Predict function

predict(df)[source]

Prediction of ammonia emissions following field fertilization.

Parameters:

df (pandas.DataFrame) –

Input DataFrame containing the environmental conditions for which predictions are made. This DataFrame must include some mandatory columns and may include optional columns.

Mandatory columns:

  • pmid: identifier for plots

  • ct: time since fertilizer application (h)

  • tan_app: total ammoniacal nitrogen applied (kgN/ha)

Optional columns:
Dynamic variables:

  • air_temp: air temperature (°C)

  • wind_2m: wind speed (m/s)

  • rain_rate: rainfall rate (mm/h)

Plot-level variables:

  • app_rate: application rate (t/ha)

  • man_dm: manure dry matter content (%)

  • man_ph: manure pH

  • app_mthd: application method (must belong to {bc, bsth, ts, os, cs} (1))

  • man_source: manure source (must belong to {pig, cat})

  • incorp: incorporation (must belong to {none, shallow, deep})

  • t_incorp: time of incorporation (h) (2)

  1. bc = broadacst, bsth = band spreading trailing hose, ts = trailing shoe, os = open slot, cs = closed slot.

  2. When incorp = none, t_incorp must be set to NaN.

Default values for optional columns: air_temp = 13.89, wind_2m = 3.11, rain_rate = 0, app_rate = 29.38, man_dm = 6.25, man_ph = 7.38, app_mthd = bsth, man_source = cat, incorp = none.

Returns:

The DataFrame completed with the column ‘prediction_ecum’ (cumulative prediction, kgN/ha) as well as the optional columns not initially provided, filled with their default values.

Return type:

pandas.DataFrame

Example

>>> import pandas as pd
>>> from nh3pred import predict

>>> # Prediction for two plots, identified with pmid = 1 and pmid = 2
>>> df = pd.DataFrame ({
...     "pmid": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2],
...     "ct": [3, 6, 10, 24, 48, 72, 1, 3, 6, 10, 21, 46],
...     "tan_app": [42, 42, 42, 42, 42, 42, 120, 120, 120, 120, 120, 120],
...     "air_temp": [18, 23, 24, 15, 21, 20, 9, 10, 11, 6, 7, 10],
...     "wind_2m": [2, 2, 1, 1, 2, 2, 4, 5, 5, 6, 3, 4],
...     "rain_rate": 0,
...     "app_rate": [20, 20, 20, 20, 20, 20, 28, 28, 28, 28, 28, 28],
...     "man_dm": [8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 13, 13, 13, 13, 13, 13],
...     "man_ph": [7.1, 7.1, 7.1, 7.1, 7.1, 7.1, 7.7, 7.7, 7.7, 7.7, 7.7, 7.7],
...     "app_mthd": ["ts", "ts", "ts", "ts", "ts", "ts", "bc", "bc", "bc", "bc", "bc", "bc"],
...     "man_source": ["cat", "cat", "cat", "cat", "cat", "cat", "pig", "pig", "pig", "pig", "pig", "pig"]
... })

>>> predict (df)
    pmid  ct  air_temp  wind_2m  rain_rate app_mthd incorp man_source  tan_app  app_rate  man_dm  man_ph  t_incorp  prediction_ecum
0      1   3        18        2          0       ts   none        cat       42        20     8.3     7.1       NaN         3.090000
1      1   6        23        2          0       ts   none        cat       42        20     8.3     7.1       NaN         5.620000
2      1  10        24        1          0       ts   none        cat       42        20     8.3     7.1       NaN         7.460000
3      1  24        15        1          0       ts   none        cat       42        20     8.3     7.1       NaN         8.030000
4      1  48        21        2          0       ts   none        cat       42        20     8.3     7.1       NaN         9.760000
5      1  72        20        2          0       ts   none        cat       42        20     8.3     7.1       NaN        10.480000
6      2   1         9        4          0       bc   none        pig      120        28    13.0     7.7       NaN         8.550000
7      2   3        10        5          0       bc   none        pig      120        28    13.0     7.7       NaN        27.150000
8      2   6        11        5          0       bc   none        pig      120        28    13.0     7.7       NaN        41.639999
9      2  10         6        6          0       bc   none        pig      120        28    13.0     7.7       NaN        48.549999
10     2  21         7        3          0       bc   none        pig      120        28    13.0     7.7       NaN        52.610001
11     2  46        10        4          0       bc   none        pig      120        28    13.0     7.7       NaN        61.220001