diff --git a/r_app/80_calculate_kpis.R b/r_app/80_calculate_kpis.R
index a825434..65496ce 100644
--- a/r_app/80_calculate_kpis.R
+++ b/r_app/80_calculate_kpis.R
@@ -370,7 +370,7 @@ main <- function() {
     current_year <- as.numeric(format(end_date, "%G"))
     
     # Call with correct signature
-    kpi_results <- calculate_all_kpis(
+    kpi_results <- calculate_all_field_analysis_agronomic_support(
       field_boundaries_sf = field_boundaries_sf,
       current_week = current_week,
       current_year = current_year,
diff --git a/r_app/80_utils_agronomic_support.R b/r_app/80_utils_agronomic_support.R
index 421b35f..d649775 100644
--- a/r_app/80_utils_agronomic_support.R
+++ b/r_app/80_utils_agronomic_support.R
@@ -1,8 +1,8 @@
 # 80_UTILS_AGRONOMIC_SUPPORT.R
 # ============================================================================
-# SPECIFIC KPI UTILITIES (SCRIPT 80 - CLIENT TYPE: agronomic_support)
+# AURA-SPECIFIC KPI UTILITIES (SCRIPT 80 - CLIENT TYPE: agronomic_support)
 #
-# Contains all 6 KPI calculation functions and helpers:
+# Contains all 6 AURA KPI calculation functions and helpers:
 #   - Field uniformity KPI (CV-based, spatial autocorrelation)
 #   - Area change KPI (week-over-week CI changes)
 #   - TCH forecasted KPI (tonnage projections from harvest data)
@@ -12,7 +12,7 @@
 #   - KPI reporting (summary tables, field details, text interpretation)
 #   - KPI export (Excel, RDS, data export)
 #
-# Orchestrator: calculate_all_kpis()
+# Orchestrator: calculate_all_field_analysis_agronomic_support()
 # Dependencies: 00_common_utils.R (safe_log), sourced from common
 # Used by: 80_calculate_kpis.R (when client_type == "agronomic_support")
 # ============================================================================
@@ -24,6 +24,8 @@ library(tidyr)
 library(readxl)
 library(writexl)
 library(spdep)
+library(caret)
+library(CAST)
 
 # ============================================================================
 # SHARED HELPER FUNCTIONS (NOW IN 80_UTILS_COMMON.R)
@@ -65,7 +67,7 @@ prepare_predictions <- function(harvest_model, field_data, scenario = "optimisti
 }
 
 # ============================================================================
-# KPI CALCULATION FUNCTIONS (6 KPIS)
+# AURA KPI CALCULATION FUNCTIONS (6 KPIS)
 # ============================================================================
 
 #' KPI 1: Calculate field uniformity based on CV and spatial autocorrelation
@@ -75,36 +77,52 @@ prepare_predictions <- function(harvest_model, field_data, scenario = "optimisti
 #'
 #' @param ci_pixels_by_field List of CI pixel arrays for each field
 #' @param field_boundaries_sf SF object with field geometries
-#' @param ci_raster Raster object with CI values (for spatial autocorrelation)
+#' @param ci_band Raster band with CI values
 #'
 #' @return Data frame with field_idx, cv_value, morans_i, uniformity_score, interpretation
-calculate_field_uniformity_kpi <- function(ci_pixels_by_field, field_boundaries_sf, ci_raster = NULL) {
-  results_list <- list()
+calculate_field_uniformity_kpi <- function(ci_pixels_by_field, field_boundaries_sf, ci_band = NULL) {
+  result <- data.frame(
+    field_idx = integer(),
+    cv_value = numeric(),
+    morans_i = numeric(),
+    uniformity_score = numeric(),
+    interpretation = character(),
+    stringsAsFactors = FALSE
+  )
   
   for (field_idx in seq_len(nrow(field_boundaries_sf))) {
     ci_pixels <- ci_pixels_by_field[[field_idx]]
     
     if (is.null(ci_pixels) || length(ci_pixels) == 0) {
-      results_list[[length(results_list) + 1]] <- list(
+      result <- rbind(result, data.frame(
         field_idx = field_idx,
         cv_value = NA_real_,
         morans_i = NA_real_,
         uniformity_score = NA_real_,
-        interpretation = "No data"
-      )
+        interpretation = "No data",
+        stringsAsFactors = FALSE
+      ))
       next
     }
     
     cv_val <- calculate_cv(ci_pixels)
     
     morans_i <- NA_real_
-    if (!is.null(ci_raster)) {
-      morans_result <- calculate_spatial_autocorrelation(ci_raster, field_boundaries_sf[field_idx, ])
-      if (is.list(morans_result)) {
-        morans_i <- morans_result$morans_i
-      } else {
-        morans_i <- morans_result
-      }
+    if (!is.null(ci_band) && inherits(ci_band, "SpatRaster")) {
+      tryCatch({
+        # Get single field geometry
+        single_field <- field_boundaries_sf[field_idx, ]
+        morans_result <- calculate_spatial_autocorrelation(ci_band, single_field)
+        
+        if (is.list(morans_result)) {
+          morans_i <- morans_result$morans_i
+        } else {
+          morans_i <- morans_result
+        }
+      }, error = function(e) {
+        message(paste("  Warning: Spatial autocorrelation failed for field", field_idx, ":", e$message))
+        morans_i <<- NA_real_
+      })
     }
     
     # Normalize CV (0-1 scale, invert so lower CV = higher score)
@@ -135,18 +153,15 @@ calculate_field_uniformity_kpi <- function(ci_pixels_by_field, field_boundaries_
       interpretation <- "Very poor uniformity"
     }
     
-    results_list[[length(results_list) + 1]] <- list(
+    result <- rbind(result, data.frame(
       field_idx = field_idx,
       cv_value = cv_val,
       morans_i = morans_i,
       uniformity_score = round(uniformity_score, 3),
-      interpretation = interpretation
-    )
-  }
-  
-  # Convert accumulated list to data frame in a single operation
-  result <- do.call(rbind, lapply(results_list, as.data.frame))
-  
+      interpretation = interpretation,
+      stringsAsFactors = FALSE
+    ))
+  } 
   return(result)
 }
 
@@ -214,12 +229,19 @@ calculate_tch_forecasted_kpi <- function(field_statistics, harvesting_data = NUL
       next
     }
     
-    if (is.na(result$mean_ci[i])) {
-      result$tch_forecasted[i] <- NA_real_
-      result$tch_lower_bound[i] <- NA_real_
-      result$tch_upper_bound[i] <- NA_real_
-      result$confidence[i] <- "No data"
-    }
+    ci_val <- result$mean_ci[i]
+    
+    # Simple linear model
+    tch_est <- 50 + (ci_val * 10)
+    
+    # Confidence interval based on CI range
+    tch_lower <- tch_est * 0.85
+    tch_upper <- tch_est * 1.15
+    
+    result$tch_forecasted[i] <- round(tch_est, 2)
+    result$tch_lower_bound[i] <- round(tch_lower, 2)
+    result$tch_upper_bound[i] <- round(tch_upper, 2)
+    result$confidence[i] <- "Medium"
   }
   
   return(result)
@@ -338,190 +360,107 @@ calculate_weed_presence_kpi <- function(ci_pixels_by_field) {
   return(result)
 }
 
-# #' Calculate Gap Filling Score KPI (placeholder)
-# #' @param ci_raster Current week CI raster
-# #' @param field_boundaries Field boundaries
-# #' @return Data frame with field-level gap filling scores
-# calculate_gap_filling_kpi <- function(ci_raster, field_boundaries) {
-#   # Handle both sf and SpatVector inputs
-#   if (!inherits(field_boundaries, "SpatVector")) {
-#     field_boundaries_vect <- terra::vect(field_boundaries)
-#   } else {
-#     field_boundaries_vect <- field_boundaries
-#   }
+#' Calculate Gap Filling Score KPI (placeholder)
+#' @param ci_raster Current week CI raster
+#' @param field_boundaries Field boundaries
+#' @return List with summary data frame and field-level results data frame
+calculate_gap_filling_kpi <- function(ci_raster, field_boundaries) {
+  # Handle both sf and SpatVector inputs
+  if (!inherits(field_boundaries, "SpatVector")) {
+    field_boundaries_vect <- terra::vect(field_boundaries)
+  } else {
+    field_boundaries_vect <- field_boundaries
+  }
 
-#   results_list <- list()
+  field_results <- data.frame()
 
-#   # Ensure field_boundaries_vect is valid and matches field_boundaries dimensions
-#   n_fields_vect <- length(field_boundaries_vect)
-#   n_fields_sf <- nrow(field_boundaries)
-  
-#   if (n_fields_sf != n_fields_vect) {
-#     warning(paste("Field boundary mismatch: nrow(field_boundaries)=", n_fields_sf, "vs length(field_boundaries_vect)=", n_fields_vect, ". Using actual SpatVector length."))
-#   }
+  for (i in seq_len(nrow(field_boundaries))) {
+    field_name <- if ("field" %in% names(field_boundaries)) field_boundaries$field[i] else NA_character_
+    sub_field_name <- if ("sub_field" %in% names(field_boundaries)) field_boundaries$sub_field[i] else NA_character_
+    field_vect <- field_boundaries_vect[i]
 
-#   for (i in seq_len(n_fields_vect)) {
-#     field_vect <- field_boundaries_vect[i]
+    # Extract CI values using helper function
+    ci_values <- extract_ci_values(ci_raster, field_vect)
+    valid_values <- ci_values[!is.na(ci_values) & is.finite(ci_values)]
 
-#     # Extract CI values using helper function
-#     ci_values <- extract_ci_values(ci_raster, field_vect)
-#     valid_values <- ci_values[!is.na(ci_values) & is.finite(ci_values)]
+    if (length(valid_values) > 1) {
+      # Gap score using 2σ below median to detect outliers
+      median_ci <- median(valid_values)
+      sd_ci <- sd(valid_values)
+      outlier_threshold <- median_ci - (2 * sd_ci)
+      low_ci_pixels <- sum(valid_values < outlier_threshold)
+      total_pixels <- length(valid_values)
+      gap_score <- round((low_ci_pixels / total_pixels) * 100, 2)
 
-#     if (length(valid_values) > 1) {
-#       # Calculate % of valid (non-NA) values = gap filling success
-#       total_pixels <- length(ci_values)
-#       valid_pixels <- length(valid_values)
-#       gap_filling_success <- (valid_pixels / total_pixels) * 100
-#       na_percent <- ((total_pixels - valid_pixels) / total_pixels) * 100
+      # Classify gap severity
+      gap_level <- dplyr::case_when(
+        gap_score < 10 ~ "Minimal",
+        gap_score < 25 ~ "Moderate", 
+        TRUE ~ "Significant"
+      )
 
-#       results_list[[length(results_list) + 1]] <- list(
-#         field_idx = i,
-#         gap_filling_success = round(gap_filling_success, 2),
-#         na_percent_pre_interpolation = round(na_percent, 2),
-#         mean_ci = round(mean(valid_values), 2)
-#       )
-#     } else {
-#       # Not enough valid data
-#       results_list[[length(results_list) + 1]] <- list(
-#         field_idx = i,
-#         gap_filling_success = NA_real_,
-#         na_percent_pre_interpolation = NA_real_,
-#         mean_ci = NA_real_
-#       )
-#     }
-#   }
+      field_results <- rbind(field_results, data.frame(
+        field = field_name,
+        sub_field = sub_field_name,
+        gap_level = gap_level,
+        gap_score = gap_score,
+        mean_ci = mean(valid_values),
+        outlier_threshold = outlier_threshold
+      ))
+    } else {
+      # Not enough valid data, fill with NA row
+      field_results <- rbind(field_results, data.frame(
+        field = field_name,
+        sub_field = sub_field_name,
+        gap_level = NA_character_,
+        gap_score = NA_real_,
+        mean_ci = NA_real_,
+        outlier_threshold = NA_real_
+      ))
+    }
+  }
+  # Summarize results
+  gap_summary <- field_results %>%
+    dplyr::group_by(gap_level) %>%
+    dplyr::summarise(field_count = n(), .groups = 'drop') %>%
+    dplyr::mutate(percent = round((field_count / sum(field_count)) * 100, 1))
 
-  # Convert accumulated list to data frame in a single operation
-  field_results <- do.call(rbind, lapply(results_list, as.data.frame))
-  
-  return(field_results)
+  return(list(summary = gap_summary, field_results = field_results))
 }
 
 # ============================================================================
 # KPI ORCHESTRATOR AND REPORTING
 # ============================================================================
 
-#' Create summary tables for all 6 KPIs (AGGREGATED farm-level summaries)
+#' Create summary tables for all 6 KPIs
 #'
-#' @param all_kpis List containing results from all 6 KPI functions (per-field data)
+#' @param all_kpis List containing results from all 6 KPI functions
 #'
-#' @return List of summary data frames ready for reporting (farm-level aggregates)
+#' @return List of summary data frames ready for reporting
 create_summary_tables <- function(all_kpis) {
-  
-  # ==========================================
-  # 1. UNIFORMITY SUMMARY (count by interpretation)
-  # ==========================================
-  uniformity_summary <- all_kpis$uniformity %>%
-    group_by(interpretation) %>%
-    summarise(
-      field_count = n(),
-      avg_cv = mean(cv_value, na.rm = TRUE),
-      avg_morans_i = mean(morans_i, na.rm = TRUE),
-      .groups = 'drop'
-    ) %>%
-    rename(
-      Status = interpretation,
-      `Field Count` = field_count,
-      `Avg CV` = avg_cv,
-      `Avg Moran's I` = avg_morans_i
-    )
-  
-  # ==========================================
-  # 2. AREA CHANGE SUMMARY (improving/stable/declining counts)
-  # ==========================================
-  area_change_summary <- all_kpis$area_change %>%
-    group_by(interpretation) %>%
-    summarise(
-      field_count = n(),
-      avg_ci_change = mean(mean_ci_pct_change, na.rm = TRUE),
-      .groups = 'drop'
-    ) %>%
-    rename(
-      Status = interpretation,
-      `Field Count` = field_count,
-      `Avg CI Change %` = avg_ci_change
-    )
-  
-  # ==========================================
-  # 3. TCH FORECAST SUMMARY (yield statistics)
-  # ==========================================
-  tch_summary <- all_kpis$tch_forecasted %>%
-    summarise(
-      avg_tch = mean(tch_forecasted, na.rm = TRUE),
-      min_tch = min(tch_forecasted, na.rm = TRUE),
-      max_tch = max(tch_forecasted, na.rm = TRUE),
-      avg_ci = mean(mean_ci, na.rm = TRUE),
-      fields_with_data = sum(!is.na(tch_forecasted))
-    ) %>%
-    rename(
-      `Avg Forecast (t/ha)` = avg_tch,
-      `Min (t/ha)` = min_tch,
-      `Max (t/ha)` = max_tch,
-      `Avg CI` = avg_ci,
-      `Fields` = fields_with_data
-    )
-  
-  # ==========================================
-  # 4. GROWTH DECLINE SUMMARY (trend interpretation)
-  # ==========================================
-  growth_summary <- all_kpis$growth_decline %>%
-    group_by(trend_interpretation) %>%
-    summarise(
-      field_count = n(),
-      avg_trend = mean(four_week_trend, na.rm = TRUE),
-      .groups = 'drop'
-    ) %>%
-    rename(
-      Trend = trend_interpretation,
-      `Field Count` = field_count,
-      `Avg 4-Week Trend` = avg_trend
-    )
-  
-  # ==========================================
-  # 5. WEED PRESSURE SUMMARY (risk level counts)
-  # ==========================================
-  weed_summary <- all_kpis$weed_presence %>%
-    group_by(weed_pressure_risk) %>%
-    summarise(
-      field_count = n(),
-      avg_fragmentation = mean(fragmentation_index, na.rm = TRUE),
-      .groups = 'drop'
-    ) %>%
-    rename(
-      `Risk Level` = weed_pressure_risk,
-      `Field Count` = field_count,
-      `Avg Fragmentation` = avg_fragmentation
-    )
-  
-  # ==========================================
-  # 6. GAP FILLING SUMMARY
-  # ==========================================
-  gap_summary <- if (!is.null(all_kpis$gap_filling) && is.data.frame(all_kpis$gap_filling) && nrow(all_kpis$gap_filling) > 0) {
-    all_kpis$gap_filling %>%
-      summarise(
-        avg_gap_filling = mean(gap_filling_success, na.rm = TRUE),
-        avg_na_percent = mean(na_percent_pre_interpolation, na.rm = TRUE),
-        fields_with_data = n()
-      ) %>%
-      rename(
-        `Avg Gap Filling Success %` = avg_gap_filling,
-        `Avg NA % Pre-Interpolation` = avg_na_percent,
-        `Fields Analyzed` = fields_with_data
-      )
-  } else {
-    data.frame(`Avg Gap Filling Success %` = NA_real_, `Avg NA % Pre-Interpolation` = NA_real_, `Fields Analyzed` = 0, check.names = FALSE)
-  }
-  
-  # Return as list (each element is a farm-level summary table)
   kpi_summary <- list(
-    uniformity = uniformity_summary,
-    area_change = area_change_summary,
-    tch_forecast = tch_summary,
-    growth_decline = growth_summary,
-    weed_pressure = weed_summary,
-    gap_filling = gap_summary
+    uniformity = all_kpis$uniformity %>%
+      select(field_idx, cv_value, morans_i, uniformity_score, interpretation),
+    
+    area_change = all_kpis$area_change %>%
+      select(field_idx, mean_ci_pct_change, interpretation),
+    
+    tch_forecast = all_kpis$tch_forecasted %>%
+      select(field_idx, mean_ci, tch_forecasted, tch_lower_bound, tch_upper_bound, confidence),
+    
+    growth_decline = all_kpis$growth_decline %>%
+      select(field_idx, four_week_trend, trend_interpretation, decline_severity),
+    
+    weed_pressure = all_kpis$weed_presence %>%
+      select(field_idx, fragmentation_index, weed_pressure_risk),
+    
+    gap_filling = if (!is.null(all_kpis$gap_filling)) {
+      all_kpis$gap_filling %>%
+        select(field_idx, gap_score, gap_level, mean_ci)
+    } else {
+      NULL
+    }
   )
-  
   return(kpi_summary)
 }
 
@@ -531,7 +470,7 @@ create_summary_tables <- function(all_kpis) {
 #' @param all_kpis List with all KPI results
 #' @param field_boundaries_sf SF object with field boundaries
 #'
-#' @return Data frame with one row per field, all KPI columns (renamed for reporting compatibility)
+#' @return Data frame with one row per field, all KPI columns
 create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
   result <- field_df %>%
     left_join(
@@ -543,7 +482,7 @@ create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
       by = c("field_idx")
     ) %>%
     left_join(
-      all_kpis$tch_forecasted %>% select(field_idx, tch_forecasted, mean_ci),
+      all_kpis$tch_forecasted %>% select(field_idx, tch_forecasted),
       by = c("field_idx")
     ) %>%
     left_join(
@@ -553,26 +492,7 @@ create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
     left_join(
       all_kpis$weed_presence %>% select(field_idx, weed_pressure_risk),
       by = c("field_idx")
-    ) %>%
-    # Rename columns to match reporting script expectations
-    rename(
-      Field = field_name,
-      `Growth Uniformity` = uniformity_interpretation,
-      `Yield Forecast (t/ha)` = tch_forecasted,
-      `Decline Risk` = decline_severity,
-      `Weed Risk` = weed_pressure_risk,
-      `CI Change %` = mean_ci_pct_change,
-      `Mean CI` = mean_ci,
-      `CV Value` = cv_value
-    ) %>%
-    # Add placeholder columns expected by reporting script (will be populated from other sources)
-    mutate(
-      `Field Size (ha)` = NA_real_,
-      `Gap Score` = NA_real_
-    ) %>%
-    select(field_idx, Field, `Field Size (ha)`, `Growth Uniformity`, `Yield Forecast (t/ha)`, 
-           `Gap Score`, `Decline Risk`, `Weed Risk`, `CI Change %`, `Mean CI`, `CV Value`)
-  
+    ) 
   return(result)
 }
 
@@ -583,7 +503,7 @@ create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
 #' @return Character string with formatted KPI summary text
 create_field_kpi_text <- function(all_kpis) {
   text_parts <- c(
-    "## KPI ANALYSIS SUMMARY\n",
+    "## AURA KPI ANALYSIS SUMMARY\n",
     "### Field Uniformity\n",
     paste(all_kpis$uniformity$interpretation, collapse = "; "), "\n",
     "### Growth Trends\n",
@@ -597,69 +517,21 @@ create_field_kpi_text <- function(all_kpis) {
 
 #' Export detailed KPI data to Excel/RDS
 #'
-#' @param all_kpis List with all KPI results (per-field data)
-#' @param kpi_summary List with summary tables (farm-level aggregates)
-#' @param project_dir Project name (for filename)
+#' @param all_kpis List with all KPI results
+#' @param kpi_summary List with summary tables
 #' @param output_dir Directory for output files
 #' @param week Week number
 #' @param year Year
-#' @param field_boundaries_sf SF object with field boundaries (optional, for field_details_table)
-#'
 #' @return List of output file paths
-export_kpi_data <- function(all_kpis, kpi_summary, project_dir, output_dir, week, year, field_boundaries_sf = NULL) {
+export_kpi_data <- function(all_kpis, kpi_summary, output_dir, week, year, project_dir) {
   # Ensure output directory exists
   if (!dir.exists(output_dir)) {
     dir.create(output_dir, recursive = TRUE)
   }
   
-  # Create unified field details table if field_boundaries_sf is provided
-  field_details_table <- NULL
-  if (!is.null(field_boundaries_sf)) {
-    tryCatch({
-      # Create a basic field_df from the boundaries
-      # Robust field name extraction with multiple fallbacks
-      field_name <- NA_character_
-      
-      # Check for 'name' column in the data.frame
-      if ("name" %in% names(field_boundaries_sf)) {
-        field_name <- field_boundaries_sf$name
-      } else if ("properties" %in% names(field_boundaries_sf)) {
-        # Extract from properties column (may be a list-column)
-        props <- field_boundaries_sf$properties
-        if (is.list(props) && length(props) > 0 && "name" %in% names(props[[1]])) {
-          field_name <- sapply(props, function(x) ifelse(is.null(x$name), NA_character_, x$name))
-        } else if (!is.list(props)) {
-          # Try direct access if properties is a simple column
-          field_name <- props
-        }
-      }
-      
-      # Ensure field_name is a character vector of appropriate length
-      if (length(field_name) != nrow(field_boundaries_sf)) {
-        field_name <- rep(NA_character_, nrow(field_boundaries_sf))
-      }
-      
-      # Replace only NA elements with fallback names, keeping valid names intact
-      na_indices <- which(is.na(field_name))
-      if (length(na_indices) > 0) {
-        field_name[na_indices] <- paste0("Field_", na_indices)
-      }
-      
-      field_df <- data.frame(
-        field_idx = 1:nrow(field_boundaries_sf),
-        field_name = field_name,
-        stringsAsFactors = FALSE
-      )
-      
-      field_details_table <- create_field_detail_table(field_df, all_kpis, field_boundaries_sf)
-      message(paste("✓ Field details table created with", nrow(field_details_table), "fields"))
-    }, error = function(e) {
-      message(paste("WARNING: Could not create field_details_table:", e$message))
-    })
-  }
-  
-  # Export all KPI tables to a single Excel file - use project_dir"
-  excel_file <- file.path(output_dir, paste0(project_dir, "_kpi_summary_tables_week", sprintf("%02d_%d", week, year), ".xlsx"))
+  # Export all KPI tables to a single Excel file
+  excel_file <- paste0(project_dir, "_field_analysis_week", sprintf("%02d_%d", week, year), ".xlsx")
+  excel_path <- file.path(output_dir, excel_file)
   
   sheets <- list(
     "Uniformity" = as.data.frame(kpi_summary$uniformity),
@@ -670,40 +542,38 @@ export_kpi_data <- function(all_kpis, kpi_summary, project_dir, output_dir, week
     "Gap_Filling" = as.data.frame(kpi_summary$gap_filling)
   )
   
-  write_xlsx(sheets, excel_file)
-  message(paste("✓ KPI data exported to:", excel_file))
+  write_xlsx(sheets, excel_path)
+  message(paste("✓ AURA KPI data exported to:", excel_path))
   
-  # Export to RDS for programmatic access (CRITICAL: Both per-field AND summary tables)
-  # The reporting script expects: summary_tables (list of 6 summary tables)
-  # We also provide: all_kpis (per-field data) and field_details (unified field view)
-  rds_file <- file.path(output_dir, paste0(project_dir, "_kpi_summary_tables_week", sprintf("%02d_%d", week, year), ".rds"))
-  
-  # Create the export structure that reporting scripts expect
-  export_data <- list(
-    summary_tables = kpi_summary,        # Farm-level aggregates (6 KPI summaries)
-    all_kpis = all_kpis,                 # Per-field data (6 KPI per-field tables)
-    field_details = field_details_table  # Unified field-level detail table
+  # Also export to RDS for programmatic access
+  rds_file <- paste0(project_dir, "_field_analysis_week", sprintf("%02d_%d", week, year), ".rds")
+  rds_path <- file.path(output_dir, rds_file)
+
+    # Save complete structure including metadata
+  kpi_export_data <- list(
+    kpis = all_kpis,
+    summary_tables = kpi_summary,
+    metadata = list(
+      week = week,
+      year = year,
+      project = project_dir,
+      created_at = Sys.time()
+    )
   )
   
-  saveRDS(export_data, rds_file)
-  message(paste("✓ KPI RDS exported to:", rds_file))
-  message("  Structure: list($summary_tables, $all_kpis, $field_details)")
+  saveRDS(kpi_export_data, rds_path)
+  message(paste("✓ AURA KPI RDS exported to:", rds_path))
   
-  # Return including field_details for orchestrator to capture
-  return(list(
-    excel = excel_file,
-    rds = rds_file,
-    field_details = field_details_table
-  ))
+  return(list(excel = excel_path, rds = rds_path))
 }
 
 # ============================================================================
 # ORCHESTRATOR FUNCTION
 # ============================================================================
 
-#' Calculate all 6 KPIs
+#' Calculate all 6 AURA KPIs
 #'
-#' Main entry point for KPI calculation.
+#' Main entry point for AURA KPI calculation.
 #' This function orchestrates the 6 KPI calculations and returns all results.
 #'
 #' @param field_boundaries_sf SF object with field geometries
@@ -714,7 +584,6 @@ export_kpi_data <- function(all_kpis, kpi_summary, project_dir, output_dir, week
 #' @param ci_rds_path Path to combined CI RDS file
 #' @param harvesting_data Data frame with harvest data (optional)
 #' @param output_dir Directory for KPI exports
-#' @param project_dir Project name (for filename in exports)
 #'
 #' @return List with results from all 6 KPI functions
 #'
@@ -722,11 +591,11 @@ export_kpi_data <- function(all_kpis, kpi_summary, project_dir, output_dir, week
 #' This function:
 #' 1. Loads current week mosaic and extracts field statistics
 #' 2. (Optionally) loads previous week mosaic for comparison metrics
-#' 3. Calculates all 6 KPIs
+#' 3. Calculates all 6 AURA KPIs
 #' 4. Creates summary tables
 #' 5. Exports results to Excel/RDS
 #'
-calculate_all_kpis <- function(
+calculate_all_field_analysis_agronomic_support <- function(
     field_boundaries_sf,
     current_week,
     current_year,
@@ -738,7 +607,7 @@ calculate_all_kpis <- function(
     project_dir = NULL
 ) {
   
-  message("\n============ KPI CALCULATION (6 KPIs) ============")
+  message("\n============ AURA KPI CALCULATION (6 KPIs) ============")
   
   # Load current week mosaic
   message("Loading current week mosaic...")
@@ -751,7 +620,12 @@ calculate_all_kpis <- function(
   # Extract field statistics
   message("Extracting field statistics from current mosaic...")
   current_stats <- extract_field_statistics_from_ci(current_mosaic, field_boundaries_sf)
-  ci_pixels_by_field <- extract_ci_values(current_mosaic, field_boundaries_sf)
+  #Extract CI pixels for each field individually
+  ci_pixels_by_field <- list()
+  for (i in seq_len(nrow(field_boundaries_sf))) {
+    field_vect <- terra::vect(field_boundaries_sf[i, ])
+    ci_pixels_by_field[[i]] <- extract_ci_values(current_mosaic, field_vect)
+  }
   
   # Load previous week mosaic (if available)
   previous_stats <- NULL
@@ -787,14 +661,19 @@ calculate_all_kpis <- function(
   
   message("Calculating KPI 4: Growth Decline...")
   growth_decline_kpi <- calculate_growth_decline_kpi(
-    ci_pixels_by_field  # Would need historical data for real trend
+    list(ci_pixels_by_field)  # Would need historical data for real trend
   )
   
   message("Calculating KPI 5: Weed Presence...")
   weed_kpi <- calculate_weed_presence_kpi(ci_pixels_by_field)
   
   message("Calculating KPI 6: Gap Filling...")
-  gap_filling_kpi <- calculate_gap_filling_kpi(current_mosaic, field_boundaries_sf)
+  gap_filling_result <- calculate_gap_filling_kpi(current_mosaic, field_boundaries_sf)
+
+  # Add field_idx to gap filling results
+  gap_filling_kpi <- gap_filling_result$field_results %>%
+  mutate(field_idx = row_number()) %>%
+  select(field_idx, gap_score, gap_level, mean_ci, outlier_threshold)
   
   # Compile results
   all_kpis <- list(
@@ -807,21 +686,21 @@ calculate_all_kpis <- function(
   )
   
   # Create summary tables
+  message("\nCreating summary tables...")
   kpi_summary <- create_summary_tables(all_kpis)
   
-  # Export - pass project_dir for proper filename and field_boundaries_sf for field details table
-  if (is.null(project_dir)) {
-    project_dir <- "AURA"  # Fallback if not provided
-  }
-  export_result <- export_kpi_data(all_kpis, kpi_summary, project_dir, output_dir, current_week, current_year, field_boundaries_sf)
+  # Export
+  message("\nExporting KPI data...")
+  export_paths <- export_kpi_data(all_kpis, kpi_summary, output_dir, current_week, current_year, project_dir)
   
-  message(paste("\n✓", project_dir, "KPI calculation complete. Week", current_week, current_year, "\n"))
+  message(paste("\n✓ AURA KPI calculation complete. Week", current_week, current_year))
   
-  # Return combined structure (for integration with 80_calculate_kpis.R)
-  # Capture field_details from export_result to propagate it out
   return(list(
-    all_kpis = all_kpis,
+    kpis = all_kpis,
     summary_tables = kpi_summary,
-    field_details = export_result$field_details  # Propagate field_details from export_kpi_data
-  ))
+    metadata = list(
+      week = current_week,
+      year = current_year,
+      project = project_dir,
+      export_paths = export_paths) ))
 }