dict:
    - GrUter_Ksyn
    - GrUterWt_FetBWbrth
    - Uter_KsynDecay
    - GrUter_KsynDecay
    - Uter_Ksyn
    - Uter_Kdeg
    - Uter_Wt_coeff
    - UterWt_FetBWbrth

This will display the functions that need to be called in order to calulate the specified value. It also provides all of the input values that are involved in the calculation.

If you are only interested in calcualting a specific value you can use the create_function() method. This will return a function that takes the required inputs and will call all of the functions required to calculate the specified value. This returns a ModelOuptut just like the nd.nasem() function.

GrUter_Wt_function = dag.create_function("GrUter_Wt")
print(inspect.signature(GrUter_Wt_function))

(animal_input, coeff_dict)

By inspecting the signature of the created function we see it requires animal_input and coeff_dict. The animal_input can be the same as the one used to run nd.nasem(), though only the specified keys are required. The coeff_dict contains default coefficients. The default can be accessed as nd.coeff_dict.