photosynthesis

Radiation use efficiency and gross assimilation.

References

SIMPLACE RadiationUseEfficiency.java and biomass block of Lintul5.java.

Equations

Gross biomass growth rate (daily):

\[ \text{GTOTAL} = \text{RUE} \cdot f_T(T) \cdot f_{CO_2} \cdot \text{NSTRESS} \cdot \text{TRANRF} \cdot \text{PARINT} \]

where RUE has units of g dry matter per MJ intercepted PAR.

Photosynthesis (Module)

Radiation use efficiency and biomass assimilation.

Source code in torchcrop/processes/photosynthesis.py

class Photosynthesis(nn.Module):
    """Radiation use efficiency and biomass assimilation."""

    def forward(
        self,
        parint: torch.Tensor,
        davtmp: torch.Tensor,
        tranrf: torch.Tensor,
        nstress: torch.Tensor,
        params: CropParameters,
    ) -> dict[str, torch.Tensor]:
        """Compute gross daily biomass production.

        Args:
            parint: PAR intercepted by the canopy [MJ m⁻² d⁻¹], shape
                ``[B]``.
            davtmp: Mean daily air temperature [°C], shape ``[B]``.
            tranrf: Water-stress reduction factor in ``[0, 1]``, shape
                ``[B]``.
            nstress: Nutrient-stress reduction factor in ``[0, 1]``, shape
                ``[B]``.
            params: Crop parameters; uses ``rue``, ``tmpf_tb``, ``co2``.

        Returns:
            Dict of ``[B]`` tensors grouped as follows.

            Rate variables (consumed by `Partitioning`):

                * ``gtotal`` [g DM m⁻² d⁻¹] — Gross total biomass
                  production rate
                  ``= rue_eff * parint * tranrf * nstress``. Split into
                  organ-specific growth rates ``g_lv``, ``g_st``,
                  ``g_root``, ``g_so`` by the partitioning module.

            Diagnostics:

                * ``rue_eff`` [g MJ⁻¹] — Effective radiation use
                  efficiency after temperature and CO₂ adjustments.
                * ``tmp_factor`` [-] — Temperature reduction factor on RUE
                  from ``AFGEN(tmpf_tb, T_avg)``, in ``[0, 1]``.
                * ``co2_factor`` [-] — CO₂ enhancement factor (1.0 at 360
                  ppm, up to 1.3 at 700 ppm).
        """
        tmp_factor = interpolate(params.tmpf_tb, davtmp)
        # Simple CO2 response (linear around reference 360 ppm, saturates at 700)
        co2_factor = 1.0 + 0.3 * torch.clamp((params.co2 - 360.0) / (700.0 - 360.0), 0.0, 1.0)
        rue_eff = params.rue * tmp_factor * co2_factor
        gtotal = rue_eff * parint * tranrf * nstress
        return {
            "rue_eff": rue_eff,
            "gtotal": gtotal,
            "tmp_factor": tmp_factor,
            "co2_factor": co2_factor,
        }

forward(self, parint, davtmp, tranrf, nstress, params)

Compute gross daily biomass production.

Parameters:

Name	Type	Description	Default
parint	torch.Tensor	PAR intercepted by the canopy [MJ m⁻² d⁻¹], shape [B].	required
davtmp	torch.Tensor	Mean daily air temperature [°C], shape [B].	required
tranrf	torch.Tensor	Water-stress reduction factor in [0, 1], shape [B].	required
nstress	torch.Tensor	Nutrient-stress reduction factor in [0, 1], shape [B].	required
params	CropParameters	Crop parameters; uses rue, tmpf_tb, co2.	required

Returns:

Type

Description

Dict of ``[B]`` tensors grouped as follows. Rate variables (consumed by `Partitioning`)

gtotal [g DM m⁻² d⁻¹] — Gross total biomass production rate = rue_eff * parint * tranrf * nstress. Split into organ-specific growth rates g_lv, g_st, g_root, g_so by the partitioning module. Diagnostics: * ``rue_eff`` [g MJ⁻¹] — Effective radiation use efficiency after temperature and CO₂ adjustments. * ``tmp_factor`` [-] — Temperature reduction factor on RUE from ``AFGEN(tmpf_tb, T_avg)``, in ``[0, 1]``. * ``co2_factor`` [-] — CO₂ enhancement factor (1.0 at 360 ppm, up to 1.3 at 700 ppm).

Source code in torchcrop/processes/photosynthesis.py

def forward(
    self,
    parint: torch.Tensor,
    davtmp: torch.Tensor,
    tranrf: torch.Tensor,
    nstress: torch.Tensor,
    params: CropParameters,
) -> dict[str, torch.Tensor]:
    """Compute gross daily biomass production.

    Args:
        parint: PAR intercepted by the canopy [MJ m⁻² d⁻¹], shape
            ``[B]``.
        davtmp: Mean daily air temperature [°C], shape ``[B]``.
        tranrf: Water-stress reduction factor in ``[0, 1]``, shape
            ``[B]``.
        nstress: Nutrient-stress reduction factor in ``[0, 1]``, shape
            ``[B]``.
        params: Crop parameters; uses ``rue``, ``tmpf_tb``, ``co2``.

    Returns:
        Dict of ``[B]`` tensors grouped as follows.

        Rate variables (consumed by `Partitioning`):

            * ``gtotal`` [g DM m⁻² d⁻¹] — Gross total biomass
              production rate
              ``= rue_eff * parint * tranrf * nstress``. Split into
              organ-specific growth rates ``g_lv``, ``g_st``,
              ``g_root``, ``g_so`` by the partitioning module.

        Diagnostics:

            * ``rue_eff`` [g MJ⁻¹] — Effective radiation use
              efficiency after temperature and CO₂ adjustments.
            * ``tmp_factor`` [-] — Temperature reduction factor on RUE
              from ``AFGEN(tmpf_tb, T_avg)``, in ``[0, 1]``.
            * ``co2_factor`` [-] — CO₂ enhancement factor (1.0 at 360
              ppm, up to 1.3 at 700 ppm).
    """
    tmp_factor = interpolate(params.tmpf_tb, davtmp)
    # Simple CO2 response (linear around reference 360 ppm, saturates at 700)
    co2_factor = 1.0 + 0.3 * torch.clamp((params.co2 - 360.0) / (700.0 - 360.0), 0.0, 1.0)
    rue_eff = params.rue * tmp_factor * co2_factor
    gtotal = rue_eff * parint * tranrf * nstress
    return {
        "rue_eff": rue_eff,
        "gtotal": gtotal,
        "tmp_factor": tmp_factor,
        "co2_factor": co2_factor,
    }