photosynthesis

Radiation use efficiency.

This module produces only RUE and the overall correction factor RTMCO. The gross biomass growth rate (GTOTAL) is produced downstream in the partitioning / model-level code, not here.

Equations

Effective daytime temperature:

\[ \text{DTEMP} = \text{TMAX} - f \cdot (\text{TMAX} - \text{TMIN}) \]

where f is day_temp_factor (default 0.25, i.e. daytime mean).

Temperature × CO\(_2\) correction factor on RUE:

\[ \text{RTMCO} = \text{TMPFTB}(\text{DTEMP}) \cdot \text{TMNFTB}(\text{TMIN}) \cdot \text{COTB}(\text{CO}_2) \]

DVS-dependent radiation use efficiency:

\[ \text{RUE} = \text{scale_factor_rue} \cdot \text{RUETB}(\text{DVS}) \]

RUE is in units of g dry matter per MJ intercepted PAR.

Photosynthesis (Module)

Radiation use efficiency component.

Outputs RUE from ruetb(DVS) and an overall correction factor RTMCO that combines daytime-temperature, minimum-temperature and CO\(_2\) responses. parint, tranrf and nstress are not consumed here — they enter the GTOTAL formula in the downstream biomass block.

Source code in torchcrop/processes/photosynthesis.py

class Photosynthesis(nn.Module):
    """Radiation use efficiency component.

    Outputs ``RUE`` from ``ruetb(DVS)`` and an overall correction
    factor ``RTMCO`` that combines daytime-temperature,
    minimum-temperature and CO\\(_2\\) responses. ``parint``,
    ``tranrf`` and ``nstress`` are *not* consumed here — they enter
    the ``GTOTAL`` formula in the downstream biomass block.
    """

    def forward(
        self,
        tmax: torch.Tensor,
        tmin: torch.Tensor,
        dvs: torch.Tensor,
        params: CropParameters,
        co2: torch.Tensor | float = 370.0,
    ) -> dict[str, torch.Tensor]:
        """Compute RUE and the RTMCO correction factor.

        Args:
            tmax: Daily maximum air temperature [°C], shape ``[B]``.
            tmin: Daily minimum air temperature [°C], shape ``[B]``.
            dvs: Development stage [-] (0–2), shape ``[B]``.
            params: Crop parameters; uses ``ruetb``, ``scale_factor_rue``,
                ``tmpftb``, ``tmnftb``, ``cotb``, ``day_temp_factor``.
            co2: Atmospheric CO₂ concentration [ppm], a scalar or
                shape broadcastable to ``[B]`` (default ``370``).
                Supplied by ``SiteParameters.co2`` in the model.

        Returns:
            Dict of ``[B]`` tensors:

            * ``rue`` [g MJ⁻¹] — DVS-dependent radiation use
              efficiency ``scale_factor_rue · RUETB(DVS)``.
            * ``rtmco`` [-] — Overall correction factor
              ``RTMP · RCO``.
            * ``rco`` [-] — CO₂ correction factor ``COTB(CO₂)``.
            * ``rtmp`` [-] — Temperature correction factor
              ``TMPFTB(DTEMP) · TMNFTB(TMIN)``.
            * ``dtemp`` [°C] — Effective daytime temperature
              ``TMAX − f · (TMAX − TMIN)``.
        """
        # DVS-dependent base RUE.
        rue = params.scale_factor_rue * interpolate(params.ruetb, dvs)

        # CO₂ correction.
        co2_t = torch.as_tensor(co2, dtype=tmax.dtype, device=tmax.device)
        co2_b = co2_t.expand_as(tmax) if co2_t.dim() == 0 else co2_t
        rco = interpolate(params.cotb, co2_b)

        # Effective daytime temperature; tuned via day_temp_factor.
        dtemp = tmax - params.day_temp_factor * (tmax - tmin)

        # Temperature reduction: daytime-temp × low-min-temp.
        rtmp = interpolate(params.tmpftb, dtemp) * interpolate(params.tmnftb, tmin)

        rtmco = rtmp * rco

        return {
            "rue": rue,
            "rtmco": rtmco,
            "rco": rco,
            "rtmp": rtmp,
            "dtemp": dtemp,
        }

forward(self, tmax, tmin, dvs, params, co2=370.0)

Compute RUE and the RTMCO correction factor.

Parameters:

Name	Type	Description	Default
tmax	torch.Tensor	Daily maximum air temperature [°C], shape [B].	required
tmin	torch.Tensor	Daily minimum air temperature [°C], shape [B].	required
dvs	torch.Tensor	Development stage [-] (0–2), shape [B].	required
params	CropParameters	Crop parameters; uses ruetb, scale_factor_rue, tmpftb, tmnftb, cotb, day_temp_factor.	required
co2	torch.Tensor | float	Atmospheric CO₂ concentration [ppm], a scalar or shape broadcastable to [B] (default 370). Supplied by SiteParameters.co2 in the model.	370.0

Returns:

Type	Description
Dict of ``[B]`` tensors	rue [g MJ⁻¹] — DVS-dependent radiation use efficiency scale_factor_rue · RUETB(DVS). rtmco [-] — Overall correction factor RTMP · RCO. rco [-] — CO₂ correction factor COTB(CO₂). rtmp [-] — Temperature correction factor TMPFTB(DTEMP) · TMNFTB(TMIN). dtemp [°C] — Effective daytime temperature TMAX − f · (TMAX − TMIN).

Source code in torchcrop/processes/photosynthesis.py

def forward(
    self,
    tmax: torch.Tensor,
    tmin: torch.Tensor,
    dvs: torch.Tensor,
    params: CropParameters,
    co2: torch.Tensor | float = 370.0,
) -> dict[str, torch.Tensor]:
    """Compute RUE and the RTMCO correction factor.

    Args:
        tmax: Daily maximum air temperature [°C], shape ``[B]``.
        tmin: Daily minimum air temperature [°C], shape ``[B]``.
        dvs: Development stage [-] (0–2), shape ``[B]``.
        params: Crop parameters; uses ``ruetb``, ``scale_factor_rue``,
            ``tmpftb``, ``tmnftb``, ``cotb``, ``day_temp_factor``.
        co2: Atmospheric CO₂ concentration [ppm], a scalar or
            shape broadcastable to ``[B]`` (default ``370``).
            Supplied by ``SiteParameters.co2`` in the model.

    Returns:
        Dict of ``[B]`` tensors:

        * ``rue`` [g MJ⁻¹] — DVS-dependent radiation use
          efficiency ``scale_factor_rue · RUETB(DVS)``.
        * ``rtmco`` [-] — Overall correction factor
          ``RTMP · RCO``.
        * ``rco`` [-] — CO₂ correction factor ``COTB(CO₂)``.
        * ``rtmp`` [-] — Temperature correction factor
          ``TMPFTB(DTEMP) · TMNFTB(TMIN)``.
        * ``dtemp`` [°C] — Effective daytime temperature
          ``TMAX − f · (TMAX − TMIN)``.
    """
    # DVS-dependent base RUE.
    rue = params.scale_factor_rue * interpolate(params.ruetb, dvs)

    # CO₂ correction.
    co2_t = torch.as_tensor(co2, dtype=tmax.dtype, device=tmax.device)
    co2_b = co2_t.expand_as(tmax) if co2_t.dim() == 0 else co2_t
    rco = interpolate(params.cotb, co2_b)

    # Effective daytime temperature; tuned via day_temp_factor.
    dtemp = tmax - params.day_temp_factor * (tmax - tmin)

    # Temperature reduction: daytime-temp × low-min-temp.
    rtmp = interpolate(params.tmpftb, dtemp) * interpolate(params.tmnftb, tmin)

    rtmco = rtmp * rco

    return {
        "rue": rue,
        "rtmco": rtmco,
        "rco": rco,
        "rtmp": rtmp,
        "dtemp": dtemp,
    }