fix score computation

Author: jnnsbrr

inseeds/components/farming/ca_behaviour.py

@@ -393,6 +393,65 @@ def pft_prices(self):
         """
         return self._fao("_pft_prices")
 
+    # -------------------------------------------------------------------------
+    # Country-Level Reference Values (for performance score normalization)
+    # -------------------------------------------------------------------------
+
+    # Default reference values for LEVELS (overwritten each year in update())
+    reference_yield_level: float = 1.0
+    reference_soilc_level: float = 1.0
+    reference_moisture_level: float = 1.0
+
+    # Default reference values for TRENDS (overwritten each year in update())
+    # These represent "meaningful" trends for normalization, computed as
+    # the standard deviation of trends across farmers (not mean, since mean
+    # trend is often near zero and would cause division issues)
+    reference_yield_trend: float = 1.0
+    reference_soilc_trend: float = 1.0
+    reference_moisture_trend: float = 1.0
+
+    def compute_reference_values(self):
+        """Compute country-mean reference values for performance normalization.
+
+        Called once per year in update() BEFORE farmers are updated.
+        This ensures all farmers see consistent reference values and avoids
+        repeated computation during performance comparisons.
+
+        Reference values for levels use country means.
+        Reference values for trends use standard deviation (spread of trends),
+        which provides a natural scale for "what counts as meaningful change".
+        """
+        yields, soilcs, moistures = [], [], []
+        yield_trends, soilc_trends, moisture_trends = [], [], []
+
+        for farmer in self.farmers:
+            if hasattr(farmer, "cropyield"):
+                yields.append(farmer.cropyield)
+            if hasattr(farmer, "soilc"):
+                soilcs.append(farmer.soilc)
+            if hasattr(farmer, "root_moisture"):
+                moistures.append(farmer.root_moisture)
+
+            # Collect trends from trackers
+            tracker = farmer.behaviour.performance_tracker
+            if tracker is not None:
+                yield_trends.append(tracker.yield_trend)
+                soilc_trends.append(tracker.soilc_trend)
+                moisture_trends.append(tracker.moisture_trend)
+
+        # Level references: country mean (with fallback to 1.0)
+        self.reference_yield_level = float(np.mean(yields)) if yields else 1.0
+        self.reference_soilc_level = float(np.mean(soilcs)) if soilcs else 1.0
+        self.reference_moisture_level = float(np.mean(moistures)) if moistures else 1.0
+
+        # Trend references: standard deviation of trends (what counts as meaningful)
+        # Use std rather than mean because mean trend can be ~0 which breaks normalization
+        # The std represents "typical variation in trends" - a natural scale
+        # Minimum of 0.1 to avoid extreme values when all trends are identical
+        self.reference_yield_trend = max(0.1, float(np.std(yield_trends))) if yield_trends else 1.0
+        self.reference_soilc_trend = max(0.1, float(np.std(soilc_trends))) if soilc_trends else 1.0
+        self.reference_moisture_trend = max(0.1, float(np.std(moisture_trends))) if moisture_trends else 1.0
+
     # -------------------------------------------------------------------------
     # Country-Level Statistics (for non-local spreading)
     # -------------------------------------------------------------------------
@@ -459,6 +518,10 @@ def update(self, t):
         Computes country-level statistics BEFORE updating farmers to ensure
         all farmers see the same country-level data for this timestep.
         """
+        # Compute reference values for performance normalization
+        # Must run BEFORE farmers update so they see consistent references
+        self.compute_reference_values()
+
         # Compute country-level statistics for non-local spreading
         # Must run BEFORE farmers update so they see current country stats
         self.compute_management_performance(t)

inseeds/components/farming/ca_country.py

@@ -440,12 +440,12 @@ def indicate_cover_crop_type(self):
         # Get environmental conditions from cell
         runoff = self.cell_runoff
         leaching_val = self.cell_leaching
-        # fertilizer_val = self.cell_fertilizer
+        fertilizer_val = self.cell_fertilizer
 
         # Get thresholds from config
         cc = self.model.config.coupled_config.practice_dimensions.cover_crop
         leaching_limit = cc.leaching_high
-        # fertilizer_limit = cc.fertilizer_high
+        fertilizer_limit = cc.fertilizer_high
 
         # Calculate leaching rate (normalized by runoff)
         leaching = leaching_val *1e3 / runoff if runoff > 0 else 0
@@ -455,7 +455,7 @@ def indicate_cover_crop_type(self):
         # -----------------------------------------------------------------
         # High leaching: soil has excess N that's being lost
         # → Use non-legume catch crop to capture nutrients
-        if leaching >= leaching_limit: # and fertilizer_val >= fertilizer_limit:
+        if leaching >= leaching_limit or fertilizer_val >= fertilizer_limit:
             return 1  # Non-legume
 
         # Otherwise: soil may be N-limited
@@ -903,6 +903,9 @@ def update(self, t):
         if self.capital < self.min_capital:
             self.behaviour.transition_blocker = BLOCKER_CAPITAL_SURVIVAL
             self.behaviour.transition_driver = DRIVER_NONE
+            # Still decrement observation counter so farmer can evaluate
+            # promptly when they recover from survival mode (avoids limbo)
+            self.behaviour.decrement_observation_years()
             return
 
         # -----------------------------------------------------------------