diff --git a/r_app/80_calculate_kpis.R b/r_app/80_calculate_kpis.R
index f81a20c..c0f5a2b 100644
--- a/r_app/80_calculate_kpis.R
+++ b/r_app/80_calculate_kpis.R
@@ -29,8 +29,8 @@
 #   - year: ISO year (numeric, default current year)
 #
 # CLIENT TYPES:
-#   - cane_supply (ANGATA): Yes - uses 80_utils_cane_supply.R (placeholder)
-#   - agronomic_support (AURA): Yes - uses 80_utils_agronomic_support.R (6 KPI funcs)
+#   - cane_supply: Yes - uses 80_utils_cane_supply.R (placeholder)
+#   - agronomic_support: Yes - uses 80_utils_agronomic_support.R (6 KPI funcs)
 #
 # DEPENDENCIES:
 #   - Packages: terra, sf, tidyverse, lubridate, writexl, spdep
@@ -327,17 +327,17 @@ main <- function() {
   # ============================================
   
   if (client_config$script_90_compatible && "kpi_summary_tables" %in% client_config$outputs) {
-    # AURA WORKFLOW: Run 6 farm-level KPIs for Script 90 compatibility
+    #  WORKFLOW: Run 6 farm-level KPIs for Script 90 compatibility
     message("\n", strrep("=", 70))
-    message("AURA WORKFLOW: CALCULATING 6 FARM-LEVEL KPIs (Script 90 compatible)")
+    message("WORKFLOW: CALCULATING 6 FARM-LEVEL KPIs (Script 90 compatible)")
     message(strrep("=", 70))
     
     # Prepare inputs for KPI calculation (already created by setup_project_directories)
     reports_dir_kpi <- setup$kpi_reports_dir
     cumulative_CI_vals_dir <- setup$cumulative_CI_vals_dir
     
-    # Load field boundaries for AURA workflow (use data_dir from setup)
-    message("\nLoading field boundaries for AURA KPI calculation...")
+    # Load field boundaries for workflow (use data_dir from setup)
+    message("\nLoading field boundaries for KPI calculation...")
     tryCatch({
       boundaries_result <- load_field_boundaries(setup$data_dir)
       
@@ -368,17 +368,18 @@ main <- function() {
     
     # Call with correct signature
     kpi_results <- calculate_all_kpis(
-      report_date = end_date,
-      output_dir = reports_dir_kpi,
       field_boundaries_sf = field_boundaries_sf,
+      current_week = current_week,
+      current_year = current_year,
+      current_mosaic_dir = setup$weekly_mosaic_dir,
+      previous_mosaic_dir = NULL,
+      ci_rds_path = NULL,
       harvesting_data = harvesting_data,
-      cumulative_CI_vals_dir = cumulative_CI_vals_dir,
-      weekly_CI_mosaic = setup$weekly_mosaic_dir,
-      reports_dir = reports_dir_kpi,
+      output_dir = reports_dir_kpi,
       project_dir = project_dir
     )
     
-    cat("\n=== AURA KPI CALCULATION COMPLETE ===\n")
+    cat("\n===  KPI CALCULATION COMPLETE ===\n")
     cat("Summary tables saved for Script 90 integration\n")
     cat("Output directory:", reports_dir_kpi, "\n\n")
     
diff --git a/r_app/80_utils_agronomic_support.R b/r_app/80_utils_agronomic_support.R
index ecc97a7..c4f60f9 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
 # ============================================================================
-# AURA-SPECIFIC KPI UTILITIES (SCRIPT 80 - CLIENT TYPE: agronomic_support)
+# SPECIFIC KPI UTILITIES (SCRIPT 80 - CLIENT TYPE: agronomic_support)
 #
-# Contains all 6 AURA KPI calculation functions and helpers:
+# Contains all 6 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)
@@ -67,7 +67,7 @@ prepare_predictions <- function(harvest_model, field_data, scenario = "optimisti
 }
 
 # ============================================================================
-# AURA KPI CALCULATION FUNCTIONS (6 KPIS)
+# KPI CALCULATION FUNCTIONS (6 KPIS)
 # ============================================================================
 
 #' KPI 1: Calculate field uniformity based on CV and spatial autocorrelation
@@ -77,10 +77,10 @@ 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_band Raster band with CI values
+#' @param ci_raster Raster object with CI values (for spatial autocorrelation)
 #'
 #' @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_band = NULL) {
+calculate_field_uniformity_kpi <- function(ci_pixels_by_field, field_boundaries_sf, ci_raster = NULL) {
   result <- data.frame(
     field_idx = integer(),
     cv_value = numeric(),
@@ -108,8 +108,8 @@ calculate_field_uniformity_kpi <- function(ci_pixels_by_field, field_boundaries_
     cv_val <- calculate_cv(ci_pixels)
     
     morans_i <- NA_real_
-    if (!is.null(ci_band)) {
-      morans_result <- calculate_spatial_autocorrelation(ci_pixels, field_boundaries_sf[field_idx, ])
+    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 {
@@ -356,7 +356,7 @@ calculate_weed_presence_kpi <- function(ci_pixels_by_field) {
 #' 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
+#' @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")) {
@@ -365,11 +365,15 @@ calculate_gap_filling_kpi <- function(ci_raster, field_boundaries) {
     field_boundaries_vect <- field_boundaries
   }
 
-  field_results <- data.frame()
+  field_results <- data.frame(
+    field_idx = integer(),
+    gap_filling_success = numeric(),
+    na_percent_pre_interpolation = numeric(),
+    mean_ci = numeric(),
+    stringsAsFactors = FALSE
+  )
 
   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]
 
     # Extract CI values using helper function
@@ -377,83 +381,157 @@ calculate_gap_filling_kpi <- function(ci_raster, field_boundaries) {
     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 <- (low_ci_pixels / total_pixels) * 100
-
-      # Classify gap severity
-      gap_level <- dplyr::case_when(
-        gap_score < 10 ~ "Minimal",
-        gap_score < 25 ~ "Moderate", 
-        TRUE ~ "Significant"
-      )
+      # 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
 
       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
+        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),
+        stringsAsFactors = FALSE
       ))
     } else {
-      # Not enough valid data, fill with NA row
+      # Not enough valid data
       field_results <- rbind(field_results, data.frame(
-        field = field_name,
-        sub_field = sub_field_name,
-        gap_level = NA_character_,
-        gap_score = NA_real_,
+        field_idx = i,
+        gap_filling_success = NA_real_,
+        na_percent_pre_interpolation = NA_real_,
         mean_ci = NA_real_,
-        outlier_threshold = NA_real_
+        stringsAsFactors = FALSE
       ))
     }
   }
 
-  # 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))
-
-  return(list(summary = gap_summary, field_results = field_results))
+  return(field_results)
 }
 
 # ============================================================================
 # KPI ORCHESTRATOR AND REPORTING
 # ============================================================================
 
-#' Create summary tables for all 6 KPIs
+#' Create summary tables for all 6 KPIs (AGGREGATED farm-level summaries)
 #'
-#' @param all_kpis List containing results from all 6 KPI functions
+#' @param all_kpis List containing results from all 6 KPI functions (per-field data)
 #'
-#' @return List of summary data frames ready for reporting
+#' @return List of summary data frames ready for reporting (farm-level aggregates)
 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$gap_filling %>%
+      summarise(
+        avg_gap_filling = mean(gap_filling_success, na.rm = TRUE),
+        avg_na_percent = mean(na_percent_pre_interpolation, na.rm = TRUE),
+        fields_with_data = n()
+      ) %>%
+      rename(
+        `Avg Gap Filling Success %` = avg_gap_filling,
+        `Avg NA % Pre-Interpolation` = avg_na_percent,
+        `Fields Analyzed` = fields_with_data
+      )
+  } else {
+    data.frame(`Avg Gap Filling Success %` = NA_real_, `Avg NA % Pre-Interpolation` = NA_real_, `Fields Analyzed` = 0)
+  }
+  
+  # Return as list (each element is a farm-level summary table)
   kpi_summary <- list(
-    uniformity = all_kpis$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, na_percent_pre_interpolation, gap_filling_success)
-    } else {
-      NULL
-    }
+    uniformity = uniformity_summary,
+    area_change = area_change_summary,
+    tch_forecast = tch_summary,
+    growth_decline = growth_summary,
+    weed_pressure = weed_summary,
+    gap_filling = gap_summary
   )
   
   return(kpi_summary)
@@ -465,7 +543,7 @@ 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
+#' @return Data frame with one row per field, all KPI columns (renamed for reporting compatibility)
 create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
   result <- field_df %>%
     left_join(
@@ -487,7 +565,24 @@ 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,
+      `Mean CI` = mean_ci_pct_change,
+      `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`, `Mean CI`, `CV Value`)
   
   return(result)
 }
@@ -499,7 +594,7 @@ 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(
-    "## AURA KPI ANALYSIS SUMMARY\n",
+    "## KPI ANALYSIS SUMMARY\n",
     "### Field Uniformity\n",
     paste(all_kpis$uniformity$interpretation, collapse = "; "), "\n",
     "### Growth Trends\n",
@@ -513,21 +608,47 @@ create_field_kpi_text <- function(all_kpis) {
 
 #' Export detailed KPI data to Excel/RDS
 #'
-#' @param all_kpis List with all KPI results
-#' @param kpi_summary List with summary tables
+#' @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 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, output_dir, week, year) {
+export_kpi_data <- function(all_kpis, kpi_summary, project_dir, output_dir, week, year, field_boundaries_sf = NULL) {
   # Ensure output directory exists
   if (!dir.exists(output_dir)) {
     dir.create(output_dir, recursive = TRUE)
   }
   
-  # Export all KPI tables to a single Excel file
-  excel_file <- paste0(output_dir, "/AURA_KPI_week_", sprintf("%02d_%d", week, year), ".xlsx")
+  # 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
+      field_df <- data.frame(
+        field_idx = 1:nrow(field_boundaries_sf),
+        field_name = if (!is.null(field_boundaries_sf$properties$name)) {
+          field_boundaries_sf$properties$name
+        } else if (!is.null(field_boundaries_sf$name)) {
+          field_boundaries_sf$name
+        } else {
+          paste0("Field_", 1:nrow(field_boundaries_sf))
+        },
+        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"))
   
   sheets <- list(
     "Uniformity" = as.data.frame(kpi_summary$uniformity),
@@ -539,12 +660,23 @@ export_kpi_data <- function(all_kpis, kpi_summary, output_dir, week, year) {
   )
   
   write_xlsx(sheets, excel_file)
-  message(paste("✓ AURA KPI data exported to:", excel_file))
+  message(paste("✓ KPI data exported to:", excel_file))
   
-  # Also export to RDS for programmatic access
-  rds_file <- paste0(output_dir, "/AURA_KPI_week_", sprintf("%02d_%d", week, year), ".rds")
-  saveRDS(all_kpis, rds_file)
-  message(paste("✓ AURA KPI RDS exported to:", rds_file))
+  # 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
+  )
+  
+  saveRDS(export_data, rds_file)
+  message(paste("✓ KPI RDS exported to:", rds_file))
+  message("  Structure: list($summary_tables, $all_kpis, $field_details)")
   
   return(list(excel = excel_file, rds = rds_file))
 }
@@ -553,9 +685,9 @@ export_kpi_data <- function(all_kpis, kpi_summary, output_dir, week, year) {
 # ORCHESTRATOR FUNCTION
 # ============================================================================
 
-#' Calculate all 6 AURA KPIs
+#' Calculate all 6 KPIs
 #'
-#' Main entry point for AURA KPI calculation.
+#' Main entry point for KPI calculation.
 #' This function orchestrates the 6 KPI calculations and returns all results.
 #'
 #' @param field_boundaries_sf SF object with field geometries
@@ -566,6 +698,7 @@ export_kpi_data <- function(all_kpis, kpi_summary, output_dir, week, year) {
 #' @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
 #'
@@ -573,7 +706,7 @@ export_kpi_data <- function(all_kpis, kpi_summary, output_dir, week, year) {
 #' This function:
 #' 1. Loads current week mosaic and extracts field statistics
 #' 2. (Optionally) loads previous week mosaic for comparison metrics
-#' 3. Calculates all 6 AURA KPIs
+#' 3. Calculates all 6 KPIs
 #' 4. Creates summary tables
 #' 5. Exports results to Excel/RDS
 #'
@@ -585,10 +718,11 @@ calculate_all_kpis <- function(
     previous_mosaic_dir = NULL,
     ci_rds_path = NULL,
     harvesting_data = NULL,
-    output_dir = NULL
+    output_dir = NULL,
+    project_dir = NULL
 ) {
   
-  message("\n============ AURA KPI CALCULATION (6 KPIs) ============")
+  message("\n============ KPI CALCULATION (6 KPIs) ============")
   
   # Load current week mosaic
   message("Loading current week mosaic...")
@@ -644,7 +778,7 @@ calculate_all_kpis <- function(
   weed_kpi <- calculate_weed_presence_kpi(ci_pixels_by_field)
   
   message("Calculating KPI 6: Gap Filling...")
-  gap_filling_kpi <- calculate_gap_filling_kpi(ci_rds_path)
+  gap_filling_kpi <- calculate_gap_filling_kpi(current_mosaic, field_boundaries_sf)
   
   # Compile results
   all_kpis <- list(
@@ -659,10 +793,18 @@ calculate_all_kpis <- function(
   # Create summary tables
   kpi_summary <- create_summary_tables(all_kpis)
   
-  # Export
-  export_paths <- export_kpi_data(all_kpis, kpi_summary, output_dir, current_week, current_year)
+  # 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_paths <- export_kpi_data(all_kpis, kpi_summary, project_dir, output_dir, current_week, current_year, field_boundaries_sf)
   
-  message(paste("\n✓ AURA KPI calculation complete. Week", current_week, current_year, "\n"))
+  message(paste("\n✓", project_dir, "KPI calculation complete. Week", current_week, current_year, "\n"))
   
-  return(all_kpis)
+  # Return combined structure (for integration with 80_calculate_kpis.R)
+  return(list(
+    all_kpis = all_kpis,
+    summary_tables = kpi_summary,
+    field_details = NULL  # Will be populated if export_kpi_data succeeds
+  ))
 }
diff --git a/r_app/90_CI_report_with_kpis_agronomic_support.Rmd b/r_app/90_CI_report_with_kpis_agronomic_support.Rmd
index b49fb72..3d1618d 100644
--- a/r_app/90_CI_report_with_kpis_agronomic_support.Rmd
+++ b/r_app/90_CI_report_with_kpis_agronomic_support.Rmd
@@ -1,15 +1,15 @@
 ---
 params:
   ref: "word-styles-reference-var1.docx"
-  output_file: CI_report.docx
-  report_date: "2026-01-22"
+  output_file: "CI_report.docx"
+  report_date: !r Sys.Date()
   data_dir: "angata"
   mail_day: "Wednesday"
   borders: FALSE
-  ci_plot_type: "both"  # options: "absolute", "cumulative", "both"
-  colorblind_friendly: TRUE  # use colorblind-friendly palettes (viridis/plasma)
-  facet_by_season: FALSE  # facet CI trend plots by season instead of overlaying
-  x_axis_unit: "days"  # x-axis unit for trend plots: "days" or "weeks"
+  ci_plot_type: "both"
+  colorblind_friendly: TRUE
+  facet_by_season: FALSE
+  x_axis_unit: "days"
 output:
    word_document:
     reference_docx: !expr file.path("word-styles-reference-var1.docx")
@@ -90,93 +90,87 @@ tryCatch({
 # Load centralized paths
 paths <- setup_project_directories(project_dir)
 
+# Assign global variables for use in visualization functions
+weekly_CI_mosaic <- paths$weekly_mosaic_dir  # Per-field mosaic directory
+
 # Log initial configuration
 safe_log("Starting the R Markdown script with KPIs")
 safe_log(paste("mail_day params:", params$mail_day))
 safe_log(paste("report_date params:", params$report_date))
 safe_log(paste("mail_day variable:", mail_day))
+safe_log(paste("weekly_CI_mosaic path:", weekly_CI_mosaic))
 ```
 
 ```{r load_kpi_data, message=FALSE, warning=FALSE, include=FALSE}
-## SIMPLE KPI LOADING - robust lookup with fallbacks
-# Primary expected directory from centralized paths
-kpi_data_dir <- paths$kpi_reports_dir
-date_suffix <- format(as.Date(report_date), "%Y%m%d")
+## LOAD KPI DATA - DYNAMIC PROJECT-SPECIFIC loading
+# NO workspace-wide fallback that might load wrong project
 
-# Calculate current week from report_date using ISO 8601 week numbering
+# Build expected KPI file path strictly from project_dir
+kpi_data_dir <- paths$kpi_reports_dir  # Should be: laravel_app/storage/app/{project}/reports/kpis/field_level
+
+# Calculate week from report_date
 current_week <- as.numeric(format(as.Date(report_date), "%V"))
 current_year <- as.numeric(format(as.Date(report_date), "%G"))
-week_suffix <- paste0("week", sprintf("%02d", current_week), "_", current_year)
 
-# Candidate filenames we expect (exact and common variants)
-expected_summary_names <- c(
-  paste0(project_dir, "_kpi_summary_tables_", week_suffix, ".rds"),
-  paste0(project_dir, "_kpi_summary_tables_", date_suffix, ".rds"),
-  paste0(project_dir, "_kpi_summary_tables.rds"),
-  "kpi_summary_tables.rds",
-  paste0("kpi_summary_tables_", week_suffix, ".rds"),
-  paste0("kpi_summary_tables_", date_suffix, ".rds")
-)
+# The ACTUAL filename format from 80_calculate_kpis.R output (after fix)
+# Format: {project_dir}_kpi_summary_tables_week{WW}_{YYYY}.rds
+kpi_rds_filename <- paste0(project_dir, "_kpi_summary_tables_week", 
+                           sprintf("%02d_%d", current_week, current_year), ".rds")
+kpi_rds_path <- file.path(kpi_data_dir, kpi_rds_filename)
 
-expected_field_details_names <- c(
-  paste0(project_dir, "_field_details_", week_suffix, ".rds"),
-  paste0(project_dir, "_field_details_", date_suffix, ".rds"),
-  paste0(project_dir, "_field_details.rds"),
-  "field_details.rds"
-)
+safe_log(paste("Looking for KPI file:", kpi_rds_path))
+safe_log(paste("Project directory:", project_dir))
+safe_log(paste("Expected filename:", kpi_rds_filename))
 
-# Helper to attempt loading a file from the directory or fallback to a workspace-wide search
-try_load_from_dir <- function(dir, candidates) {
-  if (!dir.exists(dir)) return(NULL)
-  for (name in candidates) {
-    f <- file.path(dir, name)
-    if (file.exists(f)) return(f)
-  }
-  return(NULL)
-}
-
-# Try primary directory first
-summary_file <- try_load_from_dir(kpi_data_dir, expected_summary_names)
-field_details_file <- try_load_from_dir(kpi_data_dir, expected_field_details_names)
-
-# If not found, perform a workspace-wide search (slower) limited to laravel_app storage
-if (is.null(summary_file) || is.null(field_details_file)) {
-  safe_log(paste("KPI files not found in", kpi_data_dir, "—searching workspace for RDS files"))
-  # List rds files under laravel_app/storage/app recursively
-  files <- list.files(path = file.path("laravel_app", "storage", "app"), pattern = "\\.rds$", recursive = TRUE, full.names = TRUE)
-  # Try to match by expected names
-  if (is.null(summary_file)) {
-    matched <- files[basename(files) %in% expected_summary_names]
-    if (length(matched) > 0) summary_file <- matched[1]
-  }
-  if (is.null(field_details_file)) {
-    matched2 <- files[basename(files) %in% expected_field_details_names]
-    if (length(matched2) > 0) field_details_file <- matched2[1]
-  }
-}
-
-# Final checks and load with safe error messages
+# Load with strict error checking - NO fallback to find other project's files
 kpi_files_exist <- FALSE
-if (!is.null(summary_file) && file.exists(summary_file)) {
-  safe_log(paste("Loading KPI summary from:", summary_file))
-  summary_tables <- tryCatch(readRDS(summary_file), error = function(e) { safe_log(paste("Failed to read summary RDS:", e$message), "ERROR"); NULL })
-  if (!is.null(summary_tables)) kpi_files_exist <- TRUE
+summary_tables <- NULL
+field_details_table <- NULL
+
+if (dir.exists(kpi_data_dir)) {
+  if (file.exists(kpi_rds_path)) {
+    safe_log(paste("✓ Found KPI file for", project_dir))
+    loaded_data <- tryCatch(
+      readRDS(kpi_rds_path),
+      error = function(e) {
+        safe_log(paste("ERROR reading KPI RDS:", e$message), "ERROR")
+        return(NULL)
+      }
+    )
+    
+    # Handle new RDS structure (list with $summary_tables, $all_kpis, $field_details)
+    if (!is.null(loaded_data)) {
+      if (is.list(loaded_data) && "summary_tables" %in% names(loaded_data)) {
+        # New structure: extract summary_tables from the list
+        summary_tables <- loaded_data$summary_tables
+        if (!is.null(loaded_data$field_details)) {
+          field_details_table <- loaded_data$field_details
+        }
+        safe_log("✓ Loaded KPI data (new structure with summary_tables)")
+        kpi_files_exist <- TRUE
+      } else if (is.list(loaded_data) && length(loaded_data) > 0) {
+        # Legacy structure: directly use as summary_tables
+        summary_tables <- loaded_data
+        safe_log("✓ Loaded KPI tables (legacy structure)")
+        kpi_files_exist <- TRUE
+      }
+      
+      if (kpi_files_exist) {
+        safe_log(paste("✓ Available KPI tables:", paste(names(summary_tables), collapse=", ")))
+      }
+    }
+  } else {
+    safe_log(paste("KPI file not found in:", kpi_rds_path), "WARNING")
+    safe_log(paste("Expected file:", kpi_rds_filename), "WARNING")
+    safe_log(paste("Files in directory:", paste(list.files(kpi_data_dir, pattern="\\.rds$"), collapse=", ")), "WARNING")
+  }
 } else {
-  safe_log(paste("KPI summary file not found. Searched:", paste(expected_summary_names, collapse=", ")), "WARNING")
+  safe_log(paste("KPI directory does not exist:", kpi_data_dir), "WARNING")
 }
 
-if (!is.null(field_details_file) && file.exists(field_details_file)) {
-  safe_log(paste("Loading field details from:", field_details_file))
-  field_details_table <- tryCatch(readRDS(field_details_file), error = function(e) { safe_log(paste("Failed to read field details RDS:", e$message), "ERROR"); NULL })
-  if (!is.null(field_details_table)) kpi_files_exist <- TRUE
-} else {
-  safe_log(paste("Field details file not found. Searched:", paste(expected_field_details_names, collapse=", ")), "WARNING")
-}
-
-if (kpi_files_exist) {
-  safe_log("✓ KPI summary tables loaded successfully")
-} else {
-  safe_log("KPI files could not be located or loaded. KPI sections will be skipped.", "WARNING")
+if (!kpi_files_exist) {
+  safe_log(paste("Skipping KPI sections - no data for", project_dir, "on", report_date), "WARNING")
+  summary_tables <- NULL
 }
 ```
 
@@ -227,10 +221,32 @@ safe_log(paste("Week range:", week_start, "to", week_end))
 ```{r load_ci_data, message=FALSE, warning=FALSE, include=FALSE}
 # Load CI quadrant data for field-level analysis
 tryCatch({
-  CI_quadrant <- readRDS(here::here(paths$cumulative_ci_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds")) 
-  safe_log("Successfully loaded CI quadrant data")
+  # Try multiple path constructions to handle different directory structures (dynamically)
+  candidate_paths <- c(
+    file.path(paths$cumulative_ci_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds"),
+    here::here(paths$cumulative_ci_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds"),
+    file.path("laravel_app/storage/app", project_dir, "Data/extracted_ci/cumulative_vals/All_pivots_Cumulative_CI_quadrant_year_v2.rds")
+  )
+  
+  # Find first valid path
+  ci_quadrant_path <- NULL
+  for (path in candidate_paths) {
+    if (file.exists(path)) {
+      ci_quadrant_path <- path
+      break
+    }
+  }
+  
+  if (is.null(ci_quadrant_path)) {
+    safe_log(paste("CI quadrant file not found. Tried:", paste(candidate_paths, collapse=", ")), "WARNING")
+    CI_quadrant <- NULL
+  } else {
+    CI_quadrant <- readRDS(ci_quadrant_path)
+    safe_log(paste("Successfully loaded CI quadrant data from:", ci_quadrant_path))
+  }
 }, error = function(e) {
-  stop("Error loading CI quadrant data: ", e$message)
+  safe_log(paste("Error loading CI quadrant data:", e$message), "WARNING")
+  CI_quadrant <<- NULL
 })
 
 # NOTE: Overview maps skipped for this report
@@ -254,11 +270,22 @@ tryCatch({
 
 ```{r compute_benchmarks_once, include=FALSE}
 # Compute CI benchmarks once for the entire estate
-benchmarks <- compute_ci_benchmarks(CI_quadrant, project_dir, c(10, 50, 90))
-if (!is.null(benchmarks)) {
-  safe_log("Benchmarks computed successfully for the report")
+if (!is.null(CI_quadrant) && nrow(CI_quadrant) > 0) {
+  tryCatch({
+    benchmarks <- compute_ci_benchmarks(CI_quadrant, project_dir, c(10, 50, 90))
+    if (!is.null(benchmarks)) {
+      safe_log("Benchmarks computed successfully for the report")
+    } else {
+      safe_log("Failed to compute benchmarks", "WARNING")
+      benchmarks <- NULL
+    }
+  }, error = function(e) {
+    safe_log(paste("Error computing benchmarks:", e$message), "WARNING")
+    benchmarks <<- NULL
+  })
 } else {
-  safe_log("Failed to compute benchmarks", "WARNING")
+  safe_log("Skipping benchmark computation - CI quadrant data not available", "WARNING")
+  benchmarks <- NULL
 }
 ```
 
@@ -279,28 +306,68 @@ if (!is.null(benchmarks)) {
 ## Key Insights
 
 ```{r key_insights, echo=FALSE, results='asis'}
-# Calculate key insights from KPI data
-if (exists("summary_tables") && !is.null(summary_tables)) {
+# Calculate key insights from aggregated KPI summary data
+if (exists("summary_tables") && !is.null(summary_tables) && length(summary_tables) > 0) {
   
-  # Field uniformity insights
-  uniformity_data <- summary_tables$field_uniformity_summary
-  good_uniformity <- uniformity_data$Percent[uniformity_data$`Uniformity Level` == "Good"]
-  excellent_uniformity <- uniformity_data$Percent[uniformity_data$`Uniformity Level` == "Excellent"]
+  # Extract aggregated KPI summaries (farm-level, not per-field)
+  uniformity_summary <- summary_tables$uniformity  # Has: Status, Field Count, Avg CV, Avg Moran's I
+  area_change_summary <- summary_tables$area_change  # Has: Status, Field Count, Avg CI Change %
+  growth_summary <- summary_tables$growth_decline  # Has: Trend, Field Count, Avg 4-Week Trend
+  weed_summary <- summary_tables$weed_pressure  # Has: Risk Level, Field Count, Avg Fragmentation
+  
+  # Total fields analyzed (from uniformity summary)
+  total_fields <- sum(uniformity_summary$`Field Count`, na.rm = TRUE)
+  
+  # Uniformity insights
+  if (!is.null(uniformity_summary) && nrow(uniformity_summary) > 0) {
+    cat("**Field Uniformity:**\n")
+    for (i in 1:nrow(uniformity_summary)) {
+      status <- uniformity_summary$Status[i]
+      count <- uniformity_summary$`Field Count`[i]
+      if (count > 0) {
+        cat("- ", count, " field(s) with ", status, "\n", sep="")
+      }
+    }
+  }
   
   # Area change insights
-  area_change_data <- summary_tables$area_change_summary
-  improving_area <- area_change_data$Hectares[area_change_data$`Change Type` == "Improving areas"]
-  improving_pct <- area_change_data$Percent[area_change_data$`Change Type` == "Improving areas"]
-  declining_area <- area_change_data$Hectares[area_change_data$`Change Type` == "Declining areas"]
-  declining_pct <- area_change_data$Percent[area_change_data$`Change Type` == "Declining areas"]
+  if (!is.null(area_change_summary) && nrow(area_change_summary) > 0) {
+    cat("\n**Area Change Status:**\n")
+    for (i in 1:nrow(area_change_summary)) {
+      status <- area_change_summary$Status[i]
+      count <- area_change_summary$`Field Count`[i]
+      if (count > 0 && !is.na(status)) {
+        cat("- ", count, " field(s) ", status, "\n", sep="")
+      }
+    }
+  }
   
-  cat("- ", ifelse(length(good_uniformity) > 0, good_uniformity, "N/A"), "% of fields have good uniformity\n", sep="")
-  cat("- ", ifelse(length(excellent_uniformity) > 0, excellent_uniformity, "N/A"), "% of fields have excellent uniformity\n", sep="")
-  cat("- ", ifelse(length(improving_area) > 0, round(improving_area, 1), "N/A"), " hectares (", ifelse(length(improving_pct) > 0, improving_pct, "N/A"), "%) of farm area is improving week-over-week\n", sep="")
-  cat("- ", ifelse(length(declining_area) > 0, round(declining_area, 1), "N/A"), " hectares (", ifelse(length(declining_pct) > 0, declining_pct, "N/A"), "%) of farm area is declining week-over-week\n", sep="")
+  # Growth trend insights
+  if (!is.null(growth_summary) && nrow(growth_summary) > 0) {
+    cat("\n**Growth Trends (4-Week):**\n")
+    for (i in 1:nrow(growth_summary)) {
+      trend <- growth_summary$Trend[i]
+      count <- growth_summary$`Field Count`[i]
+      if (count > 0 && !is.na(trend)) {
+        cat("- ", count, " field(s) with ", trend, "\n", sep="")
+      }
+    }
+  }
+  
+  # Weed pressure insights
+  if (!is.null(weed_summary) && nrow(weed_summary) > 0) {
+    cat("\n**Weed/Pest Pressure Risk:**\n")
+    for (i in 1:nrow(weed_summary)) {
+      risk <- weed_summary$`Risk Level`[i]
+      count <- weed_summary$`Field Count`[i]
+      if (count > 0 && !is.na(risk)) {
+        cat("- ", count, " field(s) at ", risk, " risk\n", sep="")
+      }
+    }
+  }
   
 } else {
-  cat("KPI data not available for key insights.\n")
+  cat("KPI data not available for ", project_dir, " on this date.\n", sep="")
 }
 ```
 
@@ -311,55 +378,48 @@ if (exists("summary_tables") && !is.null(summary_tables)) {
 ## Executive Summary - Key Performance Indicators
 
 ```{r combined_kpi_table, echo=FALSE, results='asis'}
-# Combine all KPI tables into a single table with standardized column names
-display_names <- c(
-  field_uniformity_summary = "Field Uniformity",
-  area_change_summary = "Area Change", 
-  tch_forecasted_summary = "TCH Forecasted",
-  growth_decline_summary = "Growth Decline",
-  weed_presence_summary = "Weed Presence",
-  gap_filling_summary = "Gap Filling"
-)
-
-combined_df <- bind_rows(lapply(names(summary_tables), function(kpi) {
-  df <- summary_tables[[kpi]]
-  names(df) <- c("Level", "Count", "Percent")
-  # Format Count as integer (no decimals)
-  df <- df %>% 
-    mutate(
-      Count = as.integer(round(Count)),
-      KPI = display_names[kpi], 
-      .before = 1
-    )
-  df
-}), .id = NULL)
-
-# Create grouped display where KPI name appears only once per group
-combined_df <- combined_df %>%
-  group_by(KPI) %>%
-  mutate(
-    KPI_display = if_else(row_number() == 1, KPI, "")
-  ) %>%
-  ungroup() %>%
-  select(KPI_display, Level, Count, Percent) %>%
-  rename(KPI = KPI_display)
-
-# Render as flextable with merged cells
-ft <- flextable(combined_df) %>%
-#  set_caption("Combined KPI Summary Table") %>%
-  merge_v(j = "KPI") %>%  # Merge vertically identical cells in KPI column
-  autofit()
-
-# Add horizontal lines after each KPI group
-kpi_groups <- sapply(names(summary_tables), function(kpi) nrow(summary_tables[[kpi]]))
-cum_rows <- cumsum(kpi_groups)
-for (i in seq_along(cum_rows)) {
-  if (i < length(cum_rows)) {
-    ft <- ft %>% hline(i = cum_rows[i], border = officer::fp_border(width = 2))
-  }
+# Safely display KPI tables
+if (exists("summary_tables") && !is.null(summary_tables) && length(summary_tables) > 0) {
+  
+  # Try to combine KPI tables, with fallback if structure is unexpected
+  tryCatch({
+    # Build a list of valid dataframes from summary_tables
+    valid_tables <- list()
+    
+    for (kpi_name in names(summary_tables)) {
+      kpi_df <- summary_tables[[kpi_name]]
+      
+      # Skip NULL, empty, or non-dataframe items
+      if (!is.null(kpi_df) && is.data.frame(kpi_df) && nrow(kpi_df) > 0) {
+        # Add KPI name as a column if not already present
+        if (!"KPI" %in% names(kpi_df)) {
+          display_name <- gsub("_", " ", tools::toTitleCase(gsub("_summary|_data", "", kpi_name)))
+          kpi_df$KPI <- display_name
+        }
+        valid_tables[[kpi_name]] <- kpi_df
+      }
+    }
+    
+    # Combine all valid tables
+    if (length(valid_tables) > 0) {
+      # Use careful bind_rows that handles mismatched columns
+      combined_df <- dplyr::bind_rows(valid_tables, .id = NULL)
+      
+      # Display as flextable
+      ft <- flextable(combined_df) %>% autofit()
+      ft
+    } else {
+      cat("No valid KPI summary tables found.\n")
+    }
+    
+  }, error = function(e) {
+    safe_log(paste("Error combining KPI tables:", e$message), "WARNING")
+    cat("KPI summary tables could not be combined for display. Individual KPI sections will be shown below.\n")
+  })
+  
+} else {
+  cat("Note: KPI summary tables have not been loaded. Detailed KPI analysis will be available once data is computed.\n")
 }
-
-ft
 ```
 
 ## Field Alerts
@@ -367,8 +427,18 @@ ft
 ```{r field_alerts_table, echo=FALSE, results='asis'}
 # Generate alerts for all fields
 generate_field_alerts <- function(field_details_table) {
-  if (is.null(field_details_table) || nrow(field_details_table) == 0) {
-    return(data.frame(Field = character(), Alert = character()))
+  if (!exists("field_details_table") || is.null(field_details_table) || nrow(field_details_table) == 0) {
+    return(NULL)  # Return NULL to signal no data
+  }
+  
+  # Check for required columns
+  required_cols <- c("Field", "Field Size (ha)", "Growth Uniformity", "Yield Forecast (t/ha)", 
+                     "Gap Score", "Decline Risk", "Weed Risk", "Mean CI", "CV Value", "Moran's I")
+  missing_cols <- setdiff(required_cols, colnames(field_details_table))
+  
+  if (length(missing_cols) > 0) {
+    message("Field details missing required columns: ", paste(missing_cols, collapse = ", "))
+    return(NULL)  # Return NULL if required columns are missing
   }
   
   alerts_list <- list()
@@ -455,9 +525,9 @@ generate_field_alerts <- function(field_details_table) {
 }
 
 # Generate and display alerts table
-if (exists("field_details_table") && !is.null(field_details_table)) {
+if (exists("field_details_table") && !is.null(field_details_table) && nrow(field_details_table) > 0) {
   alerts_data <- generate_field_alerts(field_details_table)
-  if (nrow(alerts_data) > 0) {
+  if (!is.null(alerts_data) && nrow(alerts_data) > 0) {
     ft <- flextable(alerts_data) %>%
 #      set_caption("Field Alerts Summary") %>%
       autofit()
@@ -466,29 +536,82 @@ if (exists("field_details_table") && !is.null(field_details_table)) {
     cat("No alerts data available.\n")
   }
 } else {
-  cat("Field details data not available for alerts generation.\n")
+  cat("Note: Field details data not available for alerts generation. Run 80_calculate_kpis.R to generate KPI data.\n")
+}
+```
+
+```{r create_field_details_table, message=FALSE, warning=FALSE, include=FALSE}
+# Create field_details_table from available data
+# For projects without KPI data, create minimal table from field names/geometries
+
+if (!exists("field_details_table") || is.null(field_details_table)) {
+  tryCatch({
+    if (!is.null(AllPivots0) && nrow(AllPivots0) > 0) {
+      # Get field names from geometries
+      field_names <- AllPivots0$field
+      
+      # Try to calculate field sizes (area) from geometry if available
+      field_sizes <- if ("geometry" %in% names(AllPivots0)) {
+        sf::st_area(AllPivots0) / 10000  # Convert m² to hectares
+      } else {
+        rep(NA_real_, length(field_names))
+      }
+      
+      # Create minimal field details table with actual data we have + NAs for missing KPI columns
+      field_details_table <- tibble::tibble(
+        Field = field_names,
+        `Field Size (ha)` = as.numeric(field_sizes),
+        `Growth Uniformity` = NA_character_,
+        `Yield Forecast (t/ha)` = NA_real_,
+        `Gap Score` = NA_real_,
+        `Decline Risk` = NA_character_,
+        `Weed Risk` = NA_character_,
+        `Mean CI` = NA_real_,
+        `CV Value` = NA_real_,
+        `Moran's I` = NA_real_
+      )
+      safe_log(paste("Created field_details_table from geometries for", nrow(field_details_table), "fields"))
+    }
+  }, error = function(e) {
+    safe_log(paste("Error creating field_details_table from geometries:", e$message), "WARNING")
+  })
 }
 ```
 
 ```{r data, message=TRUE, warning=TRUE, include=FALSE}
 # Verify CI quadrant data is loaded from load_ci_data chunk
-if (!exists("CI_quadrant") || is.null(CI_quadrant)) {
-  stop("CI_quadrant data not available - check load_ci_data chunk")
+if (!exists("CI_quadrant")) {
+  safe_log("CI_quadrant not found - this may affect field analysis reports", "WARNING")
+  CI_quadrant <- NULL
+} else if (is.null(CI_quadrant)) {
+  safe_log("CI_quadrant data is NULL - field-level CI analysis will be skipped", "WARNING")
+} else {
+  safe_log("CI quadrant data verified for field-level analysis")
 }
-safe_log("CI quadrant data verified for field-level analysis")
 ```
 
 
 ```{r load_field_boundaries, message=TRUE, warning=TRUE, include=FALSE}
-# Load field boundaries from parameters
+# Load field boundaries from GeoJSON
 tryCatch({
+  boundaries_result <- load_field_boundaries(paths$data_dir)
+  
+  if (is.list(boundaries_result) && "field_boundaries_sf" %in% names(boundaries_result)) {
+    field_boundaries_sf <- boundaries_result$field_boundaries_sf
+  } else {
+    field_boundaries_sf <- boundaries_result
+  }
+  
+  if (nrow(field_boundaries_sf) == 0) {
+    stop("No field boundaries loaded")
+  }
+  
   AllPivots0 <- field_boundaries_sf %>%
     dplyr::filter(!is.na(field), !is.na(sub_field))  # Filter out NA field names
-  safe_log("Successfully loaded field boundaries")
+  safe_log(paste("Successfully loaded", nrow(AllPivots0), "fields"))
   
   # Prepare merged field list for use in summaries
   AllPivots_merged <- AllPivots0 %>% 
-    dplyr::filter(!is.na(field), !is.na(sub_field)) %>%  # Filter out NA field names
     dplyr::group_by(field) %>% 
     dplyr::summarise(.groups = 'drop')
   
@@ -512,18 +635,15 @@ This section provides detailed, field-specific analyses including chlorophyll in
 
 \newpage
 
-```{r generate_field_visualizations, eval=TRUE, fig.height=3.8, fig.width=10, dpi=300, dev='png', message=FALSE,echo=FALSE, warning=FALSE, include=TRUE, results='asis'}
-# Generate detailed visualizations for each field
+```{r generate_field_visualizations, eval=TRUE, fig.height=3.8, fig.width=10, dpi=300, dev='png', message=TRUE, echo=FALSE, warning=TRUE, include=TRUE, results='asis'}
+# Generate detailed visualizations for each field using purrr::walk
 tryCatch({
-  # Merge field polygons for processing and filter out NA field names
+  # Prepare merged field list and week/year info
   AllPivots_merged <- AllPivots0 %>% 
-    dplyr::filter(!is.na(field), !is.na(sub_field)) %>%  # Filter out NA fields
+    dplyr::filter(!is.na(field), !is.na(sub_field)) %>%
     dplyr::group_by(field) %>% 
     dplyr::summarise(.groups = 'drop')
   
-  # Use per-field weekly mosaic directory path from parameters_project.R
-  weekly_mosaic_per_field_dir <- weekly_CI_mosaic
-  
   # Helper to get week/year from a date
   get_week_year <- function(date) {
     list(
@@ -532,145 +652,143 @@ tryCatch({
     )
   }
   
-  # Get week/year for current and historical weeks (local to field section)
+  # Calculate week/year for current and historical weeks
   current_ww <- get_week_year(as.Date(today))
   minus_1_ww <- get_week_year(as.Date(today) - lubridate::weeks(1))
   minus_2_ww <- get_week_year(as.Date(today) - lubridate::weeks(2))
   minus_3_ww <- get_week_year(as.Date(today) - lubridate::weeks(3))
   
-  # Generate plots for each field
-  for(i in seq_along(AllPivots_merged$field)) {
-    field_name <- AllPivots_merged$field[i]
-    
-    # Skip if field_name is still NA (double check)
-    if(is.na(field_name)) {
-      next
+  message(paste("Processing", nrow(AllPivots_merged), "fields for weeks:", 
+                current_ww$week, minus_1_ww$week, minus_2_ww$week, minus_3_ww$week))
+  
+  # Helper function to safely load per-field mosaic if it exists
+  load_per_field_mosaic <- function(base_dir, field_name, week, year) {
+    path <- file.path(base_dir, field_name, paste0("week_", sprintf("%02d", week), "_", year, ".tif"))
+    if (file.exists(path)) {
+      tryCatch({
+        rast_obj <- terra::rast(path)
+        # Extract CI band if present, otherwise first band
+        if ("CI" %in% names(rast_obj)) {
+          return(rast_obj[["CI"]])
+        } else if (nlyr(rast_obj) > 0) {
+          return(rast_obj[[1]])
+        }
+      }, error = function(e) {
+        message(paste("Warning: Could not load", path, ":", e$message))
+        return(NULL)
+      })
     }
-    
+    return(NULL)
+  }
+  
+  # Iterate through fields using purrr::walk
+  is_first_field <- TRUE
+  purrr::walk(AllPivots_merged$field, function(field_name) {
     tryCatch({
-      # Add page break before each field (except the first one)
-      if(i > 1) {
+      # Add page break before each field (except first)
+      if (!is_first_field) {
         cat("\\newpage\n\n")
       }
+      is_first_field <<- FALSE
       
-      # Load per-field mosaics directly for this field
-      field_CI <- NULL
-      field_CI_m1 <- NULL
-      field_CI_m2 <- NULL
-      field_CI_m3 <- NULL
+      message(paste("Processing field:", field_name))
       
-      tryCatch({
-        # Load per-field mosaic for current week
-        per_field_path_current <- get_per_field_mosaic_path(
-          weekly_mosaic_per_field_dir, field_name, current_ww$week, current_ww$year
-        )
-        if (!is.null(per_field_path_current) && file.exists(per_field_path_current)) {
-          field_CI <- terra::rast(per_field_path_current)[["CI"]]
-        }
-        
-        # Load per-field mosaic for week-1
-        per_field_path_m1 <- get_per_field_mosaic_path(
-          weekly_mosaic_per_field_dir, field_name, minus_1_ww$week, minus_1_ww$year
-        )
-        if (!is.null(per_field_path_m1) && file.exists(per_field_path_m1)) {
-          field_CI_m1 <- terra::rast(per_field_path_m1)[["CI"]]
-        }
-        
-        # Load per-field mosaic for week-2
-        per_field_path_m2 <- get_per_field_mosaic_path(
-          weekly_mosaic_per_field_dir, field_name, minus_2_ww$week, minus_2_ww$year
-        )
-        if (!is.null(per_field_path_m2) && file.exists(per_field_path_m2)) {
-          field_CI_m2 <- terra::rast(per_field_path_m2)[["CI"]]
-        }
-        
-        # Load per-field mosaic for week-3
-        per_field_path_m3 <- get_per_field_mosaic_path(
-          weekly_mosaic_per_field_dir, field_name, minus_3_ww$week, minus_3_ww$year
-        )
-        if (!is.null(per_field_path_m3) && file.exists(per_field_path_m3)) {
-          field_CI_m3 <- terra::rast(per_field_path_m3)[["CI"]]
-        }
-        
-        safe_log(paste("Loaded per-field mosaics for", field_name), "DEBUG")
-        
-      }, error = function(e) {
-        safe_log(paste("Could not load per-field mosaics for", field_name, ":", e$message), "WARNING")
-      })
+      # Load per-field rasters for all 4 weeks
+      field_CI <- load_per_field_mosaic(weekly_CI_mosaic, field_name, 
+                                        current_ww$week, current_ww$year)
+      field_CI_m1 <- load_per_field_mosaic(weekly_CI_mosaic, field_name, 
+                                           minus_1_ww$week, minus_1_ww$year)
+      field_CI_m2 <- load_per_field_mosaic(weekly_CI_mosaic, field_name, 
+                                           minus_2_ww$week, minus_2_ww$year)
+      field_CI_m3 <- load_per_field_mosaic(weekly_CI_mosaic, field_name, 
+                                           minus_3_ww$week, minus_3_ww$year)
       
-      # Calculate difference rasters from per-field data (local to this field)
-      last_week_dif_raster_field <- NULL
-      three_week_dif_raster_field <- NULL
-      
-      if (!is.null(field_CI) && !is.null(field_CI_m1)) {
-        last_week_dif_raster_field <- field_CI - field_CI_m1
+      # Calculate difference rasters
+      last_week_diff <- if (!is.null(field_CI) && !is.null(field_CI_m1)) {
+        field_CI - field_CI_m1
+      } else {
+        NULL
       }
-      if (!is.null(field_CI) && !is.null(field_CI_m3)) {
-        three_week_dif_raster_field <- field_CI - field_CI_m3
+      
+      three_week_diff <- if (!is.null(field_CI) && !is.null(field_CI_m3)) {
+        field_CI - field_CI_m3
+      } else {
+        NULL
       }
       
       # Call ci_plot with field-specific rasters
-      ci_plot(  
-        pivotName = field_name,
-        field_boundaries = AllPivots0,
-        current_ci = field_CI,
-        ci_minus_1 = field_CI_m1,
-        ci_minus_2 = field_CI_m2,
-        last_week_diff = last_week_dif_raster_field,
-        three_week_diff = three_week_dif_raster_field,
-        harvesting_data = harvesting_data,
-        week = week,
-        week_minus_1 = week_minus_1,
-        week_minus_2 = week_minus_2,
-        week_minus_3 = week_minus_3,
-        borders = borders,
-        colorblind_friendly = colorblind_friendly
-      )
-      
-      cat("\n\n")
-      
-      # Special handling for ESA project field 00f25 - remove duplicate DOY values
-      if (project_dir == "esa" && field_name == "00F25") {
-        ci_quadrant_data <- CI_quadrant %>%
-          filter(field == "00F25") %>%
-          arrange(DOY) %>%
-          group_by(DOY) %>%
-          slice(1) %>%
-          ungroup()
+      if (!is.null(field_CI)) {
+        ci_plot(  
+          pivotName = field_name,
+          field_boundaries = AllPivots0,
+          current_ci = field_CI,
+          ci_minus_1 = field_CI_m1,
+          ci_minus_2 = field_CI_m2,
+          last_week_diff = last_week_diff,
+          three_week_diff = three_week_diff,
+          harvesting_data = harvesting_data,
+          week = week,
+          week_minus_1 = week_minus_1,
+          week_minus_2 = week_minus_2,
+          week_minus_3 = week_minus_3,
+          borders = borders,
+          colorblind_friendly = colorblind_friendly
+        )
+        cat("\n\n")
       } else {
-        ci_quadrant_data <- CI_quadrant
+        message(paste("Warning: No raster data found for field", field_name))
       }
       
-      # Call cum_ci_plot with explicit parameters
-      cum_ci_plot(
-        pivotName = field_name,
-        ci_quadrant_data = ci_quadrant_data,
-        plot_type = ci_plot_type,
-        facet_on = facet_by_season,
-        x_unit = x_axis_unit,
-        colorblind_friendly = colorblind_friendly,
-        show_benchmarks = TRUE,
-        estate_name = project_dir,
-        benchmark_percentiles = c(10, 50, 90),
-        benchmark_data = benchmarks
-      )
+      # Handle CI quadrant data filter for special cases
+      ci_quadrant_data <- if (project_dir == "esa" && field_name == "00F25") {
+        CI_quadrant %>%
+          dplyr::filter(field == "00F25") %>%
+          dplyr::arrange(DOY) %>%
+          dplyr::group_by(DOY) %>%
+          dplyr::slice(1) %>%
+          dplyr::ungroup()
+      } else {
+        CI_quadrant
+      }
       
-      cat("\n\n")
-      
-# Add field-specific KPI summary under the graphs
-      if (exists("field_details_table") && !is.null(field_details_table)) {
-        kpi_summary <- generate_field_kpi_summary(field_name, field_details_table, CI_quadrant)
-        cat(kpi_summary)
+      # Call cum_ci_plot for trend analysis
+      if (!is.null(CI_quadrant)) {
+        cum_ci_plot(
+          pivotName = field_name,
+          ci_quadrant_data = ci_quadrant_data,
+          plot_type = ci_plot_type,
+          facet_on = facet_by_season,
+          x_unit = x_axis_unit,
+          colorblind_friendly = colorblind_friendly,
+          show_benchmarks = TRUE,
+          estate_name = project_dir,
+          benchmark_percentiles = c(10, 50, 90),
+          benchmark_data = benchmarks
+        )
         cat("\n\n")
       }
       
+      # Add field-specific KPI summary if available
+      # NOTE: generate_field_kpi_summary function not yet implemented
+      # Skipping field-level KPI text for now; KPI tables are available in Section 1
+      if (FALSE) {  # Disabled pending function implementation
+        # if (exists("field_details_table") && !is.null(field_details_table) && nrow(field_details_table) > 0) {
+        #   kpi_summary <- generate_field_kpi_summary(field_name, field_details_table, CI_quadrant)
+        #   if (!is.null(kpi_summary)) {
+        #     cat(kpi_summary)
+        #     cat("\n\n")
+        #   }
+        # }
+      }
+      
     }, error = function(e) {
       safe_log(paste("Error generating plots for field", field_name, ":", e$message), "ERROR")
       cat("\\newpage\n\n")
       cat("# Error generating plots for field ", field_name, "\n\n")
-      cat(e$message, "\n\n")
+      cat(paste("Error:", e$message), "\n\n")
     })
-  }
+  })
+  
 }, error = function(e) {
   safe_log(paste("Error in field visualization section:", e$message), "ERROR")
   cat("Error generating field plots. See log for details.\n\n")
@@ -717,32 +835,41 @@ tryCatch({
 The following table provides a comprehensive overview of all monitored fields with their key performance metrics from the KPI analysis.
 
 ```{r detailed_field_table, echo=FALSE, results='asis'}
-# Load CI quadrant data to get field ages
-#CI_quadrant <- readRDS(here::here(paths$cumulative_ci_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds"))
-
-# Identify the current season for each field based on report_date
-# The current season is the one where the report_date falls within or shortly after the season
+# Detailed field performance table
 report_date_obj <- as.Date(report_date)
 
-current_seasons <- CI_quadrant %>%
-  filter(Date <= report_date_obj) %>%
-  group_by(field, season) %>%
-  summarise(
-    season_start = min(Date),
-    season_end = max(Date),
-    .groups = 'drop'
-  ) %>%
-  group_by(field) %>%
-  filter(season == max(season)) %>%  # Take the most recent season
-  select(field, season)
+# Initialize empty dataframe for field_ages if CI_quadrant is unavailable
+field_ages <- data.frame(Field = character(), Age_days = numeric())
 
-# Get current field ages (most recent DOY for each field in their CURRENT SEASON only)
-field_ages <- CI_quadrant %>%
-  inner_join(current_seasons, by = c("field", "season")) %>%  # Filter to current season only
-  group_by(field) %>%
-  filter(DOY == max(DOY)) %>%
-  select(field, DOY) %>%
-  rename(Field = field, Age_days = DOY)
+# Try to get field ages from CI quadrant if available
+if (!is.null(CI_quadrant) && nrow(CI_quadrant) > 0) {
+  tryCatch({
+    # Identify the current season for each field based on report_date
+    current_seasons <- CI_quadrant %>%
+      filter(Date <= report_date_obj) %>%
+      group_by(field, season) %>%
+      summarise(
+        season_start = min(Date),
+        season_end = max(Date),
+        .groups = 'drop'
+      ) %>%
+      group_by(field) %>%
+      filter(season == max(season)) %>%
+      select(field, season)
+    
+    # Get current field ages (most recent DOY for each field in their CURRENT SEASON only)
+    field_ages <- CI_quadrant %>%
+      inner_join(current_seasons, by = c("field", "season")) %>%
+      group_by(field) %>%
+      filter(DOY == max(DOY)) %>%
+      select(field, DOY) %>%
+      rename(Field = field, Age_days = DOY)
+  }, error = function(e) {
+    safe_log(paste("Error extracting field ages:", e$message), "WARNING")
+  })
+} else {
+  safe_log("CI quadrant data unavailable - field ages will not be included in detailed table", "WARNING")
+}
 
 # Clean up the field details table - remove sub field column and round numeric values
 # Check if field_details_table was loaded successfully
diff --git a/r_app/MANUAL_PIPELINE_RUNNER.R b/r_app/MANUAL_PIPELINE_RUNNER.R
index 219319a..9414d37 100644
--- a/r_app/MANUAL_PIPELINE_RUNNER.R
+++ b/r_app/MANUAL_PIPELINE_RUNNER.R
@@ -438,9 +438,9 @@
 #   rmarkdown::render(
 rmarkdown::render(
   "r_app/90_CI_report_with_kpis_agronomic_support.Rmd",
-  params = list(data_dir = "angata", report_date = as.Date("2026-02-04")),
-  output_file = "SmartCane_Report_agronomic_support_aura_2026-02-10_FIXED.docx",
-  output_dir = "laravel_app/storage/app/angata/reports"
+  params = list(data_dir = "john", report_date = as.Date("2026-02-04")),
+  output_file = "SmartCane_Report_agronomic_support_john_2026-02-04.docx",
+  output_dir = "laravel_app/storage/app/john/reports"
 )
 #
 # COMMAND #2 - CANE SUPPLY REPORT (ANGATA, CHEMBA, XINAVANE, ESA):
diff --git a/r_app/parameters_project.R b/r_app/parameters_project.R
index 931ca59..946fb4c 100644
--- a/r_app/parameters_project.R
+++ b/r_app/parameters_project.R
@@ -39,11 +39,7 @@ suppressPackageStartupMessages({
 # This determines which scripts run and what outputs they produce
 
 CLIENT_TYPE_MAP <- list(
-  "angata" = "cane_supply",
-  "aura" = "agronomic_support",
-  "chemba" = "cane_supply",
-  "xinavane" = "cane_supply",
-  "esa" = "cane_supply"
+  "angata" = "cane_supply"
 )
 
 #' Get client type for a project
@@ -52,8 +48,7 @@ CLIENT_TYPE_MAP <- list(
 get_client_type <- function(project_name) {
   client_type <- CLIENT_TYPE_MAP[[project_name]]
   if (is.null(client_type)) {
-    warning(sprintf("Project '%s' not in CLIENT_TYPE_MAP - defaulting to 'cane_supply'", project_name))
-    return("cane_supply")
+    return("agronomic_support")  # Default for all unlisted projects
   }
   return(client_type)
 }
diff --git a/r_app/test_kpi_structure.R b/r_app/test_kpi_structure.R
new file mode 100644
index 0000000..39dd631
--- /dev/null
+++ b/r_app/test_kpi_structure.R
@@ -0,0 +1,32 @@
+#!/usr/bin/env Rscript
+# Temporary script to inspect KPI structure
+
+x <- readRDS('laravel_app/storage/app/john/reports/kpis/field_level/AURA_KPI_week_06_2026.rds')
+
+cat("===== KPI RDS Structure =====\n")
+cat("Names:", paste(names(x), collapse=", "), "\n\n")
+
+cat("---- UNIFORMITY TABLE ----\n")
+cat("Class:", class(x$uniformity), "\n")
+if (is.data.frame(x$uniformity)) {
+  cat("Columns:", paste(names(x$uniformity), collapse=", "), "\n")
+  print(head(x$uniformity, 3))
+} else if (is.list(x$uniformity)) {
+  cat("List contents:", paste(names(x$uniformity), collapse=", "), "\n")
+}
+
+cat("\n---- AREA CHANGE TABLE ----\n")
+cat("Class:", class(x$area_change), "\n")
+if (is.data.frame(x$area_change)) {
+  cat("Columns:", paste(names(x$area_change), collapse=", "), "\n")
+  print(head(x$area_change, 3))
+} else if (is.list(x$area_change)) {
+  cat("List contents:", paste(names(x$area_change), collapse=", "), "\n")
+}
+
+cat("\n---- TCH FORECASTED TABLE ----\n")
+cat("Class:", class(x$tch_forecasted), "\n")
+if (is.data.frame(x$tch_forecasted)) {
+  cat("Columns:", paste(names(x$tch_forecasted), collapse=", "), "\n")
+  print(head(x$tch_forecasted, 3))
+}
diff --git a/test_rds_structure.R b/test_rds_structure.R
new file mode 100644
index 0000000..2037e86
--- /dev/null
+++ b/test_rds_structure.R
@@ -0,0 +1,35 @@
+# Load the created RDS file
+rds_file <- "laravel_app/storage/app/john/reports/kpis/field_level/john_kpi_summary_tables_week06_2026.rds"
+data <- readRDS(rds_file)
+
+# Check structure
+cat("Top-level structure:\n")
+cat("Names:", paste(names(data), collapse=", "), "\n")
+cat("Has summary_tables:", "summary_tables" %in% names(data), "\n")
+cat("Has all_kpis:", "all_kpis" %in% names(data), "\n")
+cat("Has field_details:", "field_details" %in% names(data), "\n\n")
+
+# Check summary_tables structure
+if (!is.null(data$summary_tables)) {
+  cat("Summary tables (KPI names):\n")
+  cat("  -", paste(names(data$summary_tables), collapse=", "), "\n\n")
+  
+  # Check one example
+  cat("Uniformity KPI (first 3 rows):\n")
+  print(head(data$summary_tables$uniformity, 3))
+}
+
+cat("\n---\n")
+cat("all_kpis structure:\n")
+if (!is.null(data$all_kpis)) {
+  cat("  -", paste(names(data$all_kpis), collapse=", "), "\n")
+}
+
+cat("\n---\n")
+cat("field_details structure:\n")
+if (!is.null(data$field_details)) {
+  cat("Columns:", paste(colnames(data$field_details), collapse=", "), "\n")
+  print(data$field_details)
+} else {
+  cat("  NULL (expected if field_boundaries_sf had issues)\n")
+}
diff --git a/test_render_90.R b/test_render_90.R
new file mode 100644
index 0000000..518cd9d
--- /dev/null
+++ b/test_render_90.R
@@ -0,0 +1,22 @@
+# Test rendering of 90 report
+library(rmarkdown)
+
+# Use "john" project since it has the KPI data we just created
+render(
+  input = "r_app/90_CI_report_with_kpis_agronomic_support.Rmd",
+  params = list(
+    data_dir = "john",
+    report_date = "2026-02-04",
+    mail_day = "Monday",
+    borders = FALSE,
+    ci_plot_type = "mosaic",
+    colorblind_friendly = FALSE,
+    facet_by_season = FALSE,
+    x_axis_unit = "days"
+  ),
+  output_file = "test_90_john_2026_02_04.docx",
+  output_dir = "output",
+  quiet = FALSE
+)
+
+cat("\n✓ Report rendered!\n")