add a condition for pillar

scripts/bigquery_aggregate_layer.py

@@ -20,6 +20,32 @@ Narrative style:
   - Plain text, tanpa markdown bold (**)
   - Interpretatif: membaca tren, gap, anomali, konsistensi dari data nyata
   - Bilingual: narrative_en (Inggris) + narrative_id (Indonesia)
+
+KONDISI PILAR (pillar_condition_en / pillar_condition_id):
+  Kolom tambahan di agg_pillar_by_country untuk mendeskripsikan kondisi
+  tiap pilar per negara per tahun secara kontekstual dan kuantitatif.
+
+  Landasan teori:
+  1. FAO & CFS (1996 World Food Summit; CFS Reform Document 2009):
+     Definisi 4 pilar ketahanan pangan dan makna substantif masing-masing.
+     Referensi: FAO (2009). "Declaration of the World Summit on Food Security."
+                CFS (2012). "Global Strategic Framework for Food Security & Nutrition."
+  2. GFSI — Economist Impact (2022):
+     Threshold klasifikasi skor 0-100:
+       >= 75 : "Good" environment  -> label "Secure / Aman"
+       >= 60 : above threshold     -> label "Adequate / Memadai"
+       >= 40 : "Moderate" env      -> label "Moderate / Sedang"
+       >= 20 : below moderate      -> label "At Risk / Berisiko"
+       <  20 : severe              -> label "Critical / Kritis"
+     Referensi: Economist Impact (2022). "Global Food Security Index 2022."
+  3. IPC — Integrated Food Security Phase Classification (2019):
+     Klasifikasi bertingkat per pilar: dari "Moderate Risk" hingga "Critical".
+     Referensi: IPC (2019). "IPC Technical Manual Version 3.0."
+  4. FAO SOFI (2023/2024):
+     Konteks kondisi per pilar: availability (supply/stok), access (keterjangkauan),
+     utilization (nutrisi/sanitasi), stability (kerentanan terhadap guncangan).
+     Referensi: FAO et al. (2024). "The State of Food Security and Nutrition
+                in the World 2024."
 """
 
 import pandas as pd
@@ -157,6 +183,173 @@ def translate_pillar(name: str) -> str:
     return PILLAR_TRANSLATION_ID.get(name, name)
 
 
+# =============================================================================
+# PILLAR CONDITION CLASSIFIER
+# =============================================================================
+#
+# Landasan teori (lihat docstring modul di atas untuk referensi lengkap):
+#
+# Tier skor (skala 1-100, mengacu GFSI 2022 + IPC Phase Classification):
+#   >= 75 : Secure        / Aman           — performa tinggi, kondisi baik
+#   >= 60 : Adequate      / Memadai        — di atas threshold, masih ada ruang
+#   >= 40 : Moderate      / Sedang         — tantangan nyata, perlu perhatian
+#   >= 20 : At Risk       / Berisiko       — kondisi lemah, butuh intervensi
+#   <  20 : Critical      / Kritis         — sangat buruk, tindakan segera
+#
+# Label kontekstual per pilar mengacu definisi FAO/CFS empat pilar:
+#   Food Availability  : ketersediaan pasokan (produksi, stok, impor)
+#   Food Access        : keterjangkauan ekonomi & fisik terhadap pangan
+#   Food Utilization   : pemanfaatan biologis (gizi, sanitasi, kesehatan)
+#   Food Stability     : konsistensi tiga pilar di atas dari waktu ke waktu
+#   Food Other         : indikator multidimensi / suplemen
+#
+# =============================================================================
+
+# Tier thresholds (urut dari tertinggi)
+_CONDITION_TIERS = [
+    # (min_score, base_label_en,  base_label_id)
+    (75, "Secure",    "Aman"),
+    (60, "Adequate",  "Memadai"),
+    (40, "Moderate",  "Sedang"),
+    (20, "At Risk",   "Berisiko"),
+    ( 0, "Critical",  "Kritis"),
+]
+
+# Konteks kondisi per pilar per tier (EN, ID)
+# Mengacu makna substantif pilar (FAO SOFI 2024; FSC Handbook 2020;
+# IPC Technical Manual 2019).
+_PILLAR_CONTEXT: dict = {
+    # ---- Food Availability ----
+    "Food Availability": {
+        "Secure"   : ("Food supply is abundant and well-distributed",
+                      "Pasokan pangan berlimpah dan terdistribusi merata"),
+        "Adequate" : ("Food supply is sufficient with minor gaps",
+                      "Pasokan pangan cukup dengan kesenjangan minor"),
+        "Moderate" : ("Food supply shows signs of strain",
+                      "Pasokan pangan menunjukkan tanda-tanda tekanan"),
+        "At Risk"  : ("Food supply is insufficient; stocks are dwindling",
+                      "Pasokan pangan tidak mencukupi; stok mulai menipis"),
+        "Critical" : ("Severe food supply deficit; stocks critically low",
+                      "Defisit pasokan pangan parah; stok dalam kondisi kritis"),
+    },
+    # ---- Food Access ----
+    "Food Access": {
+        "Secure"   : ("Food is economically and physically accessible to all",
+                      "Pangan terjangkau secara ekonomi dan fisik bagi semua"),
+        "Adequate" : ("Food access is generally good with limited barriers",
+                      "Akses pangan umumnya baik dengan hambatan terbatas"),
+        "Moderate" : ("Portions of the population face access constraints",
+                      "Sebagian penduduk menghadapi kendala akses pangan"),
+        "At Risk"  : ("Significant affordability or physical access barriers",
+                      "Hambatan keterjangkauan atau akses fisik yang signifikan"),
+        "Critical" : ("Widespread inability to access sufficient food",
+                      "Ketidakmampuan meluas dalam mengakses pangan yang cukup"),
+    },
+    # ---- Food Utilization ----
+    "Food Utilization": {
+        "Secure"   : ("Dietary quality, nutrition, and sanitation are strong",
+                      "Kualitas gizi, nutrisi, dan sanitasi dalam kondisi baik"),
+        "Adequate" : ("Nutrition and sanitation are adequate; minor deficiencies",
+                      "Gizi dan sanitasi memadai; kekurangan minor masih ada"),
+        "Moderate" : ("Nutritional gaps or sanitation issues are evident",
+                      "Kesenjangan gizi atau masalah sanitasi mulai terlihat"),
+        "At Risk"  : ("Significant nutritional deficiencies or poor sanitation",
+                      "Kekurangan gizi atau sanitasi buruk yang signifikan"),
+        "Critical" : ("Severe malnutrition and/or critical sanitation deficits",
+                      "Malnutrisi parah dan/atau defisit sanitasi yang kritis"),
+    },
+    # ---- Food Stability ----
+    "Food Stability": {
+        "Secure"   : ("Food security is consistently maintained over time",
+                      "Ketahanan pangan terjaga konsisten dari waktu ke waktu"),
+        "Adequate" : ("Stability is generally good with manageable risks",
+                      "Stabilitas umumnya baik dengan risiko yang masih terkelola"),
+        "Moderate" : ("Periodic shocks or vulnerabilities affect stability",
+                      "Guncangan periodik atau kerentanan memengaruhi stabilitas"),
+        "At Risk"  : ("Frequent disruptions threaten food security continuity",
+                      "Gangguan berulang mengancam kesinambungan ketahanan pangan"),
+        "Critical" : ("Sustained instability; food security is highly fragile",
+                      "Ketidakstabilan berkelanjutan; ketahanan pangan sangat rapuh"),
+    },
+    # ---- Food Other / Indikator Tambahan ----
+    "Food Other": {
+        "Secure"   : ("Supplementary indicators reflect strong food system",
+                      "Indikator tambahan mencerminkan sistem pangan yang kuat"),
+        "Adequate" : ("Supplementary indicators are at acceptable levels",
+                      "Indikator tambahan berada pada level yang dapat diterima"),
+        "Moderate" : ("Supplementary indicators signal emerging challenges",
+                      "Indikator tambahan memberi sinyal tantangan yang muncul"),
+        "At Risk"  : ("Supplementary indicators show concerning levels",
+                      "Indikator tambahan menunjukkan level yang mengkhawatirkan"),
+        "Critical" : ("Supplementary indicators reflect systemic food system failure",
+                      "Indikator tambahan mencerminkan kegagalan sistemik pangan"),
+    },
+}
+
+# Fallback jika pillar_name tidak dikenali
+_PILLAR_CONTEXT_FALLBACK: dict = {
+    "Secure"   : ("Performance is high across food security indicators",
+                  "Performa tinggi pada indikator ketahanan pangan"),
+    "Adequate" : ("Performance is adequate across food security indicators",
+                  "Performa memadai pada indikator ketahanan pangan"),
+    "Moderate" : ("Performance shows moderate challenges",
+                  "Performa menunjukkan tantangan yang moderat"),
+    "At Risk"  : ("Performance indicates vulnerability in food security",
+                  "Performa mengindikasikan kerentanan ketahanan pangan"),
+    "Critical" : ("Performance is critically low; urgent action needed",
+                  "Performa sangat rendah; tindakan segera diperlukan"),
+}
+
+
+def get_pillar_condition(pillar_name: str, score: float) -> tuple:
+    """
+    Mengembalikan (condition_en, condition_id) berdasarkan skor dan nama pilar.
+
+    Tier mengacu GFSI 2022 (Economist Impact) + IPC Phase Classification (2019):
+      >= 75 -> Secure / Aman
+      >= 60 -> Adequate / Memadai
+      >= 40 -> Moderate / Sedang
+      >= 20 -> At Risk / Berisiko
+      <  20 -> Critical / Kritis
+
+    Deskripsi kontekstual mengacu FAO/CFS definisi 4 pilar (World Food Summit
+    1996; CFS 2009) dan FAO SOFI 2024.
+
+    Args:
+        pillar_name : Nama pilar dalam bahasa Inggris (e.g. "Food Availability").
+        score       : Skor ternormalisasi skala 1-100.
+
+    Returns:
+        Tuple (condition_en: str, condition_id: str)
+    """
+    if score is None or (isinstance(score, float) and np.isnan(score)):
+        return ("N/A", "N/A")
+
+    # Tentukan tier
+    tier_label_en = _CONDITION_TIERS[-1][1]  # default: Critical
+    tier_label_id = _CONDITION_TIERS[-1][2]
+    for min_score, lbl_en, lbl_id in _CONDITION_TIERS:
+        if score >= min_score:
+            tier_label_en = lbl_en
+            tier_label_id = lbl_id
+            break
+
+    # Ambil konteks per pilar
+    ctx = _PILLAR_CONTEXT.get(pillar_name, None)
+    if ctx:
+        ctx_en, ctx_id = ctx.get(
+            tier_label_en,
+            _PILLAR_CONTEXT_FALLBACK.get(tier_label_en, ("", ""))
+        )
+    else:
+        ctx_en, ctx_id = _PILLAR_CONTEXT_FALLBACK.get(tier_label_en, ("", ""))
+
+    # Format akhir: "TIER — Context"
+    condition_en = f"{tier_label_en} — {ctx_en}"
+    condition_id = f"{tier_label_id} — {ctx_id}"
+    return condition_en, condition_id
+
+
 # =============================================================================
 # WINDOWS CP1252 SAFE LOGGING
 # =============================================================================
@@ -334,8 +527,6 @@ def _find_anomaly_year(values_by_year: dict) -> tuple:
 
 # =============================================================================
 # NARRATIVE BUILDER — PILLAR
-# Digunakan untuk SEMUA baris: per negara dan ASEAN aggregate.
-# Jika is_asean=True, narasi tidak menyebut "country" melainkan "ASEAN region".
 # =============================================================================
 
 def _build_pillar_narrative(
@@ -418,7 +609,6 @@ def _build_pillar_narrative(
             sentences_en.append(s3_en)
             sentences_id.append(s3_id)
 
-    # Gap antar negara hanya relevan untuk ASEAN narrative
     if is_asean and not country_pillar_all.empty:
         gap_trend = _detect_country_gap(
             country_pillar_all[country_pillar_all["year"] <= year],
@@ -437,7 +627,6 @@ def _build_pillar_narrative(
             sentences_en.append(s4_en)
             sentences_id.append(s4_id)
 
-    # Top/bottom hanya ditampilkan untuk baris ASEAN
     if is_asean and top_country and bot_country and top_country != bot_country:
         top_country_id = translate_country(top_country)
         bot_country_id = translate_country(bot_country)
@@ -453,7 +642,6 @@ def _build_pillar_narrative(
         sentences_en.append(s5_en)
         sentences_id.append(s5_id)
 
-    # Perbandingan antar pilar
     if not all_pillar_scores_year.empty and len(all_pillar_scores_year) > 1:
         sorted_pillars = all_pillar_scores_year.sort_values("pillar_country_score_1_100", ascending=False)
         strongest = sorted_pillars.iloc[0]
@@ -530,7 +718,6 @@ class FoodSecurityAggregator:
             self.logger.warning(f"  [DIRECTION] {n_null_dir} rows NULL -> diisi 'positive'")
             self.df["direction"] = self.df["direction"].fillna("positive")
 
-        # Rename pillar_name: add 'Food ' prefix, remove Sustainability
         PILLAR_RENAME_MAP = {
             'Availability'  : 'Food Availability',
             'Access'        : 'Food Access',
@@ -542,7 +729,6 @@ class FoodSecurityAggregator:
         }
         self.df["pillar_name"] = self.df["pillar_name"].replace(PILLAR_RENAME_MAP)
 
-        # Kolom terjemahan Indonesia
         if "country_name_id" not in self.df.columns:
             self.df["country_name_id"] = self.df["country_name"].apply(translate_country)
         if "pillar_name_id" not in self.df.columns:
@@ -686,6 +872,12 @@ class FoodSecurityAggregator:
                 "asean_country_id"            : ASEAN_COUNTRY_ID,
                 "pillar_change"               : "Sustainability renamed to Food Other, all pillars prefixed with Food",
                 "architecture"                : "ASEAN merged into country tables (country_id=0)",
+                "condition_column"            : "pillar_condition_en/id added to agg_pillar_by_country",
+                "condition_reference"         : (
+                    "GFSI 2022 (Economist Impact) score tiers >= 75/60/40/20; "
+                    "IPC Technical Manual 2019; FAO/CFS 4-pillar framework 1996/2009; "
+                    "FAO SOFI 2024"
+                ),
             }),
             "validation_metrics" : json.dumps({
                 "status"   : status,
@@ -698,11 +890,6 @@ class FoodSecurityAggregator:
     # =========================================================================
 
     def _build_asean_pillar_rows(self, df_normed: pd.DataFrame) -> pd.DataFrame:
-        """
-        Hitung rata-rata ASEAN per pillar per year dari norm_value semua negara,
-        kemudian scale ulang ke 1-100 dalam konteks SELURUH tabel (negara + ASEAN).
-        Return DataFrame dengan format sama seperti baris per-negara.
-        """
         asean_agg = (
             df_normed
             .groupby(["pillar_id", "pillar_name", "year"])
@@ -716,7 +903,7 @@ class FoodSecurityAggregator:
         return asean_agg
 
     # =========================================================================
-    # STEP 2: agg_pillar_by_country (termasuk ASEAN)
+    # STEP 2: agg_pillar_by_country (termasuk ASEAN + kolom kondisi)
     # =========================================================================
 
     def calc_pillar_by_country(self) -> pd.DataFrame:
@@ -725,6 +912,7 @@ class FoodSecurityAggregator:
         self.logger.info("\n" + "=" * 70)
         self.logger.info(f"STEP 2: {table_name} -> [Gold] fs_asean_gold")
         self.logger.info("  Termasuk baris ASEAN (country_id=0) untuk filter Looker Studio")
+        self.logger.info("  Kolom baru: pillar_condition_en, pillar_condition_id")
         self.logger.info("=" * 70)
 
         try:
@@ -746,10 +934,27 @@ class FoodSecurityAggregator:
             # Gabung
             df = pd.concat([df_countries, df_asean], ignore_index=True)
 
-            # Scale 1-100 secara BERSAMA (negara + ASEAN dalam satu ruang skala)
+            # Scale 1-100 secara BERSAMA
             df["pillar_country_score_1_100"] = global_minmax(df["pillar_country_norm"])
 
-            # Rank hanya di antara negara asli (ASEAN tidak di-rank melawan dirinya sendiri)
+            # ---------------------------------------------------------------
+            # TAMBAHAN: kolom kondisi pilar
+            # Dibangkitkan SETELAH score_1_100 tersedia, sehingga tier
+            # langsung mencerminkan skor dalam skala akhir 1-100.
+            # Referensi tier: GFSI 2022 (Economist Impact); IPC 2019;
+            #                 FAO/CFS 1996/2009; FAO SOFI 2024.
+            # ---------------------------------------------------------------
+            conditions = df.apply(
+                lambda row: get_pillar_condition(
+                    row["pillar_name"],
+                    row["pillar_country_score_1_100"]
+                ),
+                axis=1
+            )
+            df["pillar_condition_en"] = conditions.apply(lambda x: x[0])
+            df["pillar_condition_id"] = conditions.apply(lambda x: x[1])
+
+            # Rank hanya di antara negara asli
             country_only = df[df["country_id"] != ASEAN_COUNTRY_ID].copy()
             country_only["rank_in_pillar_year"] = (
                 country_only.groupby(["pillar_id", "year"])["pillar_country_score_1_100"]
@@ -771,12 +976,20 @@ class FoodSecurityAggregator:
             df["pillar_country_score_1_100"] = df["pillar_country_score_1_100"].astype(float)
             df["pillar_name_id"]             = df["pillar_name_id"].astype(str)
             df["country_name_id"]            = df["country_name_id"].astype(str)
+            df["pillar_condition_en"]        = df["pillar_condition_en"].astype(str)
+            df["pillar_condition_id"]        = df["pillar_condition_id"].astype(str)
 
             self.logger.info(
                 f"  Total rows: {len(df):,} "
                 f"({len(df_countries):,} country + {len(asean_only):,} ASEAN)"
             )
 
+            # Log distribusi kondisi untuk QA
+            self.logger.info("\n  Distribusi pillar_condition_en (sample):")
+            cond_dist = df["pillar_condition_en"].value_counts().head(10)
+            for cond, cnt in cond_dist.items():
+                self.logger.info(f"    {cnt:>6,}  {cond}")
+
             schema = [
                 bigquery.SchemaField("country_id",                  "INTEGER", mode="REQUIRED"),
                 bigquery.SchemaField("country_name",                "STRING",  mode="REQUIRED"),
@@ -789,6 +1002,10 @@ class FoodSecurityAggregator:
                 bigquery.SchemaField("pillar_country_score_1_100",  "FLOAT",   mode="REQUIRED"),
                 bigquery.SchemaField("rank_in_pillar_year",         "INTEGER", mode="REQUIRED"),
                 bigquery.SchemaField("year_over_year_change",       "FLOAT",   mode="NULLABLE"),
+                # --- KOLOM KONDISI BARU ---
+                # Tier skor (GFSI 2022) + konteks substantif per pilar (FAO/CFS; IPC 2019)
+                bigquery.SchemaField("pillar_condition_en",         "STRING",  mode="REQUIRED"),
+                bigquery.SchemaField("pillar_condition_id",         "STRING",  mode="REQUIRED"),
             ]
             rows = load_to_bigquery(
                 self.client, df, table_name, layer='gold',
@@ -927,7 +1144,7 @@ class FoodSecurityAggregator:
 
             df_countries = pd.concat(parts, ignore_index=True)
 
-            # ---- ASEAN aggregate (rata-rata dari semua negara per framework per year) ----
+            # ---- ASEAN aggregate ----
             asean_parts = []
             for fw in df_countries["framework"].unique():
                 fw_df = df_countries[
@@ -958,7 +1175,6 @@ class FoodSecurityAggregator:
 
             df = check_and_dedup(df, ["country_id", "framework", "year"], context=table_name, logger=self.logger)
 
-            # Rank hanya di antara negara asli
             country_mask = df["country_id"] != ASEAN_COUNTRY_ID
             df.loc[country_mask, "rank_in_framework_year"] = (
                 df[country_mask]
@@ -1014,7 +1230,6 @@ class FoodSecurityAggregator:
         self.logger.info("\n" + "=" * 70)
         self.logger.info(f"STEP 4: {table_name} -> [Gold] fs_asean_gold")
         self.logger.info("  Termasuk baris ASEAN (country_id=0)")
-        self.logger.info("  Filter country_name='ASEAN' untuk overview regional")
         self.logger.info("=" * 70)
 
         try:
@@ -1022,7 +1237,6 @@ class FoodSecurityAggregator:
             years   = sorted(df_pillar_by_country["year"].unique())
             pillars = df_pillar_by_country["pillar_id"].unique()
 
-            # Precompute history per country x pillar
             history = {}
             for (c_id, p_id), grp in df_pillar_by_country.groupby(["country_id", "pillar_id"]):
                 history[(c_id, p_id)] = dict(
@@ -1031,8 +1245,6 @@ class FoodSecurityAggregator:
 
             for yr in years:
                 yr_df = df_pillar_by_country[df_pillar_by_country["year"] == yr]
-
-                # Semua negara asli untuk referensi top/bottom dalam narasi ASEAN
                 country_only_yr = yr_df[yr_df["country_id"] != ASEAN_COUNTRY_ID]
 
                 for p_id in pillars:
@@ -1044,11 +1256,9 @@ class FoodSecurityAggregator:
                     p_name     = str(p_name_row["pillar_name"])
                     n_pillars  = len(pillars)
 
-                    # Ranking di antara semua pillar (gunakan skor ASEAN untuk rank antar pillar)
                     asean_yr_all_pillars = yr_df[yr_df["country_id"] == ASEAN_COUNTRY_ID]
                     asean_sorted = asean_yr_all_pillars.sort_values("pillar_country_score_1_100", ascending=False).reset_index(drop=True)
 
-                    # Top/bottom di antara negara asli (untuk narasi ASEAN)
                     country_pillar_yr = country_only_yr[country_only_yr["pillar_id"] == p_id]
                     if not country_pillar_yr.empty:
                         top_row       = country_pillar_yr.loc[country_pillar_yr["pillar_country_score_1_100"].idxmax()]
@@ -1061,7 +1271,6 @@ class FoodSecurityAggregator:
                         top_country = bot_country = None
                         top_score = bot_score = None
 
-                    # Iterasi setiap baris (negara + ASEAN) pada pillar ini
                     for _, row in yr_pillar_all.iterrows():
                         c_id       = int(row["country_id"])
                         c_name     = str(row["country_name"])
@@ -1072,22 +1281,15 @@ class FoodSecurityAggregator:
                         p_name_id  = translate_pillar(p_name)
                         is_asean   = (c_id == ASEAN_COUNTRY_ID)
 
-                        # Rank pilar ini dalam konteks yang sesuai
                         if is_asean:
-                            # ASEAN: rank pilar ini di antara semua pilar ASEAN tahun ini
                             rank_sorted = asean_sorted.reset_index(drop=True)
                             p_rank = int(rank_sorted[rank_sorted["pillar_id"] == p_id].index[0]) + 1 if p_id in rank_sorted["pillar_id"].values else 0
                         else:
-                            # Negara: rank pillar ini di antara semua pillar negara ini
                             country_all_pillars = yr_df[yr_df["country_id"] == c_id].sort_values("pillar_country_score_1_100", ascending=False).reset_index(drop=True)
                             p_rank = int(country_all_pillars[country_all_pillars["pillar_id"] == p_id].index[0]) + 1 if p_id in country_all_pillars["pillar_id"].values else 0
 
                         hist_up = {y: s for y, s in history.get((c_id, p_id), {}).items() if y <= yr}
-
-                        # all_pillar_scores_year untuk perbandingan lintas pilar
                         all_pillar_yr = yr_df[yr_df["country_id"] == c_id][["pillar_name", "pillar_country_score_1_100"]].copy()
-
-                        # country_pillar_all untuk gap trend (hanya relevan untuk ASEAN)
                         cpa = df_pillar_by_country[
                             (df_pillar_by_country["pillar_id"] == p_id) &
                             (df_pillar_by_country["country_id"] != ASEAN_COUNTRY_ID)
@@ -1110,6 +1312,10 @@ class FoodSecurityAggregator:
                             is_asean              = is_asean,
                         )
 
+                        # Ambil kondisi dari kolom yang sudah dihitung di df_pillar_by_country
+                        cond_en = str(row.get("pillar_condition_en", "N/A"))
+                        cond_id = str(row.get("pillar_condition_id", "N/A"))
+
                         records.append({
                             "year":                 yr,
                             "country_id":           c_id,
@@ -1128,6 +1334,8 @@ class FoodSecurityAggregator:
                             "bottom_country_id":    translate_country(bot_country) if (is_asean and bot_country) else None,
                             "bottom_country_score": bot_score   if is_asean else None,
                             "is_asean_aggregate":   is_asean,
+                            "pillar_condition_en":  cond_en,
+                            "pillar_condition_id":  cond_id,
                             "narrative_en":         narrative_en,
                             "narrative_id":         narrative_id,
                         })
@@ -1140,6 +1348,8 @@ class FoodSecurityAggregator:
             df["is_asean_aggregate"]   = df["is_asean_aggregate"].astype(bool)
             df["pillar_name_id"]       = df["pillar_name_id"].astype(str)
             df["country_name_id"]      = df["country_name_id"].astype(str)
+            df["pillar_condition_en"]  = df["pillar_condition_en"].astype(str)
+            df["pillar_condition_id"]  = df["pillar_condition_id"].astype(str)
             df["narrative_en"]         = df["narrative_en"].astype(str)
             df["narrative_id"]         = df["narrative_id"].astype(str)
             for col in ["pillar_score", "yoy_change", "top_country_score", "bottom_country_score"]:
@@ -1148,10 +1358,6 @@ class FoodSecurityAggregator:
             self.logger.info(f"\n  Total rows: {len(df):,}")
             self.logger.info(f"  ASEAN rows: {df['is_asean_aggregate'].sum():,}")
             self.logger.info(f"  Country rows: {(~df['is_asean_aggregate']).sum():,}")
-            self.logger.info("\n  Sample ASEAN narrative_en (first):")
-            asean_sample = df[df["is_asean_aggregate"]].head(1)
-            if not asean_sample.empty:
-                self.logger.info(f"  {asean_sample.iloc[0]['narrative_en'][:300]}")
 
             schema = [
                 bigquery.SchemaField("year",                 "INTEGER", mode="REQUIRED"),
@@ -1171,6 +1377,8 @@ class FoodSecurityAggregator:
                 bigquery.SchemaField("bottom_country_id",    "STRING",  mode="NULLABLE"),
                 bigquery.SchemaField("bottom_country_score", "FLOAT",   mode="NULLABLE"),
                 bigquery.SchemaField("is_asean_aggregate",   "BOOL",    mode="REQUIRED"),
+                bigquery.SchemaField("pillar_condition_en",  "STRING",  mode="REQUIRED"),
+                bigquery.SchemaField("pillar_condition_id",  "STRING",  mode="REQUIRED"),
                 bigquery.SchemaField("narrative_en",         "STRING",  mode="REQUIRED"),
                 bigquery.SchemaField("narrative_id",         "STRING",  mode="REQUIRED"),
             ]
@@ -1244,11 +1452,12 @@ class FoodSecurityAggregator:
         self.logger.info("\n" + "=" * 70)
         self.logger.info("FOOD SECURITY AGGREGATION — 3 TABLES -> fs_asean_gold")
         self.logger.info("  ASEAN aggregate DIGABUNG ke tabel yang sama (country_id=0)")
-        self.logger.info("  Tabel dihapus: agg_pillar_composite, agg_framework_asean,")
+        self.logger.info("  Kolom baru : pillar_condition_en, pillar_condition_id")
-        self.logger.info("                 agg_narrative_overview")
         self.logger.info(f"  Performance threshold: {PERFORMANCE_THRESHOLD}")
+        self.logger.info(f"  Condition tiers (GFSI 2022): >=75 Secure | >=60 Adequate |")
+        self.logger.info(f"                               >=40 Moderate | >=20 At Risk | <20 Critical")
         self.logger.info(f"  Narrative style      : interpretive, plain text, bilingual EN/ID")
-        self.logger.info(f"  Sustainability       : renamed to 'Food Other' (EN) / 'Indikator Tambahan' (ID)")
+        self.logger.info(f"  Sustainability       : renamed to 'Food Other' / 'Indikator Tambahan'")
         self.logger.info("=" * 70)
 
         self.load_data()
@@ -1302,6 +1511,7 @@ if __name__ == "__main__":
     print(f"  NORMALIZE_FRAMEWORKS_JOINTLY : {NORMALIZE_FRAMEWORKS_JOINTLY}")
     print(f"  PERFORMANCE_THRESHOLD        : {PERFORMANCE_THRESHOLD}")
     print(f"  ASEAN_COUNTRY_ID             : {ASEAN_COUNTRY_ID}")
+    print(f"  Condition tiers (GFSI 2022)  : >=75 Secure | >=60 Adequate | >=40 Moderate | >=20 At Risk | <20 Critical")
     print("=" * 70)
 
     logger = setup_logging()