diff --git a/r_app/.gitignore b/r_app/.gitignore index d159461..7cd7b7e 100644 --- a/r_app/.gitignore +++ b/r_app/.gitignore @@ -8,8 +8,14 @@ renv *.tmp *.swp *.save + +# Ignore ALL PNG files by default (generated outputs, analysis plots, etc.) *.png +# EXCEPTIONS: Explicitly track intentional PNG assets +# Uncomment or add lines below for PNG files that should be committed to git +!r_app/CI_graph_example.png + # Ignore files related to Rproj .Rproj.user/ .Rhistory diff --git a/r_app/80_utils_agronomic_support.R b/r_app/80_utils_agronomic_support.R index adb629a..9dcf3db 100644 --- a/r_app/80_utils_agronomic_support.R +++ b/r_app/80_utils_agronomic_support.R @@ -24,8 +24,6 @@ library(tidyr) library(readxl) library(writexl) library(spdep) -library(caret) -library(CAST) # ============================================================================ # SHARED HELPER FUNCTIONS (NOW IN 80_UTILS_COMMON.R) @@ -588,7 +586,7 @@ create_summary_tables <- function(all_kpis) { #' @param current_year Current year #' #' @return Data frame with one row per field, all KPI columns -create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_week, current_year) { +create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_week, current_year, current_stats = NULL) { # Start with field identifiers AND field_idx for joining result <- field_boundaries_sf %>% @@ -602,6 +600,20 @@ create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_wee ) %>% select(field_idx, Field_id, Field_name, Week, Year) + # ============================================ + # GROUP 0: MEAN CI (from field statistics) + # ============================================ + if (!is.null(current_stats)) { + result <- result %>% + left_join( + current_stats %>% + select(Field_id, Mean_CI), + by = "Field_id" + ) + } else { + result$Mean_CI <- NA_real_ + } + # ============================================ # GROUP 1: FIELD UNIFORMITY (KPI 1) # ============================================ @@ -609,7 +621,8 @@ create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_wee left_join( all_kpis$uniformity %>% select(field_idx, CV = cv_value, - Uniformity_Category = uniformity_category), + Uniformity_Category = uniformity_category, + Uniformity_Interpretation = interpretation), by = "field_idx" ) @@ -915,9 +928,95 @@ calculate_all_field_analysis_agronomic_support <- function( data_dir = data_dir, project_dir = project_dir) message("Calculating KPI 4: Growth Decline...") - growth_decline_kpi <- calculate_growth_decline_kpi( - ci_pixels_by_field - ) + + # Load historical field statistics to build weekly mean CI time series per field + # (growth_decline_kpi expects temporal series, not spatial pixel arrays) + weekly_mean_ci_by_field <- list() + + # Build list of weekly mean CI values for each field (4-week lookback) + for (field_idx in seq_len(nrow(field_boundaries_sf))) { + weekly_ci_values <- c() + } + + # Try to load historical data for trend calculation + if (!is.null(output_dir) && !is.null(project_dir)) { + tryCatch({ + historical_data <- load_historical_field_data( + project_dir = project_dir, + current_week = current_week, + current_year = current_year, + reports_dir = output_dir, + num_weeks = 4, + auto_generate = FALSE, + field_boundaries_sf = field_boundaries_sf + ) + + if (!is.null(historical_data) && length(historical_data) > 0) { + message(" Building weekly mean CI time series from historical data...") + + # Initialize list with empty vectors for each field + for (field_idx in seq_len(nrow(field_boundaries_sf))) { + weekly_mean_ci_by_field[[field_idx]] <- c() + } + + # Extract Mean_CI from each historical week (reverse order to go chronologically) + for (hist_idx in rev(seq_along(historical_data))) { + hist_week <- historical_data[[hist_idx]] + hist_data <- hist_week$data + + # Extract Mean_CI column if available + if ("Mean_CI" %in% names(hist_data)) { + # Match fields between historical data and field_boundaries + for (field_idx in seq_len(nrow(field_boundaries_sf))) { + field_name <- field_boundaries_sf$field[field_idx] + + # Find matching row in historical data by field name/ID + field_row <- which( + (hist_data$Field_id == field_name | hist_data$Field_name == field_name) & + !is.na(hist_data$Mean_CI) + ) + + if (length(field_row) > 0) { + mean_ci_val <- as.numeric(hist_data$Mean_CI[field_row[1]]) + if (!is.na(mean_ci_val)) { + weekly_mean_ci_by_field[[field_idx]] <- c(weekly_mean_ci_by_field[[field_idx]], mean_ci_val) + } + } + } + } + } + + message(paste(" ✓ Loaded weekly Mean_CI for", sum(sapply(weekly_mean_ci_by_field, length) > 0), "fields")) + } + }, error = function(e) { + message(paste(" Note: Could not load historical field data for trend analysis:", e$message)) + }) + } + + # If no historical data available, create empty vectors (will result in "Insufficient data") + if (length(weekly_mean_ci_by_field) == 0 || all(sapply(weekly_mean_ci_by_field, length) == 0)) { + message(" Warning: No historical weekly CI data available - using current week only") + for (field_idx in seq_len(nrow(field_boundaries_sf))) { + # Use current week mean CI as single-point series (insufficient for trend) + if (!is.null(current_stats) && nrow(current_stats) > 0) { + field_name <- field_boundaries_sf$field[field_idx] + matching_row <- which( + (current_stats$Field_id == field_name | current_stats$Field_name == field_name) & + !is.na(current_stats$Mean_CI) + ) + if (length(matching_row) > 0) { + weekly_mean_ci_by_field[[field_idx]] <- c(as.numeric(current_stats$Mean_CI[matching_row[1]])) + } else { + weekly_mean_ci_by_field[[field_idx]] <- NA_real_ + } + } else { + weekly_mean_ci_by_field[[field_idx]] <- NA_real_ + } + } + } + + # Calculate growth decline using weekly time series (not spatial pixel arrays) + growth_decline_kpi <- calculate_growth_decline_kpi(weekly_mean_ci_by_field) message("Calculating KPI 5: Field Patchiness...") # Calculate patchiness using both Gini coefficient and Moran's I spatial clustering @@ -943,6 +1042,16 @@ calculate_all_field_analysis_agronomic_support <- function( # Use the common wrapper function (same as cane supply) gap_scores_result <- calculate_gap_scores(per_field_files, field_boundaries_sf) + # Guard against NULL or empty result from calculate_gap_scores + if (is.null(gap_scores_result) || nrow(gap_scores_result) == 0) { + message(" Warning: calculate_gap_scores returned NULL/empty - creating fallback") + gap_scores_result <- data.frame( + Field_id = field_boundaries_sf$field, + gap_score = NA_real_, + stringsAsFactors = FALSE + ) + } + # Convert to the format expected by orchestrator gap_filling_kpi <- gap_scores_result %>% mutate(field_idx = match(Field_id, field_boundaries_sf$field)) %>% @@ -999,7 +1108,8 @@ calculate_all_field_analysis_agronomic_support <- function( field_boundaries_sf = field_boundaries_sf, all_kpis = all_kpis, current_week = current_week, - current_year = current_year + current_year = current_year, + current_stats = current_stats ) # Create summary tables diff --git a/r_app/80_utils_cane_supply.R b/r_app/80_utils_cane_supply.R index 3877c1c..c4c1afa 100644 --- a/r_app/80_utils_cane_supply.R +++ b/r_app/80_utils_cane_supply.R @@ -27,6 +27,15 @@ library(tidyr) library(readxl) library(writexl) +# ============================================================================ +# ALERT THRESHOLDS & CONFIGURATION CONSTANTS +# ============================================================================ + +# CI change thresholds for alert categorization +# These values are project-standard and should be consistent across all workflows +CI_CHANGE_DECLINE_THRESHOLD <- -0.5 # Weekly CI change threshold for decline alerts +CI_CHANGE_INCREASE_THRESHOLD <- 0.5 # Weekly CI change threshold for increase alerts + # ============================================================================ # ANGATA-SPECIFIC HELPER FUNCTIONS (Placeholder Section) # ============================================================================ @@ -139,7 +148,7 @@ categorize_cv_trend_long_term <- function(cv_slope) { #' Determine status alert based on harvest probability and crop health #' Priority order: #' 1. harvest_ready (imminent + mature ≥12 months) -#' 2. decline_stress (drop ≥2 points but still >1.5) +#' 2. decline_stress (drop ≥CI_CHANGE_DECLINE_THRESHOLD but still >1.5) #' 3. harvested_bare (Mean CI < 1.5) #' @param imminent_prob Numeric harvest probability #' @param age_week Numeric age in weeks @@ -152,8 +161,8 @@ calculate_status_alert <- function(imminent_prob, age_week, weekly_ci_change, me return("harvest_ready") } - # Priority 2: Strong decline - if (!is.na(weekly_ci_change) && weekly_ci_change <= -2.0 && !is.na(mean_ci) && mean_ci > 1.5) { + # Priority 2: Strong decline (using configurable threshold) + if (!is.na(weekly_ci_change) && weekly_ci_change <= CI_CHANGE_DECLINE_THRESHOLD && !is.na(mean_ci) && mean_ci > 1.5) { return("decline_stress") } @@ -582,7 +591,7 @@ calculate_field_analysis_cane_supply <- function(setup, # ========== PHASE 6: LOAD HARVEST PROBABILITIES ========== message("\n4. Loading harvest probabilities from script 31...") - harvest_prob_dir <- file.path(data_dir, "..", "reports", "kpis", "field_stats") + harvest_prob_dir <- setup$kpi_field_stats_dir harvest_prob_file <- file.path(harvest_prob_dir, sprintf("%s_harvest_imminent_week_%02d_%d.csv", project_dir, current_week, current_year)) message(paste(" Looking for:", harvest_prob_file)) @@ -634,13 +643,23 @@ calculate_field_analysis_cane_supply <- function(setup, # print(head(field_analysis_df[, available_cols], 10)) # } - # # ========== PHASE 10: CALCULATE FARM-LEVEL KPIS ========== + # ========== PHASE 10: CALCULATE FARM-LEVEL KPIS ========== # farm_kpi_results <- calculate_farm_level_kpis( # field_analysis_df, # current_week, # current_year, # end_date # ) + + # For now, farm-level KPIs are not implemented in CANE_SUPPLY workflow + farm_kpi_results <- NULL + + # ========== RETURN RESULTS ========== + return(list( + field_analysis_df = field_analysis_df, + farm_kpi_results = farm_kpi_results, + export_paths = export_paths + )) } # ============================================================================ diff --git a/r_app/80_utils_common.R b/r_app/80_utils_common.R index 37ca957..d3d2ca2 100644 --- a/r_app/80_utils_common.R +++ b/r_app/80_utils_common.R @@ -17,6 +17,25 @@ # centralized in the orchestrator script. # ============================================================================ +# ============================================================================ +# LOAD PROJECT CONFIGURATION (Guard against re-sourcing) +# ============================================================================ +# Ensure parameters_project.R has been sourced to provide global configuration +# (PROJECT, data_dir, field_boundaries_path, etc.). Use a sentinel to avoid double-sourcing. +if (!exists("PROJECT", envir = .GlobalEnv)) { + tryCatch({ + source(here::here("r_app", "parameters_project.R")) + }, error = function(e) { + # Fallback: try relative path if here() doesn't work + tryCatch({ + source("parameters_project.R") + }, error = function(e2) { + warning(paste("Could not source parameters_project.R:", e2$message, + "- using defaults or expecting caller to set PROJECT/data_dir")) + }) + }) +} + # ============================================================================ # CONSTANTS (from 80_calculate_kpis.R) # ============================================================================ @@ -495,8 +514,15 @@ calculate_gap_scores <- function(per_field_files, field_boundaries_sf) { dplyr::summarise(gap_score = mean(gap_score, na.rm = TRUE), .groups = "drop") message(paste(" ✓ Calculated gap scores for", nrow(gap_scores_df), "fields")) - message(paste(" Gap score range:", round(min(gap_scores_df$gap_score, na.rm=TRUE), 2), "-", - round(max(gap_scores_df$gap_score, na.rm=TRUE), 2), "%")) + + # Guard against all-NA values which would produce Inf/-Inf warnings + if (any(is.finite(gap_scores_df$gap_score))) { + min_score <- round(min(gap_scores_df$gap_score, na.rm = TRUE), 2) + max_score <- round(max(gap_scores_df$gap_score, na.rm = TRUE), 2) + message(paste(" Gap score range:", min_score, "-", max_score, "%")) + } else { + message(" Gap score range: All values are NA (no valid gap scores)") + } } else { message(" WARNING: No gap scores calculated from per-field mosaics") gap_scores_df <- NULL @@ -645,6 +671,8 @@ load_harvest_data <- function(data_dir) { if (all(required_cols %in% names(harvesting_data))) { # Convert to data frame and ensure column types harvesting_data <- as.data.frame(harvesting_data) + # CRITICAL: Coerce field to character to preserve leading zeros (e.g., "01", "02") + harvesting_data$field <- as.character(harvesting_data$field) harvesting_data$year <- as.numeric(harvesting_data$year) harvesting_data$tonnage_ha <- as.numeric(harvesting_data$tonnage_ha) @@ -664,7 +692,12 @@ load_harvest_data <- function(data_dir) { # Fallback: create empty data frame if loading failed if (is.null(harvesting_data)) { message(" WARNING: No harvest data available. TCH yield prediction will use graceful fallback (NA values)") - harvesting_data <- data.frame(field = character(), year = numeric(), tonnage_ha = numeric()) + harvesting_data <- data.frame( + field = character(), # Explicitly character to preserve leading zeros when data is added + year = numeric(), + tonnage_ha = numeric(), + stringsAsFactors = FALSE + ) } return(harvesting_data) diff --git a/r_app/90_CI_report_with_kpis_agronomic_support.Rmd b/r_app/90_CI_report_with_kpis_agronomic_support.Rmd index 4725229..666e6b3 100644 --- a/r_app/90_CI_report_with_kpis_agronomic_support.Rmd +++ b/r_app/90_CI_report_with_kpis_agronomic_support.Rmd @@ -376,13 +376,13 @@ prev_week_1_date <- report_date_obj - 7 prev_week_2_date <- report_date_obj - 14 prev_week_3_date <- report_date_obj - 21 -week_minus_1 <- lubridate::isoweek(prev_week_1_date) +week_minus_1 <- sprintf("%02d", lubridate::isoweek(prev_week_1_date)) week_minus_1_year <- lubridate::isoyear(prev_week_1_date) -week_minus_2 <- lubridate::isoweek(prev_week_2_date) +week_minus_2 <- sprintf("%02d", lubridate::isoweek(prev_week_2_date)) week_minus_2_year <- lubridate::isoyear(prev_week_2_date) -week_minus_3 <- lubridate::isoweek(prev_week_3_date) +week_minus_3 <- sprintf("%02d", lubridate::isoweek(prev_week_3_date)) week_minus_3_year <- lubridate::isoyear(prev_week_3_date) # Format current week with leading zeros @@ -468,10 +468,14 @@ if (!is.null(CI_quadrant) && nrow(CI_quadrant) > 0) { Satellite Based Field Reporting ::: + + ::: {custom-style="Cover_subtitle" style="text-align:center; margin-top:18px;"} Chlorophyll Index (CI) Monitoring Report — `r toupper(params$data_dir)` Farm (Week `r { rd <- params$report_date; rd <- if (inherits(rd, "Date")) rd else suppressWarnings(as.Date(rd)); if (is.na(rd)) rd <- Sys.Date(); if (!is.null(params$week)) params$week else format(rd, '%V') }`, `r { rd <- params$report_date; rd <- if (inherits(rd, "Date")) rd else suppressWarnings(as.Date(rd)); if (is.na(rd)) rd <- Sys.Date(); format(rd, '%Y') }`) ::: +\newpage + ## Report Summary **Farm Location:** `r toupper(project_dir)` Estate @@ -907,8 +911,8 @@ tryCatch({ # Aggregate mosaics for three weeks: current, week-1, week-3 farm_mosaic_current <- aggregate_mosaics_safe(current_week, current_iso_year, "current week") - farm_mosaic_minus_1 <- aggregate_mosaics_safe(week_minus_1, week_minus_1_year, "week-1") - farm_mosaic_minus_3 <- aggregate_mosaics_safe(week_minus_3, week_minus_3_year, "week-3") + farm_mosaic_minus_1 <- aggregate_mosaics_safe(as.numeric(week_minus_1), week_minus_1_year, "week-1") + farm_mosaic_minus_3 <- aggregate_mosaics_safe(as.numeric(week_minus_3), week_minus_3_year, "week-3") # Extract CI band (5th band, or named "CI") from each aggregated mosaic farm_ci_current <- NULL @@ -1547,7 +1551,6 @@ if (!exists("field_details_table") || is.null(field_details_table) || nrow(field select( Field = Field_id, `Field Size (acres)` = field_size_acres, - `Growth Uniformity` = Uniformity_Interpretation, `Mean CI` = Mean_CI, `Weekly CI Change` = Weekly_CI_Change, `Yield Forecast (t/ha)` = TCH_Forecasted, @@ -1561,7 +1564,6 @@ if (!exists("field_details_table") || is.null(field_details_table) || nrow(field select( Field = Field_id, `Field Size (acres)` = field_size_acres, - `Growth Uniformity` = Uniformity_Interpretation, `Mean CI` = Mean_CI, `Yield Forecast (t/ha)` = TCH_Forecasted, `Gap Score` = Gap_Score, @@ -1616,8 +1618,9 @@ The Chlorophyll Index (CI) is a vegetation index that measures the relative amou CI values typically range from 0 (bare soil or severely stressed vegetation) to 7+ (very healthy, dense vegetation). For sugarcane, values between 3-7 generally indicate good crop health, depending on the growth stage. +
![Chlorophyll Index Example](CI_graph_example.png) - +
### What You'll Find in This Report: diff --git a/r_app/91_CI_report_with_kpis_cane_supply.Rmd b/r_app/91_CI_report_with_kpis_cane_supply.Rmd index ea0f92f..6929b80 100644 --- a/r_app/91_CI_report_with_kpis_cane_supply.Rmd +++ b/r_app/91_CI_report_with_kpis_cane_supply.Rmd @@ -487,7 +487,7 @@ if (exists("summary_data") && !is.null(summary_data) && "field_analysis" %in% na ## 1.1 Overview of cane supply area, showing zones with number of acres being harvest ready -```{r overview_map, fig.width=9, fig.height=7, fig.align="center", echo=FALSE, message=FALSE, warning=FALSE} +```{r overview_map, fig.width=8, fig.height=7, fig.align="center", echo=FALSE, message=FALSE, warning=FALSE} # Create a hexbin overview map with ggplot tryCatch({ # Use per-field field_analysis data from RDS (already loaded in load_kpi_data chunk) @@ -643,7 +643,7 @@ tryCatch({ }) ``` -\newpage + ## 1.2 Key Performance Indicators ```{r combined_kpi_table, echo=FALSE, results='asis'} @@ -931,9 +931,9 @@ CI values typically range from 0 (bare soil or severely stressed vegetation) to -```{r ci_fig, echo=FALSE, fig.align='right', out.width='40%', fig.cap="Chlorophyll Index Example"} -knitr::include_graphics("CI_graph_example.png") -``` +
+![Chlorophyll Index Example](CI_graph_example.png) +
### Data File Structure and Columns diff --git a/r_app/parameters_project.R b/r_app/parameters_project.R index daae442..517fd48 100644 --- a/r_app/parameters_project.R +++ b/r_app/parameters_project.R @@ -623,13 +623,19 @@ detect_tile_structure_from_merged_final <- function(merged_final_tif_dir, daily_ #' Detect mosaic mode from project structure #' +#' Determine mosaic architecture (legacy detection function) +#' +#' NOTE: This is a legacy function kept for backward compatibility. +#' The project has moved to per-field (single-file) architecture. +#' `weekly_tile_max` is no longer created in all_dirs, so this will always return "single-file" +#' #' Determines if project uses "tiled" (legacy) or "single-file" (per-field) mosaics #' #' @param project_dir Character. Project name -#' @return Character. "tiled" or "single-file" +#' @return Character. "tiled" or "single-file" (now always "single-file") detect_mosaic_mode <- function(project_dir) { # Per-field architecture is standard - always return "single-file" - # unless weekly_tile_max directory exists with content + # Legacy support: check if weekly_tile_max exists (it won't in standard setup) mosaic_tiled_dir <- file.path("laravel_app", "storage", "app", project_dir, "weekly_tile_max") if (dir.exists(mosaic_tiled_dir) && length(list.files(mosaic_tiled_dir)) > 0) { @@ -662,11 +668,15 @@ get_project_storage_path <- function(project_dir, subdir = NULL) { return(path) } -#' Get mosaic directory +#' Get mosaic directory (legacy function) +#' +#' NOTE: This is a legacy helper kept for backward compatibility. +#' In the standard per-field workflow, this returns weekly_mosaic directory. +#' The "tiled" mode is no longer created (weekly_tile_max_dir was removed from all_dirs). #' #' @param project_dir Character. Project name -#' @param mosaic_mode Character. "tiled" or "single-file" -#' @return Character. Full path to mosaic directory +#' @param mosaic_mode Character. "tiled" or "single-file" (auto-detects if "auto") +#' @return Character. Full path to mosaic directory (typically weekly_mosaic) get_mosaic_dir <- function(project_dir, mosaic_mode = "auto") { if (mosaic_mode == "auto") { mosaic_mode <- detect_mosaic_mode(project_dir) @@ -718,11 +728,14 @@ check_harvest_output_exists <- function(project_dir, week_num, year_num) { file.exists(path) } -#' Get mosaic verification directory +#' Get mosaic verification directory (legacy function) +#' +#' NOTE: This is a legacy helper kept for backward compatibility. +#' Standard workflow uses weekly_mosaic; tiled mode is no longer created. #' #' @param project_dir Character. Project name #' @param mosaic_mode Character. "tiled" or "single-file" -#' @return Character. Full path to mosaic directory +#' @return Character. Full path to mosaic directory for verification get_mosaic_verification_dir <- function(project_dir, mosaic_mode) { base <- file.path("laravel_app", "storage", "app", project_dir) diff --git a/r_app/report_utils.R b/r_app/report_utils.R index 3827593..8b3afa1 100644 --- a/r_app/report_utils.R +++ b/r_app/report_utils.R @@ -802,30 +802,9 @@ get_week_path <- function(mosaic_path, input_date, week_offset) { target_week <- sprintf("%02d", lubridate::isoweek(target_date)) # Left-pad week number with a zero if needed target_year <- lubridate::isoyear(target_date) - # Primary approach: Try single-file mosaic path first + # Load single-file mosaic for the given week path_to_week <- here::here(mosaic_path, paste0("week_", target_week, "_", target_year, ".tif")) - # Smart fallback: If single-file doesn't exist AND path contains "weekly_mosaic", check for tiles - if (!file.exists(path_to_week) && grepl("weekly_mosaic", mosaic_path)) { - # Try to locate tile-based mosaics in weekly_tile_max instead - tile_mosaic_path <- sub("weekly_mosaic", "weekly_tile_max", mosaic_path) - - # Look for any tile files matching the week pattern (e.g., week_XX_YYYY_00.tif, week_XX_YYYY_01.tif, etc.) - if (dir.exists(tile_mosaic_path)) { - tile_files <- list.files(tile_mosaic_path, - pattern = paste0("^week_", target_week, "_", target_year, "_(\\d{2})\\.tif$"), - full.names = TRUE) - - if (length(tile_files) > 0) { - # Found tiles - return the first tile as primary, note that multiple tiles exist - safe_log(paste("Single-file mosaic not found for week", target_week, target_year, - "but found", length(tile_files), "tile files in weekly_tile_max. Using tile approach."), "INFO") - # Return first tile - caller should aggregate if needed - path_to_week <- tile_files[1] # Return first tile; downstream can handle multiple tiles - } - } - } - # Log the path calculation safe_log(paste("Calculated path for week", target_week, "of year", target_year, ":", path_to_week), "INFO") @@ -1169,10 +1148,10 @@ generate_field_kpi_summary <- function(field_name, field_details_table, CI_quadr } kpi_text <- paste0( - "Size: ", round(field_summary$field_size, 1), " ha | Growth Uniformity: ", field_summary$uniformity_levels, + "Size: ", round(field_summary$field_size * 0.404686, 1), " ha | Mean CI: ", round(field_summary$avg_mean_ci, 2), + " | Growth Uniformity: ", field_summary$uniformity_levels, " | ", yield_text, " | Gap Score: ", round(field_summary$max_gap_score, 1), - " | Decline Risk: ", field_summary$highest_decline_risk, " | Weed Risk: ", field_summary$highest_weed_risk, - " | Mean CI: ", round(field_summary$avg_mean_ci, 2) + " | Decline Risk: ", field_summary$highest_decline_risk, " | Weed Risk: ", field_summary$highest_weed_risk ) # Wrap in smaller text HTML tags for Word output