diff --git a/r_app/80_utils_agronomic_support.R b/r_app/80_utils_agronomic_support.R
index b874ee9..8e55893 100644
--- a/r_app/80_utils_agronomic_support.R
+++ b/r_app/80_utils_agronomic_support.R
@@ -207,48 +207,57 @@ calculate_area_change_kpi <- function(current_stats, previous_stats) {
     stringsAsFactors = FALSE
   )
   
+  # Handle case where previous stats is NULL or empty
   if (is.null(previous_stats) || nrow(previous_stats) == 0) {
     result$interpretation <- "No previous data"
     return(result)
   }
   
   # Match fields between current and previous stats
+  # Handle both naming conventions (Field_id vs field_idx)
+  if ("Field_id" %in% names(current_stats)) {
+    current_field_col <- "Field_id"
+    prev_field_col <- "Field_id"
+    ci_col <- "Mean_CI"
+  } else {
+    current_field_col <- "field_idx"
+    prev_field_col <- "field_idx"
+    ci_col <- "mean_ci"
+  }
+  
+  # Create lookup for previous stats
+  prev_lookup <- setNames(
+    previous_stats[[ci_col]],
+    previous_stats[[prev_field_col]]
+  )
+  
+  # Calculate percentage change for each field
   for (i in seq_len(nrow(current_stats))) {
-    field_id <- current_stats$Field_id[i]
+    current_field_id <- current_stats[[current_field_col]][i]
+    current_ci <- current_stats[[ci_col]][i]
     
-    # Find matching field in previous stats
-    prev_idx <- which(previous_stats$Field_id == field_id)
+    # Find matching previous CI value
+    prev_ci <- prev_lookup[[as.character(current_field_id)]]
     
-    if (length(prev_idx) == 0) {
-      result$interpretation[i] <- "No previous data"
-      next
-    }
-    
-    prev_idx <- prev_idx[1]  # Take first match
-    
-    current_ci <- current_stats$Mean_CI[i]
-    previous_ci <- previous_stats$Mean_CI[prev_idx]
-    
-    if (is.na(current_ci) || is.na(previous_ci) || previous_ci == 0) {
-      result$interpretation[i] <- "No previous data"
-      next
-    }
-    
-    # Calculate percentage change
-    pct_change <- ((current_ci - previous_ci) / previous_ci) * 100
-    result$mean_ci_pct_change[i] <- round(pct_change, 2)
-    
-    # Add interpretation
-    if (pct_change > 15) {
-      result$interpretation[i] <- "Rapid growth"
-    } else if (pct_change > 5) {
-      result$interpretation[i] <- "Positive growth"
-    } else if (pct_change > -5) {
-      result$interpretation[i] <- "Stable"
-    } else if (pct_change > -15) {
-      result$interpretation[i] <- "Declining"
+    if (!is.null(prev_ci) && !is.na(prev_ci) && !is.na(current_ci) && prev_ci > 0) {
+      # Calculate percentage change
+      pct_change <- ((current_ci - prev_ci) / prev_ci) * 100
+      result$mean_ci_pct_change[i] <- round(pct_change, 2)
+      
+      # Add interpretation
+      if (pct_change > 15) {
+        result$interpretation[i] <- "Rapid growth"
+      } else if (pct_change > 5) {
+        result$interpretation[i] <- "Positive growth"
+      } else if (pct_change > -5) {
+        result$interpretation[i] <- "Stable"
+      } else if (pct_change > -15) {
+        result$interpretation[i] <- "Declining"
+      } else {
+        result$interpretation[i] <- "Rapid decline"
+      }
     } else {
-      result$interpretation[i] <- "Rapid decline"
+      result$interpretation[i] <- "No previous data"
     }
   }