Getting Started#

Installation#

The NASEM dairy package can be installed by running this command:

pip install nasem_dairy

We recommend installing the package in a virtual environment. The nasem_dairy package is compatible with Python 3.10 and above.

Using the Model#

This section will go over how to run the model and view the results. The quickest way to get started is to load one of the demo inputs provided with the package.

First, create a new Python file and import the nasem_dairy package.

import nasem_dairy as nd

Load a Demo#

The NASEM dairy model requires several inputs to run. These inputs include:

a diet to feed
animal related parameters
the equations to use
infused nutrients (optional)

More details on the inputs can be found on the model inputs page. The nd.demo() function will load a demo input for a lactating cow. We will be ignoring infusions for this example.

diet, animal_input, equation_selection, _ = nd.demo("lactating_cow_test")

Run the model#

The model can be run by calling the nd.nasem() function. This function will return a ModelOutput object with the results. Only the diet, animal inputs and equation selection are required. More details on the optional inputs can be found on the nasem API documentation.

output = nd.nasem(diet, animal_input, equation_selection)
print(output)

Printing the output will show a snapshot of the results:

=====================
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

Viewing Results#

The get_value() method can be used to get the value of a specific variable.

print(output.get_value("Mlk_Prod_comp"))
34.19721330579462

The results can be saved to a JSON file using the export_to_JSON() method.

output.export_to_JSON("output.json")

If you are unsure what variable you are looking for you can use the search() method. This will return a DataFrame with all the variables that contain the search term in the nasem or are sorted in a category including the search term. For example, searching for “CPgain” will return all the variables related to crude protein for body weight gain.

print(output.search("CPgain"))

                    Name       Value      Category           Level 1                  Level 2
            Body_CPgain    0.020052    Production  body_composition              Body_CPgain
Body_CPgain_MPalowTrg_g  481.572362    Production  body_composition  Body_CPgain_MPalowTrg_g
          Body_CPgain_g   20.051817    Production  body_composition            Body_CPgain_g
         CPGain_FrmGain    0.128702    Production  body_composition           CPGain_FrmGain
        CPGain_RsrvGain    0.068000        Inputs        coeff_dict          CPGain_RsrvGain
             Frm_CPgain    0.020052    Production         gestation               Frm_CPgain
           Frm_CPgain_g   20.051817    Production  body_composition             Frm_CPgain_g
         Gest_NCPgain_g    1.008063  Requirements           protein           Gest_NCPgain_g
            Rsrv_CPgain    0.000000    Production  body_composition              Rsrv_CPgain
          Rsrv_CPgain_g    0.000000    Production  body_composition            Rsrv_CPgain_g

More details on the ModelOutput object can be found on the model output page. For more details on using this package see the user guide page.