Yields Module (14_yields)


The yields module simulates agricultural and pasture yields based on yield data coming from LPJmL1, land use intensities coming from module 13_tc and management intensity provided by 38_factor_costs. Whether yields should be calculated under assumption of climate change or under static climate conditions is controlled in the preprocessing and can be set in the config by the setting static_inputs. The module returns yields for all crops and pasture which is used then by 30_crop and 31_pasture. Corresponding carbon densities are provided by the carbon module 52_carbon.

For information about the preprocessing of yields, see yields.R. Before yields are fed into the model they need to be calibrated which is describe in Yield calibration.



Name Description Unit A B C
$vm\_tau(i)$ agricultural land use intensity tau - x x x
$fm\_tau1995(i)$ agricultural land use intensity tau in 1995 - x x x
$vm\_mi(i)$ Management intensity - x x x

The last columns of the table indicate the usage in the different realizations (numbered with capital letters)


Name Description Unit
$vm\_yld(j,kve,w)$ yields (variable because of technical change) tonDM/ha

Interface plot

Figure 0: Information exchange among modules


(A) biocorrect (default)

The biocorrect module is reading the LPJmL data and is performing several corrections before the data send to the other moduled. First, a bioenergy yield correction (which also is the reason for the name of the realization) is performed: as there is currently no robust information of bioenergy yields available in FAO, it is assumed that LPJmL yields for bioenergy agree with yields achieved under highest currently observed land use intensification (which agrees with the values in EUR). All other bioenergy yields are downscaled proportional to the land use intensity in the given region. Yields for all other crops are calibrated on a regional level to meet regional FAO yields in 1995 by applying a calibration factor which was derived in the preprocessing (see Yield calibration).

Equation 1: Yield correction for bioenergy.

i14\_yields(t,j,\text{("begr","betr")},w) = i14\_yields(t,j,\text{("begr","betr")},w) \frac{ fm\_tau1995(i(j))}{fm\_tau1995(\text{"EUR"})}

Equation 2: Application of the yield calibration factor

i14\_yields(t,j,kve,w) = i14\_yields(t,j,kve,w)\cdot f14\_yld\_calib(i(j),kve);

For the inclusion of technology intensity and management intensity in the yield value the LPJmL yields are finally multiplied with the management intensity (MI) and the change in technology intensity relative to the 1995 values.

Equation 3: Application of land use intensity and management intensity factors on yield

vm\_yld(j,kve,w) = ic14\_yields(j,kve,w) \cdot vm\_mi(i) \frac{vm\_tau(i(j))}{fm\_tau1995(i(j))}

There are currently no known limitations of this realization

(B) biocorrect_switch

The biocorrect_switch realisation is based on the biocorrect realisation. The only difference is that biocorrect_switch allows to deactivate technological change for 2nd generation bioenergy yields.
The switch $i14\_kbe\_tc2$ determines if 2nd gen. bioenergy yields can increase due to technological change (1) or not (0). If $i14\_kbe\_tc2$ ist set to 1, biocorrect_switch behaves identical to the biocorrect realisation.

Equations 4 & 5: Different treatment of bioenergy and non-bioenergy crops

vm\_yld(j,kve14,w) &= ic14\_yields(j,kve14,w) \cdot vm\_mi(i) \frac{vm\_tau(i(j))}{fm\_tau1995(i(j))} \\
vm\_yld(j,kbe14,w) &= ic14\_yields(j,kbe14,w)\$(i14\_kbe\_tc2 = 0)\\
&+ (ic14\_yields(j,kbe14,w) \cdot vm\_mi(i) \frac{vm\_tau(i(j))}{fm\_tau1995(i(j))})\$(i14\_kbe\_tc2 = 1)

There are currently no known limitations of this realization

(C) yield_increase

The yield_increase realization is based on the biocorrect realization. Here it is possible to increase a yield for a specific crop in certain predefined clusters. This can be interesting when one wants to learn something about the impact of targeted crop improvements.

Equations 6: Application of additional, cluster-specific yield increases

i14\_yields(t,j,kve,w) = f14\_yields(t,j,kve,w)\cdot f14\_finland\_yield\_increase(t,j,kve)

There are currently no known limitations of this realization


Name Description Unit A B C
$i14\_yields(t,j,kve,w)$ biophysical input yields (excluding technological change) tonDM/ha x x x
$ic14\_yields(j,kve,w)$ biophysical input yields (excluding technological change) of current time step tonDM/ha x x x
$f14\_yields(t,j,kve,w)$ LPJ potential yields per cell (rainfed and irrigated) tonDM/ha x x x
$f14\_yld\_calib(i,kve)$ Calibration factor for the LPJ yields - x x x
$f14\_finland\_yield\_increase(t,j,kve)$ yield increase % x
$i14\_kbe\_tc$ TC on bioenery on (1) or off (0) - x
$i14\_kbe\_tc2$ TC on bioenery on (1) or off (0) - x
$kve14$ vegetal production activities without begr and betr - x
$kbe14$ begr and betr production activities - x

The last columns of the table indicate the usage in the different realizations (numbered with capital letters)


Jan Philipp Dietrich, Florian Humpenöder, Anne Biewald

See Also

13_tc, 30_crop, 31_pasture, 38_factor_costs, 52_carbon, yields.R, Yield calibration, Overview


1 [LPJml] Bondeau, A., Smith, P.C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W., Gerten, D., Lotze-Campen, H., Mueller, C., Reichstein, M., Smith, B., 2007. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 13, 679–706.

2 [MIRCA] Portman et al. 2010: MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling