Add plant reproductive tissue and gpp topslice integration.

tests/models/plants/test_plants_model.py

@@ -187,6 +187,11 @@ def test_PlantsModel_allocate_gpp(fxt_plants_model, fixture_core_components):
         # Ensure that leaf and root turnover exist and are > 0
         assert fxt_plants_model.data["leaf_turnover"][cell_id] > 0
         assert fxt_plants_model.data["root_turnover"][cell_id] > 0
+        assert fxt_plants_model.data["plant_reproductive_tissue_turnover"][cell_id] > 0
+        assert fxt_plants_model.data["propagule_c_mass"][cell_id] > 0
+        assert fxt_plants_model.data["non_propagule_c_mass"][cell_id] > 0
+        assert fxt_plants_model.data["root_carbohydrate_exudation"][cell_id] > 0
+        assert fxt_plants_model.data["plant_symbiote_carbon_supply"][cell_id] > 0
 
 
 def test_PlantsModel_update(
@@ -228,9 +233,6 @@ def test_PlantsModel_calculate_turnover(fxt_plants_model, fixture_config):
     consts = fxt_plants_model.model_constants
 
     # Check that all expected variables are generated and have the correct value
-    assert np.allclose(
-        fxt_plants_model.data["plant_reproductive_tissue_turnover"], 0.003
-    )
     assert np.allclose(fxt_plants_model.data["stem_lignin"], consts.stem_lignin)
     assert np.allclose(
         fxt_plants_model.data["senesced_leaf_lignin"], consts.senesced_leaf_lignin

virtual_ecosystem/models/plants/constants.py

@@ -59,3 +59,9 @@ class PlantsConsts(ConstantsDataclass):
 
     root_turnover_c_p_ratio: float = 656.7
     """Carbon to Phosphorous ratio of root turnover."""
+
+    root_exudates: float = 0.5
+    """Fraction of GPP topslice allocated to root exudates."""
+
+    propagules_mass: float = 0.5
+    """Fraction of reprodutive tissue allocated to propagules."""

virtual_ecosystem/models/plants/plants_model.py

@@ -605,6 +605,17 @@ def allocate_gpp(self) -> None:
         # Reset turnover to 0 as turnover from previous steps should have been allocated
         self.data["leaf_turnover"] = xr.full_like(self.data["elevation"], 0)
         self.data["root_turnover"] = xr.full_like(self.data["elevation"], 0)
+        self.data["plant_reproductive_tissue_turnover"] = xr.full_like(
+            self.data["elevation"], 0
+        )
+        self.data["propagule_c_mass"] = xr.full_like(self.data["elevation"], 0)
+        self.data["non_propagule_c_mass"] = xr.full_like(self.data["elevation"], 0)
+        self.data["root_carbohydrate_exudation"] = xr.full_like(
+            self.data["elevation"], 0
+        )
+        self.data["plant_symbiote_carbon_supply"] = xr.full_like(
+            self.data["elevation"], 0
+        )
 
         # Loop over each grid cell
         for cell_id in self.communities.keys():
@@ -622,12 +633,38 @@ def allocate_gpp(self) -> None:
             # TODO: dimension mismatch (1d vs 2d array) - check in pyrealm
             cohorts.dbh_values = cohorts.dbh_values + cohort_allocation.delta_dbh
 
-            # Calculate total turnover from all cohorts in a grid cell
+            # Sum of turnover from all cohorts in a grid cell
+            # TODO: Pyrealm provides annual turnover values. Divide by 12 to get monthly
+            #       turnover values is naive and will overestimate turnover. This should
+            #       be updated eventually to a more sophisticated approach.
             self.data["leaf_turnover"][cell_id] = np.sum(
-                cohort_allocation.foliage_turnover
+                cohort_allocation.foliage_turnover / 12
             )
             self.data["root_turnover"][cell_id] = np.sum(
-                cohort_allocation.fine_root_turnover
+                cohort_allocation.fine_root_turnover / 12
+            )
+            self.data["plant_reproductive_tissue_turnover"][cell_id] = np.sum(
+                cohort_allocation.reproductive_tissue_turnover / 12
+            )
+
+            # Reproductive tissue mass allocation to fruit
+            self.data["propagule_c_mass"][cell_id] = np.sum(
+                community.stem_allometry.reproductive_tissue_mass
+                * self.model_constants.propagules_mass
+            )
+            self.data["non_propagule_c_mass"][cell_id] = np.sum(
+                community.stem_allometry.reproductive_tissue_mass
+                * (1 - self.model_constants.propagules_mass)
+            )
+
+            # Allocate the topsliced GPP to root exudates with remainder as active
+            # nutrient pathways
+            self.data["root_carbohydrate_exudation"][cell_id] = np.sum(
+                cohort_allocation.gpp_topslice * self.model_constants.root_exudates
+            )
+            self.data["plant_symbiote_carbon_supply"][cell_id] = np.sum(
+                cohort_allocation.gpp_topslice
+                * (1 - self.model_constants.root_exudates)
             )
 
             # Update community allometry with new dbh values
@@ -681,10 +718,6 @@ def calculate_turnover(self) -> None:
             the variables it returns.
         """
 
-        self.data["plant_reproductive_tissue_turnover"] = xr.full_like(
-            self.data["elevation"], 0.003
-        )
-
         # Lignin concentrations
         self.data["stem_lignin"] = xr.full_like(
             self.data["elevation"], self.model_constants.stem_lignin
@@ -733,9 +766,6 @@ def calculate_turnover(self) -> None:
         self.data["nitrogen_fixation_carbon_supply"] = xr.full_like(
             self.data["elevation"], 0.01
         )
-        self.data["root_carbohydrate_exudation"] = xr.full_like(
-            self.data["elevation"], 0.025
-        )
 
     def calculate_nutrient_uptake(self) -> None:
         """Calculate uptake of soil nutrients by the plant community.