EPIC - crop.demand¶
- class mef_agri.models.crop.demand.model_epic.Demand(**kwargs)¶
- bmt()¶
RQ -
'biomass'from model with id'crop'\(c_{\textrm{bm},k}\ [\frac{t}{ha}]\)
- Returns:
total dry biomass (aboveground + roots)
- Return type:
- hui()¶
RQ -
'heat_unit_index'from model with id'crop.development'\(c_{\textrm{D-hui},k}\ [\ ]\)
- Returns:
current heat unit index
- Return type:
- initialize(epoch)¶
Initialization of random outputs with zero vectors.
Except: \(c_{\textrm{N-co},0} = c_{\textrm{N-c1},0} + c_{\textrm{N-c2},0}\) which is the result when setting \(c_{\textrm{D-hui},0} = 0.0\) in equ. 26 [R2] .
- Parameters:
epoch (datetime.date) – initialization epoch
- n_amount_opt()¶
MQ - Random Output
\(c_{\textrm{N-ao},k} = c_{\textrm{N-co},k}\cdot c_{\textrm{bm},k} \ [\frac{kg}{ha}]\)
- Returns:
optimal N-amount in crop biomass
- Return type:
numpy.ndarray
- n_concentration_opt()¶
MQ - Random Output
\(c_{\textrm{N-co},k}\ [\frac{kg}{t}]\) - [R2] (equ, 25, 26)
- Returns:
optimal N-concentration in crop biomass
- Return type:
numpy.ndarray
- ncoeff1()¶
MQ - Hyper-Parameter
\(c_{\textrm{N-c1},0}\ [\frac{kg}{t}]\) - [R2] (equ 26, table 2)
- Returns:
coefficient to determine optimal N-concentration in crop biomass
- Return type:
numpy.ndarray
- ncoeff2()¶
MQ - Hyper-Parameter
\(c_{\textrm{N-c2},0}\ [\frac{kg}{t}]\) - [R2] (equ 26, table 2)
- Returns:
coefficient to determine optimal N-concentration in crop biomass
- Return type:
numpy.ndarray
- ncoeff3()¶
MQ - Hyper-Parameter
\(c_{\textrm{N-c3},0}\ [\ ]\) - [R2] (equ 26, table 2)
- Returns:
coefficient to determine optimal N-concentration in crop biomass
- Return type:
numpy.ndarray
- nitrogen()¶
MQ - Random Output
\(c_{\textrm{N-dem},k}\ [\frac{kg}{ha}]\) - [R2] (equ. 25)
- Returns:
nitrogen demand of the crop
- Return type:
numpy.ndarray
- nsum()¶
RQ -
'nitrogen_sum'from model with id'crop.uptake'\(c_{\textrm{N-ups},k}\ [\frac{kg}{ha}]\)
- Returns:
sum of nitrogen uptake of current crop/vegetation period
- Return type:
- transpiration_pot()¶
RQ -
'transpiration_pot'from model with id'zone.soil.surface.evapotranspiration'\(s_{\textrm{W-tp},s,k}\ [\frac{mm}{day}]\)
- Returns:
potential transpiration
- Return type:
- update(epoch)¶
The following computations are performed
- Parameters:
epoch (datetime.date) – current evaluation epoch
- water()¶
MQ - Random Output
\(c_{\textrm{W-dem},k}\ [\ ]\)
- Returns:
water demand of the crop > is set to the potential transpiration from the soil evaporation model
- Return type:
numpy.ndarray